Supply Chain Optimization Considering Sustainability Aspects

Mohammad Ali Beheshtinia,Masood Fathi,Parisa Feizollahy

doi:10.3390/su132111873

Mohammad Ali Beheshtinia, Masood Fathi + Show 1 more

Open Access

https://doi.org/10.3390/su132111873

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Abstract

Supply chain optimization concerns the improvement of the performance and efficiency of the manufacturing and distribution supply chain by making the best use of resources. In the context of supply chain optimization, scheduling has always been a challenging task for experts, especially when considering a distributed manufacturing system (DMS). The present study aims to tackle the supply chain scheduling problem in a DMS while considering two essential sustainability aspects, namely environmental and economic. The economic aspect is addressed by optimizing the total delivery time of order, transportation cost, and production cost while optimizing environmental pollution and the quality of products contribute to the environmental aspect. To cope with the problem, it is mathematically formulated as a mixed-integer linear programming (MILP) model. Due to the complexity of the problem, an improved genetic algorithm (GA) named GA-TOPKOR is proposed. The algorithm is a combination of GA and TOPKOR, which is one of the multi-criteria decision-making techniques. To assess the efficiency of GA-TOPKOR, it is applied to a real-life case study and a set of test problems. The solutions obtained by the algorithm are compared against the traditional GA and the optimum solutions obtained from the MILP model. The results of comparisons collectively show the efficiency of the GA-TOPKOR. Analysis of results also revealed that using the TOPKOR technique in the selection operator of GA significantly improves its performance.

Highlights

A supply chain is composed of independent organizations such as suppliers, logistics providers, manufacturers, and distributors who all work in an integrated system to add value to a product [1]
This study investigates the scheduling problem in a supply chain network consisting of a manufacturer with multiple sites scattered in different regions and its customers
The performance of the genetic algorithm (GA)-TOPKOR was compared with the conventional GA for large-scale problems and with the optimum solution for small-size problems

Summary

Introduction

A supply chain is composed of independent organizations such as suppliers, logistics providers, manufacturers, and distributors who all work in an integrated system to add value to a product [1]. Nowadays, applying a distributed manufacturing system (DMS) has attracted the attention of many supply chain managers [2]. Applying the multi-site strategy may provide the manufacturers with several advantages such as less transportation costs in the supply chain, the possibility of using the environmental potentials such as cheap workforce or materials, and reducing the risk of production cease due to natural disasters such as earthquakes or floods [3]. Despite the advantages of DMS, scheduling a supply chain network that consists of several production units is a challenging task for most companies. It gives rise to a problem known as supply chain scheduling problem (SCCP)

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