bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Efficiency-Quality Trade-Off of Cross-Trained Workers
Authors: Pinker Edieal J. , Shumsky Robert A. 
Published: December 2003

Does cross-training workers allow a firm to achieve economies of scale when there is variability in the content of work, or does it create a workforce that performs many tasks with consistent mediocrity? To address this question we integrate a model of a stochastic service system with models for tenure- and experience-based service quality. When examined in isolation, the service system model confirms a well-known “rule of thumb” from the queueing literature: Flexible or cross-trained servers provide more throughput with fewer workers than specialized servers. However, in the integrated model these economies of scale are tempered by a loss in quality. Given multiple tasks, flexible workers may not gain sufficient experience to provide high-quality service to any one customer, and what is gained in efficiency is lost in quality. Through a series of numerical experiments we find that low utilization in an all-specialist system can also reduce quality, and therefore the optimal staff mix combines flexible and specialized workers. We also investigate when the performance of the system is sensitive to the staffing configuration choice. For small systems with high learning rates, the optimal staff mix provides significant benefits over either extreme case (a completely specialized or completely flexible workforce). If the system is small and the rate of learning is slow, flexible servers are preferred. For large systems with high learning rates, the model leans toward specialized servers. In a final set of experiments, the model analyzes the design options for an actual call center.
Assessing the Value of Information Sharing in a Promotional Retail Environment
Authors: Iyer Ananth. V. , Ye Jianming 
Published: December 2003

We focus on a logistics system where inventory is held at three levels: the customers, the retail store, and the warehouse. Retail customer segments are heterogeneous and differ in their reservation prices for product as well as their holding costs. They purchase product from a retail store managed by a retailer. The retailer chooses a retail pricing scheme to maximize his expected profit given a model of customer temporal response to retail pricing. This retailer is supplied product from a warehouse managed by a manufacturer. The manufacturer is responsible for maintaining inventory level at the warehouse and providing 100% service level for retailer orders. The manufacturer uses all available information to generate an inventory policy that maximizes expected profit subject to the service-level requirement. We evaluate the manufacturer's optimal expected profit under two possible schemes: (1) no information regarding the timing of retail promotion plans, and (2) full information regarding the timing of retail promotion plans. We show: (1) as the predictability of the sales impact of a promotion decreases, it may be optimal for the retailer to eliminate retail promotions; (2) increased stockpiling tendency of customers increases retailer profits and decreases manufacturer profits; and (3) retail-promotion information sharing can make retail promotions change from being less profitable than no promotions to being more profitable than no promotions for the manufacturer. We show the impact of fitting the model to a grocery store data set that provided data regarding retail sales (and associated prices) of canned tomato soup over two years. We also explore managerial insights suggested by the model.
Designing a Call Center with Impatient Customers
Authors: Garnett O. , Mandelbaum A. , Reiman M. 
Published: December 2003

The most common model to support workforce management of telephone call centers is the M/M/N/B model, in particular its special cases M/M/N (Erlang C, which models out busy signals) and M/M/N/N (Erlang B, disallowing waiting). All of these models lack a central prevalent feature, namely, that impatient customers might decide to leave (abandon) before their service begins. In this paper, we analyze the simplest abandonment model, in which customers' patience is exponentially distributed and the system's waiting capacity is unlimited (M/M/N + M). Such a model is both rich and analyzable enough to provide information that is practically important for call-center managers. We first outline a method for exact analysis of the M/M/N + M model, that while numerically tractable is not very insightful. We then proceed with an asymptotic analysis of the M/M/N + M model, in a regime that is appropriate for large call centers (many agents, high efficiency, high service level). Guided by the asymptotic behavior, we derive approximations for performance measures and propose “rules of thumb” for the design of large call centers. We thus add support to the growing acknowledgment that insights from diffusion approximations are directly applicable to management practice.
On Throughput Maximization in Constant Travel-Time Robotic Cells
Authors: Dawande Milind , Sriskandarajah Chelliah , Sethi Suresh 
Published: December 2003

We consider the problem of scheduling operations in bufferless robotic cells that produce identical parts. The objective is to find a cyclic sequence of robot moves that minimizes the long-run average time to produce a part or, equivalently, maximizes the throughput rate. The robot can be moved in simple cycles that produce one unit or, in more complicated cycles, that produce multiple units. Because one-unit cycles are the easiest to understand, implement, and control, they are widely used in industry. We analyze one-unit cycles for a class of robotic cells called constant travel-time robotic cells. We complete a structural analysis of the class of one-unit cycles and obtain a polynomial time algorithm for finding an optimal one-unit cycle. Constant travel-time robotic cells are used in real manufacturing operations that the authors have encountered during their interactions with companies. The results and the analysis in this paper offer practitioners (i) a tool to experiment with and study the design of a proposed robotic cell during a prototyping exercise, (ii) a lower bound on the throughput of a robotic cell to help them make an informed assessment of the ultimate productivity level, and (iii) a benchmark throughput level (for comparison purposes) for robotic cells whose operations differ slightly from those discussed in this paper.
Capacity Games in Assembly Systems with Uncertain Demand
Authors: Wang Yunzeng , Gerchak Yigal 
Published: December 2003

