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LAPORAN AKHIR

PENELITIAN PASCASARJNA

DANA ITS 2020

Pemodelan Multi-Criteria Sorting Problem pada Akuisisi Core dalam Sistem

Remanufaktur

Tim Peneliti : Prof. Dr. Ir. Udisubakti Ciptomulyono, M.Eng.Sc.

(Departemen Teknik Sistem dan Industri/Fakultas Teknologi Industri dan Rekayasa Sistem)

Nani Kurniati, S.T., M.T., Ph.D. (Departemen Teknik Sistem dan Industri/Fakultas Teknologi Industri dan Rekayasa Sistem)

M. Imron Mustajib, S.T., M.T. (Departemen Teknik Sistem dan Industri/Fakultas Teknologi Industri dan Rekayasa Sistem)

DIREKTORAT RISET DAN PENGABDIAN KEPADA MASYARAKAT

INSTITUT TEKNOLOGI SEPULUH NOPEMBER

SURABAYA

2020

Sesuai Surat Perjanjian Pelaksanaan Penelitian No: 930/PKS/ITS/2020

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Daftar Isi

Daftar Isi .......................................................................................................................................................... i

Daftar Tabel .................................................................................................................................................... ii

Daftar Gambar ............................................................................................................................................... iii

Daftar Lampiran ............................................................................................................................................. iv

BAB I RINGKASAN ..................................................................................................................................... 2

BAB II HASIL PENELITIAN ........................................................................................................................ 3

BAB III STATUS LUARAN .......................................................................................................................... 7

BAB IV PERAN MITRA (UntukPenelitian Kerjasama Antar Perguruan Tinggi) ........................................ 8

BAB V KENDALA PELAKSANAAN PENELITIAN ................................................................................. 9

BAB VI RENCANA TAHAPAN SELANJUTNYA ................................................................................... 10

BAB VII DAFTAR PUSTAKA ................................................................................................................... 28

BAB VIII LAMPIRAN ................................................................................................................................. 34

LAMPIRAN 1 Tabel Daftar Luaran ............................................................................................................... 6

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Daftar Tabel

Tabel 2.1 Kriteria kualitas incoming core

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Daftar Gambar

Gambar 3.1 Status manuskrip seminar internasional

Gambar 6.2 Konseptual model AHPSort untuk klasifikasi kualitas incoming core

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Daftar Lampiran

Tabel Daftar Luaran

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BAB I RINGKASAN

Remanufaktur merupakan salah satu strategi recovery produk yang sangat penting untuk

mengembalikan fungsi-fungsi produk yang telah berada pada fase end of life menjadi produk

dengan satus useful life, sehingga kualitasnya dapat disetarakan dengan produk baru. Praktik

remanufaktur tidak hanya dapat memberikan manfaat bagi perusahaan, tetapi juga bagi

konsumen, dan lingkungan sekitar. Bagi perusahaan remanufaktur, proses yang dilakukan dapat

membantu mengurangi penggunaan virgin material dan konsumsi energi. Oleh sebab itu,

praktek remanufaktur dapat mengurangi biaya dan dapat meningkatkan profit bagi perusahaan.

Sementara itu, dari sisi lingkungan praktek remanufaktur juga dapat mengurangi polusi dan

emisi. Adapun dari sudut pandang konsumen, produk remanufaktur mampu diperoleh dengan

harga yang terjangkau. Meskipun demikian, bagi sebagian konsumen kualitas produk

remanufaktur masih dipandang rendah, dan tidak sama dengan produk baru. Hal ini disebabkan

input material produk remanufaktur berasal dari produk bekas yang level kualitasnya

diasumsikan lebih rendah dari material baru atau virgin material.

Input dari proses remanufaktur adalah produk bekas atau core, yang diterima pada aktifitas

akuisisi core. Kualitas incoming core yang diterima oleh sistem remanufaktur kondisinya

cenderung bervariasi karena faktor penggunaan produk selama di tangan konsumen. Selain itu,

faktor teknologi produk dan kondisi fisik produk itu sendiri juga ikut berpengaruh. Ketiga faktor

tersebut menjadi penyebab ketidakkpastian kualitas core yang menjadikan sistem remanufaktur

perlu melakukan pengendalian kualitas pada saat aktifitas akuisi core

Sorting dan grading merupakan pengendalian kualitas pada level operasional remanufaktur

yang merupakan solusi langsung sehingg dapat memitigasi adanya ketidakpastian kualitas

incoming core. Meskipun telah banyak penelitian yang membahas tentang pengendalian

kualitas pada aktifitas akuisi core, tetapi ukuran performansi yang diusulkan untuk

mengklasifikasikan kulitas incoming core lebih banyak berorientasi pada single criteria, yaitu

aspek ekonomi. Adapun kriteria-kriteria kualitas untuk penerimaan incoming core belum

diperhatikan. Karena kriteria ekonomi saja tidak selalu cocok untuk diterapkan pada produk

yang komplek.

Berdasarkan research gap di atas, maka penelitian ini mengusulkan pengendalian kualitas pada

aktifitas akuisi core diusulkan dengan membangun model multi criteria sorting problem pada

tahapan akuisisi core dengan memperhatikan kondisi teknologi, fisik, dan penggunaan.

Kata kunci: Quality Uncertainty, Sorting, Multi-criteria, Remanufacturing Planning

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Ringkasan penelitian berisi latar belakang penelitian,tujuan dan tahapan metode

penelitian, luaran yang ditargetkan, kata kunci

BAB II HASIL PENELITIAN

Saat ini ada banyak metodologi yang tersedia yang telah dikembangkan dari berbagai

disiplin ilmu penelitian untuk menangani masalah klasifikasi dan sortir, diantaranya adalah

untuk penyelidikan sistem yang tidak pasti: matematika fuzzy, teori sistem abu-abu,

probabilitas, dan statistik. Banyak studi terbaru (misalnya Golinska et al., 2015; Xin, 2016)

telah menunjukkan bahwa metode yang didasarkan pada grey system theory yang sangat

berguna untuk masalah klasifikasi dalam keadaan operasi remanufaktur saat ini dalam kondisi

ketidakpastian, mengingat klasifikasi abu-abu sedang kompleksitas dalam perhitungan. Grey

clustering adalah metode yang dikembangkan yang dapat didefinisikan sebagai cabang grey

system theory yang berkaitan dengan pengklasifikasian indeks pengamatan atau objek

pengamatan ke dalam kelas yang dapat ditentukan menggunakan grey indeks matrix atau fungsi

kemungkinan abu-abu (Liu et al., 2016). Agar lebih memahami hubungan sistem tidak pasti

dan sistem abu-abu, Altintas et al. (2020) menjelaskan bahwa suatu sistem disebut "abu-abu"

jika memiliki informasi yang tidak lengkap dan tidak pasti, sedangkan sistem "putih" memiliki

semua informasi dan sistem "hitam" tidak memiliki data seperti yang dapat dilihat pada gambar

3. Metode ini adalah sangat berguna dalam mempelajari masalah ketidakpastian kumpulan data

kecil dan informasi yang tidak lengkap yang sulit ditangani dengan pendekatan probabilitas

dan matematika fuzzy (Liu et al., 2016).

Model grey clustering bertindak sebagai sistem yang mengubah masukan menjadi

keluaran. Input berisi objek dan sistem indeks kualitas, sedangkan outputnya adalah kelas

kualitas dari inti yang masuk. Secara umum, kelas mutu adalah kumpulan informasi tentang

sifat kualitatif dari objek yang dievaluasi, yang memungkinkan identifikasi kriteria dan

identifikasi hasil yang diperoleh (Kosacka et al., 2015).

Studi kasus yang diidentifikasi pada peneltian ini adalah perusahaan remanufaktur

Indonesia untuk suku cadang alat berat, misalnya: silinder hidrolik. Dalam praktek bisnisnya,

banyak perusahaan remanufaktur telah melakukan recovery silinder hidrolik bekas untuk

komponen alat berat (suspensi depan, suspensi belakang, dan silinder hoist) untuk alat berat

pertambangan dan konstruksi.

Untuk memberikan kriteria kualitas produk bekas, Mustajib et al. (2019) telah

menetapkan kriteria kualitas untuk pemilahan core yang masuk berdasarkan: kondisi teknologi,

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fisik, dan penggunaan. Mari kita perhatikan, jika ada delapan (I = 8) inti silinder bekas yang

diakuisisi oleh pabrikan ulang, dan perlu diklasifikasikan berdasarkan kriteria tersebut yang

kemudian dikembangkan menjadi 11 subkriteria (J = 11). Selain itu, silinder hidrolik diurutkan

menjadi tiga kelas grey yang berbeda (K = 3), yaitu: buruk, sedang, dan terbaik. Klasifikasi

untuk inti ke dalam kelas abu-abu ke-k berdasarkan nilai pengamatan inti ke-i yang dinilai

terhadap kriteria ke diindeks oleh xij. Adapaun nilai xij. Dapat diperoleh dari table 2.1 berikut

ini.

