Nghiên cứu tối ưu hóa lịch trình nhà máy tiết kiệm năng lượng với thời gian vận chuyển tại Viet Tien

A COMPARATIVE STUDY OF METAHEURISTICS FOR ENERGY- SAVING JOB SHOP SCHEDULING PROBLEM WITH TRANSPORTATION TIMES: A CASE STUDY OF VIET TIEN GARMENT CORPORATION A COMPARATIVE STUDY OF METAHEURISTICS FOR ENERGY- SAVING JOB SHOP SCHEDULING PROBLEM WITH TRANSPORTATION TIMES: A CASE STUDY OF VIET TIEN GARMENT CORPORATION 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.

Keywords: energy-saving scheduling; Production schedule; total energy consumption; Particle Swarm Optimization, Mixed integer linear programming, CPLEX software, Python code. Acknowledgements First, I would like to thank Dr. Phan Nguyen Ky Phuc, my adviser, for his important assistance and guidance throughout this challenging period. He has spent many hours offering helpful critiques, comments, and recommendations to improve the caliber of this work, for which I am very appreciative.

In addition to all the committed instructors who have generously shared their knowledge and given students pursuing a Bachelor of Engineering in Logistics and Supply Chain Management priceless educational advice, I would like to express my sincere gratitude and thanks to the distinguished Board of Deans of the School of Industrial Engineering Management. I express my gratitude to the jury members for their keen interest in this subject and for giving them time to consider my thesis. In my professional journey, I have been fortunate to acquire extensive knowledge in supply chain operational activities and to encounter and navigate significant logistical challenges in real-world business settings. These practical experiences serve as the bedrock of the insights presented in this paper, contributing a nuanced perspective to the academic discourse.

Lastly, my heartfelt gratitude is extended to my family, fellow seniors, and friends, whose unwavering emotional support and generous encouragement have been indispensable. Their collective assistance has played an instrumental role in the successful completion of this study, and I am profoundly appreciative of their steadfast commitment. The collaborative efforts and support from these individuals have been pivotal in this thesis's culmination. ii Table of Contents Abstract.iii Table of Contents.

iv List of Tables.vi List of Figures.vii List of Abbreviations.2 Problem Statement – The Need for Study:.3 Objectives of Study:.4 Scope and Limitation:.5 CHAPTER 2 RELATED WORKS.3 Candidate Solution Methods.1 Approaches Comparison and Selection:.2 Proposed System Design.24 CHAPTER 4 SOLUTION DEVELEPMENT.1 Model or Prototype Solution.33 CHAPTER 5 RESULT ANALYSIS.52 CHAPTER 6 CONCLUSION AND RECOMMENDATION. Recommendations for future research.100 iv List of Tables Table 3-1: Summary of considered approaches 23 Table 4-1: Indices tables about number if orders, number of machines and number of operations. 35 Table 4-2: Indices tables about number if orders, number of machines and number of operations. 36 Table 4-3: The processing time of operations of a Nike Pants completed by machines (minutes).37 Table 4-4: The processing time of operations of a hoodie completed by machines (minutes).38 Table 4-5: The processing time of operations of a Crop top completed by machines (minutes).39 Table 5-1: Objective solution of MILP.54 Table 5-2: Cmax result of each jobs.54 Table 5-3: Solution space.

55 Table 5-4: Start time of each jobs processed on machines.56 Table 5-5: Completion time of each jobs processed on machines.56 Table 5-6: Comparison result between MILP and PSO method.58 Table 5-7: Summay of sample data. 59 Table 5-8: Result of sample data. 60 v List of Figures Figure 2-1: Logo of Viet Tien Garment Corporation.11 Figure 2-2: Machine system of Viet Tien Garment Corporation.12 Figure 2-3: Layout of the flexible job shop environment (a).14 Figure 2-4: Layout of the flexible job shop environment (b).15 Figure 4-1: BOM of a Skirts.35 Figure 4-2: BOM of a NIKE Pant. 36 Figure 4-3: BOM of Nike hoodies.37 Figure 4-4: BOM of a Crop top.38 Figure 4-5: Procedure of PSO application.49 Figure 5-1: Objective result of MILP.52 Figure 5-2: Gantt chart solution.53 Figure 5-3: Fitness result of solution space.54 Figure 5-4: Gantt chart solution of PSO.56 iii List of Abbreviations MRP Material requirements planning MTO Make to order LP Linear Programming NLP Nonlinear Programming ILP Integer Linear Programming MILP Mixed Integer Linear Programming MRP Material Requirements Planning MPS Master Production Schedule BOM Bill of Materials JSP Job-Shop Scheduling Problem FJSP Flexible Job-Shop Scheduling Problem MIP Mixed-Integer programming FIFO First In First Out SPT Shortest Processing Time LPT Largest Processing Time TLBO Teaching–Learning-Based Optimization PSO Particle Swarm Optimization ABC Artificial Bee Colony NP Nondeterministic Polynomial iii CHAPTER 1 INTRODUCTION 1.1 Background: Allocating resources effectively to match demand is a pivotal component of production planning, acting as an essential mechanism that reveals in detail how a company's products are manufactured.

