A modified ant colony optimization algorithm for multi-objective assembly line balancing

Abstract

In this paper, a novel ant colony optimization algorithm called modified ant colony optimization algorithm (MACO) is proposed for multi-objective single-model assembly line balancing problem (SALBP). The proposed MACO presents a novel heuristic information combined with subsequent task number and deviation time that can guide ants to find better solutions for SALBP. The proposed MACO also adopts three assignment methods (i.e., forward, backward and local rebalancing assignment methods) and stratified sequential algorithm combined with Pareto-optimal front as multi-objective decision. The objectives of SALBP are to minimize the number of workstations, maximize assembly line efficiency and minimize workload variation among workstations. In the latter part of the paper, the proposed MACO has been applied to solve Scholl benchmark problems which include both small-size and large-size problems. The performance of the proposed MACO has been compared with the multi-objective genetic algorithm and the multiple assignment genetic algorithm and has obtained improved results in many test problems.

Hybridizing Ant Colony Optimization by Beam Search for the Assembly Line Balancing Problem

“…Kilbridge and Wester [16] found that task times usually follow a unimodal or bimodal distribution. Following Morrison et al [13], three kinds of task times are generated: (1) peak at the bottom: tasks times are drawn from a normal distribution with the mean centered around small times; (2) peak in the middle: task times are drawn from a normal distribution with a mean of C/2; (3) bimodal: task times are drawn from a combination of two normal distribution with means centered around small and large times. Task times are rounded to the next integer and possible rounding effects are compensated for by setting the default cycle time to 1000 to allow flexible time structures [14].…”

“…An assembly line is a continuous production line consisting of materials and workstations combined with conveyor belts, linking men and machines closely and efficiently [1]. Assembly lines are flow-oriented systems that are indispensable for both the production of high quantity standardized products and low volume production of customized products [2].…”

“…Therefore, exploring efficient heuristic methodologies to cope with large scale ALBP within an acceptable time period is clearly necessary. Recently, meta-heuristic algorithms such as the genetic algorithm, particle swarm optimization and the ant colony optimization (ACO) algorithm have been used to deal with ALBP due to these algorithms having good performance in optimization [1]. Many researchers explored ALBP with ant colony based approaches, and found ACO has good performance in solving combinatorial optimization problems [1,8].…”

“…Recently, meta-heuristic algorithms such as the genetic algorithm, particle swarm optimization and the ant colony optimization (ACO) algorithm have been used to deal with ALBP due to these algorithms having good performance in optimization [1]. Many researchers explored ALBP with ant colony based approaches, and found ACO has good performance in solving combinatorial optimization problems [1,8]. For example, to effectively address ALBP with complicating factors such as parallel workstations, stochastic task times and mixed-models, McMullen and Tarasewich [8] proposed an approach based on ant techniques and, in comparison with other heuristics, showed that the proposed method is competitive in terms of the performance measures used in the study.…”

Model and migrating birds optimization algorithm for two-sided assembly line worker assignment and balancing problem

“…The basic version of the assembly line balancing problem is simple assembly line balancing problem (ALBP) with workstation minimization criterion, referred to as type I ALBP (Boysen et al 2007;Chica et al 2015). Another widely researched objective function is minimizing cycle time, leading to a more complex ALBP referred to as type II ALBP (Zhong and Ai 2017). Type I ALBP has been criticized for lacking real-life application and being too theoretical since the environment in real industry is much more complex.…”