An algorithm for solving the job-shop problem
Management Science
A practical use of Jackson's preemptive schedule for solving the job shop problem
Annals of Operations Research
A branch and bound algorithm for the job-shop scheduling problem
Discrete Applied Mathematics - Special volume: viewpoints on optimization
A genetic algorithm for the job shop problem
Computers and Operations Research - Special issue on genetic algorithms
Evolution based learning in a job shop scheduling environment
Computers and Operations Research - Special issue on genetic algorithms
A fast taboo search algorithm for the job shop problem
Management Science
An effective hybrid optimization strategy for job-shop scheduling problems
Computers and Operations Research
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
The hybrid heuristic genetic algorithm for job shop scheduling
Computers and Industrial Engineering
Parallel GRASP with path-relinking for job shop scheduling
Parallel Computing - Special issue: Parallel computing in numerical optimization
A hybrid particle swarm optimization for job shop scheduling problem
Computers and Industrial Engineering
A very fast TS/SA algorithm for the job shop scheduling problem
Computers and Operations Research
Computers and Operations Research
A random key based genetic algorithm for the resource constrained project scheduling problem
Computers and Operations Research
Project scheduling using a competitive genetic algorithm
SMO'08 Proceedings of the 8th conference on Simulation, modelling and optimization
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Scheduling operations problems arise in diverse areas such as flexible manufacturing, production planning and scheduling, logistics, supply chain problem, etc. A common feature of many of these problems is that no efficient solution algorithms are known that solve each instance to optimality in a time bounded polynomially in the size of the problem, Dorndorf and Pesch [22]. Discrete optimization can help to overcome these difficulties. This paper presents an optimization approach to solve the complex scheduling problem in a job shop environment. This problem is also known as the Job Shop Scheduling Problem (JSSP). The JSSP is a difficult problem in combinatorial optimization for which extensive investigation has been devoted to the development of efficient algorithms. The proposed approach is based on a genetic algorithm technique. Genetic algorithms are an optimization methodology based on a direct analogy to Darwinian natural selection and mutations in biological reproduction. The scheduling rules such as SPT and MWKR are integrated into the process of genetic evolution. The chromosome representation of the problem is based on random keys. The schedules are constructed using a priority rule in which the priorities and delay times of the operations are defined by the genetic algorithm. Schedules are constructed using a procedure that generates parameterized active schedules. After a schedule is obtained a local search heuristic is applied to improve the solution. The approach is tested on a set of standard instances taken from the literature and compared with other approaches. The computation results validate the effectiveness of the proposed approach.