Sequencing with earliness and tardiness penalties: a review
Operations Research
Efficient algorithms for scheduling semiconductor burn-in operations
Operations Research
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Earliness and tardiness scheduling problems on a batch processor
Discrete Applied Mathematics
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
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
Computers and Industrial Engineering - Special issue: Focussed issue on applied meta-heuristics
Minimizing total completion time on a batch processing machine with job families
Operations Research Letters
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A GRASP approach for makespan minimization on parallel batch processing machines
Journal of Intelligent Manufacturing
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This paper considers a scheduling problem for parallel burn-in ovens in the semiconductor manufacturing industry. An oven is a batch processing machine with restricted capacity. The batch processing time is set by the longest processing time among those of all the jobs contained in the batch. All jobs are assumed to have the same due date. The objective is to minimize the sum of the absolute deviations of completion times from the due date (earliness-tardiness) of all jobs. We suggest three decomposition heuristics. The first heuristic applies the exact algorithm due to Emmons and Hall (for the nonbatching problem) in order to assign the jobs to separate early and tardy job sets for each of the parallel burn-in ovens. Then, we use job sequencing rules and dynamic programming in order to form batches for the early and tardy job sets and sequence them optimally. The second proposed heuristic is based on genetic algorithms. We use a genetic algorithm in order to assign jobs to each single burn-in oven. Then, after forming early and tardy job sets for each oven we apply again sequencing rules and dynamic programming techniques to the early and tardy jobs sets on each single machine in order to form batches. The third heuristic assigns jobs to the m early job sets and m tardy jobs sets in case of m burn-in ovens in parallel via a genetic algorithm and applies again dynamic programming and sequencing rules. We report on computational experiments based on generated test data and compare the results of the heuristics with known exact solution for small size test instances obtained from a branch and bound scheme.