A DISCRETE DYNAMIC OPTIMIZATION MODEL FOR MADE-TO-ORDER COST ANALYSIS

A model is presented that yields optimal production rates for a firm producing a contracted order. The model is unique in that it considers the influence of production rate and learning on total program cost. An application to the specific characteristics of two military production programs is presented. As demonstrated by the application, models of this type may be used as decision-making tools when negotiating the cost impact of contract modifications.     

DYNAMIC LOT SIZING FOR A SINGLE-FACILITY MULTIPRODUCT PROBLEM IN A ROLLING-HORIZON ENVIRONMENT

This paper considers a production planning model for a single-facility multiproduct problem where backlogging is not allowed. A planning-horizon theorem is derived. From that theorem, a forward algorithm for finding an optimal solution over a finite horizon and a procedure for selecting the first-period production in a rolling-horizon environment are developed. Computational results from a set of simulation experiments designed to investigate the cost effectiveness of the procedure demonstrate its effectiveness.     

A GOAL PROGRAMMING MODEL FOR OUTPUT PLANNING

This paper describes a goal programming procedure for determining satisfactory output plans for a work center. The situation being modeled is one in which work center inputs are known but vary significantly across time periods. Input levels are fixed relative to a given master production schedule, and output levels can be varied only within certain prescribed limits, at least in the short term. The similarity of the output planning problem to the more familiar aggregate planning problem is noted and discussed.     

A DYNAMIC PROGRAMMING MODEL FOR PERIODICAL SELECTION

This paper presents a model for choosing the periodical collection in a reading room. The model consists of two stages. In the first stage the probability distribution of contacts by users with a given periodical is developed and employed to determine the utility of the periodical. In the second stage the problem of choosing an optimum periodical collection is formulated as a capital budgeting problem and solved with the aid of dynamic programming. The implementation of this model at the University of Connecticut is described in detail.     

ALLOCATE: A COMPUTER MODEL FOR SALES TERRITORY PLANNING

Recent years have witnessed a renewal of interest in the application of management science techniques to personal selling related problems. Some early applications are described in [2] [7]. Cloonan has employed simulation in examination of the salesman routing problem [3] [4]. Lodish, in CALLPLAN, has devised an interactive call planning system designed to assist sales management and/or salesmen in allocating sales call time more efficiently [6]. Armstrong has devised a system he labels SCHEDULE which estimates the value of calls on accounts [1]. Hess and Samuels have designed a computer based sales districting model which is an analogue of a legislative apportionment model [5]. The objective of this paper is to explore the nature of a call planning system entitled ALLOCATE. ALLOCATE was designed to be employed by upper sales management either as an input device for sales management decisions such as sales-territory-size, or as a vehicle for determining the effects of alternative call allocation strategies on territorial revenue over multiple time periods.     

THE ACADEMIC FLOW MODEL: A MARKOV-CHAIN MODEL FOR FACULTY PLANNING*

A Markov-chain faculty planning model to be used with institution-specific data is presented to describe and better understand the complex phenomena of faculty movement through an institution and on its relationship to salary costs, composition of the faculty, and faculty turnover rate. The model updates the earlier work done at Stanford University and Oregon State University by the addition of states for fixed-term appointments and for part-time FTEs to reflect accurately the current hiring trends at many institutions. The model is implemented and tested at two different institutions. The findings suggest that the model is a viable means of gaining useful insights and quantitative data on the faculty profile, salary costs, expected departures, and part-time trends. And further, when used as a planning tool, and the model apparently is comprehensive and flexible enough to analyze the probable effects, both in the short and long run, of alternative personnel policies on the faculty composition.     

A NOTE ON DYNAMIC LOT SIZING IN A ROLLING-HORIZON ENVIRONMENT

This paper presents a modification of the dynamic lot-size algorithm of Wagner and Whitin for rolling horizon environments. The computational results show that the modified algorithm gives better cost performance than the Wagner-Whitin algorithm and the Silver-Meal heuristic. The improvements over the Wagner-Whitin algorithm require very few additional computations.     

