A Comparative Study Of Metaheuristics For Energy-Saving Job Shop Scheduling Problem With Transportation Times: A Case Study Of Viet Tien Garment Corporation

Abstract

The core objective of this paper is finding optimized methods to mitigate the total completion time of machines to performance orders early, leading to directly reducing the energy. This problem is inspired by a real situation observed in a garment manufacturer. The reduction in production will be a great competitive advantage for the company in this competitive market. This paper will examine the production schedule of Viet Tien Garment Corporation in Vietnam. Currently, the company often considers machining and assembly stages separately, which could lead to inefficiencies and a lack of holistic optimization in terms of job and operation precedence. This method is simple to use and is widely used in production planning. To increase machine utilization or minimize total energy consumption the mathematical model is used to modify the system and give the optimized production schedule for the job-shop scheduling problem. The BOM of products, processing time of machines to complete each operation are collected and integrated with the model conception (Mixed integer linear programming) to give the optimized result by running code through CPLEX software. Additionally, I develop a metaheuristic based on genetic algorithm which can efficiently address large problems by Python code. Based on that information, the company may make some suggestions to modify or minimize the total energy consumption in the long term. When the problem is solved by the best result, the sensitive analysis is conducted to evaluate methods. As a result, the company may make plans and some potential strategies to run the business.