Abstract
Lot sizing models involve operational and tactical decisions. These decisions may entail multi-level production processes such as assembly operations with multiple plants and limited capacities. Lot sizing problems are widely recognized as NP-hard problems therefore difficult to solve, especially the ones with multiple plants and capacity constraints. The level of complexity rises to an even higher level when there is an interplant transfer between the plants. This paper presents a Genetic Algorithm (GA) based solution methodology applied to large scale multi-plant capacitated lot sizing problem with interplant transfer (MPCLSP-IT). Although the GA has been a very effective and widely accepted meta-heuristic approach used to solve large scale complex problems, it has not been employed for MPCLSP-IT problem. This paper solves the MPCLSP-IT problem in large scale instances by using a genetic algorithm, and in doing so successfully obtains a better solution in terms of computation time when compared to the results obtained by the other methods such as Lagrangian relaxation, greedy randomized adaptive search procedure (GRASP) heuristics, and GRASP-path relinking techniques used in extant literature.
Highlights
Due to the globalization of the manufacturing industry, many companies run their production and distribution operations in multiple locations to meet the customer demands on time while minimizing the overall production time and cost (Vercellis, 1999)
Research While a few prior studies have proposed various solution approaches to solve MPCLSP-IT problems, the prior methods were limited in scope, in problem size
This paper proposes the genetic algorithm approach to solve a large scale MPCLSP-IT problem
Summary
Due to the globalization of the manufacturing industry, many companies run their production and distribution operations in multiple locations to meet the customer demands on time while minimizing the overall production time and cost (Vercellis, 1999). These objectives can be achieved through effective production planning and by serving customers from multiple plants. According to Gruson et al (2019), lot sizing problems are one of the widely studied problems in the production management literature thanks to their broad applications in production and supply chain planning. Our research reveals that only a few studies such as (Carvalho and Nascimento, 2016, 2018; Nascimento et al, 2010; Nascimento and Toledo, 2008; Sambasivan and Schmidt, 2002; Sambasivan and Yahya, 2005) are based on the large scale MPCLSP
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