Abstract
Production processes in Cellular Manufacturing Systems (CMS) often involve groups of parts sharing the same technological requirements in terms of tooling and setup. The issue of scheduling such parts through a flow-shop production layout is known as the Flow-Shop Group Scheduling (FSGS) problem or, whether setup times are sequence-dependent, the Flow-Shop Sequence-Dependent Group Scheduling (FSDGS) problem. This paper addresses the FSDGS issue, proposing a hybrid metaheuristic procedure integrating features from Genetic Algorithms (GAs) and Biased Random Sampling (BRS) search techniques with the aim of minimizing the total flow time, i.e., the sum of completion times of all jobs. A well-known benchmark of test cases, entailing problems with two, three, and six machines, is employed for both tuning the relevant parameters of the developed procedure and assessing its performances against two metaheuristic algorithms recently presented by literature. The obtained results and a properly arranged ANOVA analysis highlight the superiority of the proposed approach in tackling the scheduling problem under investigation.
Highlights
During the last few decades, the Cellular Manufacturing (CM) production philosophy has been implemented with favorable results in many manufacturing firms
The design of a Cellular Manufacturing System (CMS) usually starts with the cell formation phase, in which parts are clustered in families and groups of machines are identified; the second step consists in defining the machine layout within each cell and the arrangement of cells with regards to each other; a proper schedule concerning part families to be processed at each cell has to be determined; the issue of allocating tools, materials, and human resources to each cell has to be addressed [5]
The aim of the present paper is to propose a Genetic Algorithms (GAs)-based algorithm for minimizing the total flow time in the classical Flow-Shop Sequence-Dependent Group Scheduling (FSDGS) problem, able to improve the alternative optimization procedures recently presented in such field of research, namely the Particle Swarm Optimization (PSO) by Hajinejad, Salmasi, and Mokthari [27]
Summary
During the last few decades, the Cellular Manufacturing (CM) production philosophy has been implemented with favorable results in many manufacturing firms. According to CM principles, parts requiring similar production processes are grouped in distinct manufacturing cells, made by dedicated clusters of machines. The design of a Cellular Manufacturing System (CMS) usually starts with the cell formation phase, in which parts are clustered in families and groups of machines are identified; the second step consists in defining the machine layout within each cell and the arrangement of cells with regards to each other; a proper schedule concerning part families to be processed at each cell has to be determined; the issue of allocating tools, materials, and human resources to each cell has to be addressed [5]. Due to similarities among their process requirements, parts belonging to the same family generally visit machines in a cell according to the same sequence. Each family may be divided into smaller groups made by jobs sharing the same setup operations to be performed on the machines composing the cell [8]. The problem of scheduling jobs in such a manufacturing system is usually referred to as the Flow-Shop
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