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Tabel 2.1 Kriteria kualitas incoming core

Kriteria Sub kriteria Definisi Referensi Frormulasi Penilaian ( Indeks) Value

range Referen

si

Target

value

Technological

condition

Obsolescence Kondisi dimana teknologi

used product

menunjukkan tingkat

kedaluwarsa, karena umur

produk lebih lama dari

umur desain (quality

obsolescence), dan

munculya inovasi

teknologi baru

Kwak dan Kim (2012),

Mashhadi et al. (2015),

Raihanian et al. (2017), Gao et

al. (2018), Sitcharangsie et al.

(2019), Zhou dan Gupta

(2018), Gao et al. (2018)

Expert’s questionnaire

1-5

(skala)

1 Min

Upgradeability Kondisi yang

memudahkan suatu

produk dalam proses

remanufaktur untuk

ditingkatkan (diupgrade)

teknologinya sebagai

proses pengayaan

fungsional atau fitur

sehingga produk lebih

mudah beradaptasi dengan

teknologi baru untuk

menghindari keusangan

Sundin (2004), Xing et al.

(2007), Du et al. (2012), Kwak

dan Kim, (2012), Pialot et al.

(2017) Chakraborty et al.

(2017), Omwando et al. (2018),

Khan et al. (2018)

(Shafiee, Finkelstein, &

Chukova, 2011)


1-5

(skala)

5 Max

Multiple life

cycle

Kondisi daur hidup

produk bekas yang bisa

diperbarui umur

penggunaannya

Aziz et al. (2015), Suhariyanto

et al. (2017), Badurdeen, et al.

(2018), Krystofik et al. (2018),

Zhang et al. (2020)

Used products are generally experienced

one or more service cycles, which leads

to the diversity ofremaining life

characteristics of their used parts and

thus affects their EOL strategies

(Zhang, Zhang, Jiang, & Wang, 2016)

1-5

(skala))

5 Max

Diassembly

capability

Tingkat kemudahan used

product atau core untuk

dibongkar

Du et al. (2012), Omwando et al.

(2018), Ding et al. (2018)

Expert’s questionnaire 1-5

(skala)

5 Max

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Physical

condition

Damage level Tingkatan yang

mengindikasaikan adanya

cacat atau kerusakan

secara fisik. Misalnya:

retak, korosi, aus

Seliger et al. (2006), Wang et al.

(2017), Gao et al. (2018), Jiang

et al. (2019b)

𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓𝑑𝑎𝑚𝑎𝑔𝑒𝑠

𝑇𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑎𝑙𝑙𝑜𝑤𝑎𝑏𝑙𝑒 𝑑𝑎𝑚𝑎𝑔𝑒𝑠× 100%

0-100

(%)

0% Min

Completeness

of components

Tingkat kelengkapan

komponen-komponen

yang menjadi bagin

integral produk bekas

(Kosacka, 2018) 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓𝑎𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑒 𝑐𝑜𝑚𝑝𝑜𝑛𝑒𝑛𝑡𝑠

𝑇𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑐𝑜𝑚𝑝𝑜𝑛𝑒𝑛𝑡𝑠× 100%

0-100

(%)

0% Max

Traceability of

identitiy

Kemudahan penulusuran

infomasi variasi produk

yang memungkinkan

pengenalan model atau

jenis menjad ilebih

mudah. Misalnya nomor

pabrikan sebagai nomor

identifikasi.

Xia et al. (2015), (Kosacka,

2018)

1-5

(skala)

5 Max

Dimensional &

geometric

tolerance

Batas penyimpangan

dimensi atau geoetri yang

dijinkan pada core

Zhou et al. (2012), Liu et al.

(2013), Ge et al. (2014) Liu

(2016), Liu et al. (2016), Yang et

al. (2016),


(%)

0% Min

Usage

condition

Frequency of

use

Frekuensi pemakaian

produk pada saat fase

penggunaan

Gavidel dan Rickli (2017),

Diallo et al. (2017),

𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑒𝑥𝑒𝑐𝑢𝑡𝑒𝑑 𝑢𝑠𝑒𝑠 𝑖𝑛 𝑝𝑒𝑟𝑖𝑜𝑑 𝑡

𝑇𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑝𝑙𝑎𝑛𝑛𝑒𝑑 𝑢𝑠𝑒𝑠 𝑖𝑛 𝑝𝑒𝑟𝑖𝑜𝑑𝑒 𝑡× 100%

0-100

(%)

0% Min

Remaining

useful life

Sisa umur pakai Hu et al. (2014), Zhang et al.

(2016), Jiang et al. (2019b)

(Meng, Lou, Peng, & Prybutok,

2017)

Expert’s questionnaire, or

𝑅𝑈𝐿 = 𝑚𝑒𝑎𝑛 𝑢𝑠𝑒𝑓𝑢𝑙 𝑙𝑖𝑓𝑒(𝑇𝑚)− 𝑎𝑐𝑡𝑢𝑎𝑙 𝑢𝑠𝑒𝑓𝑢𝑙 𝑙𝑖𝑓𝑒 𝑜𝑓 𝑝𝑎𝑟𝑡𝑠

0-100

(%)

100% Max

Maintenance

history

Histori perawatan Diallo et al. (2017) 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑒𝑥𝑒𝑐𝑢𝑡𝑒𝑑 𝑚𝑎𝑖𝑛𝑡𝑒𝑛𝑎𝑛𝑐𝑒 𝑖𝑛 𝑝𝑒𝑟𝑖𝑜𝑑 𝑡

𝑇𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑝𝑙𝑎𝑛𝑛𝑒𝑑 𝑚𝑎𝑖𝑛𝑡𝑒𝑛𝑎𝑛𝑐𝑒 𝑖𝑛 𝑝𝑒𝑟𝑖𝑜𝑑𝑒 𝑡× 100%

0-100

(%)

100% Max

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BAB III STATUS LUARAN

Status Luaran berisi status tercapainya luaran untuk kemajuan penelitian saat ini adalah manuskrip yang

dikirim ke International Conference on Mechanical Engineering Research and Application

(iCOMERA) http://icomera.teknik.ub.ac.id/ dengan status “paper in review” seperti yang terlihat pada

gambar di bawah ini.

Gambar 3.1 Status manuskrip seminar internasional

http://icomera.teknik.ub.ac.id/

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BAB IV PERAN MITRA

Berisi uraian realisasi kerjasama dan realisasi kontribusi mitra, baik in-kinddan in-cash

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BAB V KENDALA PELAKSANAAN PENELITIAN

Kendala penelitian ini adalah proses pengambilan data yang melibatkan kuisoner yang

melibatkan pendapat pakar (expert) dari perusahaan remanufaktur alat berat guna melakukan

penilaian (assessment) kriteria-kriteria yang telah dirancang pada belum bisa didapatkan saat

ini karena adanya masih physical distancing maupun social distancing adanya Pandemi

COVID 19.

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BAB VI RENCANA TAHAPAN SELANJUTNYA

Rencana tahapan selanjutnya penyelesaian penelitian untuk mencapai luaran yang dijanjikan.

Pengembangan model pengambilan keputusan untuk penyortiran kualitas incoming core

menggunakan AHPSort dengan langkah-langkah ditunjukkan pada Gambar berikut ini.

Definisikan tujuan & bangun framework AHP Kriteria (cr), r=1,...R Alternatif (as), s=1,..S

Definisikan kelas (Cj)j=1,...J, j: jumlah kelas

Definisikan profil setiap kelas Limiting profile (lpjr), or Local central profile (cpjr)

Evaluasi secara berpasangan atas kepentingan setiap kriteria (cr), dan tetapkan bobot (wr)

Tentukan limiting profiles

Pairwise compare the points

Tentukan prioritas lokal untuk setiap alternatifpada kriteria tunggal (psr) untuk alternatif (as), dan local priority (pjr) dari limiting profile (lpjr), atau local central profile (cpjr)

Evaluasi prioritas pada kriteria

Tentukan bobot global setiap kriteria

Tentukan representative points

Bandingkan matrik berpasangan alternatif tunggal (as) dengan limiting profile (lpjr), atau local central profile (cpjr)

Tentukan prioritas global dari alternatif-alternatif yang ada (ps)

Tentukan prioritas global untuk limiting profile (lpj), and (cpj)

Tugaskan kepada kelas-kelas

A. Pendefinisian masalah

B. Proses evaluasi

C. Penugasan kepada kelas

Gambar 6.1 Konseptual model AHPSort untuk klasifikasi kualitas incoming core

Pada Gambar 6.1 diperlihatkan bahwa penyortiran kualitas incoming core menggunakan

AHPSort dimulai dengan menetapkan kriteria (c) sejumlah R (𝑐1, ⋯ , 𝑐𝑟 , ⋯ 𝑐𝑅) dengan

alternatif core (a) sebanyak S (𝑎1, ⋯ , 𝑎𝑠 , ⋯ , 𝑎𝑆). Selanjutnya, ditetapkan kelas (C) sebanyak J

(𝐶1, ⋯ , 𝐶𝑗 , ⋯ , 𝐶𝐽) untuk pengelompokan kualitas core tersebut. Pengelompokan kelas ini

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misalnya dapat digolongkan menjadi tiga, yaitu: C1 untuk alternatif core dengan kualitas yang

terbaik, C2 untuk alternatif core yang memiliki kualitas sedang, C3 untuk alternatif core yang

memiliki kualitas rendah. Hal ini dapat dilakukan dengan menetapkan sebuah pembatas yang

disebut sebagai limiting profile (lp) atau central profile (cp) pada setiap kriteria untuk setiap

kelas ke j.

