Colin O. Benjamin

A knowledge-based decision support system for locating a manufacturing facility

This paper examines the design and implementation of a knowledge-based decision support system (KBDSS) in the facility location domain. The KBDSS for locating a manufacturing facility is intended to support facilities planners in improving the quality of strategic decision-making in facility location. A survey of past location studies was conducted to identify the major considerations of location analysts and to develop a hierarchy of factors for locating a manufacturing facility in the USA. Location evaluation is accomplished via a location model that is formulated as a linear additive multiattribute utility model. The microcomputer-based DSS developed integrates an object-oriented expert system with other modeling tools, such as a database management system and a C-based interactive graphical user interface. The system incorporates subjective, judgemental evaluations from management with objective, factual location data to recommend the best location for a new manufacturing facility. The system also mak...

Comparing BP and ART II neural network classifiers for facility location

Abstract This paper compares the performance of Artificial Neural Networks (ANNs) as classifiers in the facility location domain. The ART II (Adaptive Resonance Theory) and BP (Back Propagation) paradigms are used as exemplars of ANNs developed using supervised and unsupervised learning. Their performances are compared with that obtained using a linear multi-attribute utility model (MAUM) to classify the 48 states in the continental U.S.A. based on location profiles developed from government publications. In this paper, the models are used to classify the U.S. states based on their suitability for accommodating new manufacturing facilities. For this data set, the BP ANN model displayed robust performance and showed better convergence with the MAUM.

An integrated multiobjective planning model: A case study of the Zambian copper mining industry

Abstract This study outlines a planning methodology that formally integrates major societal dimensions with mathematical programming in industry planning for a low-income developing country. An integrated approach involving the Analytic Hierarchy Process (AHP) and Linear Goal Programming (LGP) is employed. Development objectives are classified under economic, technological, social and political factors and are prioritized using the AHP according to their degree of importance. The priority structure is incorporated into the LGP framework to develop a multiobjective planning model. The application of the model is demonstrated using data from an export-oriented, semi-industrial and dominant copper mining sector in a low-income developing country in Sub-Saharan Africa.

Activity-Based Costing in Small Manufacturing Companies—The Theory/Practice Gap

ABSTRACTActivity-based costing (ABC) has become popular and has been successfully implemented by several large manufacturing companies. However, no studies have been reported on the use of these costing systems in small companies. We report the results of a survey conducted to study the applicability of ABC in small manufacturing companies in Missouri. Information received on existing costing systems, production processes, level of automation, and interest in adopting ABC revealed a large theory/practice gap among Missouris small manufacturing companies.

Classifying U.S. manufacturing plant locations using an artificial neural network

Abstract This paper examines the application of an artificial neural network (ANN) for classifying the contiguous states in the USA according to their manufacturing climate. The application uses a self-organizing paradigm, the ART2. The performance of the ANN was very encouraging. Its results were compared to those obtained from a scoring model that ranked the states utilizing the same criteria. Future work is intended to further improve the networks performance and develop an Artificial Neural System (ANS) that will assist decision makers in selecting the best locations for manufacturing facilities in the U.S.A. The ANS is seen as a key component of an intelligent decision support system for improving and enhancing plant location decision making.

Prioritizing development goals in low-income developing countries

This paper outlines a systematic approach to prioritizing the multiple and often conflicting development goals and objectives in a typical low-income developing country (LDC). First, a hierarchy of development goals and objectives is developed from an extensive review of the literature. Then, Analytic Hierarchy Process (AHP) is utilized to analyze the judgement elicited from World Bank experts and a priority structure established reflecting the perceived importance of these development goals and objectives. This methodology can assist development planners in LDCs in formulating developement plans consistent with national objectives.

A simulator-based approach to Cellular Manufacturing System design

Abstract Cellular Manufacturing (CM) is a product-oriented organizational strategy that is recognized as one of the critical elements in the rejuvenation of outdated and unproductive manufacturing plants. Successful planning, design, and implementation of Cellular Manufacturing Systems (CMS) require the development of sound, replicable system design approaches. In this paper, a simulator-based approach for the design of manufacturing cells and systems is described. The approach utilizes holistic systems design concepts and addresses Hitomis three fundamental aspects of manufacturing systems: transformational, procedural, and structural. A distinction is made between system logic and system structure and the concepts of logical design and physical design are utilized. The approach described can assist the designer by providing a systematic holistic methodology to the CMS design process.

Cellular manufacturing system design: A holistic approach

Abstract A systematic approach to the design of Cellular Manufacturing Systems is described. The design methodology, which incorporates several widely accepted design axioms, is divided into four phases; analysis, conceptual design, embodiment design, and detailed design. In the analysis phase, part-feature/process data are analyzed to identify part families. Abstract concepts, initially conceived in the conceptual design phase, are developed and refined in an iterative manner into concrete proposals in the embodiment design phase. The purpose of the Detailed Design Phase is to finalize all specifications and dimensional details of the selected “best” design from the embodiment phase. Emphasis was centered on the embodiment design phase for which a five stage approach was developed. Consistently good results were obtained when the methodology was applied to three case scenarios representing varying levels of system complexity. The methodology is intended for use by manufacturing system designers and engineers.

A winning strategy for managing simulation projects

Abstract In this paper a winning strategy for conducting successful simulation studies is proposed. The proposed approach emphasizes risk reduction through incremental model development and encourages user commitment through team work. A case study, based on a medium-sized manufacturing facility in Missouri, is used to illustrate its application. This successful strategy can be easily adopted on other similar simulation studies.

EXSEMA-An EXpert System for SElecting Simulation Software for Manufacturing Applications

This paper describes the development of EXSEMA, an expert system (ES) prototype for providing advice on the most appropriate simulation software environment for a particular manufacturing application. During a typical consultation session the user is prompted for information which describes the proposed application and the existing development and organization constraints. EXSEMA then identifies the simulation system which provides the best fit with the user’s needs and provides appropriate explanations.