Job shop scheduling by simulated annealing
Operations Research
Efficient algorithms for scheduling semiconductor burn-in operations
Operations Research
Modern heuristic techniques for combinatorial problems
Modern heuristic techniques for combinatorial problems
Minimizing the makespan on a batch machine with non-identical job sizes: an exact procedure
Computers and Operations Research
A genetic algorithm to minimize maximum lateness on a batch processing machine
Computers and Operations Research
Scheduling: Theory, Algorithms, and Systems
Scheduling: Theory, Algorithms, and Systems
Minimizing total completion time on a batch processing machine with job families
Operations Research Letters
Efficient scheduling algorithms for a single batch processing machine
Operations Research Letters
Minimizing makespan in a flow shop with two batch-processing machines using simulated annealing
Robotics and Computer-Integrated Manufacturing
Tabu search heuristic for two-machine flowshop with batch processing machines
Computers and Industrial Engineering
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This paper aims at improving the utilization of a single batch-processing machine. The batch-processing machine can process a batch of jobs, as long as the number of jobs and the total size of all the jobs in a batch do not violate the machine's capacity. The processing time of the job and its size is known. The processing time of a batch is the longest processing time among all the jobs in the batch. The objective is to minimize the makespan. Since the problem under study is NP-hard, a Simulated Annealing (SA) approach is proposed. The effectiveness of our solution procedure in terms of solution quality and run time is evaluated through experiments. The results obtained from the SA approach were compared with a commercial solver called CPLEX. Our computational study demonstrates the effectiveness of our approach in solving problem instances with 20 or more jobs in a shorter run time with better solutions.