A CONCEPTUAL DESCRIPTION OF A FINANCIAL PLANNING MODEL FOR COMMERCIAL BANKS

This paper describes a conceptually sound and powerful model to aid bank managers in short and long range decision making for the management of the financial performance of the bank. The planning model is a key element in an integrated computer-based planning system which includes a forecast of economic, monetary, and loan market conditions, an analysis of internal relationships, and a system of reports which provide management with access to historical data and planning summaries. The planning model at the heart of the system, contains an imbedded linear programming algorithm to optimize “balance sheet management” decisions within liquidity and capital adequacy constraints. The model recognizes the interaction between assets and liabilities, it recognizes the time value of money, and it accommodates the difference between the net marginal yield for decision making, and the gross average yield used for determining revenue. Finally, the model recognizes the crucial role of liquidity with an important treatment of gross cash flows from normal asset turnover as a major source of liquidity, and the liquidity aspects of liabilities, as well as the more traditional concept of asset marketability.     

A SIMULTANEOUS PLANNING MODEL FOR PRODUCTION AND WORKING CAPITAL*

A simultaneous model is proposed for the planning of production, inventory, workforce, and working capital levels over several periods. By exploiting the capabilities of linear programming, the model provides relevant information more efficiently than would be typical of the more traditional approach, in which planning is decomposed along functional lines. A detailed example is presented in order to illustrate the advantages of the simultaneous model.     

A DYNAMIC PROGRAMMING MODEL FOR OPTIMAL ALLOCATION OF DECISION TIME

Recurrent decision making by a lower-level manager can be viewed as a sequential decision process in which time and uncertainty are limiting factors. Under these conditions, the manager must determine how to best utilize his decision making time consistent with his own particular set of decision values. A dynamic programming model was devised to determine the optimal (consistent) allocation of decision time among five different types of problems for a sequence of simulated recurrent decision situations. Fifty-one lower-level managers were interviewed about their use of decision time and decision procedures. The model was validated by comparing model assumptions and results with the findings from the interviews. The model was used to determine the effects of variations in the levels of time available and uncertainty upon the optimal allocation of decision time.     

ENROLLMENT FORECASTING: USE OF A MULTIPLE-METHOD MODEL FOR PLANNING AND BUDGETING*

A multiple-method approach is presented for making enrollment projections at the departmental, school, and university level. The various methods utilized are based on the application of a cohort model (macro level) and regression techniques (micro level). The model demonstrates its flexibility in selecting one of a number of regression techniques according to a comparison approach using historical and projected results. This multiple-method approach has proved to be highly accurate and thus useful in allocating and redistributing resources to the operating units of a university. Previous efforts have relied almost solely on a single regression technique for projections and allocation purposes rather than a method based on a measure of flexibility, adaptability, and prediction confirmation.     

A DYNAMIC PROCESS MODEL OF DISSATISFACTION FOR UNFAVORABLE,NON-ROUTINE SERVICE ENCOUNTERS

This paper explores the effect of expectations and information on customer dissatisfaction in unfavorable, nonroutine service encounters. In complex services (e.g., health care) with multiple encounters and wide range of services, customers use some of the services rarely or only once. In such encounters, customers may not have clear expectations regarding the process and/or outcome of the impending service delivery. This may increase the likelihood of the customer to perceive poor service or be dissatisfied. Hypotheses regarding the nature of expectations—levels, uncertainty, consistency—and its affects on customer dissatisfaction are tested using a dynamic process model of customer dissatisfaction.     

A CASE IN ON-LINE DECISION ANALYSIS FOR PRODUCT PLANNING1

This report presents an application of quantitative decision-analytic techniques to consider an opportunity for capital investment in a new product. Decision analysis is a well-established technique for evaluating major decisions in which substantial resources and time are available for the analysis and the problem is definitely formulated at the outset. This case study, however, illustrates how decision-analytic techniques can be used on-line to improve decision making in a situation in which time and resources are limited, and the manager's perception of the problem changes in response to feedback from the analysis. The basic decision problem was whether or not the AIL division of Cutler-Hammer, Incorporated should purchase a six-month option on a flight-safety system patent. The president of AIL had only a few weeks in which to make a decision. The analytic approach focused on a probabilistic discounted earnings model. This model was refined over a two-week period through a sequence of iterations which incorporated the results of both direct and indirect assessments of probabilities and values. The analysis affected the company's decision process by providing a vehicle for structuring the ongoing communication between AIL's president and his advisors, and by focusing their attention on the most important issues. In the end, the analysis facilitated a unanimous decision from the decision-making group not to purchase the option, but to consider participating in the license as a subcontractor at a later date. This strategy had not been considered at the outset.     

