Reha Uzsoy

A REVIEW OF PRODUCTION PLANNING AND SCHEDULING MODELS IN THE SEMICONDUCTOR INDUSTRY PART I: SYSTEM CHARACTERISTICS, PERFORMANCE EVALUATION AND PRODUCTION PLANNING

Although the national importance of the semiconductor industry is widely acknowledged, it is only recently that the production planning and scheduling problems encountered in this environment have begun to be addressed using industrial engineering and operations research.techniques. These problems have several features that make them difficult and challenging: random yields and rework, complex product flows, and rapidly changing products and technologies. Hence their solution will contribute considerably to die theory and practice of production planning and control. In a two-part project we present a review of research in this area to date, discuss the applicability of the various approaches and suggest directions for future research. In this paper, Part I, we describe the characteristics of the semiconductor manufacturing environment and review models related to performance evaluation and production planning. Part II will review research on shop-floor control in this industry to date.

Executing production schedules in the face of uncertainties: A review and some future directions

We review the literature on executing production schedules in the presence of unforeseen disruptions on the shop floor. We discuss a number of issues related to problem formulation, and discuss the functions of the production schedule in the organization and provide a taxonomy of the different types of uncertainty faced by scheduling algorithms. We then review previous research relative to these issues, and suggest a number of directions for future work in this area. � 2003 Elsevier B.V. All rights reserved.

Efficient algorithms for scheduling semiconductor burn-in operations

In this paper, we study the problem of scheduling semiconductor burn-in operations, where burn-in ovens are modeled as batch processing machines. A batch processing machine is one that can process up to B jobs simultaneously. The processing time of a batch is equal to the largest processing time among all jobs in the batch. We present efficient dynamic programming-based algorithms for minimizing a number of different performance measures on a single batch processing machine. We also present heuristics for a number of problems concerning parallel identical batch processing machines and we provide worst case error bounds.

A REVIEW OF PRODUCTION PLANNING AND SCHEDULING MODELS IN THE SEMICONDUCTOR INDUSTRY PART II: SHOP-FLOOR CONTROL

Abstract In the first part of this review [62] we described the characteristics of semiconductor manufacturing environments and reviewed research on system performance evaluation and production planning. In this paper we focus on shop-floor control problems. We classify research to date by the solution techniques used, and discuss the relative advantages and disadvantages of the various approaches. We discuss the relationship between shop-floor control and production planning and suggest future research directions.

Scheduling a single batch processing machine with non-identical job sizes

The problem of scheduling jobs with non-identical capacity requirements or sizes on a single batch processing machine to minimize total completion time and makespan is studied. These problems are proven to be NP-hard and heuristics are developed for both, as well as a branch and bound algorithm for the total completion time problem. Computational experiments show that the heuristics are capable of rapidly obtaining near-optimal solutions.

Experimental Evaluation of Heuristic Optimization Algorithms: A Tutorial

Heuristic optimization algorithms seek good feasible solutions to optimization problems in circumstances where the complexities of the problem or the limited time available for solution do not allow exact solution. Although worst case and probabilistic analysis of algorithms have produced insight on some classic models, most of the heuristics developed for large optimization problem must be evaluated empirically—by applying procedures to a collection of specific instances and comparing the observed solution quality and computational burden.This paper focuses on the methodological issues that must be confronted by researchers undertaking such experimental evaluations of heuristics, including experimental design, sources of test instances, measures of algorithmic performance, analysis of results, and presentation in papers and talks. The questions are difficult, and there are no clear right answers. We seek only to highlight the main issues, present alternative ways of addressing them under different circumstances, and caution about pitfalls to avoid.

Decomposition methods for complex factory scheduling problems

Preface. 1. Introduction. 2. Industrial Context and Motivation for Decomposition Methods. 3. Review of Decomposition Methods for Factory Scheduling Problems. 4. Modelling Interactions Between Subproblems: The Disjunctive Graph Representation and Extensions. 5. Workcenter-Based Decomposition Procedures for the Classical Job Shop Environment. 6. A Generic Decomposition Procedure for Semiconductor Testing Facilities. 7. Time-Based Decomposition Procedures for Single-Machine Subproblems with Sequence-Dependent Setup Times. 8. Time-Based Decomposition Procedures for Parallel Machine Subproblems with Sequence-Dependent Setup Times. 9. Naive Rolling Horizon Procedures for Job Shop Scheduling. 10. Tailored Decomposition Procedures for Semiconductor Testing Facilities. 11. Computational Results for Job Shops with Single and Parallel Machine Workcenters. 12. The Effects of Subproblem Solution Procedures and Control Structures. 13. Conclusions and Future Directions. Author Index.

Analysis of periodic and event-driven rescheduling policies in dynamic shops

Abstract We address the problem of rescheduling production systems in the face of dynamic job arrivals. Using simple single-and parallel-machine models to gain insight, we provide worst-case and computational analyses of periodic and event-driven rescheduling policies. Our results indicate that event-driven policies can obtain high-quality schedules with less rescheduling than continuous rescheduling policies. We also show that if structure in the job arrival process is exploited very effective periodic rescheduling policies can be designed.

Scheduling batch processing machines with incompatible job families

The problem of scheduling a single batch processing machine with incompatible job families was studied, where jobs of different families cannot be processed together in the same batch. First static problems where all jobs are available simultaneously were considered and showed that for a regular performance measure there will be no unnecessary partial batches. This allowed us to develop efficient optimal algorithms to minimize makespan (Cmax), maximum lateness (Lmax) and total weighted completion time and apply some of these results to problems with parallel identical batch processing machines. Then problems withdynamic job arrivals were considered and an efficient optimal algorithm for minimizing Cmax and several heuristics to minimize Lmax were provided. Computational experiments showed that the heuristics developed for the latter problem consistently improve on dispatching solutions in very reasonable CPU times.

Predictable scheduling of a job shop subject to breakdowns

Schedule modification may delay or render infeasible the execution of external activities planned on the basis of the predictive schedule. Thus it is of interest to develop predictive schedules which can absorb disruptions without affecting planned external activities, while maintaining high shop performance. We present a predictable scheduling approach where the predictive schedule is built with such objectives. The procedure inserts additional idle time into the schedule to absorb the impacts of breakdowns. The amount and location of the additional idle time is determined from the breakdown and repair distributions as well as the structure of the predictive schedule. The effects of disruptions on planned support activities are measured by the deviations of job completion times in the realized schedule from those in the predictive schedule. We apply our approach to minimizing maximum lateness in a job shop environment with random machine breakdowns, and show that it provides high predictability with minor sacrifices in shop performance.