It facilitates the strategic use of resources such as labours, equipment, and materials, aiming primarily to reduce energy consumption. With meticulous and accurate production planning, a company can ensure that all inputs are optimized for energy efficiency and that production is scaled appropriately to resource availability. Moreover, creating an ideal scheduling plan contributes to lower energy consumption from a management standpoint. This approach to energy-efficient scheduling, despite adding a minimal financial overhead, is increasingly favoured over traditional methods like buying energy-efficient equipment or designing new products, particularly by smaller enterprises.

In Vietnam's garment industry, the Make-to-Order (MTO) strategy is extensively used. MTO allows customers to order customized products, initiating production only after an order is received, thus adhering to a pull-type supply chain where production is directly linked to actual customer demand. To manage this model effectively, it is crucial to segment and organize orders efficiently to save on production time, meet precise customer specifications, and guarantee timely delivery. In production scheduling, the critical elements include the allocation of resources and tasks—commonly machines and jobs.

The main goal in this context is to minimize energy usage, closely linked to reducing transportation times required to complete an order. Several strategies, including Linear Programming (LP), Nonlinear Programming (NLP), Integer Linear Programming (ILP), and Mixed Integer Linear Programming (MILP), are utilized to decrease the makespan and prevent delays in order fulfilment within production scheduling. These methods provide a broad spectrum of solutions to tackle the complexities of production scheduling. 1 For this thesis, the chosen approach to address these challenges is Particle Swarm Optimization.

As it is an NP-Hard problem, it is not possible to find the best solution, so over time, several types of algorithms have been proposed to find a good enough solution to the problem, one of these approaches is the bio- inspired algorithms in which they use patterns observed in nature to solve problems. One of these patterns observed in nature is the behaviour of bee swarms and the migratory movement of flocks of birds, the behaviour of these flocks is used as a heuristic in the Particle Swarm Optimization (PSO) algorithm where each individual of a population of particles uses the average between its best position ever visited (pBest) and the best position already visited among all of the population (gBest) to obtain a new position so that each round the population converges to an optimal result.2 Problem Statement – The Need for Study: 1.1 Current system description - Viet Tien company's production facilities are typically organized in a traditional job shop format where various clothing items are produced on an order-by-order basis. Each facility houses numerous specialized departments such as cutting, sewing, finishing, and quality control, which handle different stages of the garment manufacturing process. - The manufacturing process is energy-intensive, primarily due to the use of various machines such as sewing machines, cutting machines, and presses, which are essential at different stages of production.

The energy consumption varies significantly across different departments depending on the machinery and production techniques used.2 Identify the problem and the gap needed to fill this case. - According to the relevant survey, the worldwide industrial sector will consume more than half of the total energy through 2040. Against the background of the deteriorating ecological environment, environmental protection has become a serious issue for global manufacturing industries. - Suffering from growing energy costs and a worsening ecological environment, it is 2 quite necessary to adopt some measures to achieve energy saving and consumption reduction.

Reviewing this situation, Viet Tien Garment Corporation has tried to purchase more energy-efficient equipment, others attempt to redesign the products. However, these previous attempts inevitably impose substantial capital investment, which is impossible for them to afford such extra financial burden. They are finding reasonable operational management for the reduction of energy consumption. - The use of Excel and energy-efficient equipment at Viet Tien Garment Corporation presently beneficial to the FJSPs in this firm.

For FJSPs difficulties, using Excel to assign work to machines may not be the ideal strategy. Minimized transportation time and sub-lot number will minimize the transportation energy consumption, which leads to minimizing the total energy consumption. - To solve that problem, a model of FJSPs is being formed which helps the companies to get the optimal scheduling. It helps the company to ensure that products are produced on time, to the right specifications, and at the lowest possible supply, as well as the allocation of the jobs to the machines, which can be beneficial for the human resources department.

Benefits of effective production scheduling include increased efficiency, improved customer service, and increased profitability. By carefully planning and coordinating production, companies can improve efficiency, customer service, and profitability.3 What Needs to Solve or Achieve: There are several areas that need to be implemented to improve the company’s planning and scheduling. First, I need to look for several methods which may help to reduce the great amount of energy to optimize the energy of using machines and must achieve the new production schedule. Second, I must collect information about developing a production system including information about the BOM, the energy consumption to complete operations by machines with multiple tasks, and demand of customers, so I can calculate the total energy and make the schedule.3 Objectives of Study: 3 1.1 The beneficiaries of the study results - My project: This project has enriched my expertise in production planning and scheduling.

Additionally, it provided me with hands-on experience through a particular case study aimed at reducing energy usage among machine-operated jobs. I also created a Gantt chart to visualize the distribution of operations across machines. - The Viet Tien Garment Corporation: This company can enhance its production processes and address existing issues through the insights gained from this project.