scholarly journals An Investigation on a Closed-Loop Supply Chain of Product Recycling Using a Multi-Agent and Priority Based Genetic Algorithm Approach

Mathematics ◽  
2020 ◽  
Vol 8 (6) ◽  
pp. 888
Author(s):  
Yong-Tong Chen ◽  
Zhong-Chen Cao
Keyword(s):  
Genetic Algorithm ◽  
Supply Chain ◽  
Closed Loop ◽  
Reverse Flow ◽  
Computational Time ◽  
Solution Quality ◽  
Closed Loop Supply Chain ◽  
Product Flow ◽  
Multi Agent ◽  
Product Recycling

Product recycling issues have gained increasing attention in many industries in the last decade due to a variety of reasons driven by environmental, governmental and economic factors. Closed-loop supply chain (CLSC) models integrate the forward and reverse flow of products. Since the optimization of these CLSC models is known to be NP-Hard, competition on optimization quality in terms of solution quality and computational time becomes one of the main focuses in the literature in this area. A typical six-level closed-loop supply chain network is examined in this paper, which has great complexity due to the high level of echelons. The proposed solution uses a multi-agent and priority based approach which is embedded within a two-stage Genetic Algorithm (GA), decomposing the problem into (i) product flow, (ii) demand allocation and (iii) pricing bidding process. To test and demonstrate the optimization quality of the proposed algorithm, numerical experiments have been carried out based on the well-known benchmarking network. The results prove the reliability and efficiency of the proposed approach compared to LINGO and the benchmarking algorithm discussed in the literature.

Robust optimization model for sustainable supply chain for production and distribution of polyethylene pipe

2020 ◽  
Vol 15 (4) ◽  
pp. 1613-1653
Author(s):  
Jaber Valizadeh ◽  
Ehsan Sadeh ◽  
Zainolabedin Amini Sabegh ◽  
Ashkan Hafezalkotob
Keyword(s):  
Genetic Algorithm ◽  
Supply Chain ◽  
Environmental Impacts ◽  
Closed Loop ◽  
Optimal Location ◽  
Computational Time ◽  
Closed Loop Supply Chain ◽  
Content Type ◽  
Proposed Model ◽  
Production And Distribution

Purpose In this study, the authors consider the key decisions in the design of the green closed-loop supply chain (CSLC) network. These decisions include considering the optimal location of suppliers, production facilities, distribution, customers, recycling centers and disposal of non-recyclable goods. In the proposed model, the level of technology used in recycling and production centers is taken into account. Moreover, in this paper is the environmental impacts of production and distribution of products based on the eco-indicator 99 are considered. Design/methodology/approach In this study, the author consider the key decisions in the design of the green CLSC network. These decisions include considering the optimal location of suppliers, production facilities, distribution, customers, recycling centers and disposal of non-recyclable goods. In the proposed model, the level of technology used in recycling and production centers is taken into account. Moreover, the environmental impacts of production and distribution of products based on the eco-indicator 99 are considered. Findings The results indicate that the results obtained from the colonial competition algorithm have higher quality than the genetic algorithm. This quality of results includes relative percentage deviation and computational time of the algorithm and it is shown that the computational time of the colonial competition algorithm is significantly lower than the computational time of the genetic algorithm. Furthermore, the limit test and sensitivity analysis results show that the proposed model has sufficient accuracy. Originality/value Solid modeling of the green supply chain of the closed loop using the solid optimized method by Bertsimas and Sim. Development of models that considered environmental impacts to the closed loop supply chain. Considering the impact of the technology type in the manufacture of products and the recycling of waste that will reduce emissions of environmental pollutants. Another innovation of the model is the multi-cycle modeling of the closed loop of supply chain by considering the uncertainty and the fixed and variable cost of transport.

Study on Incentive Mechanism of MRCTM Closed-Loop Supply Chain Based on the Uncertainty Quality

2010 ◽  
Vol 108-111 ◽  
pp. 341-346
Author(s):  
Huang Ling
Keyword(s):  
Supply Chain ◽  
Closed Loop ◽  
Conversion Factor ◽  
Incentive Mechanism ◽  
Incentive Payment ◽  
Closed Loop Supply Chain ◽  
Optimal Analysis ◽  
The Incentive Mechanism ◽  
Product Recycling

In this paper, the MRCTM model of closed-loop supply chain was investigated. An effective incentive mechanism that considered the uncertainty quality of product recycling was designed by the introduction of brand conversion factor. The author made an optimal analysis of the incentive mechanism. Moreover, the influence of different parameters, such as quality and brand to the fixed reward and the unit incentive payment was analyzed and some useful results were obtained.

scholarly journals Intelligent Optimization Algorithms: A Stochastic Closed-Loop Supply Chain Network Problem Involving Oligopolistic Competition for Multiproducts and Their Product Flow Routings

