Edward R. Clayton

A Goal Programming Approach to the Optimization of Multi response Simulation Models

Abstract This paper presents a direct search approach to the optimization of multiresponse simulation models. The paper develops an approach within the framework of goal programming and uses a modified pattern search routine developed for this purpose. The algorithm and a graphical example are presented. The advantages and disadvantages of the approach determined from computational experiences with the solution procedure are discussed.

Solving Multiple Response Simulation Models Using Modified Response Surface Methodology Within A Lexicographic Goal Programming Framework

Abstract paper describes a new procedure for obtaining satisfactory solutions to multiple-response, multiple-input simulation models. A modified version of response surface methodology is incorporated to obtain input values which meet user specified goals for the responses. The approach is illustrated with three examples which demonstrate the method. The desirability of incorporating this approach into an interactive computer mode is also discussed.

A goal programming approach to multi-period production line scheduling

Abstract This paper considers the problem of scheduling multiple products on several different production lines (i.e. the n-job, m-machine scheduling problem) when multiple objectives exist. Specifically a goal programming model is developed and demonstrated via a case example. The production setting modeled in this paper includes three separate production lines that merge into a single inspection facility. Prior to the description and demonstration of the goal programming model, a brief overview of the multi-product scheduling problem and the various solution approaches to the problem is presented.

Evaluating sequences for paced, mixed-model assembly lines with JIT component fabrication

Evaluation measures of mixed-model assembly lines are examined. The squared deviation from linear component usage and related measures are often cited due to their apparent consistency with the goals of a just-in-time fabrication shop. This paper develops a different statistic which more directly measures deviations from linear component usage. It is shown that, for the environment studied, sequencing methods developed to achieve linear component usage, are not always better than purely random sequencing when the actual time of component use is measured. Next the sequencing procedures are compared, based on their ability to achieve desired production targets. It is shown that the sequencing method which smooths assembly times on the simulated assembly line, as well as methods which perform well based on the traditional measure of linear component use, achieve the highest levels of production. It is concluded that, in the environment studied, direct measures of system productivity are more useful than meas...

Analysis of a multi-criteria project crashing model

Abstract The purpose of this paper is to report on the model formulation and results of a multi-criteria time-cost (crashing) CPM project network. In the project of interest (the installation of a bank paper processing system), multiple and conflicting objectives concerning both time and cost must be incorporated into the decision analysis. The project network is, therefore, formulated as a goal programming model, including the various managerial objectives as goal constraints and associated priorities.

An Evolutionary Algorithm for Sequencing Production on a Paced Assembly Line

A new sequencing method for mixed-model assembly lines is developed and tested. This method, called the Evolutionary Production Sequencer (EPS) is designed to maximize production on an assembly line. The performance of EPS is evaluated using three measures: minimum cycle time necessary to achieve 100% completion without rework, percent of items completed without rework for a given cycle time, and sequence “smoothness.” The first two of these measures are based on a simulated production system. Characteristics of the system, such as assembly line station length, assembly time and cycle time, are varied to better gauge the performance of EPS. More fundamental variation is studied by modeling two production systems. In one set of tests, the system consists of an assembly line in isolation (i.e., a single-level system). In another set of tests, the production system consists of the assembly line and the fabrication system supplying components to the line (i.e., a two-level system). Sequence smoothness is measured by the mean absolute deviation (MAD) between actual component usage and the ideal usage at each point in the production sequence. The performance of EPS is compared to those of well-known assembly line sequencing techniques developed by Miltenburg (1989), Okamura and Yamashina (1979), and Yano and Rachamadugu (1991). EPS performed very well under all test conditions when the criterion of success was either minimum cycle time necessary to achieve 100% production without rework or percent of items completed without rework for a given cycle time. When MAD was the criterion of success, EPS was found inferior to the Miltenburg heuristic but better than the other two production-oriented techniques.

Analysis of material and capacity requirements with Q-GERT

Abstract The purpose of this paper is to report on research conducted to develop a simulation model which incorporates the basic MRP logic into the modelled production process. The objective of the modelling effort is to provide information necessary for planning and controlling of the material and capacity requirements of a production process that includes stochastic processes. The modelling and analysis procedure exploits the fact that the structure of the production process can be logically modelled as a network flow process. The technique employed to facilitate the network modelling is Q-GERT (Graphical Evaluation and Review Technique) modelling and simulation language. A case application is presented to demonstrate a prototype model, followed by a report of the simulation results. The model is designed to provide answers and insights to the following questions: (1) how long must each production centre operate to yield the specified quantities of finished products, (2) what is the expected lead time r...

Redesign of primary response areas for county ambulance services

Abstract This paper describes a modeling approach used to redesign primary response areas (sectors) for an existing county ambulance service. The model employs multicriteria analysis of the problem including goals to minimize and balance travel times and balance workloads among ambulance service units. The model is quite flexible in that optimal designs can be made subject to budgetary and personnel constraints and sector continuousness is maintained. Given inputs such as the location of the ambulance bases and demographic information, the model generates efficient and equitable designs. The model is solved using a modified multicriteria gradient search technique for nonlinear models. The sensitivity of the model to reordering planning priorities is also evaluated.

A network simulation model for police patrol vehicle maintenance and replacement analysis

Abstract Research in the field of urban police patrol-car analysis has tended to concentrate in the areas of vehicle deployment, fleetsize and location, etc., with very little emphasis in the area of vehicle maintenance and replacement policy. As such, the purpose of this paper is to develop and demonstrate a simulation model for patrol-vehicle replacement and maintenance analysis. The simulation model will encompass a Markov-type framework of state movements where the states represent the various stages of a patrol vehicles life from new to inoperable. The model will be developed in the form of a Q-Gert network, and, demonstrated via a case example of an urban patrol fleet of 100 vehicles.

Simulation of a production line system with machine breakdowns using network modeling

Abstract In a manufacturing system, several factors can affect the reliability of the system in producing expected output levels, including resource input rates, labor rate variability, product quality, and machine failures. Although all of these factors will be reflected in the modeling process presented in this paper, the primary focus will be on the effect of machine breakdowns on system output. Network modeling and simulation with Q-GERT is the vehicle of analysis that is employed. This technique will be demonstrated via a realistic case example encompassing a complex production system consisting of several assembly lines, each containing several machines. The statistical results of the simulation of the example system are presented and discussed. In addition, examples of how the simulation model can be used to test changes in machine repair times and breakdown rates will be presented.