Abstract
This paper presents the evaluation of two metaheuristics to solve the Unrelated Parallel Machine Scheduling Problem with Sequence Machine Dependent Setup Time. Considering such a problem, there is no relation between the time to process each task and the machine; and this is why the machines are referred to as unrelated. Furthermore, the setup time between the executions of two tasks depends on both, the task sequence and its associated machine. A metaheuristic genetic algorithm and a variable neighborhood search were used in order to solve the problem due to the difference among their characteristics. The maximal time for the schedule to be completed, also called makespan, was the performance measure used to evaluate the solutions. The results obtained by both metaheuristics were directly compared according to their performance to try to reduce this makespan. The results showed that the variable neighborhood algorithm search outperformed the genetic algorithm regarding the solutions quality and execution time.
Highlights
The task scheduling is an important decision problem at operational level, which appears in different contexts in modern production systems
This paper aims at expanding research on the resolution methods for the unrelated parallel machine scheduling problem, with a sequence and machine dependent on setup time by considering the reduction of the maximum completion time as a performance measure
With the aim of showing a comparison between the computational results obtained by each of the metaheuristics, the data set averages were analyzed for each combination of machines and and tasks under three related aspects: makespan reduction, processing time, and time processing amplitude for each solution
Summary
The task scheduling is an important decision problem at operational level, which appears in different contexts in modern production systems. The sequencing of parallel machines is necessary because several real systems have more than one machine to perform the same set of activities This issue fits the sub-problems context of greater complexity, such as a flow shop or a job shop, whose set of workstations containing parallel machines works simultaneously. According to Ying, Lee, and Lin (2012), this is a common scenario in current production systems, especially in the textile, chemical, electronics, and implementation services, as well as in the maintenance industry. This problem is highly complex, because of its dimension, but mainly because of the peculiar features arising from the situation to be resolved
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