Efficient algorithms for scheduling semiconductor burn-in operations
Operations Research
Scheduling independent tasks to reduce mean finishing time
Communications of the ACM
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
The Batch Loading and Scheduling Problem
Operations Research
Minimizing total weighted tardiness on a single batch process machine with incompatible job families
Computers and Operations Research
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Motivated by scheduling challenges of burn-in ovens in back-end semiconductor manufacturing, we propose a linear-programming-based algorithm, an integer-programming-based algorithm, and a heuristic-based algorithm to schedule nonhomogenous parallel batch machines with nonidentical job sizes and incompatible job families. We consider the common scheduling of consecutive steps that are linked together through secondary scarce resources. Our approach addresses the availability and compatibility of several resources required to make each process possible. The algorithms strive to meet short-term production targets expressed by product and step. The algorithms are shown to be effective and computationally efficient for this purpose. Taken together with previously developed methodology for the practical translation of target output schedules into short-term local production targets, this article suggests how a complex supply chain manufacturing system can be efficiently and effectively managed by decentralized local scheduling algorithms striving to meet short-term production targets that in turn ensure maintenance of an appropriate dynamic profile across production steps for work-in-process.