Fuzzy Scheduling Problem on Unrelated Parallel Machine in JIT Production System

Abstract

This paper deals with unrelated parallel machines scheduling problem with sequence dependent setup times under fully fuzzy environment to minimize total weighted fuzzy earliness and tardiness penalties, which belongs to NP-hard class. Due to inherent uncertainty in Processing times, setup times and due dates of jobs, they are considered here with triangular and trapezoidal fuzzy numbers in order to take into account the unpredictability of parameters in practical settings. Although this study is not the first one to study on fuzzy parallel machines scheduling problem, it advances this area of research in three fields: (1) it selects a fuzzy environment to cover the whole area of the considered problem not just part of it, and also, it chooses an appropriate fuzzy method based on an in-depth investigation of the effect of spread of fuzziness on the variables; (2) It introduces a mathematical programming model for the addressed problem as an exact method; and (3) due to NP-hardness of the problem, it develops an existing algorithm in the literature for the considered problem through extensive simulated experiments and statistical tests on the same benchmark problem test by proposing a genetic algorithm (GA) and a modified simulated annealing (SA) methods to solve this hard combinatorial optimization problem. The result shows the superiority of our modified SA.

Minimizing JIT scheduling of unrelated parallel machine with family setups and soft time window constraints

“…Rezaeian et al studied a fuzzy unrelated parallel machine scheduling problem. Sequence dependent setup times was taken into consideration and a genetic algorithm, as well as a modified simulated annealing were proposed to minimize total weighted fuzzy early/tardy penalties [16]. Kamalahmadi et al investigated the impacts of JIT scheduling on workers productivity in a restaurant chain.…”

Uniform Parallel Machines Scheduling with Setup Time, Learning Effect, Machine Idle Time, and Processing Set Restrictions to Minimize Earliness/Tardiness Costs

“…They presented a restricted simulated annealing (RSA) algorithm to minimize makespan [3]. Rezaeian et al dealt with unrelated parallel machine scheduling problems with sequence-dependent setup times under a fully fuzzy environment to minimize total weighted fuzzy earliness and tardiness penalties [4]. Ramezani et al studied a no-wait scheduling problem in a flexible flow shop environment with uniform parallel machines considering anticipatory sequence-dependent setup times to minimize makespan and developed a hybrid meta-heuristic to tackle the problem [5].…”