Nasrin Asgari

Survey: Covering problems in facility location: A review

In this study, we review the covering problems in facility location. Here, besides a number of reviews on covering problems, a comprehensive review of models, solutions and applications related to the covering problem is presented after Schilling, Jayaraman, and Barkhi (1993). This survey tries to review all aspects of the covering problems by stressing the works after Schilling, Jayaraman, and Barkhi (1993). We first present the covering problems and then investigate solutions and applications. A summary and future works conclude the paper.

Combination of MCDM and covering techniques in a hierarchical model for facility location: A case study

Abstract In this paper, locating some warehouses as distribution centers (DCs) in a real-world military logistics system will be investigated. There are two objectives: finding the least number of DCs and locating them in the best possible locations. The first objective implies the minimum cost of locating the facilities and the latter expresses the quality of the DCs locations, which is evaluated by studying the value of appropriate attributes affecting the quality of a location. Quality of a location depends on a number of attributes; so the value of each location is determined by using Multi Attribute Decision Making models, by considering the feasible alternatives, the related attributes and their weights according to decision maker’s (DM) point of view. Then, regarding the obtained values and the minimum number of DCs, the two objective functions are formed. Constraints imposed on these two objectives cover all centers, which must be supported by the DCs. Using Multiple Objective Decision Making techniques, the locations of DCs are determined. In the final phase, we use a simple set partitioning model to assign each supported center to only one of the located DCs.

Single facility location and relocation problem with time dependent weights and discrete planning horizon

In this paper a single facility location problem with multiple relocation opportunities is investigated. The weight associated with each demand point is a known function of time. We consider either rectilinear, or squared Euclidean, or Euclidean distances. Relocations can take place at pre-determined times. The objective function is to minimize the total location and relocation costs. An algorithm which finds the optimal locations, relocation times and the total cost, for all three types of distance measurements and various weight functions, is developed. Locations are found using constant weights, and relocations times are the solution to a Dynamic Programming or Binary Integer Programming (BIP) model. The time horizon can be finite or infinite.

A joint economic lot-size model for an integrated supply network using genetic algorithm

Competitive environment calls for cost reduction in tree-like inventory systems. One of the key elements to cost reduction of a supply chain is integration of inventory system. In this paper, we consider an integrated four-stage supply chain system, incorporating one supplier, multiple producers, multiple distributors multiple retailers. The aim of this model is to determine order quantity of each stage (from its upstream) and shortage level of each stage (for its downstream) such that the total cost of the supply chain to be minimized. Thus, for three possible situations of relation between supplier and producers replenishment intervals, an integrated inventory model, making joint economic lot-size of shortage, holding, ordering and setup costs in all stages is developed. Then a heuristic approach based on genetic algorithm for solving this problem is presented.

Developing model-based software to optimise wheat storage and transportation: A real-world application

This paper investigates a real-world case of a logistical management problem. We determine the optimal amounts of wheat to be transported from each producing province to each consuming province per month across the year. The problem was formulated as a linear integer programming (LIP) model, which could then be solved using LINGO optimisation software. As the LIP model needs to be run each month, a genetic algorithm (GA) was developed to solve the real-size problems in a reasonable time period. The solutions obtained by LINGO are compared with those obtained from the GA and the results show that the developed GA is efficient in terms of computational time and the quality of the solutions obtained.

A memetic algorithm for a multi-objective obnoxious waste location-routing problem: a case study

This paper proposes a model for obnoxious waste location-routing problem (LRP) considering various types of wastes and several treatment technologies. The investigated distribution network includes three echelons of generation nodes, treatment and disposal facilities. A multi-objective LRP model is developed with three objective functions minimizing the treatment and disposal facility undesirability, different costs related to the problem, and eventually the risk associated with transportation of untreated materials. An effective memetic algorithm is developed in which a tabu search algorithm performs the local search. Comparison of exact and meta-heuristic methods run times confirms that the proposed method is effective. Eventually, the developed algorithm is tested on a real-life case study.

OR models in urban service facility location: A critical review of applications and future developments

Abstract Facility location models are well established in various application areas with more than a century of history in academia. Since the 1970s the trend has been shifting from manufacturing to service industries. Due to their nature, service industries are frequently located in or near urban areas that results in additional assumptions, objectives and constraints other than those in more traditional manufacturing location models. This survey focuses on the location of service facilities in urban areas. We studied 110 research papers across different journals and disciplines. We have analyzed these papers on two levels. On the first, we take an Operations Research perspective to investigate the papers in terms of types of decisions, location space, main assumptions, input parameters, objective functions and constraints. On the second level, we compare and contrast the papers in each of these applications categories: (a) Waste management systems (WMS), (b) Large-scale disaster (LSD), (c) Small-scale emergency (SSE), (d) General service and infrastructure (GSI), (e) Non-emergency healthcare systems (NEH) and (f) Transportation systems and their infrastructure (TSI). Each of these categories is critically analyzed in terms of application, assumptions, decision variables, input parameters, constraints, objective functions and solution techniques. Gaps, research opportunities and trends are identified within each category. Finally, some general lessons learned based on the practicality of the models is synthesized to suggest avenues of future research.

Spatial Analysis and Land-Use Planning

In this chapter, we will discuss spatial planning and analysis for the logistics industry. After a brief definition of the concept, we examine the geographic concepts of logistics. Then, we proceed to describe the role of public sector in planning for the logistics sector, especially from spatial planning aspect, and then we proceed to present some real world examples of spatial planning and spatial analysis for logistics activities.