Devanath Tirupati

Dynamic Capacity Expansion Problem with Multiple Products: Technology Selection and Timing of Capacity Additions

This paper examines a multiproduct dynamic investment model for making technology choices and expansion decisions over a finite planning horizon. The motivation for our problem comes from recent developments in the field of flexible technology such as CAD, CAM, and CIM that permit firms to invest in these more expensive, flexible technologies to provide a competitive edge in the form of an ability to respond rapidly to changing product mix. On the other hand, more specialized dedicated equipment may be less costly. The decisions on appropriate mixes of dedicated and flexible capacity involve many complex considerations such as economies of scale, demand patterns, and mix flexibility. We formulate the problem as a mathematical program with the objective of minimizing total investment cost. Since the problem is difficult to solve optimally, we develop a two-phased approach and present heuristics to obtain good expansion schedules. These procedures are based on an easily solvable sequence of subproblems derived from the planning problem. Our computational results suggest that these methods work well and provide acceptable solutions with reasonable effort.

Tradeoff Curves, Targeting and Balancing in Manufacturing Queueing Networks

In this paper, we introduce the notions of tradeoff curves, targeting and balancing in manufacturing systems to describe the relationship between variables such as work-in-process, lead-time and capacity. We consider multiproduct manufacturing systems modeled by open networks of queues and formulate the targeting TP and balancing BP problems as nonlinear programs. These formulations are based primarily on parametric decomposition methods for estimating performance measures in open queueing networks. Since TP and BP typically are hard to solve, we show that under fairly realistic conditions they can be approximated by easily solvable convex programs. We present heuristics to obtain approximate solutions to these problems and to derive tradeoff curves. We also provide bounds on the performance of the heuristics, relative to the approximation problems, and show that they are asymptotically optimal under mild conditions.

An optimization approach to capacity expansion in semiconductor manufacturing facilities

For a given demand and planning horizon, the general facility design problem faced by semiconductor manufacturers is to decide how much capacity to build into their systems. When the technology is known and only a small number of products is to be manufactured, the specific problem is to find a tool-set configuration that minimizes the average cycle time within a prescribed budget. In this paper, it is shown that this version of the capacity expansion problem can be modelled as a nonlinear integer program in which the decision variables correspond to the number of tools at a workstation. The major difficulty encountered in trying to find solutions is that no closed form expressions exist for the waiting time, primarily due to the presence of re-entrant flow. This means that it has to be approximated. At the outset, it was observed that previously proposed approximation methods based on parametric decomposition provided extremely poor results. In response, a new set of expressions, in the form of simultane...

A Model-Based Approach for Planning and Developing a Family of Technology-Based Products

In this paper, we address the product-family design problem of a firm in a market in which customers choose products based on some measure of product performance. By developing products as a family, the firm can reduce the cost of developing individual product variants due to the reuse of a common product platform. Such a platform, designed in an aggregate-planning phase that precedes the development of individual product variants, is itself expensive to develop. Hence, its costs must be weighted against the benefits of its reuse in a family. We offer a model for capturing costs of product development when the family consists of variants based on a common platform. It is shown that the model can be converted into a network-optimization problem, and the optimal product-family can be identified under fairly general conditions by determining the shortest path of its network formulation. We also analytically examine the effect of alternative product designs on product-family composition, and discuss the implications of investing in new-product technology. Finally, we illustrate our model and managerial insights with an application from the electronics industry.

Capacity planning in manufacturing networks with discrete options

We consider multiproduct manufacturing systems modeled by open networks of queues with general distributions for arrival patterns and service times. Since exact solutions are not available for measuring mean number of jobs in these systems, we rely on approximate analyses based on the decomposition approach developed, among others, by Reiser and Kobayashi [16], Kuehn [14], Shanthikumar and Buzacott [19], Whitt [29], and extensions by Bitran and Tirupati [2]. The targeting problem (TP) presented in this paper addresses capacity planning issues in multiproduct manufacturing systems. Since TP is a nonlinear integer program that is not easy to solve, we present a heuristic to obtain an approximate solution. We also provide bounds on the performance of this heuristic and illustrate our approach by means of a numerical example.

Component Fixture Positioning/Sequencing for Printed Circuit Board Assembly with Concurrent Operations

This paper considers the problem of positioning component fixtures on the carriers of computer, numerically controlled dual delivery machines used for populating printed circuit boards with surface mounted technology. This reel positioning problem RPP is one of a series of optimization problems that are critical for improving system productivity and realizing the full potential of concurrent operations. We formulate the RPP as a mathematical program and establish its complexity. Since the problem is NP-complete we focus on the development of heuristics. Our solution procedure was prompted by engineering considerations that included concerns for minimizing the changes in the carrier direction and total movement. We also present encouraging results with test problems. The method has been implemented and achieved 7 to 8% reductions in cycle time.

Technology choice with stochastic demands and dynamic capacity allocation: A two-product analysis

Abstract In this paper we examine issues related to technology choice and capacity expansion in an environment characterized by two product families with stochastic demands. The motivation for our work comes from developments in modern technologies such as FMS and CIM that provide considerable operational flexibility. In this paper we focus on the tradeoffs between product mix flexibility of integrated technologies and dedicated facilities designed to produce efficiently a limited range of products. We present an investment model to determine optimal mix of technology and capacity choices in order to satisfy specified service levels. Based on the detailed analysis of a special case with uniform distributions, we develop a solution procedure that is amenable for extension to other demand distributions. The scope of this approach for deriving managerial insights is illustrated with computational results.

Chapter 10 Hierarchical production planning

Publisher Summary This chapter discusses basic ingredients of Hierarchical Production Planning (HPP) systems and describes models for single and multi-stage systems. Some important issues related to aggregation and disaggregation in hierarchical systems have been discussed. The role of feedback mechanisms in HPP has been described and two different interpretations of this term have been discussed. The hierarchical models described are primarily deterministic in nature. Hierarchical models represent a stochastic, multi-level decision process in which decisions at higher levels are often based on aggregate imperfect information. These models span a wide variety of manufacturing environments ranging from continuous processes to discrete systems such as batch and job shops. The approach described is a development in the application of the hierarchical approach to control and scheduling problems in discrete manufacturing systems. The approach has been successful on two-dimensions. First, the hierarchical framework is attractive to practitioners as evidenced by the several applications that have been reported. Second, considerable amount of research has been generated in developing appropriate models and solution methods.

Approximate performance modeling and decision making for manufacturing systems: A queueing network optimization framework

In this paper we discuss queueing network methodology as a framework to address issues that arise in the design and planning of discrete manufacturing systems. Our review focuses on three aspects: modeling of manufacturing facilities, performance evaluation and optimization with queueing networks. We describe both open and closed network models and present several examples from the literature illustrating applications of the methodology. We also provide a brief outline of outstanding research issues. The paper is directed towards the practitioner with operations research background and the operations management researcher with interest in this topic.

Impact of product mix flexibility and allocation policies on technology

In this article we present two contrasting models-(i) a static allocation model (SAM) that does not capture benefits of operational flexibility and (ii) a dynamic allocation model (DAM) that recognizes explicitly the impact of operational policies. Since the resulting optimization models are intractable, we develop solution procedures suitable for practical problems. In addition, we report results of a simulation study to examine the impact of alternative allocation policies and to verify the solutions provided by the procedures developed in this article. Our results suggest that explicit inclusion of the effect of operating policies in making technology and capacity choices can make a significant difference. The results also indicate that service levels are not overly sensitive to the alternate operating policies. Together these results imply that capacity strategies generated by our dynamic allocation model are robust with respect to the alternate operating policies considered in our experiments.