An assembler needs sets of components, each produced by a different supplier. To produce the components and assemble the final product, the firms involved need to construct their individual production capacities before observing the actual demand. The firms have an incentive scheme (contract) to induce a “proper” capacity build-up. The key parameters of the contract are the set of transfer prices the assembler pays each supplier for a unit of its component. We consider two game settings as to how the terms of the contract are determined. The first is one where the assembler sets the prices, and the second is for the suppliers to simultaneously select the prices each wants to charge for its component. We first characterize the optimal capacity decision when the system is centralized, and then derive the decentralized equilibrium capacities under each of the two game settings. We show that the decentralized channel performances depend heavily on system structure/parameters. In particular, under the first setting, the performance improves as the assembler's share of the systemwide capacity cost increases and it is not affected by the number of suppliers in the system, while under the second setting, the performance degrades both in the assembler's share of capacity cost and in the number of suppliers. We show that the first setting dominates the second in terms of system performance if and only if the assembler's share of capacity cost is larger than the reciprocal of the number of firms involved.
The Batch Loading and Scheduling Problem
Authors: Dobson Gregory , Nambimadom Ramakrishnan S. 
Published: December 2003

This paper discusses the problem of batching and scheduling of certain kinds of batch processors. Examples of these processors include heat treatment facilities, particularly in the steel and ceramics industries, as well as a variety of operations in the manufacture of integrated circuits. In general, for our problem there is a set of jobs waiting to be processed. Each job is associated with a given family and has a weight or delay cost and a volume. The scheduler must organize jobs into batches in which each batch consists of jobs from a single family and in which the total volume of jobs in a batch does not exceed the capacity of the processor. The scheduler must then sequence all the batches. The processing time for a batch depends only on the family and not on the number or the volume of jobs in the batch. The objective is to minimize the mean weighted flow time. The paper presents an integer programming formulation for this problem, generates a lower bound from a partial LP relaxation, provides a polynomial algorithm to solve a special case, and tests a set of heuristics on the general problem. The ability to pack jobs into batches is the key to efficient solutions and is the basis of the different solution procedures in this paper. The heuristics include a greedy heuristic, a successive knapsack heuristic, and a generalized assignment heuristic. Optimal solutions are obtained by complete enumeration for small problems. The conclusions of the computational study show that the successive knapsack and generalized assignment heuristics perform better than the greedy. The generalized assignment heuristic does slightly better than the successive knapsack heuristic in some cases, but the latter is substantially faster and more robust. For problems with few jobs, the generalized assignment heuristic and the knapsack heuristic almost always provide optimal solutions. For problems with more jobs, we compare the heruistic solutions' values to lower bounds; the computational work suggests that the heuristics continue to provide solutions that are optimal or close to the optimal. The study also shows that the volume of the job relative to the capacity of the facility and the number of jobs in a family affect the performance of the heuristics, whereas the number of families does not. Finally, we give a worst-case analysis of the greedy heuristic.
Stocking Retail Assortments Under Dynamic Consumer Substitution
Authors: Mahajan Siddharth , van Ryzin Garrett 
Published: December 2003

We analyze a single-period, stochastic inventory model (newsboy-like model) in which a sequence of heterogeneous customers dynamically substitute among product variants within a retail assortment when inventory is depleted. The customer choice decisions are based on a natural and classical utility maximization criterion. Faced with such substitution behavior, the retailer must choose initial inventory levels for the assortment to maximize expected profits. Using a sample path analysis, we analyze structural properties of the expected profit function. We show that, under very general assumptions on the demand process, total sales of each product are concave in their own inventory levels and possess the so-called decreasing differences property, meaning that the marginal value of an additional unit of the given product is decreasing in the inventory levels of all other products. For a continuous relaxation of the problem, we then show, via counterexamples, that the expected profit function is in general not even quasiconcave. Thus, global optimization may be difficult. However, we propose and analyze a stochastic gradient algorithm for the problem, and prove that it converges to a stationary point of the expected profit function under mild conditions. Finally, we apply the algorithm to a set of numerical examples and compare the resulting inventory decisions to those of some simpler, naive heuristics. The examples show that substitution effects can have a significant impact on an assortment's gross profits. The examples also illustrate some systematic distortions in inventory decisions if substitution effects are ignored. In particular, under substitution one should stock relatively more of popular variants and relatively less of unpopular variants than a traditional newsboy analysis indicates.
Stochastic Inventory Systems in a Supply Chain with Asymmetric Information: Cycle Stocks, Safety Stocks, and Consignment Stock
Authors: Corbett Charles J. 
Published: December 2003

