A controlled search simulated annealing method for the single machine weighted tardiness problem
Annals of Operations Research
Minimizing total tardiness on one machine is NP-hard
Mathematics of Operations Research
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Minmax earliness/tardiness scheduling in identical parallel machine system using genetic algorithms
ICC&IE '94 Proceedings of the 17th international conference on Computers and industrial engineering
Bicriteria scheduling problem for unrelated parallel machines
Computers and Industrial Engineering
Scheduling parallel machines to minimize total weighted and unweighted tardiness
Computers and Operations Research
AllelesLociand the Traveling Salesman Problem
Proceedings of the 1st International Conference on Genetic Algorithms
Computers and Operations Research
Scheduling unrelated parallel machines to minimize total weighted tardiness
Computers and Operations Research
The complexity of scheduling job families about a common due date
Operations Research Letters
Computers and Industrial Engineering
Advances in Engineering Software
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This paper presents the comparative use of Simulated Annealing (SA) and Genetic Algorithm (GA) in a scheduling problem of unrelated parallel machines with set-up times. The problem accounts for allotting batched work parts to the unrelated parallel machines, where each batch is composed of a fixed number of identical jobs. Processing time of each job of a batch is determined according to the machine group as well as the batch group to which the job belongs. Major or minor set-up times are required between two subsequent batches, depending on the batch sequence but yet independent of machines. The objective of the problem is to minimise the Total Weighted Tardiness (TWT) for the unrelated parallel machine scheduling. SA and GA algorithms are proposed to obtain near-optimal solutions of the problem. The performance of proposed heuristics is compared through computational experiments with real data from dicing operations of a compound semiconductor manufacturing facility.