This article surveys a new generation of analytical tools for capacity planning and management, especially in high-tech industries such as semiconductors, electronics and bio-techs. The objectives of the article are to (1) identify fundamental theory driving current research in capacity management, (2) review emerging models in operations research, game theory, and economics that address strategic, tactical and operational decision models for high-tech capacity management, and (3) take an in-depth look at capacity-optimization models developed in the specific context of semiconductor manufacturing. The goal of this survey is to go beyond typical production-planning and capacity-management literature and to examine research that can potentially broaden capacity-planning research. For instance, we explore the role of option theory and real options in modeling capacity decisions. We not only examine capacity-planning problems from the perspective of a particular firm, but also the interaction of cap...

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Managing Capacity in the High-Tech Industry: A Review of Literature

In many applications, the network design problem (NDP) faces significant uncertainty in transportation costs and demand, as it can be difficult to estimate current (and future values) of these quantities. In this paper, we present a robust optimization-based formulation for the NDP under transportation cost and demand uncertainty. We show that solving an approximation to this robust formulation of the NDP can be done efficiently for a network with single origin and destination per commodity and general uncertainty in transportation costs and demand that are independent of each other. For a network with path constraints, we propose an efficient column generation procedure to solve the linear programming relaxation. We also present computational results that show that the approximate robust solution found provides significant savings in the worst case while incurring only minor sub-optimality for specific instances of the uncertainty.

https://link.springer.com/content/pdf/10.1057%2Fpalgrave.jors.2602362.pdf

Robust solutions for network design under transportation cost and demand uncertainty

We consider the problem of expanding arc capacities in a network subject to demand and travel time uncertainty. We propose a robust optimization approach to obtain capacity expansion solutions that are insensitive to this uncertainty. Our results show that, under reasonable assumptions for network flow applications, such robust solutions can be computed by solving tractable conic linear problems. For example, the robust solution for a multicommodity flow problem is obtained by solving a linear program if the problem has a single source and sink per commodity and the uncertainty in demand and travel time is given by independent bounded polyhedral sets. Preliminary computational results show that the robust solution is attractive, as it can reduce the worst case cost by more than 20%, while incurring a 5% loss in optimality when compared to the optimal solution of a representative scenario. © 2007 Wiley Periodicals, Inc. NETWORKS, Vol. 50(2), 136145 2007

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Robust capacity expansion of network flows

A review of mathematical programming models for strategic capacity planning in manufacturing

Flexible long-term capacity planning in closed-loop supply chains with remanufacturing

We consider a discrete-time capacity expansion problem involving multiple product families, multiple machine types, and non-stationary stochastic demand. Capacity expansion decisions are made to strike an optimal balance between investment costs and lost sales costs. Motivated by current practices in the semiconductor and other high-tech industries, we assume that only minimal amounts of finished-goods inventories are held, due to the risk of obsolescence. We assume that when capacity is in short supply, management desires to ensure that a minimal service level for all product families is obtained. Our approach uses a novel assumption that demand can be approximated by a distribution whose support is a collection of rays emanating from a point and contained in real multi-dimensional space. These assumptions allow us to solve the problem as a max-flow, min-cut problem. Computational experiments show that large problems can be solved efficiently.

/pdf/optimal-capacity-expansion-for-multi-product-multi-machine-3u6pmatns5.pdf

Optimal capacity expansion for multi-product, multi-machine manufacturing systems with stochastic demand

In this paper we propose an analytical framework that intelligently combines multiple forecasts into a single and more accurate combined forecast. Four different combination methods are derived by using different weight estimation and forecast selection techniques. We examine our methods and four other commonly used combination methods in computational experiments. The results suggest that combination does improve accuracy and our methods have better and more stable performance than the others.

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Feng Zhang

Papers

Optimal capacity expansion for multi-product, multi-machine manufacturing systems with stochastic demand

Improving Forecast Accuracy by Combination