Modelling and optimization of closed-loop supply chain networks considering product recovery

Pao Yue-kong Library Electronic Theses Database

Modelling and optimization of closed-loop supply chain networks considering product recovery

 

Author: Chen, Yongtong
Title: Modelling and optimization of closed-loop supply chain networks considering product recovery
Degree: Ph.D.
Year: 2016
Subject: Business logistics.
Business logistics -- Environmental aspects.
Recycling (Waste, etc.)
Hong Kong Polytechnic University -- Dissertations
Department: Dept. of Industrial and Systems Engineering
Pages: xvii, 221 pages : color illustrations
Language: English
InnoPac Record: http://library.polyu.edu.hk/record=b2929124
URI: http://theses.lib.polyu.edu.hk/handle/200/8790
Abstract: Nowadays, environmental pollutions caused by improper abandoned end of life products are increasing dramatically. To improve the pollution situation caused by abandoned products, product recovery became popular in the research area of closed-loop supply chain recently. To accelerate the recovery of end of life products, so as to reduce the environmental pollution, the network of product recovery within closed-loop supply chain needs better planning. Since effective optimization design of product recovery network leads to efficiency and profitable business operation, thereby attracts more practitioners, it is significant to develop a closed-loop supply chain model considering product recovery. Correspondingly, the analysis of uncertainties became necessary. However, few of the work to date focused on the product recovery issues in closed-loop supply chain. This research focuses on modeling and optimization of Closed-Loop Supply Chain (CLSC) network considering product recovery. Firstly, an integrated CLSC model considering product recovery is formulated. This model optimizes facility location and product delivery in CLSC network, considering product recovery simultaneously. Eight partners in CLSC are considered, including suppliers, manufacturers, warehouses, retailers, customer regions, collection points, recycling centers, and waste disposal plant. In the literature, many closed-loop supply chain models were established and studied, but few of them analyzed the delivery activity for different kinds of recycled materials, and also only a few papers studied the situation that end of life products are classified according to their quality level. In this model, the delivery activities of different materials are considered and the end of life products are classified into good quality ones and poor quality ones. Producers will have different methods to process them. To address the proposed closed-loop supply chain model considering product recovery, a modified two-stage Genetic Algorithm is proposed. The two-stage encoding algorithm in the proposed Genetic Algorithm reinforces the genetic searching ability in tackling this kind of problem. To demonstrate the ability of the developed Genetic Algorithm, Integer Programming is implemented to solve the testing instances and benchmarked with the proposed algorithm. The results show that this proposed Genetic Algorithm can obtain a more reliable and higher quality solution in a much shorter computational time.
To further inspect the process of product recovery in a CLSC, a multi-period decision model considering uncertainties is established. This model simulates the process of product refurbishment in a CLSC and discusses uncertainties in end of life products collection, including customer demand uncertainty, returned product quality uncertainty and returned product quantity uncertainty. In the literature, few research studies have focused on the process of returned products refurbishment in closed-loop supply chain networks. Considering the deepening crisis caused by abandoned end of life products, especially electronic waste, it is important to propose a model like this. This proposed model structures the end of life products refurbishment process and provides decision supports for end of life products collection, considering the uncertainties in both quantity and quality. In this decision model, a two-layer fuzzy controller embedded with a quality indicator is developed. This proposed method effectively deals with the uncertainties of both supplies and demands in multi-period production planning of returned products refurbishment. A simulation system based on this model is implemented, which proves the effectiveness of the proposed fuzzy controller. It also proves the efficiency of the quality indicator dealing with the quality uncertainty. The developed simulation system provides decision support for end of life products collection for responsible manufacturers. Additionally, the increasing profits of end of life products refurbishment encourage product recovery to some extent. The originality and significance of this research lies in the proposed models and algorithms. This research contributes to the body of knowledge by: (i) Establishing a closed-loop supply chain model with product recovery, which filling the gap of considering multiple materials with different quality in CLSC networks, (ii) developing a two-stage priority based encoding Genetic Algorithm to reinforce the genetic searching ability in tackling NP-hard problems in closed-loop supply chain, (iii) establishing a multi-period product refurbishment model in closed-loop supply chain, which fill the gap of describing product refurbishment process considering uncertainties, (iv) developing a two-layer fuzzy controller embedded with a quality indicator to deal with uncertainties effectively in product refurbishment, and (v) implementing a simulation system to simulate the process of product refurbishment, which providing decision supports for end of life product collection and in turn mitigate environmental problems. This research provides an important means to better understand the product recovery in closed-loop supply chain and contributes significantly to the further improvement of the performance of end of life product collection.

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