Optimization of the scheduling strategy using meta-heuristics approach in the context of cellular manufacturing with multiples products

Abstract

Production scheduling is at the heart of the plant's planning and control system. One of the attractive production planning systems is Cellular Manufacturing Systems (CMS). CMS is a structural system based on group theory. Several advantages of the SFC concept mainly include the reduction of makespan and flow time. CMS is an NP-hard optimization problem. Depending on the size of the problem, the number of machines, and the number of parts, the computing time required to obtain the optimal solution increases exponentially. To solve NPhard optimization problems, metaheuristic algorithms are the best solution to get good solutions in a reasonable time. In this work, we propose a new methodology to optimize parts' sequence in each manufacturing cell, including exceptional items. This technique is based on the RC-Filter algorithm. The proposed methodology was used to optimize the sequence of parts, including exceptional items, to minimize the time required. The proposed approach has been validated using thirteen problems taken from the literature. The results were compared to those provided by the Extended Great Deluge. A cellular manufacturing system is a structured system based on the concept of a group. One of the advantages of this concept is that it can reduce production time. Optimizing cell manufacturing systems are categorized as NP-Hard, where the computational time increases exponentially with the problem's size. Utilizing metaheuristic algorithms will be an excellent solution to solve the NP-Hard problem in a reasonable time. In this work, we proposed a new hybrid approach to optimize the sequence of parts in each cell and exceptional elements. The proposed hybrid methodology is based on the RC-Filter algorithm and the Extended Great Deluge algorithm. The proposed tool is used to optimize the sequence of parts, including exceptional items in each cell, in order to minimize makespan. The proposed approach was validated using 13 problems, and the results were compared with those provided by other algorithms. A cellular manufacturing environment is generally the most efficient environment for minimizing makespan, flow time, and handling. On the other hand, in most cases, a cell production environment requires distinctive elements' performance. This task generates many delays and intercellular movements. This problem is seen as a fundamental challenge to be solved to achieve the minimum makespan with exceptional elements and the minimum intercellular movement. In this work, a methodology was proposed, called the simulated annealing meta-heuristic algorithm, to obtain the best sequence of parts, allowing the minimum makespan. This work has been divided into two steps, and each step contains two parts; these steps are as follows: In the first part of the first step, we used simulated annealing to find the best sequence of exceptional elements without changing the cell's architecture. This is an optimization step as a jobshop problem, as there was much intercellular movement. In the second part, we optimized the sequence in each cell. These two parts represent the first stage of this work. To reduce intercellular movements, we used a dynamic cellular environment. In the second step of this work, a dynamic manufacturing cell was used. This step has two parts. In the first part, new cells were designed using only exceptional elements. Likewise, the machines used to manufacture exceptional items have been transferred to other cells. During the first part, the exceptional elements were made from a specific cell architecture. The goal of this part was to give the minimum of inter-cellular movements and manipulations. The second part of this step was to save the original cells' configuration, and the part sequences were optimized in each of them. Part sequence optimization was performed using the simulated annealing algorithm.

Date	28 Jun 2021
Original language	American English
Awarding Institution	École de technologie supérieure
Supervisor	Thien-My Dao (Supervisor)