Peter W. Mullarkey

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.

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.

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.

Simulation of a signal quality survey

This paper describes a simulator for a large outdoor operation called a signal quality survey. Design and implementation of the simulator follows an object oriented approach with a primary focus of modeling operations cycles. The simulator is implemented in Visual C++/CSIM17. Excel acts as the primary user interface. A Microsoft Foundations Classes Single Document Interface application acts as a secondary user interface for simulation animation. We discuss the benefits of using discrete event simulation for this application. We also report on some of the challenges encountered during the implementation stages of the project.

Schlumberger Uses Simulation in Bidding and Executing Land Seismic Surveys

Schlumberger and its competitors use seismic surveying, the process of mapping subterranean rock formations with reflected sound waves, as an important first step in identification and recovery of oil and gas reserves. This complicated logistical operation commonly lasts two to six months, covers hundreds of square miles, employs scores of people, and utilizes a large variety of equipment. To win these jobs, Schlumberger participates in a closed bidding process organized by the oil companies. To succeed, it must quickly and accurately estimate the costs of seismic surveys. We developed a simulation tool to evaluate the impact of crew sizes (people and equipment), survey area, geographical region, and weather conditions on survey costs and durations. Schlumberger uses it to obtain and profit from a larger portion of the global seismic survey market. We demonstrated cost savings to clients of about

Dynamic output analysis for simulations of manufacturing environments

2 million on four surveys. Based on the number of surveys that Schlumberger conducts each year, it should save about

Simulation Helps Maxager Shorten Its Sales Cycle

1.5 to

Method and apparatus for knowledge-based signal monitoring and analysis

3 million each year.

Apparatus and method for monitoring well logging information

We describe the design and implementation of a generic, real time, in-line output analysis procedure for controlling simulations of discrete manufacturing environments. We implemented this capability in the commercial simulation software Extend(R). The main issues we faced were: (1) specifying the products to evaluate; (2) determining the batch sizes for output analysis; and (3) defining the stopping conditions based on the confidence intervals. We implemented a significance test for correlation and used this test to dynamically adjust the batch sizes used in confidence interval estimation done using batch means. When the stability conditions have been met, the simulation prompts the user to consider stopping the simulation. On the other hand, if at the end of the run length selected by the user, the statistical conditions were not satisfied, the tool notifies the user of that fact. This capability enabled us to significantly reduce the simulation run lengths, and ensures, with little additional computational effort, that the results were reliable. We used this tool to control simulations of electronics, steel, automotive, and metal processing industries. In general, using this tool we realized a reduction of more than 40% in the time required for simulation.

Method and system of simulating and optimizing land seismic operations

We developed a simulation approach to reduce the time and cost of selling the Maxager system, a manufacturing decision-support system consisting of both hardware and software. To enable customers to understand the impact Maxager could have on their profitability, we used to perform pilot studies in which we installed our hardware, trained their people, collected data, and performed the analysis. These pilot studies lasted for three to six months and cost hundreds of thousands of dollars. To save time and money, we simulated Maxagers data-collection systems and used the Maxager software to analyze the data. This enabled us, with some aggregate information provided by the prospective customers, to illustrate the impact Maxager could have on their systems using simulated data of their products, processes, and operating procedures without having to install our hardware. As a result, (1) we reduced sales cycles from over 12 months long to less than six months long, and (2) we reduced the cost of a sales cycle by approximately