Tuncay Özcan

Comparative analysis of multi-criteria decision making methodologies and implementation of a warehouse location selection problem

For the solution of decision making problems with multi criteria, the literature presents many methodologies under the title of decision theory. In this context, AHP, TOPSIS, ELECTRE and Grey Theory are well-known and the most acceptable methodologies. Firstly, in this study; these methodologies are compared in terms of main characteristic of decision theory and thus advantages and disadvantages of these methodologies are offered. Later, the application of these methodologies on the warehouse selection problem, which is one of the main topics of logistics management that has a wide range of applications with multi-criteria decision making methodologies, is presented as a case study which is characterized in retail sector, that maintains high uncertainity and product variety and then how to choose the best warehouse location among many alternatives has been shown.

Modified Grey Relational Analysis Integrated with Grey Dematel Approach for the Performance Evaluation of Retail Stores

Performance evaluation is one of the most important problems for retail chains and may have effect on tactical and strategic decisions. This paper proposes a grey-based multi-criteria performance evaluation model for retail sector. This model integrates Decision-Making Trial and Evaluation Laboratory (DEMATEL) and modified Grey Relational Analysis (GRA) methods. First, the grey-based DEMATEL method is used for determining the importance of performance indicators to be used in GRA based on the experts’ assessments. Then, the proposed modified GRA method is used for the performance evaluation and ranking of retail stores with respect to the predetermined performance indicators. Finally, the effectiveness and the applicability of the developed approach are illustrated with a case study with the actual data taken from a retail chain in Turkey.

A Discrete Constrained Optimization Using Genetic Algorithms for A Bookstore Layout

Abstract In retail industry, one of the most important decisions of shelf space management is the shelf location decision for products and product categories to be displayed in-store. The shelf location that products are displayed has a significant impact on product sales. At the same time, displaying complementary products close to each other increases the possibility of cross-selling of products. In this study, firstly, for a bookstore retailer, a mathematical model is developed based on association rule mining for store layout problem which includes the determination of the position of products and product categories which are displayed in-store shelves. Then, because of the NP-hard nature of the developed model, an original heuristic approach is developed based on genetic algorithms for solving large-scale real-life problems. In order to compare the performance of the genetic algorithm based heuristic with other methods, another heuristic approach based on tabu search and a simple heuristic that is co...

A heuristic approach based on artificial bee colony algorithm for retail shelf space optimization

Due to high product variety and changing consumer demands, shelf space is one of the most scarce resources in retail management. At this point, the efficient allocation of the limited shelf space carries critical importance for maximizing the financial performance. On the other hand, because of NP-Hard nature of the shelf space allocation problem, heuristic approaches are required to solve real world problems. In this paper, different from existing studies in the literature, a heuristic approach based on artificial bee colony algorithm is presented for shelf space allocation problem by using a model which considers the space and cross elasticity. In order to demonstrate the efficiency of the developed approach, another heuristic approach based on particle swarm optimization is proposed. The performance analysis of these approaches is realized with problem instances including different number of products, shelves and categories. Experimental results show that the developed artificial bee colony algorithm is efficient methodology through near-optimal solutions and reasonable solving time for large sized shelf space allocation problems.

Healthcare Expenditure Prediction in Turkey by Using Genetic Algorithm Based Grey Forecasting Models

This chapter aims to predict the health care expenditure (HCE) per capita which is an important indicator of a country’s health status and economic growth. Accurate estimation of HCE can guide efficient health care policy making and resource allocation. Grey forecasting models are applied for predicting the HCE per capita of Turkey. Three different strategies are proposed which are rolling mechanism, training data size optimization and parameter optimization to improve the forecasting accuracy of these models. Genetic algorithm (GA) which is one of the most widely used meta-heuristic optimization techniques is applied for training data size and parameter optimization of the grey forecasting models. The application results indicate that the optimization of parameters and training data size together with rolling mechanism highly improve the forecasting performance of the grey models.