The two critical factors distinguishing inventory management in a multifirm supply-chain context from the more traditional centrally planned perspective are incentive conflicts and information asymmetries. We study the well-known order quantity/reorder point (Q, r) model in a two-player context, using a framework inspired by observations during a case study. We show how traditional allocations of decision rights to supplier and buyer lead to inefficient outcomes, and we use principal-agent models to study the effects of information asymmetries about setup cost and backorder cost, respectively. We analyze two “opposite” models of contracting on inventory policies. First, we derive the buyer's optimal menu of contracts when the supplier has private information about setup cost, and we show how consignment stock can help reduce the impact of this information asymmetry. Next, we study consignment and assume the supplier cannot observe the buyer's backorder cost. We derive the supplier's optimal menu of contracts on consigned stock level and show that in this case, the supplier effectively has to overcompensate the buyer for the cost of each stockout. Our theoretical analysis and the case study suggest that consignment stock helps reduce cycle stock by providing the supplier with an additional incentive to decrease batch size, but simultaneously gives the buyer an incentive to increase safety stock by exaggerating backorder costs. This framework immediately points to practical recommendations on how supply-chain incentives should be realigned to overcome existing information asymmetries.
Inventory Competition Under Dynamic Consumer Choice
Authors: Mahajan Siddharth , van Ryzin Garrett 
Published: December 2003

We analyze a model of inventory competition among n firms that provide competing, substitutable goods. Each firm chooses initial inventory levels for their good in a single period (newsboy-like) inventory model. Customers choose dynamically based on current availability, so the inventory levels at one firm affect the demand of all competing firms. This creates a strategic interaction among the firms' inventory decisions. Our work extends earlier work on variations of this problem by Karjalainen (1992), Lippman and McCardle (1997) and Parlar (1988). Specifically, we model demand in a more realistic way as a stochastic sequence of heterogeneous consumers who choose dynamically from among the available goods (or choose not to purchase) based on a utility maximization criterion. We also use a sample path analysis, so minimal assumptions are imposed on this demand process. We characterize the Nash equilibrium of the resulting stocking game and prove it is unique in the symmetric case. We show there is a bias toward overstocking caused by competition; specifically, reducing the quantity stocked at any equilibrium of the game increases total system profits, and at any joint-optimal set of stocking levels, each firm has an individual incentive to increase its own stock. For the symmetric case, we show that as the number of competing firms increases, the overstocking becomes so severe that total system (and individual firm) profits approach zero. Finally, we propose a stochastic gradient algorithm for computing equilibria and provide several numerical examples.
Due-Date Scheduling: Asymptotic Optimality of Generalized Longest Queue and Generalized Largest Delay Rules
Authors: Van Mieghem Jan A. 
Published: December 2003

Consider the following due-date scheduling problem in a multiclass, acyclic, single-station service system: Any class k job arriving at time t must be served by its due date t+Dk. Equivalently, its delay τk must not exceed a given delay or lead-time Dk. In a stochastic system, the constraint τk≤Dk must be interpreted in a probabilistic sense. Regardless of the precise probabilistic formulation, however, the associated optimal control problem is intractable with exact analysis. This article proposes a new formulation which incorporates the constraint through a sequence of convex-increasing delay cost functions. This formulation reduces the intractable optimal scheduling problem into one for which the Generalized cμ (Gcμ) scheduling rule is known to be asymptotically optimal. The Gcμ rule simplifies here to a generalized longest queue (GLQ) or generalized largest delay (GLD) rule, which are defined as follows. Let Nk be the number of class k jobs in system, λk their arrival rate, and ak the age of their oldest job in the system. GLQ and GLD are dynamic priority rules, parameterized by θ: GLQ(θ) serves FIFO within class and prioritizes the class with highest index θkNk, whereas GLD(θ) uses index θkλkak. The argument is presented first intuitively, but is followed by a limit analysis that expresses the cost objective in terms of the maximal due-date violation probability. This proves that GLQ(θ*) and GLD(θ*), where θ*,k = 1/λkDk, asymptotically minimize the probability of maximal due-date violation in heavy traffic. Specifically, they minimize [Formula: see text] for all positive t and x, where τk(s) is the delay of the most recent class k job that arrived before time s. GLQ with appropriate parameter θα also reduces “total variability” because it asymptotically minimizes a weighted sum of αth delay moments. Properties of GLQ and GLD, including an expression for their asymptotic delay distributions, are presented.
Continuity of DEA Efficiency Measures
Authors: Scheel Holger , Scholtes Stefan 
Published: December 2003