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A Multi-Criteria Quality Sorting Model Based on AHP Grey Clustering for

Incoming Core Grading in Remanufacturing System

Mohamad Imron Mustajib1,2, Udisubakti Ciptomulyono3, Nani Kurniati4

1 Department of Industrial and Systems Engineering, Institut Teknologi Sepuluh Nopember.

Surabaya, Jawa Timur 60111, Indonesia

Email: [email protected], [email protected], [email protected]

2 Department of Industrial and Mechanical Engineering, Universitas Trunojoyo Madura.

Bangkalan, Jawa Timur 69162, Indonesia

Abstracts

Remanufacturing is the backbone of the circular economy, which helps in salvaging the used products

by extending its life cycle to be as good as new products. Cores acquisition in remanufacturing is

challenging for remanufacturers due to uncertain quality, time, and volume of returns. Therefore,

quality sorting plays an important role in core acquisition for remanufacturing systems to mitigate the

quality uncertainty of incoming core as an immediate solution. In this paper, we present the usefulness

of grey systems for handling quality uncertainty information for sorting incoming core in the

remanufacturing system. Grey systems are a powerful method to handle uncertainty with small data.

For this reason, we propose a multi-criteria quality sorting model base on the Analytical Hierarchy

Process (AHP) is coupled with grey clustering using possibility functions. The quality criteria for sorting

the incoming core according to the technological, physical, and usage conditions. To provide the

practical contribution of this research, a case study of the quality sorting problem in heavy equipment

remanufacturer was presented.

Keywords: core acquisition, quality uncertainty, grading, multi-criteria, grey decision making

1. Introduction

In the new global economy, circular economy has become a central issue for current

international concern. Many studies by researchers and policymakers in recent years have

focused on the circular economy as a possible solution to pursue

the global issue of sustainable development goals. There are multiple definitions of a circular

economy. In general terms, the circular economy can be viewed as a closed-loop industrial

system that has activities for reducing, reusing, and recycling resources usage for sustainability

achievement.

Remanufacturing is the backbone of the circular economy, which helps in salvaging

the used products by extending its life cycle to be as good as new products. Three main

activities in the remanufacturing system are cores acquisition, remanufacturing operations,

and re-marketing. The acquisition of cores is challenging for remanufacturers as

mailto:[email protected]


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remanufacturing in closed-loop supply chains characterized by uncertain quality, time, and

volume of returns. The term uncertainty is generally understood to mean lack or

incompleteness of information, known only incompletely, or imprecisely. This uncertainty

leads to economic (cost, feasibility) and technical (remanufacturability, scheduling, process

planning) risks for remanufacturing companies. Therefore, these uncertainties need to be

handled technically by mitigations for desired performance.

Quality is an essential element for the performance evaluation of used products,

which is a key driver in remanufacturing decisions. The quality of incoming cores has a

significant effect on the remanufacturing cost and cost of quality. In order to return the used

products into a good functional state, best quality cores need limited reconditioning (thus

entail lower process costs), whereas worst quality cores will need comprehensive processes

or replacement of parts (Diallo et al. 2017). Therefore, sorting and quality grading play a major

part in core acquisition for remanufacturing systems to handle the quality uncertainty of

incoming core. The sorting operation in core acquisition is vital for two important reasons.

First, to identify physical, usage, and technological conditions of incoming cores are sorted

under their quality level before the remanufacturing processes. Two, this operation is an

immediate solution to mitigate the quality uncertainty condition in the core acquisition (Li et

al. 2016; Mashhadi and Behdad, 2017).

Sorting can broadly be defined as the case where a set of alternatives are grouped in an

ordinal manner according to the absolute evaluation, beginning with those that include the most

preferred alternatives to those that include the least preferred alternatives (Zopounidis and Doumpos,

2002). Quality sorting in the remanufacturing system plays an important role

to grade the cores according to their different conditions to plan the process and the

cost of remanufacturing required. Cores with similar quality grades can be remanufactured in

dedicated processes so that the time and cost can be handled efficiently. Quality classification and

sorting policies are urgent and direct solutions that are used in remanufacturing systems to

handle this source of variability in incoming products (Mashhadi and Behdad, 2017). Complete

information is needed in order to decide the quality level of incoming cores. Unfortunately, when the

cores are sorted into different quality grades, the uncertain conditions of the incoming core makes it

difficult to estimate the associated quality level and the process planning stage becomes difficult.

Recent evidence suggests that fast sorting in long term quality control can be

accomplished by installing information and communication technology, an automatic sorting

system using radio frequency identification (RFID), sensors, bar codes, and other technologies

to automate product monitoring and testing that can maintain valuable usage data to assess

remanufacturing feasibility (Loomba & Nakashima, 2012). For instance, in order to record

operating hours and speed, Bosch integrates chips into electric motor power tools (Gavidel

and Rickli, 2017). After that, they evaluate tool quality and sorts them into two classes;

remanufacturable or non-remanufacturable. Unfortunately, these methods do not always

https://thesaurus.yourdictionary.com/in-accordance-with

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guarantee usefulness for many remanufacturers. These technologies are only useful for the

remanufacturers who originally manufactured or have control over the product design and

wish to use such these to invest in long-term payback periods only if they are economically

feasible.

Alternatives approaches are necessary to solve the problem of quality sorting for

remanufacturing as short term quality control. In this case quality uncertainty refers to the

information in which conditions of the incoming core are not known precisely. Several

methods currently exist to deal with uncertainty. A well-known example of these methods is

the probability for randomness behavior is based on density function and fuzzy sets for

fuzziness problem according to membership function for ambiguity. However, the probability

statistic approach requires large sample data to determine its probability density function. On

the other hand, the fuzzy mathematic method depends on experience and cognitive aspects

to develop a fuzzy membership function.

The quality sorting problem of incoming core can be subjective or objective uncertain

depends on the facts. For example, the classification of physical condition based on damage

level on cores by labor visual inspection is subjective, as it can change from inspector to

inspector. In contrast, the classification of cores according to their usage condition according

to the frequency of use is objective. As a result, there remains a need for an efficient method

that can handle quality uncertainty with small data in the sorting problem of the incoming

core for remanufacturing

In 1982, the theory of grey systems was first proposed by Professor Julong Deng from

Huazhong University of Science and Technology, as a model for limited and incomplete data

(Yang et al. 2019). Grey system theory has been widely used in various fields. This paper set

out to investigate the usefulness of grey systems for handling quality uncertainty information

for sorting incoming core in the remanufacturing system. The concern of grey systems theory

is about the uncertainty issues of limited data or missing information that is difficult to address

with possibility theory. In grey systems, grey sets employ the basic concept of grey numbers

and deal with the characteristic function values of a set as grey numbers. A grey number is a

number that has clear upper and lower limits but which has an unknown location within the

limits. Moreover, grey clustering is a method that may be defined as the branch of grey system

theory (GST) which is concerned with the Classification of observation indices or observation

objects into definable groups using grey incidence matrices or grey possibility functions (Liu

et al. 2016). Grey clustering evaluation models using possibility functions have been

extensively studied for uncertain systems analysis. Many recent studies (Golinska et al, 2015,

Xin, 2016) have shown that a method based on a grey clustering decision was helpful for

classification problems in the current state of remanufacturing operations under uncertain

conditions because grey classification is moderate complexity in the computations.

https://en.wikipedia.org/wiki/Huazhong_University_of_Science_and_Technology

15

The rest of this paper is structured as follows. Section 2 below describes the sorting

methodology by using grey clustering and the analytical hierarchy process (AHP) approach.

The problem of quality sorting of incoming core in heavy equipment remanufacturer is

discussed in the case study of section 3. presented, conclusions are in section 4.

2. Methodology

The multi-criteria decision-making problem for sorting problems on the incoming core

was faced with conditions of quality uncertainty, which can be tackled by combining grey

clustering and the AHP approach. The development of a multi-criteria quality sorting model

based on AHP grey clustering for incoming core in the remanufacturing system is illustrated

by the diagram in figure 1. In general, the quality sorting model based on AHP grey clustering

is divided into three stages; problem structuring, evaluation process, and assignment to

classes.

The problem structuring focus on the effective structuring of the problem situation in

sorting by defining a set of criteria to evaluate the quality of incoming core, setting core

alternatives, and dividing the quality of incoming cores into several classes. To classify the

quality of incoming cores, there are J quality classes, and we refer to the quality class j as a

subscript to differentiate between the different qualities of cores, j= 1, 2,..., J. The smaller the

j, the better the quality class of the core. The quality level of the cores in the same quality class

is almost the same in the remanufacturing process need. Overall, the decision model can be

described in Figure 1 as follows

16

Development of: Criteria to evaluate quality of incoming core Indexes system to indicate quality level of the criteria (1,...r,...R)

Setting the incoming cores into object 1...s,...S Suppose there are R evaluation criteria of one evaluation object s Dividing the quality classification into grey classes(1,...j,...J)

Determining the priority orders of objects according to the class and value of clustering coefficient.

