S. David Wu

ROBUSTNESS MEASURES AND ROBUST SCHEDULING FOR JOB SHOPS

Abstract A robust schedule is defined as a schedule that is insensitive to unforeseen shop floor disturbances given an assumed control policy. In this paper, a definition of schedule robustness is developed which comprises two components: post-disturbance make-span and post-disturbance makespan variability. We have developed robustness measures and robust scheduling methods for the case where a “right-shift” control policy is used. On occurrence of a disruption, the right-shift policy maintains the scheduling sequence while delaying the unfinished jobs as much as necessary to accommodate the disruption. An exact measure of schedule robustness is derived for the case in which only a single disruption occurs within the planning horizon. A surrogate measure is developed for the more complex case in which multiple disruptions may occur. This surrogate measure is then embedded in a genetic algorithm to generate robust schedules for job-shops. Experimental results show that robust schedules significantly outper...

One-machine rescheduling heuristics with efficiency and stability as criteria

Abstract Heuristics for the problem of rescheduling a machine on occurrence of an unforeseen disruption are developed. The criteria include minimization of the makespan (schedule efficiency) and the impact of the schedule change (schedule stability). The impact of schedule change is a non-regular performance measure defined in two ways: (1) the starting time deviations between the new schedule and the original schedule, and (2) a measure of the sequence difference between the two schedules. Three local search procedures are developed for the bicriterion problem and a set of experiments are conducted to test the efficacy of the heuristics. The heuristic solutions are shown to be effective in that the schedule stability can be increased significantly with little or no sacrifice in makespan.

On combinatorial auction and Lagrangean relaxation for distributed resource scheduling

Most existing methods for scheduling are based on centralized or hierarchical decision making using monolithic models. In this study, we investigate a new method based on a distributed and locally autonomous decision structure using the notion of combinatorial auction. In combinatorial auction the bidders demand a combination of dependent objects with a single bid. We show that not only can we use this auction mechanism to handle complex resource scheduling problems, but there exist strong links between combinatorial auction and Lagrangean-based decomposition. Exploring some of these properties, we characterize combinatorial auction using auction protocols and payment functions. This study is a first step toward developing a distributed scheduling framework that maintains system-wide performance while accommodating local preferences and objectives. We provide some insights to this framework by demonstrating four different versions of the auction mechanism using job shop scheduling problems.

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

Abstract 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 capacity investment among supply chain partners. Not only are these issues increasingly important in the fast-changing high-tech environment, they draw on new tools from different disciplines and pose significant intellectual challenges. We also examine papers that represent the multifaceted nature of high-tech capacity planning, integrating capacity decisions with issues related to contracting, coordination, sourcing, and capacity configurations.

Coordinating Strategic Capacity Planning in the Semiconductor Industry

We study strategic capacity planning in the semiconductor industry. Working with a major US semiconductor manufacturer on the configuration of their worldwide production facilities, we identify two unique characteristics of this problem as follows: (1) wafer demands and manufacturing capacity are both main sources of uncertainty, and (2) capacity planning must consider the distinct viewpoints from marketing and manufacturing. We formulate a multi-stage stochastic program with demand and capacity uncertainties. To reconcile the marketing and manufacturing perspectives, we consider a decomposition of the planning problem resembling decentralized decision-making. We develop recourse approximation schemes representing different decentralization schemes, which vary in information requirements and complexity. We show that it is possible to arrive at near optimal solutions (within 6.5%) with information decentralization while using a fraction (16.2%) of the computer time.

Capacity reservation contracts for high-tech industry

Abstract Capacity reservation provides a risk-sharing mechanism that encourages a manufacturer to expand its capacity more. We propose a deductible reservation (DR) contract where customers reserve future capacity with a fee that is deductible from the purchasing price. The manufacturer’s ex ante announcement of the “excess” capacity that she will have in addition to the reservation amount is a unique feature of the DR contract. An individually rational DR contract that provides channel coordination always exists. Since there is a unique Nash equilibrium for the reservation game among multiple customers, the main results of the one-customer case can be extended to the n -customer case. The DR contract is compared with another capacity reservation contract called take-or-pay. While the manufacturer may gain more profit under a take-or-pay contract, there may not be a channel-coordinated contract that is also individually rational for the customer. Finally, the similarities and differences between the capacity reservation contracts and other well-known supply contracts are discussed.

