bluuebunny/crossref_metadata_2025_split huggingface dataset:
Scheduling Economic Lot Size Production Runs
Authors: Davis Samuel G. 
Published: November 2008

Single capacitated production facilities where multiple products are produced pose particularly challenging scheduling problems. The traditional economic lot size calculation reflects the optimum production batch size in the light of the tradeoff between inventory carrying costs and order preparation/setup costs. However, these calculations are straightforward only when applied to a single product. When multiple products are produced on the same facility, the production quantities must be translated into a schedule which is within the capacity of the facility and results in the timely satisfaction of demand for each product. Previously published solution procedures directly address the capacity issue. A far less tractable issue is the stockout concern. This issue has previously been addressed by either (1) imposing (often overly restrictive) constraints a priori to guarantee feasibility from the outset possibly eliminating more economic schedule alternatives from consideration or (2) heuristic enumeration procedures where trial solutions are evaluated for feasibility a posteriori but the escape from infeasibility often poses algorithmic difficulties. In this paper, the focus is on providing a workable production sequence through a posteriori tests. Mathematical programming approaches are defined which include alternative formulations to better address the stockout concern. Goal programming procedures are proposed which provide a mechanism to directly examine the tradeoffs between scheduling efficiency and delivery performance.
A Model to Evaluate Variables Impacting the Productivity of Software Maintenance Projects
Authors: Banker Rajiv D. , Datar Srikant M. , Kemerer Chris F. 
Published: November 2008

The cost of maintaining application software has been rapidly escalating, and is currently estimated to comprise from 50–80% of corporate information systems department budgets. In this research we develop an estimable production frontier model of software maintenance, using a new methodology that allows the simultaneous estimation of both the production frontier and the effects of several productivity factors. Our model allows deviations on both sides of the estimated frontier to reflect the impact of both production inefficiencies and random effects such as measurement errors. The model is then estimated using an empirical dataset of 65 software maintenance projects from a large commercial bank. The insights obtained from the estimation results are found to be quite consistent for reasonable variations in the specification of the model. Estimates of the marginal impacts of all of the included productivity factors are obtained to aid managers in improving productivity in software maintenance.
Evaluation of One-for-One Replenishment Policies for Multiechelon Inventory Systems
Authors: Svoronos Antony , Zipkin Paul 
Published: November 2008

This paper is concerned with multiechelon inventory systems, where each demand triggers a separate order (one-for-one replenishment policies). The exogenous demands are independent Poisson processes. The transit times between locations may be stochastic. The way we model these transit times follows closely the standard treatment of stochastic leadtimes in single-location models. This is different, however, from the usual approach in the multiechelon setting (e.g., METRIC). We describe simple methods for computing or approximating the steady-state behavior of the system. The results show that, in sharp contrast to prior multiechelon models, transit-time variances play an important role in system performance.
Informational Dynamics of Censored Observations
Authors: Braden David J. , Freimer Marshall 
Published: November 2008

The analysis of stochastic models is often greatly complicated if there are censored observations of the random variables. This paper characterizes families of distributions which help keep tractable the analysis of such models. Our primary motivation is to provide guidance to practitioners in the selection of distributions: If a modeler feels that no member of the families we characterize is a reasonable approximation, then he will almost surely encounter serious analytic and computational problems if his data include censored observations. We characterize a family of distributions for which there exist fixed-dimensional sufficient statistics of purely censored observations. We also characterize an important subset of this family, appropriate for situations where data include both censored and exact observations. We derive the corresponding predictive distributions using arbitrary priors and present some general results relating stochastic dominance among predictive distributions to the parameters of the prior. We also analyze the cases of discrete and mixed random variables.
Competitive Strategies for Two Firms with Asymmetric Production Cost Structures
Authors: Eliashberg Jehoshua , Steinberg Richard 
Published: November 2008

We model joint production-marketing strategies for two firms with asymmetric production cost structures in competition. The first firm, called the “Production-smoother,” faces a convex production cost and a linear inventory holding cost. The second firm, called the “Order-taker,” faces a linear production cost and holds no inventory. Each firm is assumed to vary continuously over time both its production rate and its price in view of an unstable “surge” pattern of demand. The underlying theoretical motivation is to investigate the temporal nature of the equilibrium policies of two competing firms, one operating at or near capacity (the Production-smoother), and one operating significantly below capacity (the Order-taker). We characterize and compare the equilibrium strategies of the two competing firms. Among our results, we show that if the duopolistic Production-smoother finds it optimal to hold inventory, then he will begin the season by building up inventory, continue by drawing down inventory until it reaches zero, and conclude by following a “zero inventory” policy until the end of the season. This result, which is robust with respect to the market structure, is compared with a monopolistic Production-smoother policy. We also show that due to the “coupling” effect between the two competing firms, the time at which the Production-smoother begins his zero inventory policy is also critical for the Order-taker who divides his production and pricing strategies into two parts determined by the Production-smoother's zero inventory point. Numerical examples that illustrate certain aspects of our analyses are provided as well.
The Minimum Common-Cycle Algorithm for Cyclic Scheduling of Two Material Handling Hoists with Time Window Constraints
Authors: Lei Lei , Wang Tzyh-Jong 
Published: November 2008

