On applications of ant colony optimisation techniques in solving assembly line balancing problems

PublishedArticleRecently, there is an increasing interest in applications of meta-heuristic approaches in solving various engineering problems. Meta-heuristics help both academics and practitioners to get not only feasible but also near optimal solutions where obtaining a solution for the relevant problem is not possible in a reasonable time using traditional optimisation techniques. Ant colony optimisation algorithm is inspired from the collective behaviour of ants and one of the most efficient meta-heuristics in solving combinatorial optimisation problems. One of the main application areas of ant colony optimisation algorithm is assembly line balancing problem. In this paper, we first give the running principle of ant colony optimisation algorithm and then review the applications of ant colony optimisation based algorithms on assembly line balancing problems in the literature. Strengths and weaknesses of proposed algorithms to solve various problem types in the literature have also been discussed in this research. The main aim is to lead new researches in this domain and spread the application areas of ant colony optimisation techniques in various aspects of line balancing problems. Existing researches in the literature indicate that ant colony optimisation methodology has a promising solution performance to solve line balancing problems especially when integrated with other heuristic and/or meta-heuristic methodologies

Ant colony optimization for the single model U-type assembly line balancing problem

Cataloged from PDF version of article.The assembly line is a production line in which units move continuously through a sequence of stations. The assembly line balancing problem is the allocation of tasks to an ordered sequence of stations subject to the precedence constraints with the objective of minimizing the number of stations. In a U-line the line is configured into a U-shape topology. In this research, a new heuristic, Ant Colony Optimization (ACO) meta-heuristic, and its variants are proposed for the single model U-type assembly line balancing problem (UALBP). We develop a number of algorithms that can be grouped as: (i) direct methods, (ii) modified methods and (iii) methods in which ACO approach is augmented with some metaheuristic. We also construct an extensive experimental study and compare the performance of the proposed algorithms against the procedures reported in the literature.Alp, ArdaM.S

A mixed-integer programming model for cycle time minimization in assembly line balancing: Using rework stations for performing parallel tasks

[EN] In assembly lines, rework stations are generally used for reprocessing defective items. On the other hand, using rework stations for this purpose only might cause inefficient usage of the resources in this station especially in an assembly line with a low defective rate. In this study, a mixed-integer programming model for cycle time minimization is proposed by considering the use of rework stations for performing parallel tasks. By linearizing the non-linear constraint about parallel tasks using a variate transformation, the model is transformed to a linear-mixed-integer form. In addition to different defective rates, different rework station positions are also considered using the proposed model. The performance of the model is analyzed on several test problems from the related literature.Cavdur, F.; Kaymaz, E. (2020). A mixed-integer programming model for cycle time minimization in assembly line balancing: Using rework stations for performing parallel tasks. 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Multi-objective discrete particle swarm optimisation algorithm for integrated assembly sequence planning and assembly line balancing

In assembly optimisation, assembly sequence planning and assembly line balancing have been extensively studied because both activities are directly linked with assembly efficiency that influences the final assembly costs. Both activities are categorised as NP-hard and usually performed separately. Assembly sequence planning and assembly line balancing optimisation presents a good opportunity to be integrated, considering the benefits such as larger search space that leads to better solution quality, reduces error rate in planning and speeds up time-to-market for a product. In order to optimise an integrated assembly sequence planning and assembly line balancing, this work proposes a multi-objective discrete particle swarm optimisation algorithm that used discrete procedures to update its position and velocity in finding Pareto optimal solution. A computational experiment with 51 test problems at different difficulty levels was used to test the multi-objective discrete particle swarm optimisation performance compared with the existing algorithms. A statistical test of the algorithm performance indicates that the proposed multi-objective discrete particle swarm optimisation algorithm presents significant improvement in terms of the quality of the solution set towards the Pareto optimal set

Iterative Beam Search for Simple Assembly Line Balancing with a Fixed Number of Work Stations

The simple assembly line balancing problem (SALBP) concerns the assignment of tasks with pre-defined processing times to work stations that are arranged in a line. Hereby, precedence constraints between the tasks must be respected. The optimization goal of the SALBP-2 version of the problem concerns the minimization of the so-called cycle time, that is, the time in which the tasks of each work station must be completed. In this work we propose to tackle this problem with an iterative search method based on beam search. The proposed algorithm is able to obtain optimal, respectively best-known, solutions in 283 out of 302 test cases. Moreover, for 9 further test cases the algorithm is able to produce new best-known solutions. These numbers indicate that the proposed iterative beam search algorithm is currently a state-of-the-art method for the SALBP-2

Ant colony optimization for the single model U-type assembly line balancing problem

Cataloged from PDF version of article.An assembly line is a production line in which units move continuously through a sequence of stations. The assembly line balancing problem is defined as the allocation of tasks to an ordered sequence of stations subject to precedence constraints with the objective of optimizing a performance measure. In this paper, we propose ant colony algorithms to solve the single-model U-type assembly line balancing problem. We conduct an extensive experimental study in which the performance of the proposed algorithm is compared against best known algorithms reported in the literature. The results indicate that the proposed algorithms display very competitive performance against them. & 2009 Elsevier B.V. All rights reserved

Simultaneous balancing and sequencing of mixed-model parallel two-sided assembly lines

Copyright © 2014 Taylor & Francis. This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Production Research on 31 January 2014, available online: http://www.tandfonline.com/10.1080/00207543.2013.879618Growing interests from customers in customised products and increasing competitions among peers necessitate companies to configure their manufacturing systems more effectively than ever before. We propose a new assembly line system configuration for companies that need intelligent solutions to satisfy customised demands on time with existing resources. A mixed-model parallel two-sided assembly line system is introduced based on the parallel two-sided assembly line system previously proposed by Ozcan et al. (Balancing parallel two-sided assembly lines, International Journal of Production Research, 48 (16), 4767-4784, 2010). The mixed-model parallel two-sided assembly line balancing problem is illustrated with examples from the perspective of simultaneous balancing and sequencing. An agent based ant colony optimisation algorithm is proposed to solve the problem. This algorithm is the first attempt in the literature to solve an assembly line balancing problem with an agent based ant colony optimisation approach. The algorithm is illustrated with an example and its operational procedures and principles explained and discussed