Selection of Concrete Production Facility Location Integrating Fuzzy AHP with TOPSIS Method

Selection of Concrete Production Facility Location Integrating Fuzzy AHP with TOPSIS Method

Golam Kabir (University of British Columbia, Canada) and Razia Sultana Sumi (Stamford University Bangladesh, Bangladesh)
DOI: 10.4018/ijpmat.2012010104
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Abstract

Evaluation and selection of a concrete production facility location is an important strategic decision making problems for both the public and private sector. The multi-dimensional, multi-criteria nature of the concrete production facility location problem limits the usefulness of any particular single objective model. In this study, social, economical, technological, environmental, and transportation factors and sub criteria have been derived to make the optimal concrete production facility location selection decision more realistic and effectual. This study shows an improved and appropriate concrete production facility location evaluation and selection model has been developed by integrating Modified Delphi and Fuzzy Analytic Hierarchy Process (FAHP) with Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method. An example is presented to show applicability and performance of the proposed methodology followed by a sensitivity analysis to discuss and explain the results.
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1. Introduction

From a low cost and rather unusual material in the late nineteenth century, concrete became “the stone” of the twentieth. From construction elements to urban furniture, a large variety of concrete objects surround us nowadays. Concrete is one of the most widely used building materials in roads, buildings, bridges and other infrastructures. On average, approximately 1 ton of concrete is produced each year for every human being in the world (Lippiatt and Ahmad, 2004). When compared to other construction materials, concrete has more strength, ease of product and ease of maintenance. Since more than 150 years, research on cement concrete have contributed to improve its mechanicals (strength, durability) and casting (self-compacting.) characteristics (Cazacliu and Ventura, 2010). With these characteristics, concrete is the most common construction material in the world as well as in Bangladesh. In industrial countries the number of constructions is growing as a result of urbanization and industrial investment. And concrete is preferred as a construction material increasingly.

In recent years, to meet the construction needs concrete production facilities have been built in different areas. The problem of facility location is common to all businesses. The strategic planning of facility location is critical to a company’s eventual success. A suitable location can provide favorable contributions to a company’s market competitiveness. More and more firms are clearly dispersing parts of their production process to locations around the world to take advantage of national differences in the cost and quality of labor, talent, energy, facilities and capital (Partovi, 2006). The decision of the location selection of concrete production facility is a crucial aspect as it depends on multiple factors or constraints like economical, environmental, social, political, technological etc. (Cazacliu and Ventura, 2010; Chowdhurya, Apul, and Fry, 2010; Rajendran and Gambatese, 2007; Turhan et al., 2010; Zapata and Gambatese, 2005). In this complex and conflicting situation, a multi criteria analysis (MCA) is inevitable for locating concrete production facility where both quantitative and qualitative objectives can be considered. Real-life applications of multicriteria decision-making methods require the processing of imprecise, uncertain, qualitative or vague data (Joshi, Banwet, and Shankar, 2011; Kabir and Hasin, in press; Rajesh, Pugazhendhi, and Ganesh, 2009). Therefore the main objective of this research is to formulate a decision support model (system), which would facilitate in the decision making and to evaluate the best concrete production facility location among a set of discrete potential locations. An improved and more accurate concrete production facility location evaluation and selection model has been developed by integrating modified Delphi method and Fuzzy AHP with TOPSIS method.

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