2024 : 11 : 24
Jamal Arkat

Jamal Arkat

Academic rank: Professor
ORCID:
Education: PhD.
ScopusId: 55912953100
HIndex:
Faculty: Faculty of Engineering
Address: Department of Industrial Engineering, University of Kurdistan, Sanandaj, Iran
Phone: 08733660073

Research

Title
Using common redundancy components for suppliers in a supply chain network design problem considering energy costs and environmental effects
Type
JournalPaper
Keywords
Green supply chain management Location-inventory model Risk pooling Redundancy allocation
Year
2024
Journal Expert Systems with Applications
DOI
Researchers farid Abdi ، Hiwa Farughi ، Heibatolah Sadeghi ، Jamal Arkat

Abstract

Improving the reliability of factories and suppliers through appropriate allocation of redundancy components is crucial in responding to customer demand. However, if individual factories independently buy and allocate redundancy components, huge investments will be required. To optimize the reliability level and costs of the supply chain network, the present study allocates these components to suppliers based on customer demand. While allocating redundancy components to factories improves the chain’s reliability, it increases energy consumption and greenhouse gas emissions. Therefore, a balance between cost, reliability, and pollution needs to be established. The study considers a three-level supply chain, including the supplier (factories), distributor, and retailer, with stochastic and normally distributed demand assumed for each retailer. To address demand fluctuations, the risk pooling effect is implemented. As the problem belongs to the class of NP-hard problems, the study develops three multi-objective meta-heuristic algorithms, namely Non-dominated Sorting Genetic Algorithm II (NSGAII), Archived Multi-Objective Simulated Annealing (AMOSA), and Multi-Objective Artificial Bee Colony (MOABC) to solve it. The performance of these algorithms is evaluated using the results of the complete enumeration method. The comparison results based on six multi-objective performance metrics show that the MOABC algorithm has better performance in finding high-quality solutions in less time, while the NSGAII algorithm provides more diverse Pareto optimal solutions.