End

Start

Determining the clustering weight ηr of each index.

Set the whitenization weight functions of each index,Quality class j of criteria r generally is set as:

Calculating the clustering weight vector:

Calculating the clustering coefficient matrix:

Determining the quality class that certain object belongs to the clustering coefficient matrix.

Object s belongs to class j* when:

Calculating the grey fixed weight clustering coefficient:

Pro

ble

m s

tru

ctu

rin

gE

valu

ati

on

pro

ces

se

sA

ssig

me

nt

to t

he c

lasse

s

Constructing an Analytical Hierarchy Process:

A hierarchy framework Establishing sub criteria for each

criterion Creating indicator for each sub criteria

Establishing the pairwise comparison matrix

Calculation the eigenvalue Testing cosistency of ecah comparison Estimation of the relative of each

comparison

Figure 1. Flow diagram of the AHP grey cluster model for sorting problem

3. Case Study Many researchers (Zhou et al., 2012; Xu et al. 2018; Saidani et al., 2020) have

utilized a case study of heavy-duty equipment remanufacturing to show the practical contribution of their research. The following emergent case study was identified from the

17

Indonesian remanufacturer company for heavy equipment parts, for instance: hydraulic cylinder (see figure 2). In business practice, many remanufacturing companies have recovered the used hydraulic cylinder for heavy equipment components (front suspension, rear suspension, and hoist cylinders) for mining and construction heavy equipment.

Figure 2. Remanufactured hydraulic cylinder

(Source: http://www.komi.co.id) Although the eight dimensions of quality for new products have been successfully

proposed by Garvin (1987), these dimensions cannot always be recognized to evaluate the quality level of used products. This is because the used products have entered the end of life phase so they cannot perform their main functionality. Therefore, there remains a need for compatible quality criteria with used products. In order to provide the quality criteria for the used product in remanufacturing, Mustajib et al. (2019) have established that the quality criteria for sorting the incoming core based on: technological, physical, and usage conditions.

Technological

Condition

Quality sorting of incoming cores

based on conditions

Physical Condition

Level 1

Goal/objective

Level 2

Criteria (cr)Usage Condition

Frequency of useObsolescence Damage level

Multiple lifecycles Remaining useful Life

UpgradabilityCompleteness of components

Dimensional and geometrical tolerance

Maintenance historyLevel 3

Sub Criteria (cro)

Level 4

Set of decision

Alternatives (as)

Core 1(a1)

Core s(as)

Core S(aS)

Traceablity of identity

Disassemblability. . .

. . . . . .

Figure 3. Hierarchy of decision levels for incoming core quality sorting problem

The indicators of sub-criteria in Figure 3 can be calculated in a simplified way based

on the expertise of decision-makers, in the absence of detailed data for estimation, as seen in Tables 1 and 2. Furthermore, the sub-criteria for each criterion is defined as follows:

Obsolescence An Obsolescence happens when products are "out of use" or "out of date” (Rai and Terpenny, 2007). Rapid innovations and technology developments have led a significant to shorten the life cycle of products to be obsolescence. A product is technical or functional obsolescence as customers are more interested in new products with better quality performance as a result of new technology. To assess

18

the grades for the technical obsolescence, Gao et al. (2018) proposed five criteria to guide a qualitative evaluation of used products.

Upgradability An upgrade is a technical mitigation to handle the uncertain quality of used products. Meanwhile, the term upgradeability is used here refer to the level of potential of used products to be upgraded efficiently and effectively to keep its admissible on the market. Upgradeability represents the relative technological ease or viability of fostering continuous system renewal and enhancement at the level of engineering characteristics, part level, and level of the overall system. Remanufacturing with component upgrades may be an efficient alternative to used product obsolescence. In addition, an upgrade action in remanufacturing will improve the reliability of the used product.

Multiple lifecycles The principle of multiple lifecycles products is a key technique in product development for remanufacturing. Since it is one of the strategies for prolonging the product lifecycle after the end of life. Durable products are more effective for multiple lifecycles. To assess the average number of lives times a component, Geyer et al. (2003) have proposed a quantitative approach by dividing the average component life by average product use.

Disassemblability Disassembly is characterized as a complete assembly being dismantled down to its individual parts. Meanwhile, disassemblability can be loosely described as a level of ease with which a used product could be disassembled. The principles of ease for disassemblability are disassembly without force and by simple mechanism (Mok et al., 1997), due to disassembly is a labor-intensive task. For this reason, Xing et al. (2007) dan Gao et al. (2018) used five criteria to give a qualitative assessment of for disassemblability. Meanwhile, Nof et al. (1997) recommended a guideline for easy disassembly. On contrary, Ali (2017) has suggested that comprehensive methods to quantitatively evaluate the disassemblability based on product design, process technology, and incoming quality assessment.

Damage level Used products may have degraded features with different degrees of damage. This indicator is typically calculated by fault features such as corrosions, cracks, wear, and so on. The damage level can be quantified and classified according to the size of the damage (Wang et al. 2017; Jiang et al. 2019)

Components completeness Completeness is defined by Yoe (2019) as all the necessary parts are accounted for and included in the option. It is means the incoming core should not be broken down into constituent parts and should be delivered entirely without missing parts. In case of any uncertainty as to the completeness of the incoming core, an extended inspection is carried out to verify the inner structure of the used product (Golinska-dawson, 2018).

Traceability of identity Product identification and traceability are essential for the quality acceptance of used products. The core should have an original equipment manufacturer's identification number (e.g. manufacturer stickers) because of the wide variety of products, allowing the model, type, parameters to be recognized (Golinska-dawson, 2018). The availability of a used product’s identity such as text, readable labels, and barcodes that do not missing or fading over the use phase of life’s product allows easy recognition for quality sorting.

19

Dimensional and geometrical tolerance The term dimensional tolerance is generally understood to mean minimum and maximum values allowable of dimension for the parts to works properly. Meanwhile, geometric tolerance is a significant assessment factor that depends on the consistency of the parts of used products. Being classified, the sorted parts of used products will be reprocessed based on the quality loss degree according to an allowable tolerance. If the used products have the highest deformation from ideal dimension and shape (exceeding the acceptable tolerance), it can not be reconditioned to its original performance by remanufacturing they are then recycled (Liao et al., 2019)

Frequency of use Usage condition is influenced by the user’s behavior during the use phase of a product. During the middle of life, a product's performance deteriorates with the frequency of use and operations. This is evident in the case of quality of used parts in heavy equipment is evaluated based on hours of operation.

Remaining useful life Being used for a period of time, the remaining life of a used part can be defined as the lifetime of residual operation that can be predicted. To assess the degree of the remaining life of the used product can be categorized into several levels according to the minimum and maximum values of the remaining useful life values obtained (Jiang et al., 2019).

Maintenance history Maintenance has characterized a set of actions taken to allow the product to work at predetermined levels during the use phase of a product. Proper maintenance strategy will extend its original life of a product, then makes it compatible with remanufacturing (Go et al., 2015). Due to the potentially lower reliability of the used products requires an appropriate maintenance strategy. Moreover, Stadnicka et al. (2014) have proposed a set of criteria to carry out the classification of equipment maintenance based on the failure frequency (amount of failure registry entries each year).

20

Table 1. Criteria for assessing the quality level of incoming cores Sub-Criteria

(Assessment indicator)

Description Assessment Formula (index) Value range Reference

Target

value

Technological conditions:

C11:

Obsolescence

The condition in which the technology of

used product has shown it's out of date, as

the product's life cycle was longer than the

design life and the emergence of new

technological innovations


1-5

(scale)

5 Max

C12:

Upgradability

The ability of a used product when in the

remanufacturing process is easier to upgrade

for functional or feature enrichment process

so that the product is easier to adapt to new

technology to avoid obsolescence


1-5

(scale)

5 Max

C13:

Multiple lifecycles

The condition of the used product life cycle

that can be recovered for its useful life


(scale)

5 Max

C14:

Disassemblability

The ability of the product or core to be

easier for dismantling


(scale)

5 Max

Physical conditions:

C21:

Damage level

Indicating a grade of physical defect or

damage. For example cracks, corrosion,

wear


(scale)

5 Min

C22:

Components

completeness

The level of completeness of the components

as a whole system of used products

𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓𝑎𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑒 𝑐𝑜𝑚𝑝𝑜𝑛𝑒𝑛𝑡𝑠


0-100

(%)

0% Max

C23:

Traceability of identity

Easiness of tracing for the product variation

information of the model or type. For

example, the manufacturer's number is an

identification number.


(scale)

5 Max

C24:

Geometric and

dimensional tolerance

The allowable dimensional or geometrical

variation limit at the core


(scale)

5 Min

Usage conditions:

C31:

Frequency of uses

The frequency of using the product during

the usage phase


(scale)

5 Min

C32:

Maintenance frequency

The intensity of product maintenance is

carried out during the use phase


1-5

(scale)

5 Max

C32:

Remaining useful of life

The remaining usable time for a specified

period.


1-5

(scale)

5 Max

21

Table 2. Example of an expert’s questionnaire

Sub Criteria Expert’s questionnaire Answer

Linguistics value Description Scale

c11: Obsolescence How is the equality of the conditions of the used

product technology to the emergence of new

technological innovations?

