Modeling the Scheduling Problem in Cellular Manufacturing Systems Using Genetic Algorithm as an Efficient Meta-Heuristic Approach

Amin Rezaeipanah,Musa Mojarad

doi:10.37965/jait.2021.0018

Abstract

This paper presents a new, bi-criteria mixed-integer programming model for scheduling cells and pieces within each cell in a manufacturing cellular system. The objective of this model is to minimize the makespan and inter-cell movements simultaneously, while considering sequence-dependent cell setup times. In the CMS design and planning, three main steps must be considered, namely cell formation (i.e., piece families and machine grouping), inter and intra-cell layouts, and scheduling issue. Due to the fact that the Cellular Manufacturing Systems (CMS) problem is NP-Hard, a Genetic Algorithm (GA) as an efficient meta-heuristic method is proposed to solve such a hard problem. Finally, a number of test problems are solved to show the efficiency of the proposed GA and the related computational results are compared with the results obtained by the use of an optimization tool.

Highlights

One of the key decisions in cell production is the timing of components in each cell
In cellular manufacturing (CM) scheduling, a set of components must be processed in cells by a set of machines [1]
Different criteria can be considered for a cell production scheduling problem that can be minimized to completion time, minimum weighted total completion time, minimized maximum delay, minimized total delay, minimized total weighted delay, minimized number of delayed tasks, and minimized the number of intercellular translocations [3]–[5]

Summary

INTRODUCTION

One of the key decisions in cell production is the timing of components in each cell. The goal is to find the sequence of performing pieces in each group and the sequence of performing groups of pieces in cells on a set of machines; in such a way that the desired criterion is optimized in the schedule [2]. In explaining the start-up times depending on the sequence of cells, it is important to note that in the cell production system, the start-up cost of machines varies according to the sequence of the family of pieces belonging to each cell. Most algorithms developed for the group scheduling problem have two steps: the first step determines the sequence of pieces in groups and the second stage determines the sequence of performing groups of pieces in cells [6].

RELATED WORKS

PROPOSED METHOD

MATHEMATICAL MODEL

PROPOSED GENETIC ALGORITHM

RESULTS AND DISCUSSION

CONCLUSION