An improved lower bound for minimizing weighted completion times with deadlines
Operations Research
A Simulated Annealing Approach to Bicriteria Scheduling Problems on a Single Machine
Journal of Heuristics
Dynamic scheduling problem of batch processing machine in semiconductor burn-in operations
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part IV
Branch-and-bound and simulated annealing algorithms for a two-agent scheduling problem
Expert Systems with Applications: An International Journal
Optimizing patrol force deployment using a genetic algorithm
Expert Systems with Applications: An International Journal
Computers and Industrial Engineering
Optimal reduction of a spatial monitoring grid: Proposals and applications in process control
Computational Statistics & Data Analysis
Hi-index | 12.05 |
The dynamic scheduling problem of semiconductor burn-in operations is studied in this paper. The burn-in oven is a batch-processing machine and the size of each job is independent of the oven's capacity. The processing time for each batch is represented by the largest processing time of the jobs in a batch. The objective function of this problem is to minimize the total weighted completion time subject to deadline constraints. A mixed integer programming model is formulated to solve small size of problems optimally, and then a SA with a probability matrix integrated with a greedy heuristic is developed to solve the problem in practical sizes. The greedy heuristic has a backtrack procedure to ensure the obtained solutions can avoid the infeasible region and the solutions derived can be further applied as initial solutions to the proposed novel SA procedure. In the novel SA, the probability matrix is designed as a probabilistic neighbor-generation procedure to guide the searching directions. Computational experiments indicated the novel SA could effectively and efficiently obtain optimal solutions for small size of problems and provide high-quality solutions efficiently for large size of problems.