A Fuzzy Approach to Job-Shop Scheduling Problem Based on Imprecise Processing Times

2003 ◽  
pp. 91-106 ◽  
Author(s):  
Feng-Tse Lin
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Scheduling Problem ◽  
Fuzzy Approach ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Processing Times

A simheuristic approach for the flexible job shop scheduling problem with stochastic processing times

SIMULATION ◽  
2020 ◽  
pp. 003754972096889
Author(s):  
Rylan H Caldeira ◽  
A Gnanavelbabu
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Flexible Job Shop ◽  
Scheduling Problem ◽  
Flexible Job Shop Scheduling ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Processing Times ◽  
Benchmark Instances ◽  
Stochastic Processing Times

In this work, we address the flexible job shop scheduling problem (FJSSP), which is a classification of the well-known job shop scheduling problem. This problem can be encountered in real-life applications such as automobile assembly, aeronautical, textile, and semiconductor manufacturing industries. To represent inherent uncertainties in the production process, we consider stochastic flexible job shop scheduling problem (SFJSSP) with operation processing times represented by random variables following a known probability distribution. To solve this stochastic combinatorial optimization problem we propose a simulation-optimization approach to minimize the expected makespan. Our approach employs Monte Carlo simulation integrated into a Jaya algorithm framework. Due to the unavailability of standard benchmark instances in SFJSSP, our algorithm is evaluated on an extensive set of well-known FJSSP benchmark instances that are extended to SFJSSP instances. Computational results demonstrate the performance of the algorithm at different variability levels through the use of reliability-based methods.

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