Sustainable and Resilient Garment Supply Chain Network Design with Fuzzy Multi-Objectives under Uncertainty

Mari, Sonia Irshad; Lee, Young Hae; Memon, Muhammad Saad

doi:10.3390/su8101038

Cited by 43 publications

(41 citation statements)

References 17 publications

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“…Nine key drivers of supply chain risks are considered and grouped into three categories namely: economic, risk quality, and supply chain factors. These nine drivers include local supplier quality, quality of fire risk management, GDP per capita, oil intensity, quality of hazard risk management, exposure to natural hazards, corruption control, infrastructure, and political risks [49]. Transit time is the last resilient criteria for supplier selection considered in this study.…”

Section: Literature Reviewmentioning

confidence: 99%

Interactive Fuzzy Multi Criteria Decision Making Approach for Supplier Selection and Order Allocation in a Resilient Supply Chain

et al. 2019

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Modern supply chains are vulnerable to high impact, low probability disruption risks. A supply chain usually operates in such a network of entities where the resilience of one supplier is critical to overall supply chain resilience. Therefore, resilient planning is a key strategic requirement in supplier selection decisions for a competitive supply chain. The aim of this research is to develop quantitative resilient criteria for supplier selection and order allocation in a fuzzy environment. To serve the purpose, a possibilistic fuzzy multi-objective approach was proposed and an interactive fuzzy optimization solution methodology was developed. Using the proposed approach, organizations can tradeoff between cost and resilience in supply networks. The approach is illustrated using a supply chain case from a garments manufacturing company.

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Section: Literature Reviewmentioning

confidence: 99%

Interactive Fuzzy Multi Criteria Decision Making Approach for Supplier Selection and Order Allocation in a Resilient Supply Chain

et al. 2019

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“…In this way, decision makers will obtain a set of Pareto optimal solutions. Several popular methods, such as the ε-constraint approach [64], the weight-sum method [65], and fuzzy multi-objective programming [66] are frequently applied to solve the multi-objective model. Mavrotas [67] has proved that the ε-constraint approach has several advantages over the weight-sum method, and the procedure of fuzzy multi-objective programming is more complex.…”

Section: Multi-objective Techniquementioning

confidence: 99%

A Robust Fuzzy Optimization Model for Closed-Loop Supply Chain Networks Considering Sustainability

Zhang

Zhao

et al. 2019

Sustainability

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Supply chain network design (SCND) is an important strategic decision determining the structure of each entity in the supply chain, which has an important impact on the long-term development of a company. An efficient and effective supply chain network is of vital importance for improving customer satisfaction, optimizing the allocation of resources, and increasing profitability. The environmental concerns and social responsibility awareness of the whole society have spurred researchers and managers to design sustainable supply chains (SSCs) integrating the economic, environmental, and social factors. In addition, the innate uncertainty of the SCND problem requires an integrated method to cope. In this regard, this study develops a multi-echelon multi-objective robust fuzzy closed-loop supply chain network (CLSCN) design model under uncertainty including all three dimensions of sustainability. This model considers the total cost minimization, carbon caps, and social impact maximization concurrently to realize supply chain sustainability, and is able to make a balance between the conflicting multiple objectives. Meanwhile, the uncertainty of the parameters is divided into two categories and addressed with two approaches: the first category is missed working days related to social impact, which is solved by the fuzzy membership theory; the second category is the demand and remanufacturing rate, which is settled by a robust optimization method. To validate the ability and applicability of the model and solution approach, a numerical example is conducted and solved using ILOG CPLEX. The result shows that the supply chain network structure and the value of the optimization objectives will change when considering sustainability and different degrees of uncertainty. This will enable supply chain managers to reduce the environmental impact and enhance the social benefits of their supply chain activities, and design a more stable supply chain to better cope with the influence of uncertainty.

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“…The most likely value of each parameter is picked from various sources, and all required calculations are done beforehand. Thereafter, two random numbers n 1 and n 2 are generated between 0.2 and 0.8 using a uniform distribution, and the pessimistic β pes and the optimistic value β opt of a fuzzy number β are estimated using Equations (9) and (10), respectively [43].…”

Section: Debris Allocation To the Selected Temporary Disaster Debris mentioning

confidence: 99%

“…To generate various solutions, the decision maker may vary the value of α from 0-1 [43]. Equations (16) The concept was further modified by Jiménez et al [45] using the "expected interval" and "expected value" of a fuzzy number, which was originally developed by Dubois and Prade [46].…”

Section: Debris Allocation To the Selected Temporary Disaster Debris mentioning

confidence: 99%

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An Integrated Location-Allocation Model for Temporary Disaster Debris Management under an Uncertain Environment

Habib

Sarkar

2017

Sustainability

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Natural disasters always generate an overwhelming amount of debris. Reusing and recycling waste from disasters are essential for sustainable debris management. Before recycling the debris, it is necessary to sort this mixed waste. To perform the sorting process efficiently, a Temporary Disaster Debris Management Site (TDDMS) is required, and the selection of TDDMS is a multi-criteria decision-making problem due to its numerous regional and municipal constraints. This paper provides a two-phase framework for sustainable debris management during the response phase of disasters. In the first phase, a methodology for TDDMS selection is proposed that consists of Analytical Network Process (ANP) and a fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). In the second phase, a debris allocation optimization model is developed to allocate the debris from disaster-affected regions to the selected TDDMS. A city prone to hurricane damage is selected to illustrate the proposed framework. For the debris allocation purpose, five TDDMS are chosen, among which three sites are selected using the proposed methodology. To illustrate the utilization of the proposed study, a numerical example with two different scenarios is provided. Numerical outcomes prove that the model results in a sustainable debris management system for disasters.

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Sustainable and Resilient Garment Supply Chain Network Design with Fuzzy Multi-Objectives under Uncertainty

Cited by 43 publications

References 17 publications

Interactive Fuzzy Multi Criteria Decision Making Approach for Supplier Selection and Order Allocation in a Resilient Supply Chain

Interactive Fuzzy Multi Criteria Decision Making Approach for Supplier Selection and Order Allocation in a Resilient Supply Chain

A Robust Fuzzy Optimization Model for Closed-Loop Supply Chain Networks Considering Sustainability

An Integrated Location-Allocation Model for Temporary Disaster Debris Management under an Uncertain Environment

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