Balancing assembly lines operating with heterogeneous workers under uncertainty in task processing times

PurposeThis paper considers the assembly line worker assignment and balancing problem of type-2 (ALWABP-2) with fuzzy task times. This problem is an extension of the (simple) SALBP-2 in which task times are worker-dependent and concurrently uncertain. Two criteria are simultaneously considered for minimization, namely, fuzzy cycle time and fuzzy smoothness index.Design/methodology/approachFirst, we show how fuzzy concepts can be used for managing uncertain task times. Then, we present a multiobjective genetic algorithm (MOGA) to solve the problem. MOGA is devoted to the search for Pareto-optimal solutions. For facilitating effective trade-off decision-making, two different MO approaches are implemented and tested within MOGA: a weighted-sum based approach and a Pareto-based approach.FindingsExperiments over a set of fuzzified test problems show the effect of these approaches on the performance of MOGA while verifying its efficiency in terms of both solution and time quality.Originality/valueTo the author’s knowledge, no previous published work in the literature has studied the biobjective assembly line worker assignment and balancing problem of type-2 (ALWABP-2) with fuzzy task times.

Modelling and Solving Assembly Line Worker Assignment and Balancing Problem with Sequence-Dependent Setup Times

Assembly lines appear with various differentiations in order to better include the disabled in the labor market and to increase production efficiency. In this way, the optimal workforce assignment problem that emerges heterogeneously is called assembly line worker assignment and balancing problem (ALWABP). This paper addresses the ALWABP where the simple version is enriched by considering sequence-dependent setup times between tasks. A mixed integer linear programming model is presented and a simulated annealing algorithm is developed such as an NP-hard problem. In order to test the proposed solutions, 640 benchmark problems in the literature were combined and used. The solutions obtained through using the proposed algorithm are compared with the mixed integer programming model on the small-size test problems. Experimental results show that the proposed algorithm is more effective and robust for a large set of benchmark problems.