Louis A. Martin-Vega

Bio: Louis A. Martin-Vega is an academic researcher from University of Florida. The author has contributed to research in topics: Scheduling (production processes) & Job shop. The author has an hindex of 11, co-authored 21 publications receiving 1761 citations. Previous affiliations of Louis A. Martin-Vega include National Science Foundation.

A review of production planning and scheduling models in the semiconductor industry part i: system characteristics, performance evaluation and production planning

Abstract: 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.

Efficient algorithms for scheduling semiconductor burn-in operations

Abstract: 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.

Production scheduling algorithms for a semiconductor test facility

Abstract: The authors develop production scheduling algorithms for semiconductor test operations. The operations in the facility under study are characterized by a broad product mix, variable lot sizes and yields, long and variable setup times, and limited test equipment capacity. The approach presented starts by dividing the facility or job shop into a number of work centers. The method then proceeds to sequence one work center at a time. A disjunctive graph representation of the entire facility is used to capture interactions between work centers. The introduction of different management objectives leads to different work center problems and different production scheduling algorithms. The authors present algorithms for two different work center problems. Direction for future research are discussed. >

Scheduling semiconductor test operations: Minimizing maximum lateness and number of tardy jobs on a single machine

Abstract: We examine a class of single-machine scheduling problems with sequence-dependent setup times that arise in the context of semiconductor test operations. We present heuristics for the problems of minimizing maximum lateness with dynamic arrivals and minimizing number of tardy jobs. We exploit special problem structure to derive worst-case error bounds. The special problem structure also enables us to derive dynamic programming procedures for the problems where all jobs are available simultaneously.

A review of scheduling research involving setup considerations

Abstract: The majority of scheduling research assumes setup as negligible or part of the processing time. While this assumption simplifies the analysis and/or reflects certain applications, it adversely affects the solution quality for many applications which require explicit treatment of setup. Such applications, coupled with the emergence of production concepts like time-based competition and group technology, have motivated increasing interest to include setup considerations in scheduling problems. This paper provides a comprehensive review of the literature on scheduling problems involving setup times (costs). It classifies scheduling problems into batch and non-batch, sequence-independent and sequence-dependent setup, and categorizes the literature according to the shop environments of single machine, parallel machines, flowshops, and job shops. The suggested classification scheme organizes the scheduling literature involving setup considerations, summarizes the current research results for different problem types, and finally provides guidelines for future research.

A Green Vehicle Routing Problem

Abstract: A Green Vehicle Routing Problem (G-VRP) is formulated and solution techniques are developed to aid organizations with alternative fuel-powered vehicle fleets in overcoming difficulties that exist as a result of limited vehicle driving range in conjunction with limited refueling infrastructure The G-VRP is formulated as a mixed integer linear program Two construction heuristics, the Modified Clarke and Wright Savings heuristic and the Density-Based Clustering Algorithm, and a customized improvement technique, are developed Results of numerical experiments show that the heuristics perform well Moreover, problem feasibility depends on customer and station location configurations Implications of technology adoption on operations are discussed

A review of production planning and scheduling models in the semiconductor industry part i: system characteristics, performance evaluation and production planning

Abstract: 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.

Efficient algorithms for scheduling semiconductor burn-in operations

Abstract: 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.