2015 ◽  
Vol 2015 ◽  
pp. 1-22 ◽  
Author(s):  
Yan Zhou ◽  
Chi Kin Chan ◽  
Kar Hung Wong ◽  
Y. C. E. Lee
Keyword(s):  
Supply Chain ◽  
Nash Equilibrium ◽  
Closed Loop ◽  
Real Life ◽  
Supply Chain Network ◽  
Oligopolistic Competition ◽  
Objective Functions ◽  
Closed Loop Supply Chain ◽  
Product Flow ◽  
Nonsmooth Problems

Recently, the first oligopolistic competition model of the closed-loop supply chain network involving uncertain demand and return has been established. This model belongs to the context of oligopolistic firms that compete noncooperatively in a Cournot-Nash framework. In this paper, we modify the above model in two different directions. (i) For each returned product from demand market to firm in the reverse logistics, we calculate the percentage of its optimal product flows in each individual path connecting the demand market to the firm. This modification provides the optimal product flow routings for each product in the supply chain and increases the optimal profit of each firm at the Cournot-Nash equilibrium. (ii) Our model extends the method of finding the Cournot-Nash equilibrium involving smooth objective functions to problems involving nondifferentiable objective functions. This modification caters for more real-life applications as a lot of supply chain problems involve nonsmooth functions. Existence of the Cournot-Nash equilibrium is established without the assumption of differentiability of the given functions. Intelligent algorithms, such as the particle swarm optimization algorithm and the genetic algorithm, are applied to find the Cournot-Nash equilibrium for such nonsmooth problems. Numerical examples are solved to illustrate the efficiency of these algorithms.

scholarly journals Sustainable Closed-Loop Supply Chain Design Problem: A Hybrid Genetic Algorithm Approach

Mathematics ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 84 ◽  
Author(s):  
YoungSu Yun ◽  
Anudari Chuluunsukh ◽  
Mitsuo Gen
Keyword(s):  
Genetic Algorithm ◽  
Supply Chain ◽  
Design Problem ◽  
Closed Loop ◽  
Hybrid Genetic Algorithm ◽  
Distribution Channel ◽  
Distribution Channels ◽  
Supply Chain Design ◽  
Closed Loop Supply Chain ◽  
Normal Delivery

In this paper, we propose a solution to the sustainable closed-loop supply chain (SCLSC) design problem. Three factors (economic, environmental, and social) are considered for the problem and the three following requirements are addressed while satisfying associated constraint conditions: (i) minimizing the total cost; (ii) minimizing the total amount of CO2 emission during production and transportation of products; (iii) maximizing the social influence. Further, to ensure the efficient distribution of products through the SCLSC network, three types of distribution channels (normal delivery, direct delivery, and direct shipment) are considered, enabling a reformulation of the problem as a multi-objective optimization problem that can be solved using Pareto optimal solutions. A mathematical formulation is proposed for the problem, and it is solved using a hybrid genetic algorithm (pro-HGA) approach. The performance of the pro-HGA approach is compared with those of other conventional approaches at varying scales, and the performances of the SCLSC design problems with and without three types of distribution channels are also compared. Finally, we prove that the pro-HGA approach outperforms its competitors, and that the SCLSC design problem with three types of distribution channels is more efficient than that with a single distribution channel.

scholarly journals A Hybrid Approach to Solve a Model of Closed-Loop Supply Chain

2015 ◽  
Vol 2015 ◽  
pp. 1-18 ◽  
Author(s):  
Nafiseh Tokhmehchi ◽  
Ahmad Makui ◽  
Soheil Sadi-Nezhad
Keyword(s):  
Supply Chain ◽  
Closed Loop ◽  
Reverse Flow ◽  
Hybrid Approach ◽  
The Other ◽  
Mixed Integer ◽  
Closed Loop Supply Chain ◽  
Total Cost ◽  
Large Size ◽  
Hybrid Forms

This paper investigates a closed-loop supply chain network, including plants, demand centers, as well as collection centers, and disposal centers. In forward flow, the products are directly sent to demand centers, after being produced by plants, but in the reverse flow, reused products are returned to collection centers and, after investigating, are partly sent to disposal centers and the other part is resent to plants for remanufacturing. The proposed mathematical model is based on mixed-integer programming and helps minimizing the total cost. Total costs include the expenditure of establishing new centers, producing new products, cargo transport in the network, and disposal. The model aims to answer these two questions. (1) What number and in which places the plants, collection centers, and disposal centers will be constructed. (2) What amount of products will be flowing in each segment of the chain, in order to minimize the total cost. Four types of tuned metaheuristic algorithms were used, which are hybrid forms of genetic and firefly algorithms. Finally an adequate number of instances are generated to analyse the behavior of proposed algorithms. Computational results reveal that iterative sequentialization hybrid provides better solution compared with the other approaches in large size.

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