Efficient algorithms for scheduling semiconductor burn-in operations
Operations Research
Minimizing makespan on a single burn-in oven with job families and dynamic job arrivals
Computers and Operations Research
A genetic algorithm to minimize maximum lateness on a batch processing machine
Computers and Operations Research
Ant Colony Optimization for the Total Weighted Tardiness Problem
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Ant Colony Optimization
Ant colony optimization combined with taboo search for the job shop scheduling problem
Computers and Operations Research
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Efficient scheduling algorithms for a single batch processing machine
Operations Research Letters
Engineering Applications of Artificial Intelligence
Computers and Operations Research
Scheduling unrelated parallel batch processing machines with non-identical job sizes
Computers and Operations Research
Computers and Operations Research
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This paper investigates the problem of minimizing makespan on a single batch-processing machine, and the machine can process multiple jobs simultaneously. Each job is characterized by release time, processing time, and job size. We established a mixed integer programming model and proposed a valid lower bound for this problem. By introducing a definition of waste and idle space (WIS), this problem is proven to be equivalent to minimizing the WIS for the schedule. Since the problem is NP-hard, we proposed a heuristic and an ant colony optimization (ACO) algorithm based on the theorems presented. A candidate list strategy and a new method to construct heuristic information were introduced for the ACO approach to achieve a satisfactory solution in a reasonable computational time. Through extensive computational experiments, appropriate ACO parameter values were chosen and the effectiveness of the proposed algorithms was evaluated by solution quality and run time. The results showed that the ACO algorithm combined with the candidate list was more robust and consistently outperformed genetic algorithm (GA), CPLEX, and the other two heuristics, especially for large job instances.