Very high Equal as new technology 5

High Very good 4

Moderate Good 3

Low Acceptable 2

Very low Overtime 1

c12: Upgradability How the ability of used products can be

upgraded to improve the functionality or feature

enrichment so that more easily adapt to new

technologies?

Very high Minimal repair 5

High Imperfect repair 4

Moderate Replacement with younger parts 3

Low Complete/perfect repair 2

Very low Replacement with new parts 1

c13:

Multiple lifecycles How many times the ability to a used product life

cycle that can be recovered for its useful life>

Very high More than four-cycle 5

High Four-cycles 4

Moderate Three-cycles 3

Low Two-cycles 2

Very low One-cycle 1

c14:

Disassemblability

How easy is the used product or core to be

disassembled?

Very high No connections

Disassembly will not lead to any damages to the parts; Manual operation; and Quick disassembly

5

High Flexible assembly

Screw (to be removed)

Connections are not destroyed for disassembly; Joints disassembly will not lead to any damages to the parts; Manual operation; and Quick disassembly

4

Moderate Flexible connections

Snap-fit (to be opened)

Disassembly will not lead to any damages to the parts; Manual operation is possible and Time-consuming

3

Low Flexible connections

Clip (to be removed)

Disassembly will not lead to any damages to the parts; Powered tools are often needed; Time consuming

2

22

Very low Permanent connection (to be broken)

Disassembly will lead to damages to the parts; Large powered tools are required, and Time-consuming

1

c21:

Damage level What is the volume of damage? Very high 𝑥 ≥ 4𝑚𝑚3 5

High 3𝑚𝑚3 ≤ 𝑥 < 4𝑚𝑚3 4

Moderate 2𝑚𝑚3 ≤ 𝑥 < 3𝑚𝑚3 3

Low 1𝑚𝑚3 ≤ 𝑥 < 2𝑚𝑚3 2

Very low 0 < 𝑥 < 1𝑚𝑚3 1

c24:

Geometric and

dimensional

tolerance

How is tolerance deformation from ideal shape

and dimension?

Very high Highest deformation from ideal dimension and shape (exceeding the acceptable tolerance)

5

High Higher deformation from ideal shape and dimension (still acceptable tolerance)

4

Moderate Moderate deformation from ideal shape and dimension (still acceptable tolerance)

3

Low Lower deformation from ideal shape and dimension (still acceptable tolerance)

2

Very low No deformation from ideal shape and dimension 1

C32:

Maintenance history How many failures frequency (number of entries

in the shutdown register per year)

Very high 5

High 4

Moderate Approximately 24–47 times (i.e. on average 1–4 failures per month)

3

Low Approximately 12–23 times (i.e. on average 1–4 failures per month)

2

Very low Approximately 0–11 times (i.e. on average 1–4 failures per month)

1

C33:

Remaining useful of

life

How long is the remaining useful life (RUL) of the

incoming core, which is calculated from the end

of the period of use to the end of the useful life?

Very high More than four years 5

High Four years 4

Moderate Three years 3

Low Two years 2

Very low One year 1

23

Let us consider, there were eight (𝑆 = 8) the incoming core of used hydraulic cylinder which was acquired by the remanufacturer, and they needed to be classified according to

these criteria which are then expanded into 11 sub-criteria (𝑅 = 11). Moreover, the hydraulic

cylinder is sorted out into three distinctive grey classes(𝐽 = 3), namely: best, middle, and worst quality. The classification for the sth core into the jth grey class according to the observed value of the sth core judged against the rth criterion is indexed by xsr

Table 4. Matrix for pairwise comparisons for criteria

𝑐1 𝑐2 𝑐3 Weight (Wr)

𝑐1 1 5 3 0.62

𝑐2 1/5 1 3 0.24

𝑐3 1/3 1/3 1 0,14

Table 5. Matrix for pairwise comparison for sub criteria of technological conditions

𝑐11 𝑐12 𝑐13 𝑐14 Weight (�̃�𝑟𝑜)

𝑐11 1 1/5 1/9 3 0.10

𝑐12 5 1 1/7 1/7 0.13

𝑐13 9 7 1 9 0.62

𝑐14 1/3 7 1/9 1 0.16

Table 6. Matrix for pairwise comparison for sub criteria of physical condition

𝑐21 𝑐22 𝑐23 𝑐24 Weight (�̃�𝑟𝑜)

𝑐21 1 1/3 5 5 0.34

𝑐22 3 1 1/3 5 0.32

𝑐23 1/5 3 1 5 0.29

𝑐24 1/5 1/5 1/5 1 0.05

Table 7. Matrix for pairwise comparison for sub criteria of usage condition

𝑐31 𝑐32 𝑐33 Weight (�̃�𝑟𝑜)

𝑐31 1 1 3 0.39

𝑐32 1 1 7 0.51

𝑐33 1/3 1/7 1 0.1

In summary, then we can get global weight for each sub-criteria 𝑐𝑟𝑜 can be

obtained

Table 3. The observed value of each criterion on each incoming core s (Xsr)

Core alternative (as)

Sub criteria (𝑐𝑟𝑜) and global weights

𝑐11 𝑐12 𝑐13 𝑐14 𝑐21 𝑐22 𝑐23 𝑐24 𝑐31 𝑐32 𝑐33

0.06 0.08 0.38 0.10 0.08 0.08 0.07 0.01 0.05 0.07 0.01

𝑎1 3 3 4 2 2 80% 3 2 3 4 4

𝑎2 4 3 3 3 1 90% 4 2 2 3 3

24

𝑎3 2 4 5 2 2 90% 4 1 2 4 4

𝑎4 3 3 5 2 2 80% 5 3 3 3 5

𝑎5 3 4 4 2 2 90% 3 2 1 4 4

𝑎6 4 4 3 3 3 85% 4 1 2 3 3

𝑎7 4 2 4 2 2 88% 4 2 2 4 4

𝑎8 5 3 5 3 1 90% 3 3 2 3 5

𝑎9 2 4 5 2 3 95% 3 2 3 3 4

Assessment indicators are used to measure the quality condition level of a core. In order to evaluate these indicators, we need to establish an evaluation index system used to control and measure uniformity. The index developed in these indicators are presented in Table 1. But, due to the high uncertainty in the core conditions, sometimes it is very hard to determine the technical index quantitatively for each criterion as its complexity and difficulty; thus, it can only be measured qualitatively by expert assessment as can be seen in table 1. Furthermore, to assess the qualitative indicators, the expert’s questionnaire in Table 2 was used. Thus, the values for the lower, middle, and best classes can be obtained by applying the whitenization weight function (figure 5) as proposed by formula 1 until 9 as follows.

1 1 1

0 0 0X X XXr

j(1) Xrj(2) Xr

j(4)Xrj(3)Xr

j(1) Xrj(2) Xr

j(4)

B. Best of quality class C. Middle of quality class D. Lower of quality class

1

0 XXr

j(1) Xrj(2) Xr

j(4)

A. Typical whitenization function

frj ( )

Xrj(3)

frj ( ) fr

j ( ) frj ( )

Figure 5. The whitenization weight function

For example the criterion 𝑐22, the whitenization weight function is given as:

𝑓𝑟1(𝑋𝑟𝑠) = {

0, 𝑋 < 0 %𝑋

85, 0% ≤ 𝑋 < 85%

1, 85% ≤ 𝑋 ≤ 100%

(1)

𝑓𝑟2(𝑋𝑟𝑠) =

{

0, 𝑋 < 0𝑋77.5

, 0 ≤ 𝑋 ≤ 77.5%

100 − 𝑋22.5

77.5% < 𝑋 ≤ 100%

0, 𝑋 > 100%

(2)

25


{

0, 𝑋 < 0 1, 0 ≤ 𝑋 ≤ 66%

100 − 𝑋34 66 < 𝑋 ≤ 100%

0, 𝑋 > 100%

(3)

Meanwhile, the criteria with r 𝑐11, 𝑐12,𝑐13, 𝑐14, 𝑐21, 𝑐24 𝑐32, 𝑐33,the whitenization weight

function are expressed with:


0, 𝑋 < 0 𝑋

5, 0 ≤ 𝑋 < 5

1, 𝑋 ≥ 5

(4)


{

0, 𝑋 < 0𝑋2.5

, 0 ≤ 𝑋 ≤ 5

5 − 𝑋2.5

2.5 < 𝑋 ≤ 5

0, 𝑋 > 5

(5)


{

0, 𝑋 < 0 1, 0 ≤ 𝑋 ≤ 2.55 − 𝑋2.5

2.5 < 𝑋 ≤ 5

0, 𝑋 > 5

(6)

Moreover, the criteria with r 𝑐21, 𝑐24 𝑐31, 𝑡he whitenization weight function are expressed with:


0, 𝑋 < 0 5 − 𝑋

5, 0 ≤ 𝑋 < 5

1, 𝑋 ≥ 5

(7)


{

0, 𝑋 < 0𝑋2.5

, 0 ≤ 𝑋 ≤ 2.5

5 − 𝑋2.5

2.5 < 𝑋 ≤ 5

0, 𝑋 > 5

(8)


