instances of Experiment 1
Abstract
This file contains the instances utilized in Experiment 1 described in the paper "Coupling a genetic algorithm approach and a discrete event simulator to design mixed-model un-paced assembly lines with parallel workstations and stochastic task times"
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The buffer allocation problem (BAP) and the assembly line balancing problem (ALBP) are amongst the most studied problems in the literature on production systems. However they have been so far approached separately, although they are closely interrelated. This paper for the first time considers these two problems simultaneously. An innovative approach, consisting in coupling the most recent advances of simulation techniques with a genetic algorithm approach, is presented to solve a very complex problem: the Mixed Model Assembly Line Balancing Problem (MALBP) with stochastic task times, parallel workstations, and buffers between workstations. An opportune chromosomal representation allows the solutions space to be explored very efficiently, varying simultaneously task assignments and buffer capacities among workstations. A parametric simulator has been used to calculate the objective function of each individual, evaluating at the same time the effect of task assignment and buffer allocation decisions on the line throughput. The results of extensive experimentation demonstrate that using buffers can improve line efficiency. Even when considering a cost per unit buffer space, it is often possible to find solutions that provide higher throughput than for the case without buffers, and at the same time have a lower design cost.
In the paper, an innovative approach to deal with the Mixed Model Assembly Line Balancing Problem (MALBP) with stochastic task times and parallel workstations is presented. At the current stage of research, advances in solving realistic and complex assembly line balancing problem, as the one analyzed, are often limited by the poor capability to effectively evaluate the line throughput. Although algorithms are potentially able to consider many features of realistic problems and to effectively explore the solution space, a lack of precision in their objective function evaluation (which usually includes a performance parameter, as the throughput) limits in fact their capability to find good solutions. Traditionally, algorithms use indirect measures of throughput (such as workload smoothness), that are easy to calculate, but whose correlation with the throughput is often poor, especially when the complexity of the problem increases. Algorithms are thus substantially driven towards wrong objectives. The aim of this paper is to show how a decisive step forward can be done in this filed by coupling the most recent advances of simulation techniques with a genetic algorithm approach. A parametric simulator, developed under the event/object oriented paradigm, has been embedded in a genetic algorithm for the evaluation of the objective function, which contains the simulated throughput. The results of an ample simulation study, in which the proposed approach has been compared with other two traditional approaches from the literature, demonstrate that significant improvements are obtainable.
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