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

Including different kinds of preferences in a multi-objective ant algorithm for time and space assembly line balancing on different Nissan scenarios

Most of the decision support systems for balancing industrial assembly lines are designed to report a huge number of possible line configurations, according to several criteria. In this contribution, we tackle a more realistic variant of the classical assembly line problem formulation, time and space assembly line balancing. Our goal is to study the influence of incorporating user preferences based on Nissan automotive domain knowledge to guide the multi-objective search process with two different aims. First, to reduce the number of equally preferred assembly line configurations (i.e., solutions in the decision space) according to Nissan plants requirements. Second, to only provide the plant managers with configurations of their contextual interest in the objective space (i.e., solutions within their preferred Pareto front region) based on real-world economical variables. We face the said problem with a multi-objective ant colony optimisation algorithm. Using the real data of the Nissan Pathfinder engine, a solid empirical study is carried out to obtain the most useful solutions for the decision makers in six different Nissan scenarios around the world.Peer Reviewe

Including different kinds of preferences in a multi-objective ant algorithm for time and space assembly line balancing on different Nissan scenarios

Most of the decision support systems for balancing industrial assembly lines are designed to report a huge number of possible line configurations, according to several criteria. In this contribution, we tackle a more realistic variant of the classical assembly line problem formulation, time and space assembly line balancing. Our goal is to study the influence of incorporating user preferences based on Nissan automotive domain knowledge to guide the multi-objective search process with two different aims. First, to reduce the number of equally preferred assembly line configurations (i.e., solutions in the decision space) according to Nissan plants requirements. Second, to only provide the plant managers with configurations of their contextual interest in the objective space (i.e., solutions within their preferred Pareto front region) based on real-world economical variables. We face the said problem with a multi-objective ant colony optimisation algorithm. Using the real data of the Nissan Pathfinder engine, a solid empirical study is carried out to obtain the most useful solutions for the decision makers in six different Nissan scenarios around the world.Peer Reviewe

Articles publicats per investigadors de l'ETSEIB indexats al Journal Citation Reports: 2011

Informe que recull els 296 treballs publicats per 220 investigadors de l'Escola Tècnica Superior d'Enginyeria Industrial de Barcelona (ETSEIB) en revistes indexades al Journal Citation Reports durant l’any 2011Preprin

PROBLÈMES COMBINATOIRES EN CONFIGURATION DES LIGNES DE FABRICATION (ANALYSE DE COMPLEXITÉ ET OPTIMISATION)

L'objectif de la thèse est de créer et développer de nouvelles méthodes de résolution efficaces des problèmes combinatoires en configuration des lignes de fabrication. Deux problèmes ont été particulièrement étudiés: le problème d'équilibrage et de choix d'équipement pour des lignes dédiées et le problème de minimisation des coûts de changements de séries pour des lignes multi-produits. Une solution du premier problème consiste en une affectation admissible des ressources à un nombre de stations à déterminer de sorte que le coût total soit minimal. Afin de résoudre ce problème, nous l'avons réduit au problème de partition d'ensemble et l'avons résolu par des heuristiques gloutonnes et une méthode exacte de génération de contraintes. Les expérimentations sur différentes instances ont montré que la nouvelle approche de résolution surclasse les approches antérieures de la littérature en termes de qualité de solution et de temps de calcul. Pour le second problème deux critères sont considérés lexicographiquement : la minimisation du nombre de stations et la minimisation du coût de changement de séries. Nous avons examiné successivement les cas d'exécution parallèle et séquentielle des opérations. Des solutions approchées ont été trouvées par des heuristiques gloutonnes. Ensuite, nous avons proposé deux modèles de programmation linéaire en nombres entiers (PLNE) afin de trouver le nombre de stations minimal et ensuite d'obtenir le coût de changement de séries minimal. Les résultats des expérimentations sur ces nouveaux problèmes se sont avérés prometteurs à la fois en termes de qualité de solution et de temps de calcul.The objective of this thesis is to create and develop new effective solution methods for production line configuration problems. Two problems were studied: the equipment selection and balancing problem for dedicated lines and the setup cost minimization problem for multi-product lines. A solution for the first problem consists in a feasible assignment of the resources to an unknown number of stations so that the total cost is minimized. In order to solve this problem, we reduced it to the set partitioning problem and solved it by greedy heuristics and an exact method of constraint generation. The computer experiments on different problem instances showed that the new solution approach outperforms the previous methods from the literature both in terms of solution quality and computational time. For the second problem two criteria were considered lexicographically: the minimization of the number of stations and the minimization of the total setup cost. We examined successively the cases with parallel and sequential execution of operations. Approximate solutions were found by greedy heuristics. Then, we proposed two integer programming models in order to obtain the minimal number of stations and then the minimal setup cost. The experimental results for this new problem proved to be promising both in terms of solution quality and computational time.ST ETIENNE-ENS des Mines (422182304) / SudocSudocFranceF

Modelling and Solving Mixed-model Parallel Two-sided Assembly Line Problems

The global competitive environment and the growing demand for personalised products have increased the interest of companies in producing similar product models on the same assembly line. Companies are forced to make significant structural changes to rapidly respond to diversified demands and convert their existing single-model lines into mixed-model lines in order to avoid unnecessary new line construction cost for each new product model. Mixed-model assembly lines play a key role in increasing productivity without compromising quality for manufacturing enterprises. The literature is extensive on assembling small-sized products in an intermixed sequence and assembling large-sized products in large volumes on single-model lines. However, a mixed-model parallel two-sided line system, where two or more similar products or similar models of a large-sized product are assembled on each of the parallel two-sided lines in an intermixed sequence, has not been of interest to academia so far. Moreover, taking model sequencing problem into consideration on a mixed-model parallel two-sided line system is a novel research topic in this domain. Within this context, the problem of simultaneous balancing and sequencing of mixed-model parallel two-sided lines is defined and described using illustrative examples for the first time in the literature. The mathematical model of the problem is also developed to exhibit the main characteristics of the problem and to explore the logic underlying the algorithms developed. The benefits of utilising multi-line stations between two adjacent lines are discussed and numerical examples are provided. An agent-based ant colony optimisation algorithm (called ABACO) is developed to obtain a generic solution that conforms to any model sequence and it is enhanced step-by-step to increase the quality of the solutions obtained. Then, the algorithm is modified with the integration of a model sequencing procedure (where the modified version is called ABACO/S) to balance lines by tracking the product model changes on each workstation in a complex production environment where each of the parallel lines may a have different cycle time. Finally, a genetic algorithm based model sequencing mechanism is integrated to the algorithm to increase the robustness of the obtained solutions. Computational tests are performed using test cases to observe the performances of the developed algorithms. Statistical tests are conducted through obtained results and test results establish that balancing mixed-model parallel two-sided lines together has a significant effect on the sought performance measures (a weighted summation of line length and the number of workstations) in comparison with balancing those lines separately. Another important finding of the research is that considering model sequencing problem along with the line balancing problem helps algorithm find better line balances with better performance measures. The results also indicate that the developed ABACO and ABACO/S algorithms outperform other test heuristics commonly used in the literature in solving various line balancing problems; and integrating a genetic algorithm based model sequencing mechanism into ABACO/S helps the algorithm find better solutions with less amount of computational effort