{

0, 𝑋 < 0 1, 0 ≤ 𝑋 ≤ 2.5𝑋5 2.5 ≤ 𝑋 ≤ 5

0, 𝑋 > 5

(9)

Table 4. The value of the gray fixed weight cluster coefficient for each class (𝜎𝑠𝑗)

𝑎𝑠 𝜎𝑠𝑗=∑𝑓𝑟

𝑗

𝑅

𝑟=1

(𝑋𝑟) ∙ 𝜂𝑟

Maximum coefficient value Grey Class

𝑗 = 1 𝑗 = 2 𝑗 = 3 𝜎𝑠𝑗∗= 𝑚𝑎𝑥1≤𝑗≤3{𝜎𝑠

𝑗}

𝑎1 0.428 0.372 0.2 0.428 𝑗∗ =1

𝑎2 0.250 0,453 0,297 0,453 𝑗∗ =2

𝑎3 0,275 0,125 0,6 0,6 𝑗∗ = 3

𝑎4 0,313 0.436 0.251 0.436 𝑗∗ =2

𝑎5 0,153 0,521 0.326 0,521 𝑗∗ =2

26

𝑎6 0,368 0,220 0,412 0,412 𝑗∗ =3

𝑎7 0,212 0,290 0,498 0,498 𝑗∗ =3

𝑎8 0.406 0,344 0,250 0.406 𝑗∗ =1

𝑎9 0.360 0,50 0,14 0,50 𝑗∗ =2

It is apparent from this table 4 that alternative

𝑎1, 𝑎8 are classified tot class 1. Meanwhile core alternative a2, a4, a5, a9 are classified to class 2, and the remaining are classified to class 3. This research has been success conducted to assess the importance of the quality level in used products. The most interesting finding was that the assessment formula can be obtained quantitatively and qualitatively. Another important finding was that the grey quality class of the incoming core can be achieved by applying the cluster model.

4. Conclusion This study set out to propose a multi-criteria quality sorting model base on the Analytical Hierarchy

Process (AHP) is coupled with grey clustering using possibility functions. The quality criteria for sorting

the incoming core according to the technological, physical, and usage conditions. The research has

also shown that the practical contribution of this research, a case study of the quality sorting problem

in heavy equipment remanufacturer

5. Acknowledgments The authors would like to thanks DRPM Institut Teknologi Sepuluh Nopember for the funding

Postgraduate Research Grant of the year 2020 Dissertation Doctoral Research Grant, No. Contract:

930/PKS/ITS/2020

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BAB VIII LAMPIRAN

Lampiran ini berisi manuskrip yang dikirim ke The 2nd International Conference on Mechanical

Engineering Research and Application (iCOMERA) http://icomera.teknik.ub.ac.id/ sebagai berikut:

Sorting the Incoming Core Quality of Remanufacturing Based

On Grey Cluster Model

Mohamad Imron Mustajib1,2, Udisubakti Ciptomulyono3, Nani Kurniati4

1 Department of Industrial and Systems Engineering, Institut Teknologi Sepuluh Nopember.

Surabaya, Jawa Timur 60111, Indonesia

Email: [email protected], [email protected], [email protected]

2 Department of Industrial and Mechanical Engineering, Universitas Trunojoyo Madura.

Bangkalan, Jawa Timur 69162, Indonesia

Abstract. Recently, a considerable literature has grown up around the theme of remanufacturing. In the past decade has seen the rapid of remanufacturing practices in many heavy equipment companies. A key aspect of remanufacturing is core acquisition management, one of which is the main problem is the uncertainty of the quality of incoming cores. Sorting and quality classification plays an important role in the operational level of remanufacturing systems to handle the variability condition of incoming cores. The purpose of this study is to extend an existing approach to classify/sort the incoming core quality into predefined classes by using grey decision making. Firstly, the set of criteria for quality of the used product were presented. Secondly, the index system was proposed to determine the level of criteria for quality assessment of incoming core. After that, the grey cluster model was employed to assign certain incoming cores belongs to quality classes based on the clustering coefficient matrix. Finally, a numerical example is presented in detail using a case of remanufacturing of the heavy equipment part. Overall, we proposed eleven indicators based on technological, physical and usage conditions to assess quality of incoming core for case study in a heavy equipment remanufacturing company.

Keywords: core acquisition, quality uncertainty, grading, multi-criteria, grey decision making

1. Introduction

An increasing number of heavy equipment demand in Indonesia has been demonstrated by Simatupang (2012). However, it has been found that the supply of new equipment was far from adequate, due to the limited capacity of local heavy equipment manufacturers, import regulations for special heavy equipment are complicated, and procurement time for ordering equipment is quite long. In particular, Simatupang (2013) emphasized that the availability of heavy equipment for construction cases in Indonesia is only 20% of total demand. It can be seen from figure 1 that there was a significant gap between demand and availability of heavy equipment. There remains a need for an efficient method that can fulfill the gap between supply and demand for heavy equipment. One way to overcome this gap is to recover the old (used) heavy equipment by using a remanufacturing strategy. Recently, heavy equipment companies in Indonesia have shown an increased interest in




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remanufacturing (see figure 1), for example, many of them are members of the Heavy Equipment Manufacturers Association of Indonesia (HINABI), https://www.hinabi.org/.

Figure 1. The increasing interest in remanufacturing of heavy equipment (Simatupang, 2012)

Remanufacturing is an industrial process which is not only using technical manners to restores the worn-out product (core) to at least the same performance as good as new product working condition, but also considering economic and environmental aspects (Sundin & Bras, 2005). Remanufacturing can play an important role in addressing the issue of sustainable manufacturing to value retention for a worn-out product, which has widespread attention around the world. Remanufacturing has paid attention extensively due to its possible benefits and potential applications. For example, the cost savings (up to 50%) due to the reduction in energy consumption (up to 60%) and material usage cost (up to 70%) are the economic benefits that could be generated from remanufacturing practice (Jiang et al., 2016). These costs are much less than in manufacturing, so that allow remanufacturer to offer remanufactured products at market prices around 50-80% cheaper than new products with a profit margin of around 20% (Gutowski et al., 2011: Ilgın & Gupta, 2012). Furthermore, remanufacturing able to reduce environmental impacts, by reducing carbon emission during processes and minimizing waste output. Moreover, labor-intensive of remanufacturing operations (such as grading, sorting, disassembly, and reassembly) has a social benefit to create new jobs. Therefore, remanufacturing can also be seen as a win-win solution; saving the money (for remanufacturers and customers), protecting the environment, and improving social welfare.

Remanufacturing has many potential economic and environmental benefits over conventional (forward) manufacturing, on the other hand remanufacturing practice involves a complex system that includes many activities such as core acquisition, a series of operations: inspection, sorting, cleaning, testing, disassembly, reconditioning, reassembly, and remarketing. In remanufacturing, the quality condition of cores can vary significantly, affecting remanufacturing operations and cost of remanufacturing. One of the greatest challenges is the process of managing returned products of remanufacturing under complexity and uncertainties in volume, time, and quality conditions (Wei

https://www.hinabi.org/

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et al., 2015; Rizova et al. , 2020). The term quality condition has been used to refer to the characteristics of the used products in which such as the effect of the remaining

Incoming cores(uncertain quality condition)

Sorting & classification

Quality class 1: Set of qualified cores

Quality class k: Set of qualified cores

Quality class K: Set of qualified cores

Quality limit between classes

Quality limit between classes

Figure 2. The problem of sorting and classification for incoming core in the remanufacturing system

the life cycle of the parts/products. It can be measured by two characteristics, namely physical condition and technological obsolescence (Gao et al., 2018). One of the main obstacles in the core acquisition is how to manage the quality uncertainty condition of the incoming core. As the quality of the incoming cores are not known beforehand, the quality level of cores are subject to uncertainty. The term uncertainty was used to refer to situations in the lack or incompleteness of information. In theory, this is also called epistemological uncertainty which is often referred to as confusion, subjective uncertainty, knowledge-based uncertainty, and phenomenological uncertainty, as well as reducible uncertainty (Rausand and Haugen, 2020). Even though it was difficult to handle the quality uncertainty in remanufacturing system, a quality control framework have been proposed by Mustajib et al. (2017). In their study, suggested a possible quality control to

reduce the uncertainty of core variability by dividing into three levels of the area; strategic, tactical, and operational. At an operational level, core variability is graded by sorting and classifying manually which can be time-consuming and depends on the labour skills and knowledge, whereas fast sorting can be realized by installing an information and communication technology application.

Sorting and quality classification are important components in quality control and play a key role in the operational level of remanufacturing systems to handle the variability of the incoming core. In the literature of decision science (Zopounidis & Doumpos, 2002; Doumpos & Zopounidis, 2004), the term classification has come to be used to refer to the assignment of a finite set of alternatives into predefined clusters or classes (see fig. 1). On the other hand, sorting refers to problems where the groups are defined in an ordinal way. These definition have been widely used by multi-criteria decision aiding (MCDA) researchers. The decision making of sorting and quality classification is technically challenging, as any classification process covers a high degree of uncertainty implicit in the quantified information. According to Golinska et al. (2015) this is occurred because of an approximation when using the experts' knowledge.

