An adaptable scheduling algorithm for flexible flow lines
Operations Research
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
A genetic algorithm for flowshop sequencing
Computers and Operations Research - Special issue on genetic algorithms
A genetic algorithm for the job shop problem
Computers and Operations Research - Special issue on genetic algorithms
A tutorial survey of job-shop scheduling problems using genetic algorithms—I: representation
Computers and Industrial Engineering
Open Shop Scheduling to Minimize Finish Time
Journal of the ACM (JACM)
Computers and Industrial Engineering
Genetic Algorithms and Simulated Annealing
Genetic Algorithms and Simulated Annealing
Job Shop Scheduling with Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Multi-operation multi-machine scheduling
HPCN Europe '95 Proceedings of the International Conference and Exhibition on High-Performance Computing and Networking
Genetic Local Search Algorithms for the Travelling Salesman Problem
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
An empirical and theoretical study of outpatient scheduling problems employing simulation and genetic algorithm methodologies
Scheduling: Theory, Algorithms, and Systems
Scheduling: Theory, Algorithms, and Systems
Scheduling open shops with parallel machines
Operations Research Letters
Approximation algorithms for the multiprocessor open shop scheduling problem
Operations Research Letters
A Novel Online Test-Sheet Composition Approach Using Genetic Algorithm
ISICA '09 Proceedings of the 4th International Symposium on Advances in Computation and Intelligence
Towards a centralised appointments system to optimise the length of patient stay
Decision Support Systems
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The multiprocessor open shop (MPOS) scheduling problem is NP-complete, a category of hard combinatorial optimization problems that have not received much attention in the literature. In this work, a special MPOS-a proportionate one-is introduced for the first time. Two original mixed integer programming formulations for the proportionate MPOS are developed and their complexity is discussed. Due to the complexity of the MPOS, this paper develops a compu-search methodology (a genetic algorithm (GA)) to schedule the shop with the objective of minimizing the makespan. In this novel GA, a clever chromosome representation of a schedule is developed that succinctly encodes a schedule of jobs across multiple stages. The innovative design of this chromosome enables any permutation of its genes to yield a feasible solution. This simple representation of an otherwise very complex schedule enables the genetic operators of crossover and mutation to easily manipulate a schedule as the algorithm iteratively searches for better schedules. A testbed of difficult instances of the problem are created to evaluate the performance of the GA. The solution for each instance is compared with a derived lower bound. Computational results reveal that the algorithm performs extremely well, demonstrating its potential to efficiently schedule MPOS problems. More importantly, successful experiments on large-scale problem instances suggest the readiness of the GA for industrial use.