SOME COMMENTS ON “A SYSTEMS PERSPECTIVE ON MATERIALS REQUIREMENTS PLANNING”*

We believe there are some serious deficiencies in the Morecroft article on MRP problems which appeared in a recent issue of this journal. The article, at the most, shows that MRP, inappropriately imposed on an existing production system, can result in undesirable system performance. This does not represent a new or startling conclusion. Four problem areas in the article are discussed, and suggested alternative approaches are offered.     

Importance of interpolation when constructing double-bootstrap confidence intervals

We show that, in the context of double-bootstrap confidence intervals, linear interpolation at the second level of the double bootstrap can reduce the simulation error component of coverage error by an order of magnitude. Intervals that are indistinguishable in terms of coverage error with theoretical, infinite simulation, double-bootstrap confidence intervals may be obtained at substantially less computational expense than by using the standard Monte Carlo approximation method. The intervals retain the simplicity of uniform bootstrap sampling and require no special analysis or computational techniques. Interpolation at the first level of the double bootstrap is shown to have a relatively minor effect on the simulation error.     

A VARIANCE-REDUCTION STRATEGY FOR RSM SIMULATION STUDIES

The application of optimization techniques in digital simulation experiments is frequently complicated by the presence of large experimental error variances. Two of the more widely accepted design strategies for the resolution of this problem include the assignment of common pseudorandom number streams and the assignment of antithetic pseudorandom number streams to the experimental points. When considered separately, however, each of these variance-reduction procedures has rather restrictive limitations. This paper examines the simultaneous use of these two techniques as a variance-reduction strategy in response surface methodology (RSM) analysis of simulation models. A simulation of an inventory system is used to illustrate the application and benefits of this assignment procedure, as well as the basic components of an RSM analysis.     

Integrative Cycle Scheduling Approach for a Capacitated Flexible Assembly System*

This study revisits the traditional single stage, multi-item, capacitated lot-sizing problem (CLSP) with a new integrative focus on problem structuring. Unlike past research, we develop integrative cycle scheduling approaches which simultaneously address lot-sizing, capacity, and sequencing issues. Our purposes are to (1) explore the effect of sequencing on inventory levels, (2) examine the problem of infeasibility in the economic lot scheduling problem (ELSP), and (3) provide a simple methodology of generating low-cost cycle schedules in an environment with discrete shipping, dynamic demands, limited capacity, zero setup cost, and sequence-independent setup times. Our procedures are compared to benchmark cycle scheduling approaches in terms of both inventory cost and computation time under different demand scenarios, using the operating data from a flexible assembly system (FAS) at the Ford Motor Company's Sandusky, Ohio plant.     

An Evaluation of Capacity Sensitive Order Review and Release Procedures in Job Shops

We investigate the performance of capacity-sensitive order review and release (ORR) procedures in job shop environments that have not been previously explored. Previous research has ignored the case of job shops which must perform to very tight due-dates because of time-sensitive customers. We propose and test a new capacity sensitive ORR procedure called path based bottleneck (PBB) in such environments, along with the modified infinite loading (MIL) procedure which has been shown to work well in several studies. We compare the performance of these two controlled release rules with that of immediate release rule under different conditions of capacity utilization and customer specified exogenous duedates. Our results indicate that PBB performs well in lowering total costs when due-dates are tight, while MIL is a better procedure with relatively loose to medium due-dates. We also show that in many cases, the shortest processing time (SPT) dispatching rule is a superior performer than a due-date based rule like critical ratio (CR); a conclusion which is contrary to the existing research in this area. In addition, the shop floor control policies recommended are shown to be sensitive to the cost structure of the firm. The managerial implications of this research in providing effective shop floor control in job shops operating under tight due-date conditions are also discussed.     

Two-Group Classification Using Neural Networks*

Artificial neural networks are new methods for classification. We investigate two important issues in building neural network models; network architecture and size of training samples. Experiments were designed and carried out on two-group classification problems to find answers to these model building questions. The first experiment deals with selection of architecture and sample size for different classification problems. Results show that choice of architecture and choice of sample size depend on the objective: to maximize the classification rate of training samples, or to maximize the generalizability of neural networks. The second experiment compares neural network models with classical models such as linear discriminant analysis and quadratic discriminant analysis, and nonparametric methods such as k-nearest-neighbor and linear programming. Results show that neural networks are comparable to, if not better than, these other methods in terms of classification rates in the training samples but not in the test samples.     

The Impact of Learning and Labor Attrition on Worker Flexibility in Dual Resource Constrained Job Shops

This study investigates the impact of worker learning, worker flexibility, and labor attrition on the system performance of a dual resource constrained (DRC) job-shop. The effects of learning and labor attrition have not been previously addressed in DRC literature. Results from the study, consistent with previous literature, show that the greatest benefits are achieved when inter-departmental worker flexibility is incrementally introduced into the system. In addition, the learning environment, which depends on the initial processing time of jobs and the learning rates of workers, is shown to impact the acquisition of flexibility. The study also shows that the impact of labor attrition on system performance under certain shop conditions may be significant.     

Pearson-Tukey Three-Point Approximations Versus Monte Carlo Simulation

Accuracy of the Pearson-Tukey three-point approximation is measured in units of standard deviation and compared with that of Monte Carlo simulation. Using a variety of well-known distributions, comparisons are made for the mean of a random variable and for common functions of one and two random variables. Comparisons are also made for the mean of an assortment of risk-analysis (Monte Carlo) models drawn from the literature. The results suggest that the Pearson-Tukey approximation is a useful alternative to simulation in risk-analysis situations.     

The Effects of Personality Type on Choices Made in Strategic Decision Situations*

Jung's personality-theory typology is used as a framework for exploring the effects of cognitive style on the type and radicalness of choices made in strategic decision situations. Extending the work of Haley and Stumpf [23], it is proposed that individuals with different personality-type preferences exhibit cognitive styles that are associated with specific biases in the pattern of choices they make. Through participation in an interactive behavioral simulation, 407 participants confronted over one hundred ill-structured decision situations and proposed whatever actions they perceived appropriate. The results support the hypothesized relationships that individuals with different personality-type preferences (i.e., sensing-thinking, intuition-thinking, sensing-feeling, and intuition-feeling) take patterns of actions that reflect specific biases (i.e., selective perception, positivity, social desirability, and reasoning-by-analogy, respectively). The implications of these findings for evaluating the likely effectiveness of strategic decisions and making senior-level staffing decisions are discussed.     

Master Production Rescheduling Policy in Capacity-Constrained Just-In-Time Make-To-Stock Environments

This paper describes a simulation experiment to evaluate the relative effectiveness of rescheduling policies in capacity-constrained, just-in-time (JIT) make-to-stock production environments. Three performance measures were analyzed: average finished goods inventory, total units of sales lost, and a measure of schedule instability. Among the three loading methods we studied (level loading, front loading, and back loading), none was superior across all three performance measures. Level loading was the most robust, while back loading had an interaction effect with forecasting model and capacity level. Time fence scenario was found not to be as significant a factor in system performance as reported by previous research. Once an acceptable time fence scenario is selected, attempts to optimize this factor do not appear to offer significant improvement. It was found that higher demand variation, larger forecast errors, and tight capacity lead to the degradation of system performance. Judicious use of slack capacity helps a system to incorporate the periodic schedule changes typical of JIT systems.