In recent years, there has been an increasing interest study on sorting and quality grading for evaluating the quality of used products. It is only since the work of Guide & Wassenhove, (2001) that the study of sorting and grading of used mobile phones quality has gained momentum. Five years later, Seliger et al. (2006) reported a case of quality classification for Liquid Crystal Display (LCD) monitor components based on visual, mechanical, electrical, audio, display, and logical testing. In their major study, Behdad & Thurston (2011) have proposed an analytical approach to evaluate the process of upgrading used house-hold electric appliances which are graded into different quality levels. A quality evaluation model based on the fuzzy analytic hierarchy process (AHP) to evaluate the reusability degree of the end-of-life wheel loader was presented by Zhou et al. (2012). Their model and its management system are useful to increase the efficiency of workflow. Meanwhile, to determine the best upgrade level for a received product with a certain quality grade level, Mashhadi et al. (2015) developed a stochastic optimization model based on chance-constrained programming.

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In another major study, Mashhadi & Behdad (2017) have proposed a new sorting method based on both of product’s internal and external factors to better decision making in remanufacturing. This data-driven method was provided by an application of sensor data. In two related analyses are carried, first, they proposed a reusability index of used products, second, they built a clustering algorithm to identify similar characteristics of products based on the index.

As noted by Xin (2016) the condition of the used product have a different degree of uncertainty has made the evaluation process is more complicated, therefore need index evaluation model more scientific to improve the decision-making process. Liu et al. (2016) pointed out that the fundamental characteristics of uncertain systems are not only incompleteness in the information but also naturally occurring inaccuracy in available data. It is necessary to provide an index system to address the variability quality condition of incoming cores. To do this, the aim of this study was to presents a set of criteria and index systems for sorting the incoming core into predefined classes by using grey cluster model to handle quality uncertainty. Although in many ways similar, the proposed criteria and the index are significantly different from Xin (2016).

Furthermore, this paper is organized as follows. First of all, section 1 introduces the backgrounds and problems, literature reviews, and limitations of previous works. Second, in section 2 we provide descriptions about our proposed methodology for sorting problems in incoming core quality classification by using a grey clustering model. Section 3 focuses on results and discussion based on a case study presented of heavy equipment part remanufacturing to show the application of a grey clustering model for quality classification. Finally, we conclude with some summary.

2. Methods and Materials

There are many methodologies available that have been developed from a wide range of

research disciplines for addressing the classification and sorting problems. Several methods

currently exist for the investigation of uncertain systems: fuzzy mathematics, grey system

theory, probability, and statistics. Many recent studies (e.g. Golinska et al., 2015; Xin, 2016)

have shown that a method based on a grey clustering decision was helpful for classification

problems in the current state of remanufacturing operations under uncertainty conditions, in

view of grey classification is moderate complexity in the computations. Grey clustering is a

developed method that may be defined as the branch of grey system theory which is concerned

for classifying observation indices or observation objects into definable classes using grey

incidence matrices or grey possibility functions (S. Liu et al., 2016). Grey clustering evaluation

models using possibility functions have been extensively studied for uncertain systems analysis.

To better understand the relationship of uncertain systems and grey system, Altintas et al.

(2020) described that a system is called "grey” if it has incomplete and uncertain information,

while a “white” system has all the information and a “black” system has no data as can be seen

in figure 3. This method is particularly useful in studying the uncertainty problems of small

data sets and poor/incomplete information which are difficult to handle by probability and fuzzy

mathematics (S. Liu et al., 2016). In the theory of grey systems, the concepts of uncertainty and

inaccuracy are essentially the same.

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Unknown information

Known information

Grey variablesGrey variables

Grey system

Input Output

Figure 3. The concept of grey system (modified from Li et al., 2007)

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Development of: Criteria to evaluate quality of incoming core Indexes system to indicate quality level of the criteria (1,...j,...J)

Setting the incoming cores into object 1...i,...I Dividing the quality classification into grey classes (1,...k,...K)

Determining the priority orders of objects according to the class and value of clustering coefficient.

End

Start

Transfer data form according to the polarity of different indexes.

Index normalizing to each observation:

Determining the clustering weight ηj of each index.

Set the whitenization weight functions of each index,Quality class k of index j generally is set as:

Calculating the clustering weight vector:

Calculating the clustering coefficient matrix:

Determining the quality class that certain object belongs to the clustering coefficient matrix.

Object i belongs to class k* when:

Calculating the grey fixed weight clustering coefficient:

Figure 4. Flow diagram of the grey cluster model

40

The grey clustering model acts as a system that transforms an input into an output. The input

contains an object and quality index system, while the output is the quality class of the incoming

cores. Generally speaking, the quality class is a collection of information on the qualitative

properties of the evaluated object, which enables the identification of the criteria and the

identification of the results obtained (Kosacka et al., 2015).

There are several steps of grey clustering currently being adopted from previous research into

this work. Most of the steps are adapted from Liu et al. (2016) and Xin (2016) as can be seen

in figure 4. The first step in this sorting process was to determine the criteria for classifying the

incoming core. Once the criteria (1,⋯ 𝑗,⋯ 𝐽) were available, the sub criteria and its index

system to measure the quality level can be defined. After that, setting for the incoming core into

object of(1,⋯ 𝑖,⋯ 𝐼) and the quality classes were divided into (1,⋯𝑘,⋯𝐾). When dividing

quality classes, care was taken to the evaluation requirements. Following this step, the data

form was transferred according to the different indexes polarity. Prior to determining the

clustering weight 𝜂𝑗(1,⋯ 𝑗,⋯ 𝐽) of each index, the indexes were whitened using possibility

functions for whitenization. A value of 𝜂𝑗 that closer 1 means that the most important. After

being whitened, the grey fixed weight clustering coefficient was calculated. Moreover,

calculate the clustering weight vector according to the fixed weight coefficient of each class,

and then find the clustering coefficient matrix. After the matrix was found, the class that certain

object belongs to according to the clustering coefficient matrix can be determined. The final

step of grey clustering model is to determine the priority orders of objects based on the class

and value of clustering coefficient.

3. Results and Discussion

The following emergent case study was identified from the Indonesian remanufacturer company for heavy equipment parts, for instance: hydraulic cylinder (see figure 5). In business practice, many remanufacturing companies have recovered the used hydraulic cylinder for heavy equipment components (front suspension, rear suspension, and hoist cylinders) for mining and construction heavy equipment.

The eight dimensions of quality for new products have been proposed by Garvin (1987). Unfortunately, these dimensions do not always can be recognized on used products. There remains need for compatible quality criteria with used products. In order to provide the quality criteria for used product, Mustajib et al. (2019) have established that the quality criteria for sorting the incoming core based on: technological, physical, and usage conditions. Let us consider, there were eight (𝐼 = 8) incoming core of used hydraulic cylinder which were acquired by the remanufacturer, and they needed to be classified based on these criteria which are then expanded into 11 sub-criteria (𝐽 = 11). Moreover, the hydraulic cylinder are sorted out into three distinctive grey

41

classes(𝐾 = 3), namely: bad, middle, and best. The classification for the ith core into the kth grey class based on the observed value of the ith core judged against the jth criterion is indexed by xij

Figure 5. Remanufactured hydraulic cylinder

(http://www.komi.co.id/product/hydraulic-remanufacturing)

42

Table 1. Criteria for assessing the quality level of incoming cores

Sub-Criteria

(Assessment

indicator)

Description Assessment Formula Value

range

Referen

ce

Targe

t

value

Technological conditions:

A1:

Obsolescence

The condition in which the

technology of used product has

shown its out of date, as the

product's life cycle was longer than

the design life and the emergence of

new technological innovations


By using the generational difference of a part

refers to the gap between its generation and that

of the cutting-edge part

1-5

(scale)

1 Min

A2:

Upgradability

The ability of a used product when

in the remanufacturing process is

easier to upgrade for functional or

feature enrichment process so that

the product is easier to adapt to new

technology to avoid obsolescence


By using Product upgradability Index;

1-5

(scale)

5 Max

A3:

Multi-lifecycle

The condition of the used product

life cycle that can be recovered for

its useful life


(scale)

5 Max

43

A4:

Disassembly

capability

The ability of the product or core to

be easier for dismantling


(scale)

5 Max

Physical conditions:

B1:

Damage level

Indicating a grade of physical defect

or damage. For example cracks,

corrosion, wear

𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓𝑑𝑎𝑚𝑎𝑔𝑒𝑠

𝑇𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑎𝑙𝑙𝑜𝑤𝑎𝑏𝑙𝑒 𝑑𝑎𝑚𝑎𝑔𝑒𝑠× 100%

0-100

(%)

0% Min

B2:

Components

completeness

The level of completeness of the

components as a whole system of

used products

𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓𝑎𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑒 𝑐𝑜𝑚𝑝𝑜𝑛𝑒𝑛𝑡𝑠


0-100

(%)

0% Max

B3:

Traceability of

identity

Easiness of tracing for the product

variation information of the model

or type. For example the

manufacturer's number as an

identification number.


