Operation patterns analysis of automotive components remanufacturing industry development in China

Tian, Guangdong; Zhang, Honghao; Feng, Ying; Zhang, Chaoyong; Jiang, Zhigang; Li, Zhiwu; Li, Peigen

doi:10.1016/j.jclepro.2017.07.028

Cited by 181 publications

(94 citation statements)

References 66 publications

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“…the MCDM problem [38], that is evaluating the design alternatives by the gray relational closeness index (R) among data sequences as a measurement scale through analyzing similarity curve geometry and geometric relations among data sequences [27,28]. Note that the closer the curve, the larger the R. The detailed procedures could be summarized as follows:…”

Section: Gra Methods Gra Methods Was Proposed To Solvementioning

confidence: 99%

“…An overview of main methodologies for solving this problem by former researchers is revealed briefly in this section. Some MCDM methods, e.g., AHP, analytic network process (ANP), GRA, the technique for order preference by similarity to ideal solution (TOPSIS), and Decision-Making and Evaluation Laboratory (DEMATEL) and integrated approaches, e.g., gray-based DEMATEL and GC-TOPSIS, have been successfully presented and utilized to solve the GSCM problem [3,13,[25][26][27][28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

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Green supply chain management of automotive manufacturing industry considering multiperspective indices

Huang

Zhang

2019

IEEJ Transactions Elec Engng

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Considering strategic targets and fierce competition, high‐efficiency supply chain management (SCM) and selection strategy has a significant impact on the automotive manufacturing industry to improve its competitive advantage and economic benefit. However, the selection process of supply chain is often inevitably affected by multiperspective factors, e.g., societal and economic properties. Thus, multicriteria decision‐making as an effective method is needed. This article establishes a systematic hierarchical structure of multiperspective indices for SCM of automotive manufacturing industry including economic, environmental, societal, and technical properties. An integrated method that combines gray relational analysis and analytic hierarchy process is applied to select the optimal pattern of supply chain. An empirical case about three types of supply chain for automotive manufacturing industry is applied to test the effectiveness of multicriteria decision‐making method and the hierarchical structure. In addition, a comparison with other methods and a sensitivity analysis are conducted to validate the reliability of this research. The results show this method can be effectively used in the decision‐making process of SCM in the automotive manufacturing industry. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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Section: Gra Methods Gra Methods Was Proposed To Solvementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Green supply chain management of automotive manufacturing industry considering multiperspective indices

Huang

Zhang

2019

IEEJ Transactions Elec Engng

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“…A set of the combinations (m, n) is generated, where m and n are the number of machines and jobs, respectively. In this study, all the instances are (3, 10), (4,15), (5,24), (6,32), (7,26), (8,42), and (9, 56), respectively. The total amount of resources in each combination (m, n) is set to R = m + 1, m + 2, m + 3, respectively.…”

Section: Test Instance Generationmentioning

confidence: 99%

“…For future work, it is necessary to carry out a sensitivity analysis on the parameter setting in the proposed algorithm. It is also interesting to deal with the dynamic resource allocation in flexible manufacturing systems [39] and process industry systems [40][41][42][43][44]. In addition, multiobjective scheduling with dynamic resource allocation is also valuable because of its widespread applications.…”

mentioning

confidence: 99%

Parallel machine scheduling with dynamic resource allocation via a master–slave genetic algorithm

Tian

et al. 2018

IEEJ Transactions Elec Engng

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Scheduling problems with resource allocation have become of increasing interest in the academic and industrial areas in recent years. This study investigates parallel machine scheduling with dynamic resource allocation under the objective of minimizing the makespan, where the processing of jobs needs an additional resource that can be assigned and reassigned among all jobs, and the processing time of jobs is a function of the amount of allocated resources. Four decisions must be made in a schedule, namely job assignment, job sequence, resource allocation and jobs' starting time. In order to solve it efficiently, a master-slave genetic algorithm is proposed to determine the decisions of job assignment, job sequence and resource allocation, and a greedy heuristic rule is designed to determine the jobs' starting time. To examine the performance of the proposed algorithm, simulation experiments are carried out on a set of instances, and similar existing algorithms and the standard genetic algorithm are chosen as for comparison. The experimental results show that the proposed algorithm is a promising optimizer in solving the investigated problem.

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“…The second type is the objective weighting method, such as entropy weight method [14], variation coefficient method, correlation coefficient method, weighted average planning method [15] and TOPSIS method [16,17]. The other type is the combination method of subjectivity and objectivity [18][19][20], such as ELECTRE method, fuzzy comprehensive evaluation method [21,22] and PROMETHEE and so forth [23]. Moreover, two weight optimization methods based on variance maximization are proposed in a previous research [24], which discusses the multi-index comprehensive evaluation model considering two different conditions respectively (for example with prior information and without prior information).…”

Section: Introductionmentioning

confidence: 99%

A Variance Maximization Based Weight Optimization Method for Railway Transportation Safety Performance Measurement

et al. 2018

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Based on the idea of maximizing variances, a weight optimization method is proposed in this research to improve railway transportation safety evaluation. Firstly, the main evaluation indicators that can reflect the safety of railway transportation are selected as the independent variables. Secondly, in order to avoid the influence of experts' empowerment on the evaluation results of railway transport safety, fuzzy set theory is introduced to generate the variation range of the weights of each evaluation index, which is used as the constraint of weight optimization model. Then, the weight optimization model for railway transportation safety performance measurement is established based on the principle of maximum variance. The structure of the optimization model shows the characteristics of the quadratic programming model. Therefore, the optimal weight is calculated by using the branch bounded algorithm, which is one of the quadratic programming model solution algorithms. Finally, the empirical analysis of the safety performance measurement for 18 railway bureaus shows that using the optimized index weight for safety performance measurement can not only make full use of prior information but also ensure that 18 railway bureaus can be distinguished to the maximum extent.

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Operation patterns analysis of automotive components remanufacturing industry development in China

Cited by 181 publications

References 66 publications

Green supply chain management of automotive manufacturing industry considering multiperspective indices

Green supply chain management of automotive manufacturing industry considering multiperspective indices

Parallel machine scheduling with dynamic resource allocation via a master–slave genetic algorithm

A Variance Maximization Based Weight Optimization Method for Railway Transportation Safety Performance Measurement

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