Efficient Heuristics for MRP Lot Sizing with Variable Production/Purchasing Costs*

Two heuristics based on branch and bound (B&B) are developed to solve closed-loop material requirements planning (MRP) lot-sizing problems that have general product structures and variable costs. A “look ahead method'’(LAM) heuristic allows for variable production/purchasing costs and uses a single-level B&B procedure to rapidly improve lower bound values; thus, LAM efficiently uses computer-storage capacity and allows solution of larger problems. The “total average modification'’(TAM) heuristic uses B&B, applied level by level, and modified setup and carrying costs to solve the variable production/purchasing costs MRP lot-sizing problem. LAM and TAM are tested on problems and compared to heuristics in the literature. TAM may be used to solve large MRP lot-sizing problems encountered in practice.     

Lowering Inventory Systems Costs by Using Regression-Derived Estimators of Demand Variability*

Scientific techniques for inventory management typically are applied to systems containing many items. Such techniques require an estimation of the demand variance (and mean) of each item from historical data. This research demonstrates a significant potential for improvement in system cost performance from using least-squares regression fits of a variance-to-mean functional relation instead of the standard statistical variance estimate. Even when there is a moderate degree of heterogeneity among items and when the form of the variance-to-mean relation is misspecified, substantial cost savings may be realized. The cost of statistical uncertainty may be reduced by half. The research also provides evidence that system cost is fairly insensitive to the number of items used to fit the regression. This paper provides the underlying reason why a regression-derived variance estimator yields lower cost: it is less variable than the usual individual item variance estimator.     

On the Joint Lot-Sizing Problem with Zero Setup Costs

In a recent paper, Pinto and Mabert [5] presented a lot-sizing rule and an improvement procedure for the joint lot-sizing problem with zero setup costs. We show that this procedure often yields infeasible schedules. We also present and discuss two interesting properties of the joint lot-sizing problem with zero setup costs. A numerical example to illustrate the second property is provided.     

Modeling Synergy and Learning under Multiple Advanced Manufacturing Technologies

The advent of the wide variety of new, highly integrated, advanced manufacturing technologies available for acquisition by a firm's managers has brought to light an accompanying set of unexpected issues. These issues include expectations for the benefits and costs of these technologies, determining the appropriate order of implementation and finding a way to justify acquisition when many of the benefits are a function of the technology's learning and synergistic effects on other operations and technologies. We present here a model that captures the interaction effects of these highly integrated technologies and discuss the data requirements for application of the construct. We then illustrate the model's workings with a number of multitechnology examples and show the danger of ignoring the synergistic and learning effects of these technologies when considering their acquisition.     

Wealth Holdings and Portfolio Allocation of the Elderly: The Role of Marital History

This paper investigates the role of marital history in terms of explaining differences in wealth holdings and portfolio allocation of older individuals by studying data from the first wave of Health and Retirement Study which was conducted in 1992. The results generally suggest that both men and women suffer from the negative shocks of past marital dissolutions in terms of household wealth accumulation. The significance level, however, differs across currently married couples, single males, and single females. The examination of the asset components of net worth also indicates that both the probability of owning a particular asset and the fraction of wealth allocated to that asset might vary depending on the elderly individuals’ marital history.

IMPLICIT OPTIMAL AND HEURISTIC LABOR STAFFING IN A MULTIOBJECTIVE,MULTILOCATION ENVIRONMENT*

This paper presents a tractable set of integer programming models for the days-off scheduling of a mix of full- and part-time employees working α to β days/week (cycle) in a multiple-objective, multiple-location environment. Previous models were formulated to specifically schedule part-time employees working either two or three days per week. These models were intractable because they required complete employee schedule information. The new models are deemed implicit optimal since they are required to supply only essential information. While the number of variables in previous models is an exponential increasing function of β-α, the size of three of the new models is independent of α and β. The first three models developed here (as in [18]) deal with the trade-offs between idle time, the number of employees required to work at multiple “locations,” and the size of the total labor pool. The inherent flexibility of the implicit modeling approach is illustrated by the presentation of various modifications of the basic models. These modifications permit the use of preference weights on the number of employee work days/week (cycle) or the minimization of payroll costs where differential pay rates exist. These latter models may also be formulated such that idle time is ignored, constrained or minimized. The execution time for the implicit models (on a CDC CYBER 730 computer with commercially available software) averaged well under five seconds on 1200 trial problems for the type of application considered in [18]. A solution was obtained in less than 46 seconds of CPU time for a trial problem which would have required over 1.4 million integer variables with previous models. The availability of optimal solutions was invaluable in the development of two heuristics designed to deal with the trade-offs of [16]. In an experimental analysis a previous heuristic produced results which averaged from 74 to 508 percent above optimum across six experimental conditions. The comparable new heuristic produced results which averaged from 3 to 8 percent above optimum for the same experimental conditions. The paper concludes by developing a framework to integrate the results of this research with the tour scheduling problem and by identifying several other areas for related research.     

SCHEDULING PARALLEL PROCESSORS WITH SETUP COSTS AND RESOURCE LIMITATIONS

An integer linear programming model is presented for the scheduling of n products on m identical machines. The particular problem studied is one that occurs frequently in the fiberglass and textile industries. The model incorporates setup costs, lost production costs, and overtime costs. Due to the structure of the model, integer solutions can be obtained by explicitly restricting only a small number of the integer variables. This allows those responsible for scheduling to solve realistically sized problems in an efficient manner. Computational results are provided for a set of generated test problems.     

DIVERSIFICATION GAINS FROM INCLUDING REAL ESTATE IN MIXED-ASSET PORTFOLIOS*

Diversification gains in mean-variance efficiency derived from including real estate in financial asset portfolios are examined. Optimal financial and mixed-asset portfolios were generated by selecting from an investment universe including several distinct financial and real estate media. Deficiencies of previous studies were overcome by employing data with improved representativeness and comparability. The efficient mixed-asset portfolios dominated the efficient financial asset portfolios implying that purely financial asset diversification is inefficient. The optimal mixed-asset portfolio prescribed that approximately two-thirds of the investment wealth be allocated to real estate and one-third to the financial media.     

INSTRUCTING OPERATIONS DESIGN: AN EXPERIENTIAL APPROACH

Teaching an operations design course has major pedagogical challenges. (1) The design topics—output, process, facility, and work design—are necessarily taught sequentially, yet the decisions are integrative. (2) Instruction must be generic to service or product producers. (3) Discussing output design is difficult since students typically have had no exposure to the “product language” of engineering graphics. (4) No text is available which examines in sufficient depth all the operations design decisions. (5) Cases necessarily depict historical situations—process technologies and economic data—while operations managers must plan future directions for their productive systems. (6) While the commercial world contains fresh information and data, students are inexperienced in obtaining knowledge from the real world. (7) While the course presents an operations management perspective, students must recognize the information, data, and cooperation necessary from the other functional areas to successfully complete the operations design. To help overcome these seven pedagogical challenges, the students in the undergraduate and graduate operation design courses complete a comprehensive feasibility study for a new product or service and the entire productive system.     

Order Promising and the Master Production Schedule*

Previous research on material requirements planning (MRP) systems has rarely considered the impact of the master production scheduling method used to promise customer orders and to allocate production capacity. Based on a simulation study of an MRP environment, we show that the correct selection of a master production schedule (MPS) method depends on the variance of end-item demand. In addition, we find evidence that the effectiveness of a particular MPS method can be enhanced by holding buffer inventory at the same level in the product structure as in the MPS.