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.

Efficient algorithms for scheduling semiconductor burn-in operations

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.

A review of production planning and scheduling models in the semiconductor industry part ii: shop-floor control

Economies of scale are fundamental to manufacturing operations. With respect to scheduling, this phenomenon manifests itself in efficiencies gained from grouping similar jobs together. This paper reviews the rapidly growing literature on single-machine scheduling models that incorporate benefits from job grouping. We focus on three basic models known as family scheduling with item availability, family scheduling with batch availability, and batch processing. We present known results and introduce new results, and we pay special attention to key theoretical properties and the use of these properties in optimization procedures.

Scheduling groups of jobs on a single machine

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.

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

In this paper, we present a literature review, classification schemes and a simple meta-analysis for scheduling of batch processors (SBP) research in semiconductor manufacturing (SM). This review is based on a study of journals and web-based documents/articles, which include conference materials, lecture notes in computer science, working papers, etc. There are 98 articles published in various publication outlets between 1986 and October 2004. Based on the literature review carried out and the nature of SBP research observed in SM, we have introduced two classification schemes to systematically organize the published articles. The first classification scheme is based on the problem configurations of SBP research in SM and the second one is based on the solution methodology followed by the researchers. These classification schemes indicate that there is much research scope on SBP research in SM. Furthermore, a simple meta-analysis is carried out to enhance understanding on the development and evolution of SBP research in SM and to identify potential research areas for further research and for improvement. The results show that there is an increasing trend in SBP research in SM. A comprehensive list of references is presented. This study is expected to provide a source of reference for other researchers (or readers), who are interested in SBP research particularly in SM and help stimulate further interest.

Mark Gimple

Papers

Efficient scheduling algorithms for a single batch processing machine