A NOVEL MODEL FOR THE CALCULATION OF SAFETY STOCK OF PERISHABLE PRODUCTS WITH A TOTAL WASTE CONSTRAINT

Perishable products cover a high percentage of all goods. The variability, long lead times, risk period, and high service level increase the safety stock level. An increase in safety stock will also increase the probability of perished products because of the increased probability of sales of less than stock during shelf life. This study proposes a model for calculating safety stocks of perishable products besides showing the effect of perishability on service level. The effects of long lead times, risk periods, high sales and lead-time variance, and short shelf life adversely affect perished products. The study investigates and proposes a novel model for calculating total expected waste and costs with a waste quantity constraint. A real-life example compares a proposed model with waste constraints and the traditional safety stock model based on costs and waste quantity. The case study shows the better results of the proposed models

Time-Windowed Vehicle Routing Problem: Tabu Search Algorithm Approach

Vehicle routing problem (VRP); it is defined as the problem of planning the best distribution or collection routes of the vehicles assigned to serve the scattered centers from one or more warehouses in order to meet the demands of the customers. Vehicle routing problem has been a kind of problem in which various studies have been done in recent years. Many vehicle routing problems include scheduling visits to customers who are available during certain time windows. These problems are known as vehicle routing problems with time windows (VRPTWs). In this study, a tabu search optimization is proposed for the solution of time window vehicle routing problem (VRPTWs). The results were compared with the current situation and the results were interpreted

Solving Uncapacitated Planar Multi-facility Location Problems by a Revised Weighted Fuzzy c-means Clustering Algorithm

In this study, a revised weighted fuzzy c-means algorithm is proposed for uncapacitated planar multi-facility location problems. It eliminates the obligation to sequentially use different methods such as classical fuzzy c-means algorithm, combination of fuzzy c-means and center of gravity, and particle swarm optimization algorithm. Performance of the proposed algorithm for uncapacitated planar multi-facility location problem is tested on well-known research data sets. This new algorithm is compared with the methods including fuzzy c-means, fuzzy c-means based center of gravity and particle swarm optimization. Results indicate that the proposed revised weighted fuzzy c-means algorithm based method is superior in terms of cost minimization and CPU time

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

A fuzzy clustering-based hybrid method for a multi-facility location problem

A fuzzy clustering-based hybrid method for a multi-facility location problem is presented in this study. It is assumed that capacity of each facility is unlimited. The method uses different approaches sequentially. Initially, customers are grouped by spherical and elliptical fuzzy cluster analysis methods in respect to their geographical locations. Different numbers of clusters are experimented. Then facilities are located at the proposed cluster centers. Finally each cluster is solved as a single facility location problem. The center of gravity method, which optimizes transportation costs is employed to fine tune the facility location. In order to compare logistical performance of the method, a real world data is gathered. Results of existing and proposed locations are reported

Hybrid revised weighted fuzzy c-means clustering with Nelder-Mead simplex algorithm for generalized multisource Weber problem

Purpose The purpose of this paper is to propose hybrid revised weighted fuzzy c-means (RWFCM) clustering and Nelder-Mead (NM) simplex algorithm, called as RWFCM-NM, for generalized multisource Weber problem (MWP)

A Discrete Constrained Optimization Using Genetic Algorithms for A Bookstore Layout

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 commonly used by retailers are proposed. Finally, the effectiveness and applicability of the developed approaches are illustrated with numerical examples and a case study with data taken from a bookstore

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. (C) 2011 Elsevier Ltd. All rights reserved

A revised weighted fuzzy c-means and Nelder-Mead algorithm for probabilistic demand and customer positions

Facility location selection is a vital decision for companies that affects both cost and delivery time over the years. However, determination of the facility location is a NP-hard problem. A hybrid algorithm that combines revised weighted fuzzy c-means with Nelder Mead (RWFCM-NM) performs well when compared with well-known algorithms for the facility location problem (FLP) with deterministic customer demands and positions. The motivation of the study is both analyzing performance of the RWFCM-NM algorithm with probabilistic customer demands and positions and proposing a new approach for this problem. This paper develops two new algorithms for FLP when customer demands and positions are probabilistic. The proposed algorithms are a probabilistic fuzzy c-means algorithm and Nelder-Mead (Probabilistic FCM-NM), a probabilistic revised weighted fuzzy c-means algorithm and Nelder Mead (Probabilistic RWFCM-NM) for the un-capacitated planar multi-facility location problem when customer positions and customer demands are probabilistic with predetermined service level. Proposed algorithms performances were tested with 13 data sets and comparisons were made with four well known algorithms. According to the experimental results, probabilistic RWFCM-NM algorithm demonstrates superiority on compared algorithms in terms of total transportation costs