The problem of cyclic scheduling of two hoists is defined as follows. There are N + 1 workstations, S0, S1, …, SN, and two identical hoists that move jobs between stations. Jobs are identical and each job has to visit all stations in the order that stations are numbered. It is assumed that the hoist traveling times between stations are given constants. Each station can hold one job at a time, and each job must remain at station Si, for a period of at least ai and at most bi time units. Both ai and bi, i = 2, …, N, are given constants. Define mi, 0 ≤ i ≤ N, to be a move of a job from Si to Si+1. A cyclic schedule determines the order of N + 1 moves, mi, i = 0, 1, …, N, an assignment of these moves to hoists, and the start time of the moves in a cycle. The problem is to find a cyclic schedule that minimizes the total time (the cycle time) to complete all the N + 1 moves. Previous approaches toward solving cyclic hoist scheduling problems have been limited to single-hoist cases. In this paper, we propose a heuristic algorithm that finds schedules for systems with two hoists, and discuss an extension to the problem with more than two hoists. The algorithm uses a partitioning approach by which a system is partitioned into two sets of contiguous workstations and each hoist is assigned to a set. A sequence of alternative partitions is evaluated. For each partition, two single-hoist subproblems are formed and the optimal (minimal) cycle time for each subproblem is independently computed. The two optimal single-hoist schedules are then coupled and refined into a feasible two-hoist schedule with a common-cycle time for both hoists. The best of the generated schedules, that results in the minimum common-cycle time among the different partitions, is given as the final solution. Computational experience with both randomly generated problems and a benchmark problem arising from a real system is discussed.
Cyclical Schedules for Capacitated Lot Sizing with Dynamic Demands
Authors: Campbell Gerard M. , Mabert Vincent A. 
Published: November 2008

Cyclical scheduling, where the time between production periods for each item is constant, offers simplicity and ease of control compared with noncyclical scheduling, where production periods are irregularly spaced. However, when demands are dynamic, flexibility in the spacing of production periods permits noncyclical scheduling to result in lower total costs. This study investigates the additional cost of cyclical scheduling. We focus upon the capacitated lot sizing problem (CLSP), which deals with planning production on a single, capacitated machine serving multiple items with dynamic demands. To establish cyclical schedules, a mathematical programming model is developed along with a heuristic solution technique and a Lagrangian-based lower bounding procedure. For problems solved in this study, differences between solution costs and lower bounds average less than 1%. Experiments are performed to evaluate the heuristic technique and to compare cyclical schedules with noncyclical ones. For comparison purposes, a noncyclical heuristic and problem sets from the literature are used. New problems based upon operating data from Ford Motor Company are also generated and solved. In this study, cyclical schedules average 4.4% higher in cost than noncyclical ones. Coefficient of demand variation most seriously influences differences in costs. Other significant factors include capacity utilization, setup time, time between orders, and number of items.
Measuring the Long-Term Effects of Public Policy: The Case of Narcotics Use and Property Crime
Authors: Powers Keiko , Hanssens Dominique M. , Hser Yih-Ing , Anglin M. Douglas 
Published: November 2008

The effects of treatment and legal supervision on narcotics use and criminal activities were assessed by applying newly developed time-series methods that disentangle the long-term (permanent) and the short-term (temporary) effects of intervention. A multivariate systems approach was used to characterize the dynamic interplay of several related behaviors at a group level over a long period of time. Five variables—abstinence from narcotics use, daily narcotics use (or addiction), property crime, methadone maintenance treatment, and legal supervision—were derived by aggregating information from over 600 narcotic addiction histories averaging 12 years in length. Because of the long assessment period, age was also included as a control variable. Overall, the system dynamics among the variables were characterized by long-term rather than short-term relationships. Neither methadone maintenance nor legal supervision had short-term effects on narcotics use or property crime. Methadone maintenance treatment demonstrated long-term benefits by reducing narcotics use and criminal activities. Legal supervision, on the other hand, did not reduce either narcotics use or property crime in the long run. Instead, there was a positive long-term relationship in which a higher level of legal supervision was related to higher levels of narcotics use and criminal activity. This latter finding is consistent with the observation that either narcotics use or criminal activity is likely to bring addicts to the attention of the legal system. However, these addicts, as a group, did not directly respond to legal supervision by changing their narcotics use or crime involvement except perhaps through coerced treatment. The paper explores the policy implications of these findings.
Some Extensions of the Discrete Lotsizing and Scheduling Problem
Authors: Salomon Marc , Kroon Leo G. , Kuik Roelof , Van Wassenhove Luk N. 
Published: November 2008

