Abstract
This paper considers an unrelated parallel machine problem with job release times and maintenance activities, in which machines have to periodically undergo maintenance since the status of the machines will be deteriorated by job-induced dirt. The problem is inspired by a wet station for cleaning operations in a semiconductor manufacturing process. The objective is to minimize the makespan. Since the considered problem is proven to be NP-hard, obtaining optimal solutions is almost impossible in a reasonable computational time when the problem becomes large. We develop specific feature-extraction procedures to recognize important information in a job sequence and linkage encoding (LE) procedures to generate new job sequences. The two above procedures are embedded into an iterated algorithm, called a feature-extraction-based iterated algorithm (FEBIA), to obtain optimal or better solutions for the considered problem. To examine the performance of the FEBIA, the FEBIA is compared with two population-based algorithms, the particle swarm optimization (PSO) algorithm and the genetic algorithm (GA), using many test data. The results reveal that the proposed FEBIA perform better than the two population-based algorithms, demonstrating the potential of the FEBIA to solve the unrelated parallel machine problem with periodic maintenance and job release times.
Highlights
This paper considers job scheduling and maintenance activity optimization problems for unrelated parallel machines with dynamic job release times
FEATURE-EXTRACTION-BASED ITERATED ALGORITHM (FEBIA) In this paper, we propose a population-based stochastic search algorithm, called a feature-extraction-based iterated algorithm (FEBIA), where a group of solutions is maintained in each iteration using linking encoding (LE) procedures based on the mentioned feature-extraction matrices to solve Rm|rj, dij ≤ Ti, fpa|Cmax problems
In this paper, we introduce the unrelated parallel machine scheduling problem with periodic maintenance activities and a dynamic job release time, which is denoted as Rm|rj, dij ≤ Ti, fpa|Cmax
Summary
This paper considers job scheduling and maintenance activity optimization problems for unrelated parallel machines with dynamic job release times. The objective was to minimize the total completion time, and they proposed a mixed integer linear programming model and a genetic algorithm (GA) to solve both small- and large-sized problems Another well-known flexible maintenance activity in which maintenance must be started and finished in a predetermined maintenance interval [u, v] was defined. Hidri et al [17] considered a parallel-machine problem with a single robot server, where the availability interval of the machines and the duration of PM activity were deterministic and known in advance For the problem, they developed lower bound, simulated annealing (SA), tabu search (TS), and GA to find better solutions under the objective of minimizing the makespan.
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