Job Shop Scheduling by Simulated Annealing

We describe an approximation algorithm for the problem of finding the minimum makespan in a job shop. The algorithm is based on simulated annealing, a generalization of the well known iterative improvement approach to combinatorial optimization problems. The generalization involves the acceptance of...

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Vydáno v:Operations research Ročník 40; číslo 1; s. 113 - 125
Hlavní autoři: van Laarhoven, Peter J. M, Aarts, Emile H. L, Lenstra, Jan Karel
Médium: Journal Article
Jazyk:angličtina
Vydáno: Linthicum, MD INFORMS 01.01.1992
Operations Research Society of America
Institute for Operations Research and the Management Sciences
Témata:
Algorithms
analysis of algorithms
Applied sciences
Approximation
Combinatorial optimization
Cooling
Cost efficiency
deterministic
Ergodic theory
Exact sciences and technology
Experiments
Heuristics
job shop scheduling
Job shops
Markov chains
multiple machine
Operational research and scientific management
Operational research. Management science
Operations research
Optimization
Production scheduling
production/scheduling, sequencing
Scheduling
Scheduling, sequencing
Simulated annealing
Simulation
suboptimal algorithms
Markov chain
Experimental result
Approximation
Simulated annealing
Job shop
Scheduling
Asymptotic convergence
Algorithm analysis
ISSN:0030-364X, 1526-5463
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Shrnutí:We describe an approximation algorithm for the problem of finding the minimum makespan in a job shop. The algorithm is based on simulated annealing, a generalization of the well known iterative improvement approach to combinatorial optimization problems. The generalization involves the acceptance of cost-increasing transitions with a nonzero probability to avoid getting stuck in local minima. We prove that our algorithm asymptotically converges in probability to a globally minimal solution, despite the fact that the Markov chains generated by the algorithm are generally not irreducible. Computational experiments show that our algorithm can find shorter makespans than two recent approximation approaches that are more tailored to the job shop scheduling problem. This is, however, at the cost of large running times.
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ISSN:0030-364X
1526-5463
DOI:10.1287/opre.40.1.113