Abstract
In the real-world manufacturing system, various uncertain events can occur and disrupt the normal production activities. This paper addresses the multi-objective job shop scheduling problem with random machine breakdowns. As the key of our approach, the robustness of a schedule is considered jointly with the makespan and is defined as expected makespan delay, for which a meta-model is designed by using a data-driven response surface method. Correspondingly, a multi-objective evolutionary algorithm (MOEA) is proposed based on the meta-model to solve the multi-objective optimization problem. Extensive experiments based on the job shop benchmark problems are conducted. The results demonstrate that the Pareto solution sets of the MOEA are much better in both convergence and diversity than those of the algorithms based on the existing slack-based surrogate measures. The MOEA is also compared with the algorithm based on Monte Carlo approximation, showing that their Pareto solution sets are close to each other while the MOEA is much more computationally efficient.
Highlights
Production scheduling is of great significance in both scientific study and engineering applications [1,2,3,4,5]
In practice, the execution of a schedule is usually confronted with disruptions and unforeseen events, such as random machine breakdowns (RMDs), which make the actual performance of a schedule hard to predict
We will focus on the multi-objective robust job shop scheduling problem (JSS) under RMDs with the goal of optimizing the makespan and the robustness simultaneously
Summary
Production scheduling is of great significance in both scientific study and engineering applications [1,2,3,4,5]. In practice, the execution of a schedule is usually confronted with disruptions and unforeseen events, such as random machine breakdowns (RMDs), which make the actual performance of a schedule hard to predict Against this background, we will focus on the multi-objective robust JSS under RMDs with the goal of optimizing the makespan and the robustness simultaneously. Known uncertainties are those events about which some information is available in advance, such as machine breakdowns [16,17,18] whose frequency and duration may be characterized by probability distributions Under these uncertainties, a schedule will be difficult to execute as planned, and the actual performance of the schedule will deteriorate.
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