Location models in the public sector

The past four decades have witnessed an explosive growth in the field of networkbased facility location modeling. This is not at all surprising since location policy is one of the most profitable areas of applied systems analysis in regional science and ample theoretical and applied challenges are offered. Location-allocation models seek the location of facilities and/or services (e.g., schools, hospitals, and warehouses) so as to optimize one or several objectives generally related to the efficiency of the system or to the allocation of resources. This paper concerns the location of facilities or services in discrete space or networks, that are related to the public sector, such as emergency services (ambulances, fire stations, and police units), school systems and postal facilities. The paper is structured as follows: first, we will focus on public facility location models that use some type of coverage criterion, with special emphasis in emergency services. The second section will examine models based on the P-Median problem and some of the issues faced by planners when implementing this formulation in real world locational decisions. Finally, the last section will examine new trends in public sector facility location modeling.Location analysis, public facilities, covering models

Mathematical Model and Stochastic Multi-Criteria Acceptability Analysis for Facility Location Problem

This paper studies a real-life public sector facility location problem. The problem fundamentally originated from the idea of downsizing the number of service centres. However, opening of new facilities is also considered in case the current facilities fail to fulfil general management demands. Two operation research methodologies are used to solve the problem and the obtained results are compared. First, a mathematical programming model is introduced to determine where the new facilities will be located, and which districts get service from which facilities, as if there were currently no existing facilities. Second, the Stochastic Multi-criteria Acceptability Analysis-TRI (SMAA-TRI) method is used to select the best suitable places for service centres among the existing facilities. It is noted that the application of mathematical programming model and SMAA-TRI integration approach on facility location problem is the first study in literature. Compression of outcomes shows that mixed integer linear programming (MILP) model tries to open facilities in districts which are favoured by SMAA-TRI solution.</span

Redesign of Three-Echelon Multi-Commodity Distribution Network

This research studies the distribution network redesign of an actual electronics company. The problems are formulated based on multi-echelon capacitated Location Routing Problem (LRP) with two commodities: home products and service items. The objective function consists of three components: facility cost, closing cost of facility and transportation cost. We propose solution method based on clustering technique. The problem is decomposed into the Facility Location Allocation Problem (FLAP) and the Multi-Depot Vehicle Routing Problem (MDVRP). MDVRP is solved by clustering method and feed the results to the modified FLAP to allocate the demand nodes to facilities and configure all distribution networks, for the 2nd and 3rd echelon. The distribution is divided into five region zones. Previously, each region was operated independently but this research compares the solutions from solving each region independently and solving all five zones simultaneously. The results indicate that the proposed solution method can achieve computation time and total cost that are comparable to ones obtained from solving the problem to optimality. Exact approach can only solve small and medium problems, whereas the proposed solution method provides the acceptable solution of real-life largest problem in limit of computation time. Finally, we perform sensitivity analysis on the results

Experimental Evaluation of Meta-Heuristics for Multi-Objective Capacitated Multiple Allocation Hub Location Problem

Multi-objective capacitated multiple allocation hub location problem (MOCMAHLP) is a variation of classic hub location problem, which deals with network design, considering both the number and the location of the hubs and the connections between hubs and spokes, as well as routing of flow on the network. In this study, we offer two meta-heuristic approaches based on the non-dominated sorting genetic algorithm (NSGA-II) and archived multi-objective simulated annealing method (AMOSA) to solve MOCMAHLP. We attuned AMOSA based approach to obtain feasible solutions for the problem and developed five different neighborhood operators in this approach. Moreover, for NSGA-II based approach, we developed two novel problem-specific mutation operators. To statistically analyze the behavior of both algorithms, we conducted experiments on two well-known data sets, namely Turkish and Australian Post (AP). Hypervolume indicator is used as the performance metric to measure the effectiveness of both approaches on the given data sets. In the experimental study, thorough tests are conducted to fine-tune the proposed mutation types for NSGA-II and proposed neighborhood operators for AMOSA. Fine-tuning tests reveal that for NSGA-II, mutation probability does not have a real effect on Turkish data set, whereas lower mutation probabilities are slightly better for AP data set. Moreover, among the AMOSA based neighborhood operators, the one which adds/removes a specific number of links according to temperature (NS-5) performs better than the others for both data sets. After analyzing different operators for both algorithms, a comparison between our NSGA-II based and AMOSA based approaches is performed with the best settings. As a result, we conclude that both of our algorithms are able to find feasible solutions of the problem. Moreover, NSGA-II performs better for larger, whereas AMOSA performs better for smaller size networks

The design of cement distribution network in Myanmar : a case study of "X" cement industry

The network design problem is one of the most comprehensive strategic decision issues that need to be optimized for the long-term efficient operation of whole supply chain. The problem treated in this thesis is a capacitated location allocation planning of distribution centers for the distribution network design. The distribution network in this research is considered from plants to distribution centers and distribution centers to demand points. The research will explore the optimal number and locations of cement distribution center of “X” cement industry in Myanmar. The Mixed Integer Linear Programming (MILP) was developed as a tool to solve optimization problem which involves 3 manufacturing plants, 6 distribution centers and 6 market regions. The data collection was done by the company. The (MILP) model provides useful information for the Company about which distribution centers should be opened and what would be the best distribution network in order to maximize profit while still satisfies the customers’ demand. In this study, we proposed three scenarios which are scenario two, six and eight. In all scenarios, the solution was to have only two distribution centers from Mandalay and Meikhtila markets are recommended to open in the distribution network