Local Search Genetic Algorithms for the Job Shop Scheduling Problem
Applied Intelligence
A new approach to solve hybrid flow shop scheduling problems by artificial immune system
Future Generation Computer Systems - Special issue: Computational science of lattice Boltzmann modelling
Computers and Industrial Engineering - Special issue: Selected papers from the 30th international conference on computers; industrial engineering
Computers and Industrial Engineering
Computers and Operations Research
An ant algorithm for balanced job scheduling in grids
Future Generation Computer Systems
An artificial immune algorithm for the flexible job-shop scheduling problem
Future Generation Computer Systems
A high performing metaheuristic for job shop scheduling with sequence-dependent setup times
Applied Soft Computing
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
Tabu search and lower bound for an industrial complex shop scheduling problem
Computers and Industrial Engineering
Vehicle routing scheduling using an enhanced hybrid optimization approach
Journal of Intelligent Manufacturing
An artificial immune system for solving production scheduling problems: a review
Artificial Intelligence Review
Minimizing the total completion time in a distributed two stage assembly system with setup times
Computers and Operations Research
Hi-index | 0.00 |
Production Scheduling problems are typically named bases on the processing routes of their jobs on different processors and also the number of processors in each stage. In this paper, we consider the problem of scheduling a job shop (JSS) where set-up times are sequence-dependent (SDST) to minimize the maximum completion times of operations or makespan. Our problem is generally formulated as J/STsd/C"m"a"x. To tackle such an NP-hard problem, a recent effective metaheuristic algorithm known as variable neighborhood search (VNS) is employed. VNS algorithms have shown excellent capability to solve scheduling problems to optimal or near-optimal schedule. Our proposed VNS is readily intelligible yet is a robust solution technique for the problem of SDST JSS. VNS is categorized as a local search-based algorithm armed with systematic neighborhood search structures. Our proposed VNS obviates the notorious myopic behavior of local search-based metaheuristic algorithms by the means of several systematic insertion neighborhood search structures. An experimental design based on Taillard's benchmark is conducted to evaluate the efficiency and effectiveness of our proposed algorithm against some effective algorithms in the literature. The obtained results strongly support the high performance of our proposed algorithm with respect to other well-known heuristic and metaheuristic algorithms.