A Solution Approach for Agricultural Service Center Location Problem Using TOPSIS, DEA and SAW Techniques

Abstract

In this research, agricultural service centers have been introduced as a strategic decision to improve efficiency of agricultural supply chain. Since the location problems in agriculture exhibit several features, such as their large scope and size, and demonstrate increased levels of complexity, so the main aim of this study is developing an integrated Multi-Attribute Decision Making (MADM) approach considering all aspects of the Agricultural Service Center Location Problem (ASCLP) including: customers, service suppliers, and also technical suitability of candidate location. In this regard two main MADM approaches are developed. The first approach was the combination of Simple Additive Weighting (SAW), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Data Envelopment Analysis (DEA). The second approach is developed to improve the solutions for some cases which the first approach was unable to reach to the non-dominated solutions. Before completing the MADM approaches, initially a Delphi fuzzy-analytical hierarchy process survey has been completed to extract the objectives and attributes of ASCLP and their local weight, which is published in previous work by the authors. The main contribution of current chapter is to consider more comprehensively the attributes in solving a service facility location problem such as: distances to demand points and also suppliers, cultivated area of demand points, population of demand points, number of cultivated crops, and the ration of irrigated cultivated lands to dried cultivated lands. The developed approach was able to use all mentioned decision methods, simultaneously. In the second approach, the developed attributes are used, two scores (maxi-min and maxi-max) for each candidate location is computed and nondominated solutions are identified. To prove the capability of the selected attributes one case study is addressed. The results reveal that the chosen attributes to a very large degree can lead to the non-dominated solutions.

A hybrid decision support model using axiomatic fuzzy set theory in AHP and TOPSIS for multicriteria route selection

“…The positive-ideal solution is a solution that maximizes the benefit criteria and minimizes the cost criteria, whereas the negative-ideal solution maximizes the cost criteria and minimizes the benefit criteria [45]. In literature, there have been a large number of studies using TOPSIS for the solution of complex decision-making problems ( [2][3][4]24,25,41,48,49]). For this present study, the procedure of TOPSIS can be expressed in following steps:…”