Murat Erkoc

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.

Impact of product pricing and timing of investment decisions on supply chain co-opetition

Abstract In supply chain co-opetition, firms simultaneously compete and co-operate in order to maximize their profits. We consider the nature of co-opetition between two firms: The product supplier invests in the technology to improve quality, and the purchasing firm (buyer) invests in selling effort to develop the market for the product before uncertainty in demand is resolved. We consider three different decision making structures and discuss the optimal configuration from each firm’s perspective. In case 1, the supplier invests in product quality and sets the wholesale price for the product. The buyer then exerts selling effort to develop the market and following demand potential realization, sets the resale price. In case 2, the supplier invests in product quality followed by the buyer’s investment in selling effort. Then, after demand potential is observed, the supplier sets the wholesale price and the buyer sets the resale price. Finally, in case 3, both firms simultaneously invest in product quality and selling effort, respectively. Subsequently, observing the demand potential, the supplier sets the wholesale price and the buyer sets the resale price. We compare all configuration options from both the perspective of the supplier and the buyer, and show that the level of investment by the firms depends on the nature of competition between them and the level of uncertainty in demand. Our analysis reveals that although configuration 1 results in the highest profits for the integrated channel, there is no clear dominating preference on system configuration from the perspective of both parties. The incentives of the co-opetition partners and the investment levels are mainly governed by the cost structure and the level of uncertainty in demand. We examine and discuss the relation between system parameters and the incentives in desiging the supply contract structure.

Multiple unit auctions with economies and diseconomies of scale

Abstract This paper discusses multiple unit auctions for industrial procurement where the cost structures of suppliers capture economies and diseconomies of scale caused by the nature of the production cost and the opportunity value of suppliers’ capacities. The problem of winner determination and demand allocation is proven to be NP-complete. We propose a binary tree algorithm with bounds (BTB) which efficiently exploits the model’s optimality properties. BTB outperforms general integer optimization software in computational time, especially with existence of substantial economies and diseconomies of scale. The algorithm complexity is linear in demand volume. This property allows for efficient handling of high volume auctions and thus leads to increased benefit for the overall system. Under the assumption of the myopic best response strategies, we investigate the behavior of suppliers and price dynamics for iterative (multiple round) bidding with appropriate allocation and stopping rules. The allocation rules, featured by several tie breakers for multiple optimal solutions to the allocation model in each round, are proposed to induce suppliers’ dominant strategies and to improve the system’s performance.

Design of predictable production scheduling model using control theoretic approach

As one of the most important planning and operational issues in manufacturing systems, production scheduling generally deals with allocating a set of resources over time to perform a set of tasks. Recently, control theoretic approaches based on nonlinear dynamics of continuous variables have been proposed to solve production scheduling problems as an alternative to traditional production scheduling methods that deal with decision-making components in discrete nature. The major goal of this paper is to improve predictability and performance of an existing scheduling model that employs the control theoretic approach, called distributed arrival time controller (DATC), to manage arrival times of parts using an integral controller. In this paper, we first review and investigate unique dynamic characteristics of the DATC in regards to convergence and chattering of arrival times. We then propose a new arrival time controller for the DATC that can improve predictability and performance in production scheduling. We call the new mechanism the double integral arrival-time controller (DIAC). We analyse unique characteristics of the DIAC such as oscillatory trajectory of arrival times, their oscillation frequency, and sequence visiting mechanism. In addition, we compare scheduling performance of the DIAC to the existing DATC model through computational experiments. The results show that the proposed system can be used as a mathematical and simulation model for designing adaptable manufacturing systems in the future.

New ways of thinking about nurse scheduling

Purpose – The purpose of this paper is to present a new look at solving the national nursing shortage problem by discussing new approaches to minimize the impact of nursing miss‐utilization and or miss‐allocation.Design/methodology/approach – Among the approaches explored is the well‐known financial engineering derivative interest rate swap as well as new scheduling applications including inventory management and queuing models.Findings – The study found that there is great interest in the field of the nurse scheduling and staffing problem. The problems are, however, studied in isolation from one another, while their respective nature is inter‐related: staffing policies impact nursing problem model constraints. This gap may be one of the contributing factors to a well‐known gap between industry application and academic literature. Industry solutions focus encouraging the growth of nurses in the field, most of which result in a financial paradox due to increasing rate capitation for health service faciliti...

Overhaul planning and exchange scheduling for maintenance services with rotable inventory and limited processing capacity

We introduce a mathematical model with integrality property for maintenance and component exchange scheduling.The objective is to minimize total earliness for exchanges of rotable components.We propose an exact solution algorithm for the problem.A case study from the airline MRO industry is used to illustrate our methodology. Maintenance, repair and overhauling (MRO) of high cost equipment used in many industries are typically subject to regulations set by local governments or international agencies. For example in the aviation industry, critical equipment must be overhauled at certain intervals for continuing permission of use. As such, the overhaul must be completed by strict deadlines. Since the overhaul is typically a long process, MRO companies may implement exchange programs where they carry so called rotable inventory for exchanging expensive modules that require overhaul so that the equipment can continue its services with minimal interruption. The extracted module is overhauled in a capacitated facility and rotated back to the inventory for a future exchange. Since both the rotable inventory and the overhaul process capacity are limited, it may be necessary to carry out some of the exchanges earlier than their deadlines. Early exchanges results in a decrease in the maintenance cycle time of the equipment, which is not desirable for the equipment user. In this paper, we propose an integer programming model so as to minimize total earliness by generating optimal overhaul start times for rotables on parallel processing lines and exchange timetables for orders. We show that the LP relaxation of the proposed model has the integrality property. We develop a practical exact solution algorithm for the model based on a full-delay scheduling approach with backward allocation. The proposed procedure is demonstrated through both a numerical study and a case study from the airline MRO service industry.

