Using metaheuristic algorithms for solving a mixed model assembly line balancing problem considering express parallel line and learning effect

Abstract

Mixed-model assembly line attracts many manufacturing centers' attentions, since it enables them to manufacture different models of one product in the same line. The present work proposes a new mathematical model to balancing mixed-model assembly two parallel lines, in which first one is a common line and the other is an express line due to more modern technology or operators with higher skills. Therefore, the cost of equipment and skilled labor in the express line is higher, and also, the learning effect on resource dependent task times and setup times is considered in the assemble-to-order environment. The aim of this study is to minimize the cycle time and the total operating cost and smoothness index by configuration of tasks in stations, according to their precedence diagrams. Also, assigning the assistants to some tasks in some stations and for some models is allowed. This problem is categorized as an NP-hard problem and for solving this multi-objective problem, non-dominated sorting genetic algorithm ІІ (NSGA-II) and multi-objective particle swarm optimization (MOPSO) are applied. Finally, for comparing the proposed methods some numerical examples are implemented and the result show that MOPSO outperforms NSGAII.

A Developed Framework for Sequencing of Mixed-model Assembly Line with Customer’s Satisfaction and Heterogeneous Operators

“…Time factors have similar effects as cost, but cost factors have a greater impact on total costs (Sungur and Yavuz, 2015). Masood Rabbani et al (2018a) consider MMAL balancing problems with a parallel line in a make-to-order environment. One of the assembly lines utilizes higher skills workers or modern technology.…”

“…They proposed a hybrid algorithm that consists of Tabu search, Large Neighborhood Search (TLNS), Parallel Constructive Heuristic (PCH), and the Small Neighborhood Search (SNS). The comparison of two algorithms NSGA-II and Multi-objective with Particle Swarm Optimization (MOPSO) is shown by Rabbani et al (2018a). They concluded that NSGA-II is more efficient in the sequencing problem.…”

A hybrid approach development to solving the storage location assignment problem in a picker-to-parts system

“…For this, many authors argue that, for a decision maker (DM) consider all of these aspects simultaneously to define the best storage location is extremely difficult (Fontana and Cavalcante, 2013;Fontana and Nepomuceno, 2017). Thus, a Multi-Objective Genetic Algorithms (MOGA) can be used (Rabbani et al, 2018). "The Genetic Algorithms are considered to be a powerful technique of stochastic optimization and, probably the most important evolutionary computer techniques" (Serra Costa, 2011).…”