PLANNING MAIL SURVEYS: A MANAGERIAL MODEL

Typically the market research manager faced with implementing a multiple-wave mail survey has certain requirements in terms of response rate, quality, and representativeness. The existing research literature provides some excellent insights into the effects of certain Wave 1 response stimuli. Unfortunately, very little research has been conducted on post-Wave 1 stimuli, and no research has been done on multiple-wave managerial strategy. The authors have developed a prototypic managerial planning model for evaluating various mail survey strategies over multiple-response waves. Subject Areas: Marketing Management and Marketing Research.     

A COMPARISON OF RULES FOR ALLOCATING SETUP-REDUCTION INVESTMENTS IN A CAPACITATED ENVIRONMENT

This paper builds on a recent empirical study of the setup-reduction process that suggests setup-reduction proceeds through three major stages and that each stage is dominated by a particular type of investment function. Specifically, it examines the question of how to best prioritize investments during the stage that emphasizes standardizing setups across a work center. We compare different investment-allocation rules in a multi-item, capacity-constrained, dynamic demand environment under a variety of cost, demand, and investment assumptions. This analysis shows that significant differences in benefits can be achieved depending on the way setup-reduction investments are prioritized.     

PLANNING FOR TRUCK FLEET SIZE IN THE PRESENCE OF A COMMON-CARRIER OPTION

This paper considers a class of network optimization problems in which certain directed arcs must be covered by a set of cycles. Our study was motivated by a distribution planning problem of a commercial firm that had to make deliveries over several origin-destination pairs (directed arcs) and that could service any demand arc by using a vehicle in its own fleet or by paying a common carrier. The problem is to determine an optimal fleet size and the resulting vehicle routes while satisfying maximum route-time restrictions. We formulate the problem, describe some approximate solution strategies, and discuss important implementation issues.     

MATERIAL PLANNING FOR A REMANUFACTURING FACILITY

This article describes the first fully integrated material planning system to facilitate the management of a remanufacturing facility. A number of firms are already engaged in this activity. They remanufacture automobile, truck, and other vehicle components, like starters, alternators, transmissions, and so forth. These firms take in used components, disassemble them, and assemble saleable products from the good parts they find. There is considerable uncertainty in the supply of used components, the good parts in those components, and the demand for remanufactured products. Our system is based on material requirements planning logic, something that many firms in the industry are already familiar with. Meetings with experts in the industry were used to set the parameters of the system and evaluate its approach.     

A MODEL FOR SWITCHING DISPATCHING RULES IN REAL TIME IN A FLEXIBLE MANUFACTURING CELL

We model choice of dispatching rules in real time (system state dependent) as a pattern recognition problem, using a modified version of Data Envelopment Analysis. A data base of system state and performance values is created from extensive simulation, and this data base is used to train the pattern-recognition model. Our results show that the model is very effective in choosing a mix of dispatching rules over a period of time, varying the mix with system objectives, and performing better than the strategy of using fixed rules. We show how “If-Then” decision rules can be created from the model and portrayed in a decision-tree-like diagram. Since such decision rules are based on rigorous mathematical foundations, optimization will be ensured in our approach.     

A STOCHASTIC MODEL FOR ANALYSIS OF VARIATIONS IN AIR POLLUTION LEVELS

Examination of environmental data seems to suggest that variations in air pollution levels from one point in time to another tend to follow some form of observable pattern. In this article, the authors explore an approach to observing, describing and analyzing these variations. They develop a model which is essentially a Markov framework and use it to suggest a number of interesting questions and to facilitate their solution. In this demonstration, the focus is on a single component of air pollution, suspended particulate matter. For implementation, a model would need to incorporate other elements of the pollution mix. As the composition of air pollution differs from one air shed to another, so will the parameters of the model.