Semiconductor capacity planning: stochastic modelingand computational studies

This paper presents a multistage stochastic programming model for strategic capacity planning at a major US semiconductor manufacturer. Main sources of uncertainty in this multi-year planning problem include demand of different technologies and capacity estimations for each fabrication (fab) facility. We test the model using real-world scenarios requiring the determination of capacity planning for 29 technology categories among five fab facilities. The objective of the model is to minimize the gaps between product demands and the capacity allocated to the technology specified by each product. We consider two different scenario-analysis constructs: first, an independent scenario structure where we assume no prior information and the model systematically enumerates possible states in each period. The states from one period to another are independent from each other. Second, we consider an arbitrary scenario construct, which allows the planner to sample/evaluate arbitrary multi-period scenarios that captures the dependency between periods. In both cases, a scenario is defined as a multi-period path from the root to a leaf in the scenario tree. We conduct intensive computational experiments on these models using real data supplied by the semiconductor manufacturer. The purpose of our experiments is two-fold: first to examine different degree of scenario aggregation and its effects on the independent model to achieve high-quality solution. Using this as a benchmark, we then compare the results from the arbitrary model and illustrate the different uses of the two scenario constructs. We show that the independent model allows a varying degree of scenario aggregation without significant prior information, while the arbitrary model allows planners to play out specific scenarios given prior information.

Procurement Planning to Maintain Both Short-Term Adaptiveness and Long-Term Perspective

We study the fuel procurement problem for electrical utilities under uncertain demand and market price. Long-term contractual supply commitments are made at a set price with fuel suppliers at the beginning of each year. Each month the procurement planner can use fuel from these contracts or purchase fuel at the current market price. Motivated by practical insights from this market, we propose a two-phase dynamic procedure to determine a procurement plan. In the first phase, the minimum contract purchases for each month are determined at the beginning of the year. In the second phase, given the minimum contract purchases, the more detailed procurement decisions are determined at the beginning of each month with the most up-to-date information. We perform intensive computational experiments that show that this procedure produces high-quality solutions comparable to a rolling-horizon stochastic-programming heuristic, is easier to maintain and generalize, is computationally faster, and is robust to random fluctuations in demand requirements, spot market prices, and other sources of uncertainty.

Problem and Heuristic Space Search Strategies for Job Shop Scheduling

In a recent paper we discussed “problem” and “heuristic” spaces which serve as a basis for local search in job shop scheduling problems. By encoding schedules as heuristic, problem pairs (H,P) search spaces can be defined by perturbing problem data and/or heuristic parameters. In this paper we attempt to determined, through computational testing, how these spaces can be successfully searched. Well known local search strategies are applied in problem and heuristic space and compared to Shifting Bottleneck heuristics, and to probabilistic dispatching methods. An interesting result is the good performance of genetic algorithms in problem space. INFORMS Journal on Computing , ISSN 1091-9856, was published as ORSA Journal on Computing from 1989 to 1995 under ISSN 0899-1499.

Managing Short Life-Cycle Technology Products for Agere Systems

Over the past decade, the high-tech industry has been rapidly innovating technology and introducing new products. Firms have moved from vertically integrated operations to horizontally integrated operations that include contract manufacturers. In September 2002, Agere Systems recognized that it needed new tools for managing the capacity in its increasingly complex, global supply chain. Agere and the Center for Value Chain Research at Lehigh University formed a team to develop new methods for characterizing the demands for short life-cycle technology products. The team developed a leading-indicator engine that identifies products that provide advanced warning of demand changes for a group of products. For a data set including 3,500 semiconductor products, the analysis identified leading indicators that predicted the demand pattern of the product group one to seven months ahead of time with correlation values ranging from 0.51 to 0.95. The leading-indicator concept provides a new perspective on demand forecasting and can be extended to other corporate planning functions, such as financial forecasting and inventory forecasting.