Routing and scheduling in a flexible job shop by tabu search
Annals of Operations Research - Special issue on Tabu search
Experimental Evaluation of Heuristic Optimization Algorithms: A Tutorial
Journal of Heuristics
A Mixed Closure-CSP Method to Solve Scheduling Problems
Proceedings of the 14th International conference on Industrial and engineering applications of artificial intelligence and expert systems: engineering of intelligent systems
An effective hybrid optimization approach for multi-objective flexible job-shop scheduling problems
Computers and Industrial Engineering
Computers and Operations Research
A genetic algorithm for the Flexible Job-shop Scheduling Problem
Computers and Operations Research
An artificial immune algorithm for the flexible job-shop scheduling problem
Future Generation Computer Systems
An optimal dynamic threat evaluation and weapon scheduling technique
Knowledge-Based Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A multiagent evolutionary algorithm for constraint satisfaction problems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A decision support system for managing combinatorial problems in container terminals
Knowledge-Based Systems
A novel algorithm for dynamic task scheduling
Future Generation Computer Systems
Computers and Operations Research
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The aim of the job-shop scheduling problem is to optimize the task planning in an industrial plant satisfying time and technological constraints. The existing algorithmic and mathematical methods for solving this problem usually have high computational complexities making them intractable. Flexible job-shop scheduling becomes even more complex, since it allows one to assign each operation to a resource from a set of suitable ones. Alternative heuristic methods are only able to satisfy part of the constraints applicable to the problem. Moreover, these solutions usually offer little flexibility to adapt them to new requirements. This paper describes research within heuristic methods that combines genetic algorithms with repair heuristics. Firstly, it uses a genetic algorithm to provide a non-optimal solution for the problem, which does not satisfy all its constraints. Then, it applies repair heuristics to refine this solution. There are different types of heuristics, which correspond to the different types of constraints. A heuristic is intended to evaluate and slightly modify a solution that violates a constraint in a way that avoids or mitigates such violation. This approach improves the adaptability of the solution to a problem, as some changes can be addressed just modifying the considered chromosome or heuristics. The proposed solution has been tested in order to analyse its level of constraint satisfaction and its makespan, which are two of the main parameters considered in these types of problems. The paper discusses this experimentation showing the improvements over existing methods.