Douglas J. Morrice

A Multiple Attribute Utility Theory Approach to Ranking and Selection

Managers of large industrial projects often measure performance by multiple attributes. For example, our paper is motivated by the simulation of a large industrial project called a land seismic survey, in which project performance is based on duration, cost, and resource utilization. To address these types of problems, we develop a ranking and selection procedure for making comparisons of systems e.g., project configurations that have multiple performance measures. The procedure combines multiple attribute utility theory with statistical ranking and selection to select the best configuration from a set of possible configurations using the indifference-zone approach. We apply our procedure to results generated by the simulator for a land seismic survey that has six performance measures, and describe a particular type of sensitivity analysis that can be used as a robustness check.

A production scheduling heuristic for an electronics manufacturer with sequence-dependent setup costs

Abstract In this paper, we develop a three-step heuristic to address a production scheduling problem at a high volume assemble-to-order electronics manufacturer. The heuristic provides a solution for scheduling multiple product families on parallel, identical production lines so as to minimize setup costs. The heuristic involves assignment, sequencing, and time scheduling steps, with an optimization approach developed for each step. For the most complex step, the sequencing step, we develop a greedy randomized adaptive search procedure (GRASP). We compare the setup costs resulting from the use of our scheduling heuristic against a heuristic previously developed and implemented at the electronics manufacturer that assumes approximately equal, sequence-independent, setup costs. By explicitly considering the sequence-dependent setup costs and applying GRASP, our empirical results show a reduction in setups costs for an entire factory of 14–21% with a range of single production line reductions from 0% to 49%.

An approach to ranking and selection for multiple performance measures

We develop a ranking and selection procedure for making multiple comparisons of systems that have multiple performance measures. The procedure combines multiple attribute utility theory with ranking and selection to select the best configuration from a set of K configurations using the indifference zone approach. We demonstrate our procedure on a simulation model of a large project that has six performance measures.

A strategic supply chain simulation model

The authors describe a simulation game designed to quantify the benefits of an enterprise resource planning system coupled with the balanced scorecard framework in an extended enterprise. We present three scenarios of the same enterprise: a base case scenario with a non-integrated legacy system, a scenario with an integrated, enterprise resource planning system, and a scenario with an enterprise resource planning system using the balanced scorecard framework. Results from this game support our research and teaching activities on the benefits of systems integration, data and process standardization, visibility across the business enterprise, improved decision support functionality, and operationalizing strategy.

The implementation of temporal intervals in qualitative simulation graphs

In this paper we develop and implement a simulation modeling methodology that combines discrete event simulation with qualitive simulation. Our main reason for doing so is to extend the application of discrete event simulation to systems found in business for which precise quantitative information is lacking. The approach discussed in this paper is the implementation of temporal interval specifications in the discrete event model and the construction of a temporal interval clock for the qualitative simulation model.

Managing capacity flexibility in make-to-order production environments

This paper addresses the problem of managing flexible production capacity in a make-to-order (MTO) manufacturing environment. We present a multi-period capacity management model where we distinguish between process flexibility (the ability to produce multiple products on multiple production lines) and operational flexibility (the ability to dynamically change capacity allocations among different product families over time). For operational flexibility, we consider two polices: a fixed allocation policy where the capacity allocations are fixed throughout the planning horizon and a dynamic allocation policy where the capacity allocations change from period to period. The former approach is modeled as a single-stage stochastic program and solved using a cutting-plane method. The latter approach is modeled as a multi-stage stochastic program and a sampling-based decomposition method is presented to identify a feasible policy and assess the quality of that policy. A computational experiment quantifies the benefits of operational flexibility and demonstrates that it is most beneficial when the demand and capacity are well-balanced and the demand variability is high. Additionally, our results reveal that myopic operating policies may lead a firm to adopt more process flexibility and form denser flexibility configuration chains. That is, process flexibility may be over-valued in the literature since it is assumed that a firm will operate optimally after the process flexibility decision. We also show that the value of process flexibility increases with the number of periods in the planning horizon if an optimal operating policy is employed. This result is reversed if a myopic allocation policy is adopted instead.

A simulation model to study the dynamics in a service-oriented supply chain

In this paper, we investigate the dynamic behavior of a simple service-oriented supply chain in the presence of non-stationary demand using simulation. The supply chain contains four stages in series. Each stage holds no finished goods inventory. Rather, the order backlog can only be managed by adjusting capacity. These conditions reflect the reality of many service (and custom manufacturing) supply chains. The simulation model is used to compare various capacity management strategies. Measures of performance include application completion rate, backlog levels, and total cumulative costs.

A Weighted Least Squares Approach to Computer Simulation Factor Screening

In this paper, we develop a weighted least squares WLS approach to computer simulation factor screening. Like frequency domain methodology FDM, the regression analysis is performed in the frequency domain. However, unlike FDM, where unknown nuisance parameters are eliminated by cancellation in a ratio statistic, the WLS procedure is designed to estimate these parameters the WLS weights and incorporate this information into the regression analysis. We propose and compare one-and two-run approaches for estimating the weights in the WLS approach.

Discrete event simulation in supply chain planning and inventory control at Freescale Semiconductor Inc

The supply chain of Freescale Semiconductor from fabrication through final test and delivery was modeled and analyzed using discrete event simulation in Arena. Freescale starts products in manufacturing based on a make-to-order and make-to-stock master production schedule. Since customer lead time is often less than the supply chain cycle time, Freescale maintains strategic safety stock throughout the supply chain and as finished goods inventory. Manufacturing entry rate is determined by the amount of product in WIP and inventory. Our analysis concentrates on the relationship between on-time delivery in the major supply chain segments and on-time delivery to the customer in an environment of significant inventory and WIP level changes. The goal is to predict the effect of internal on-time delivery, inventory and WIP changes on the customer order fulfillment service level. In our analysis, we evaluate supply chain production and inventory control policies and the impact of lead time reductions

Sensitivity analysis in ranking and selection for multiple performance measures

We conduct sensitivity analysis on a ranking and selection procedure for making multiple comparisons of systems that have multiple performance measures. The procedure combines multiple attribute utility theory with ranking and selection to select the best configuration from a set of K configurations using the indifference zone approach. Specifically, we consider sensitivity analysis on the weights generated by the multiple attribute utility assessment procedure. We demonstrate our analysis on a simulation model of a large project that has six performance measures.