Data envelopment analysis (DEA) is a methodology that allows, in one way or other, the assignment of efficiency scores to members of a group of decision-making units. We call an efficiency measure “continuous” if small perturbations of the input-output data cause only small changes in the score. Continuity is a desirable property of an efficiency measure, in particular in the presence of measurement tolerances. Continuity is also desirable from a numerical point of view because the scores are computed by linear programming software.Focusing on convex production possibility sets, we give examples where radial DEA measures fail to be continuous, i.e., they “jump” under small data perturbations. We present necessary and sufficient conditions for continuity in terms of the data and show that these conditions are satisfied for “almost all” data. We also discuss continuity of nonradial measures and identify possible problems of “multistage approaches” to compute mix efficiencies.
A Call-Routing Problem with Service-Level Constraints
Authors: Gans Noah , Zhou Yong-Pin 
Published: December 2003

We consider a queueing system, commonly found in inbound telephone call centers, that processes two types of work. Type-H jobs arrive at rate λH, are processed at rate μH , and are served first come, first served within class. A service-level constraint of the form E[delay] ≤ α or P {delay ≤ β} ≥ limits the delay in queue that these jobs may face. An infinite backlog of type-L jobs awaits processing at rate μH, and there is no service-level constraint on this type of work. A pool of c identical servers processes all jobs, and a system controller must maximize the rate at which type-L jobs are processed, subject to the service-level constraint placed on the type-H work. We formulate the problem as a constrained, average-cost Markov decision process and determine the structure of effective routing policies. When the expected service times of the two classes are the same, these policies are globally optimal, and the computation time required to find the optimal policy is about that required to calculate the normalizing constant for a simple M/M/c system. When the expected service times of the two classes differ, the policies are optimal within the class of priority policies, and the determination of optimal policy parameters can be determined through the solution of a linear program with O(c3) variables and O(c2) constraints.
Still Flowing: Approaches to Traffic Flow and Traffic Jam Modeling
Authors: Nagel Kai , Wagner Peter , Woesler Richard 
Published: December 2003

Certain aspects of traffic flow measurements imply the existence of a phase transition. Models known from chaos and fractals, such as nonlinear analysis of coupled differential equations, cellular automata, or coupled maps, can generate behavior which indeed resembles a phase transition in the flow behavior. Other measurements point out that the same behavior could be generated by geometrical constraints of the scenario. This paper looks at some of the empirical evidence, but mostly focuses on different modeling approaches. The theory of traffic jam dynamics is reviewed in some detail, starting from the well-established theory of kinematic waves and then veering into the area of phase transitions. One aspect of the theory of phase transitions is that, by changing one single parameter, a system can be moved from displaying a phase transition to not displaying a phase transition. This implies that models for traffic can be tuned so that they display a phase transition or not. This paper focuses on microscopic modeling, i.e., coupled differential equations, cellular automata, and coupled maps. The phase transition behavior of these models, as far as it is known, is discussed. Similarly, fluid-dynamical models for the same questions are considered. A large portion of this paper is given to the discussion of extensions and open questions, which makes clear that the question of traffic jam dynamics is, albeit important, only a small part of an interesting and vibrant field. As our outlook shows, the whole field is moving away from a rather static view of traffic toward a dynamic view, which uses simulation as an important tool.
Is Organization Theory Obvious to Practitioners? A Test of One Established Theory
Authors: Priem Richard L. , Rosenstein Joseph 
Published: December 2003

Critics have argued that organization theories which “work” are obvious to practitioners; that is, the theories simply confirm relationships that are already well understood by experienced managers. In our study, four types of respondents—Chief Executive Officers (CEOs) with business school education, CEOs without such education, graduating MBA students, and liberal arts graduate students—were presented with theory-based performance-rating tasks. These tasks identified respondents' beliefs regarding high-performance alignments of businesslevel strategy, structure, and environment for manufacturing firms. The respondents' cause maps were then compared to one another and to the alignment-performance relationships prescribed by business-level contingency theory. The graduating MBAs' cause maps reflected contingency theory most closely. The MBAs' cause maps were much closer to prescriptions of the theory, however, than were those of either the liberal arts graduate students or the experienced CEOs. This suggests that business-level contingency theory is not obvious to educated laypersons, or to highly experienced practitioners; both groups would find that the theory disconfirms aspects of their causal expectations. Further, each of the four respondent types in our study emphasized different contingency factors during the decision-making exercise. We discuss these results and their implications.