A game theoretic approach for load-shifting in the smart grid

In this paper, load-shifting within the context of smart grid demand response is investigated for an electricity market composed of a single energy provider and multiple consumers. The problem is formulated as a Stackelberg game in which the provider, acting as leader, moves first and offers price discounts across a finite time horizon to motivate consumers to shift their energy consumptions from peak periods. The consumers, acting as followers, respond by deciding if and how they shift their consumption from their nominal demand. In this model, the aim of the energy provider is to maximize its profits, while the consumers aim to minimize their total costs related to both the energy consumption and inconvenience of deviating from the nominal demand. Within this setting, a procedure is proposed to obtain equilibrium outcomes and managerial insights are derived by investigating the impact of various factors, including consumer types and market diversity, on the interactions between the energy provider and its customers. Our results show that price discounts may provide significant leverage for achieving lower peak-to-average ratios while improving the service providers profits. Our results demonstrate that when load-shifting is sacrificial for the consumers, equilibrium discounts and server provider profits not only depend on the size of the demand (market depth) but also the composition and the number of consumers (market breadth).

Minimizing total weighted tardiness and overtime costs for single machine preemptive scheduling

We study the single machine preemptive scheduling problem with both regular and overtime modes.The objective is to minimize the total tardiness and overtime costs.We propose a heuristic solution methodology for the problem.The efficiency of the heuristic is tested with upper bounds generated by the mathematical model. This paper studies the scheduling of a finite set of jobs on a single resource that operates under both regular and overtime capacity modes. Jobs, which can be preempted, have associated release and due dates. Limited overtime capacity can be utilized to reduce tardiness. However, since overtime is costly, justification of the overtime use depends on the trade-off between the tardiness and the overtime costs. The overall objective is to minimize the total cost of tardiness and overtime. To achieve this objective, we develop a holistic method composed of three-stages. We first provide a heuristic based on an effective priority rule for the base case where no overtime capacity is considered. This heuristic is later employed in the first-stage to produce a compact non-delay schedule built based on the assumption that overtime capacity incurs no additional cost. In the second stage, the overtime usage is reduced by shifting workload and generating a full-delay schedule without altering the tardiness of jobs produced in the first stage. The third stage improves the total costs by altering the tardiness of jobs in return for savings in overtime utilization. Using computational tests, we compare the performance of our heuristics to the upper bounds generated by the exact mixed-integer programming formulation. The results show that the proposed method is efficient in obtaining solutions that are considerably better than the generated upper bounds in significantly short times and as such, it can be quite useful as an effective solution approach especially for large size problems.

Order Admission and Optimal Pricing for Regular Jobs and Big Deals at a Service Company

This paper studies optimal pricing and demand management policies for a firm that faces two streams of order types: one is composed of recurring regular jobs with pre-determined prices exogenous prices and the other involves big deals that require pricing proposals endogenous prices. The probability to secure the big deals diminishes with the quoted price. The authors develop and compare optimization models for different demand management settings. Specifically, we consider two distinct strategies: a pure strategy in which the firm commits to bid for deals only and a mixed strategy where the firm switches its allocation of capacity between regular jobs and deals. The authors compare optimal pricing strategies under two demand management strategies that differ in how they allocate capacity across regular jobs and deals, and order acceptance policies that they adopt. The authors observe that the differences between two strategies in terms of pricing and average gain are in accord. Under any given set of systems parameters, one of the strategies leads to both higher prices and average gains. Typically, the preferable strategy depends on the exogenous price of regular orders and the price sensitivity of the deals. The authors conclude that the threshold values for these two parameters are determined primarily by the demand rate of the deals and the service rate of the standard jobs.

CAPACITY INVESTMENT, PRICING, AND PRODUCTION ALLOCATION ACROSS INTERNATIONAL MARKETS WITH EXCHANGE RATE UNCERTAINTY

In this paper, we investigate joint optimal capacity investment, pricing and production decisions for a multinational manufacturer who faces exchange rate uncertainties. We consider a manufacturer who sells its product in both domestic and foreign markets over a multiperiod season. Because of long-lead times, the capacity investment must be committed before the selling season begins. The exchange rate between the two countries fluctuates across periods and the demand in both markets are price dependent. Our model considers three scenarios: (1) early commitment to price and quantity with central sourcing, (2) postponement of prices and quantities with central sourcing, and (3) local sourcing. We derive the optimal capacity and the optimal prices for each scenario, and investigate the impact of the exchange rate parameters and the length of the selling season. We observe that while the price and production decisions in the domestic market are independent of the exchange rate under early commitment and local sourcing scenarios, the exchange rate between two countries directly impacts these decisions under the postponement setting. We identify thresholds and gain insights on capacity and production costs, exchange rate movement, and selling season length for the choice of entering a foreign market under all scenarios.