In this paper the Discrete Lotsizing and Scheduling Problem (DLSP) is considered. DLSP relates to capacitated lotsizing as well as to job scheduling problems and is concerned with determining a feasible production schedule with minimal total costs in a single-stage manufacturing process. This involves the sequencing and sizing of production lots for a number of different items over a discrete and finite planning horizon. Feasibility of production schedules is subject to production quantities being within bounds set by capacity. A problem classification for DLSP is introduced and results on computational complexity are derived for a number of single and parallel machine problems. Furthermore, efficient algorithms are discussed for solving special single and parallel machine variants of DLSP.
Random Review Production/Inventory Systems with Compound Poisson Demands and Arbitrary Processing Times
Authors: Srinivasan Mandyam M. , Lee Hyo-Seong 
Published: November 2008

A production/inventory system is considered in which a single production facility produces items of a given type. The demand for the item is assumed to arrive according to a compound Poisson process. The time required to produce an item is assumed to follow an arbitrary distribution. An (s, S) policy is considered in which production stops at the instant that the inventory level is raised to S and production resumes at an inspection point when the inventory level is observed to have dropped to or below s for the first time. The time interval between two successive inspection points during a nonproduction period is a random variable which follows an arbitrary distribution. Under a cost structure which includes a set-up cost, a linear holding cost and a linear backorder cost, an expression for the expected cost per unit time is obtained for given control values. The operating cost during a cycle is shown to be convex in S for a given value of S-s. For the continuous review simple Poisson demand system, as well as some other special cases of the system considered here, it is shown that the expected cost per unit time is unimodal. Based on these properties, a procedure to find the optimal (s, S) policy is presented.
A Methodology for Collective Evaluation and Selection of Industrial R&D Projects
Authors: Oral Muhittin , Kettani Ossama , Lang Pascal 
Published: November 2008

This paper proposes a methodology for evaluating and selecting R&D projects in a collective decision setting, especially useful at sectorial and national levels. It consists of two major phases: Evaluation and Selection. The evaluation process repeatedly uses mathematical programming models to determine the “relative values” of a given R&D project from the viewpoint of the other R&D projects. The selection process of R&D projects is based on these “relative values” and is done through a model-based outranking method. The salient features of the methodology developed are its ability to (1) permit the evaluation of an R&D project from the viewpoint of the other R&D projects without at first imposing a uniform evaluation scheme, and (2) maximize the level of consensus as to which projects should not be retained in the R&D Program being funded, thus minimizing the level of possible resentment in those organizations or departments whose projects are not included in the R&D Program. Also discussed in this paper is an application of the methodology to evaluate and select major R&D projects in the iron and steel industry of Turkey.
An Analytic Congestion Model for Closed Production Systems with IFR Processing Times
Authors: Spearman Mark L. 
Published: November 2008

We present an analytic model relating the mean cycle time or throughput as a function of the number of jobs in a closed production system composed of a tandem network of queues having exponential and/or IFR processing times. This model exhibits macroscopic behavior that is predicted by results from queueing theory and involves three meaningful parameters: the bottleneck rate and the “raw process” time that can be determined from first moment data; and a dimensionless congestion coefficient that is typically obtained from a single WIP/average cycle time observation (e.g., simulation). The derivation of the model is based on observations of the behavior of the relation between mean cycle time and WIP. This “engineering” approach is different from a purely “probabilistic” one in that we do not consider the distribution of processing times at individual station. We compare the accuracy of the model in predicting mean cycle times to other techniques such as mean value analysis and simulation.
On Properties of Stochastic Inventory Systems
Authors: Zheng Yu-Sheng 
Published: November 2008