(scale)

5 Max

B4:

Dimensional

tolerance

The allowable dimensional or

geometrical variation limit at the

core

𝐴𝑚𝑜𝑢𝑛𝑡 𝑜𝑓 𝑣𝑎𝑟𝑖𝑎𝑡𝑖𝑜𝑛

𝐴𝑚𝑜𝑢𝑛𝑡 𝑜𝑓 𝑣𝑎𝑟𝑖𝑎𝑡𝑖𝑜𝑛 𝑙𝑖𝑚𝑖𝑡× 100%

0-100

(%)

0% Min

Usage conditions:

C1:

Frequency of uses

The frequency of using the product

during the usage phase

𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑒𝑥𝑒𝑐𝑢𝑡𝑒𝑑 𝑢𝑠𝑒𝑠 𝑖𝑛 𝑝𝑒𝑟𝑖𝑜𝑑 𝑡

𝑇𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑝𝑙𝑎𝑛𝑛𝑒𝑑 𝑢𝑠𝑒𝑠 𝑖𝑛 𝑝𝑒𝑟𝑖𝑜𝑑𝑒 𝑡× 100%

0-100

(%)

0% Min

44

C2:

Maintenance

frequency

The intensity of product

maintenance is carried out during

the use phase

𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑒𝑥𝑒𝑐𝑢𝑡𝑒𝑑 𝑚𝑎𝑖𝑛𝑡𝑒𝑛𝑎𝑛𝑐𝑒 𝑖𝑛 𝑝𝑒𝑟𝑖𝑜𝑑 𝑡

𝑇𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑝𝑙𝑎𝑛𝑛𝑒𝑑 𝑚𝑎𝑖𝑛𝑡𝑒𝑛𝑎𝑛𝑐𝑒 𝑖𝑛 𝑝𝑒𝑟𝑖𝑜𝑑𝑒 𝑡× 100%

0-100

(%)

100% Max

C3:

Remaining useful

of life

The remaining usable time for a

specified period.


𝑅𝑈𝐿 = 𝑚𝑒𝑎𝑛 𝑢𝑠𝑒𝑓𝑢𝑙 𝑙𝑖𝑓𝑒(𝑇𝑚)

− 𝑎𝑐𝑡𝑢𝑎𝑙 𝑢𝑠𝑒𝑓𝑢𝑙 𝑙𝑖𝑓𝑒 𝑜𝑓 𝑝𝑎𝑟𝑡𝑠

1-5

(scale)

5 Max

Table 2. Example of an expert’s questionnaire

Criterion Expert’s questionnaire Answer

Linguistics value Description Scale

A1: Obsolescence

How are the differences in the conditions of the used product technology to the emergence of new technological innovations

Very high Overtime 5

High Acceptable 4

Moderate Good 3

Low Very good 2

Very low Equal as new technology 1

A2: Upgradability

How the ability of used products be upgraded to improve the functionality or feature enrichment so that more easily adapt to new technologies

Very high Minimal repair 5

High Imperfect repair 4

Moderate Replacement with younger parts

3

Low Complete/perfect repair 2

Very low Replacement with new parts 1

A3:

Multi-lifecycle

How the ability of used product life cycle that can be recovered for its useful life The remaining useful lifetime of a component after being remanufactured should correspond to more than at least one usage period, such that the quality of remanufactured product can be ensured

Very high More than four cycle 5

High Four cycle 4

Moderate Three cycle 3

Low Two cycle 2

Very low One cycle 1

Very high Imperfect repair 5

45

A4: Disassembly capability

How easy is the used product or core to be disassembled

High Replacement with younger parts

4

Moderate Complete/perfect repair 3

Low Replacement with new parts 2

Very low Connection (to be broken) 1

B3:

Traceability of identity

Very high 5

High 4

Moderate 3

Low 2

Very low 1

C3:

Remaining useful of life

The remaining useful life (RUL) of an asset or system is defined as the length from the current time to the end of the useful life. In order to quantify the degree of the remaining life of used components, the remaining life of components are equally classified into three levels

Very high More than four years 5

High Four years 4

Moderate Three years 3

Low Two years 2

Very low One year 1

1

Assessment indicators are used measure the quality condition level of a core. In order to evaluate these indicators, we need to establish an evaluation index system used to control and measure uniformity. Due to the high uncertainty in the core conditions, sometimes it is very hard to determine the technical index quantitatively for each criterion as its complexity and the difficulty; thus, it can only be measured qualitatively by expert assessment as can be seen in the table 1 and table 2

1 1 1

0 0 0X X X

fjk

fjk

fjk

Xjk(1) Xj

k(2) Xjk(4)Xj

k(3)Xjk(1) Xj

k(2) Xjk(4)

A. Best of quality class B. Middle of quality class C. Lower of quality class

Figure 6. The whitenization weight function

For example the criterion j=1, the whitenization weight function is described as follows:

𝑓𝑗=11 (𝑋𝑗) = {

0, 𝑋 < 0 𝑋

5, 0 ≤ 𝑋 < 5

1, 𝑋 ≥ 5

(1)

𝑓𝑗=12 (𝑋𝑗) =

{

0, 𝑋 < 0𝑋2.5

, 0 ≤ 𝑋 ≤ 5

5 − 𝑋2.5

2.5 < 𝑋 ≤ 5

0, 𝑋 > 5

(2)

𝑓𝑗=13 (𝑋𝑗) =

{

0, 𝑋 < 0 1, 0 ≤ 𝑋 ≤ 2.55 − 𝑋2.5

2.5 < 𝑋 ≤ 5

0, 𝑋 > 5

(3)

Meanwhile, the criteria j=2, 3, 4, the whitenization weight function are expressed with:

𝑓𝑗=2,3,4,71 (𝑋𝑗) = {

0, 𝑋 < 0 𝑋

5, 0 ≤ 𝑋 < 5

1, 𝑋 ≥ 5

(4)

𝑓𝑗=2,3,4,72 (𝑋𝑗) =

{

0, 𝑋 < 0𝑋2.5

, 0 ≤ 𝑋 ≤ 5

5 − 𝑋2.5

2.5 < 𝑋 ≤ 5

0, 𝑋 > 5

(5)

𝑓𝑗=2,3,4,73 (𝑋𝑗) =

{

0, 𝑋 < 0 1, 0 ≤ 𝑋 ≤ 2.55 − 𝑋2.5

2.5 < 𝑋 ≤ 5

0, 𝑋 > 5

(6)

2

Conclusion

The purpose of the current study was to extend an existing approach to classify/sort the incoming core quality into predefined classes by using grey decision making. The results of this study show that grey clustering is a powerful method which is concerned to classify observation index or observation objects into definable groups by using a grey index matrix or a grey possibility function. An implication of this is the possibility that quality criteria for sorting the incoming core based on: technological, physical, and usage conditions. This study provides the comprehensive quality assessment of incoming core in the core acquistion of remanufacturing system.

Acknowledgement

The authors would like to thanks Kemenristek BRIN for the funding Postgraduate Research Grant of the

year 2020 Dissertation Doctoral Research Grant, No. Contract: 930/PKS/ITS/2020

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6

LAMPIRAN 1 Tabel Daftar Luaran

Program : Penelitian Pascasarjana

Nama Ketua Tim : Prof. Dr. Ir. Udisubakti Ciptomulyono, M.Eng.Sc

Judul : Pemodelan Multi-Criteria Sorting Problem pada Akuisisi

Core dalam Sistem Remanufaktur

1.Artikel Jurnal

No Judul Artikel Nama Jurnal Status Kemajuan*)

1. Multi-Criteria Sorting Problem in

Core Acquisition For

Remanufacturing Based on Grey-

AHPShort

Sustainability,

MDPI

Draft manuskrip (in

progress)

*) Status kemajuan: Persiapan, submitted, under review, accepted, published

2. Artikel Konferensi

No Judul Artikel Nama Konferensi (Nama

Penyelenggara, Tempat,

Tanggal)

Status Kemajuan*)

1 Sorting the Incoming Core Quality

of Remanufacturing Based On

Grey Cluster Model

International Conference on

Mechanical Engineering

Research and Application

(iCOMERA)


Jurusan Teknik Mesin

Universitas Brawijaya.

Oktober 7 – 9, 2020

Submitted (paper in

review)

*) Status kemajuan: Persiapan, submitted, under review, accepted, presented

3. Paten

No Judul Usulan Paten Status Kemajuan

*) Status kemajuan: Persiapan, submitted, under review

4. Buku

No Judul Buku (Rencana) Penerbit Status Kemajuan*)


7

*) Status kemajuan: Persiapan, under review, published

5. Hasil Lain

No Nama Output Detail Output Status Kemajuan*)

*) Status kemajuan: cantumkan status kemajuan sesuai kondisi saat ini

6. Disertasi/Tesis/Tugas Akhir/PKM yang dihasilkan

No Nama Mahasiswa NRP Judul Status*)

1 M. Imron Mustajib 02411560010003 Model

Remanufacturing

Planning dengan

Mempertimbangkan

Kondisi

Ketidakpastian

Kualitas

in progress

*) Status kemajuan: cantumkan lulus dan tahun kelulusan atau in progress

laporan akhir penelitian pascasarjna dana its 2020 · 2020. 11. 30. · i laporan akhir penelitian...

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