For most order quantity/reorder point inventory systems, the stochastic model, which specifies the demands as stochastic processes, is often more accurate than its deterministic counterpart—the EOQ model. However, the application of the stochastic model has been limited because of the absence of insightful analytical results on the model. This paper analyzes the stochastic order quantity/reorder point model in comparison with a corresponding deterministic EOQ model. Based on simple optimality conditions for the control variables derived in the paper, a sensitivity analysis is carried out, and a number of basic qualitative properties are established for the optimal control parameters. Our main results include the following: (1) in contrast to the deterministic EOQ model, the controllable costs of the stochastic model due to selection of the order quantity (assuming the reorder point is chosen optimally for every order quantity) are actually smaller, while the total costs are clearly larger; the optimal order quantity is larger, but the difference is relatively small when the quantity is large; the cost performance is even less sensitive to choices of the order quantity; (2) the relative increase of the costs incurred by using the quantity determined by the EOQ instead of the optimal from the stochastic model is no more than 1/8, and vanishes when the ordering costs are significant relative to other costs.
Estimating Loyalty and Switching with an Application to the Automobile Market
Authors: McCarthy Patrick S. , Kannan P. K. , Chandrasekharan Radha , Wright Gordon P. 
Published: November 2008

A brand switching model that considers the choices: previous choice, current choice, and substitute choice, if the current choice were not available, is developed and estimated. An important assumption of the model is that the market consists of two types of consumers: “Loyals” and “Shoppers.” The model provides estimates for: (1) the proportion of Loyals in a submarket, and (2) the success of each submarket in capturing Shoppers. We illustrate how the model can be used to characterize the competitive structure of a market. We also show how the estimated parameters of the model can be validated using simulation and a theoretically derived covariance matrix. Our empirical application to automobile data from J. D. Power and Associates provides insights into several interesting marketing phenomena including: (1) the competitiveness of domestic versus foreign makes of automobiles, (2) competition between submarkets defined by form, and (3) the effect of previous dealer experience on loyalty and shopping.
Complete Prepayment Models for Mortgage-Backed Securities
Authors: Kang Pan , Zenios Stavros A. 
Published: November 2008

The estimation of prepayment rates for pools of mortgages is a critical component in determining the value of mortgage-backed securities—MBS for short—and derivative products. This paper discusses the development of prepayment models for pools of fixed-rate mortgages. The models are complete: calibrated functional forms are given for all of the factors that determine prepayment rates. Hence, the models can be used as benchmarks against the simple models of the Public Securities Association, the Federal Housing Administration experience, or the variety of projected prepayment rates generated by proprietary industry models. The key factors that determine prepayment rates are: (1) refinancing incentive, (2) seasonal variations, (3) seasoning of the mortgage pool, and (4) burnout effect. Each factor is modeled separately and is calibrated using historical data. A multiplicative relationship determines the prepayment rate of the mortgage pool. A novel feature of our model is the use of basis functions that capture the complex interactions between the control variables, i.e., interest rate differentials and time, and the response parameter, i.e., prepayment rates.
Stochastic Dominance and Expected Utility: Survey and Analysis
Authors: Levy Haim 
Published: November 2008

While Stochastic Dominance has been employed in various forms as early as 1932, it has only been since 1969–1970 that the notion has been developed and extensively employed in the area of economics, finance, agriculture, statistics, marketing and operations research. In this survey, the first-, second- and third-order stochastic dominance rules are discussed with an emphasis on the development in the area since the 1980s.
Stopped Myopic Policies in Some Inventory Models with Generalized Demand Processes
Authors: Lovejoy William S. 
Published: November 2008

This paper considers single-item inventory systems with immediate delivery and no economies of scale. Bounds are provided on the value loss relative to optimal cost for restricting attention to the class of inventory stocking policies that behave myopically up to a specified stopping time. The stopping time may be random, and may depend on demand histories as well as information exogenous to the firm. The bounds are robust to the nature of the demand process faced after the stopping time, so are applicable when the statistics of demand after the stopping time are unknown. Stopping times that are large with high probability imply that near-term decisions are completely specified with high probability. The general bounding results allow one to consider demand processes that may otherwise be analytically intractable. It is shown that the class of demand models for which the assumption of additive i.i.d. shocks is appropriate is the same class admitting effective myopic stocking policies. The bounds also make rigorous the intuitive notion that myopic policies are least effective in systems with precipitous drops in demand coupled with an inability to recover cash invested in inventory. The option of selling inventory at discount is a reality in many real systems and enhances the attractiveness of the myopic stocking policy. In numerical examples, the myopic policy is shown to be an effective competitor in a range of systems for which optimal policies are not known. The results suggest that for systems with immediate delivery, no economies of scale, and no currently known optimal policy, the myopic stocking rule is a reasonable default policy to adopt.