Tabu Search for Large Location-Allocation Problems

1997 ◽  
Vol 48 (7) ◽  
pp. 745
Author(s):  
M. Ohlemuller
Keyword(s):  
Tabu Search ◽  
Location Allocation ◽  
Allocation Problems

scholarly journals The Capacitated Location-Allocation Problem Using the VAOMP (Vector Assignment Ordered Median Problem) Unified Approach in GIS (Geospatial Information Systam)

2020 ◽  
Vol 10 (23) ◽  
pp. 8505
Author(s):  
Alireza Vafaeinejad ◽  
Samira Bolouri ◽  
Ali Asghar Alesheikh ◽  
Mahdi Panahi ◽  
Chang-Wook Lee
Keyword(s):  
Tabu Search ◽  
Search Algorithm ◽  
Unified Approach ◽  
Exact Methods ◽  
Median Problem ◽  
Geospatial Information ◽  
Location Allocation ◽  
Ordered Median ◽  
Allocation Problems ◽  
Fire Stations

The Vector Assignment Ordered Median Problem (VAOMP) is a new unified approach for location-allocation problems, which are one of the most important forms of applied analysis in GIS (Geospatial Information System). Solving location-allocation problems with exact methods is difficult and time-consuming, especially when the number of objectives and criteria increases. One of the most important criteria in location-allocation problems is the capacity of facilities. Firstly, this study develops a new VAOMP approach by including capacity as a criterion, resulting in a new model known as VAOCMP (Vector Assignment Ordered Capacitated Median Problem). Then secondly, the results of applying VAOMP, in scenario 1, and VAOCMP, in scenario 2, for the location-allocation of fire stations in Tehran, with the objective of minimizing the arrival time of fire engines to an incident site to no more than 5 min, are examined using both the Tabu Search and Simulated Annealing algorithms in GIS. The results of scenario 1 show that 52,840 demands were unable to be served with 10 existing stations. In scenario 2, given that each facility could not accept demand above its capacity, the number of demands without service increased to 59,080, revealing that the number of stations in the study area is insufficient. Adding 35 candidate stations and performing relocation-reallocation revealed that at least three other stations are needed for optimal service. Thirdly, and finally, the VAOMP and VAOCMP were implemented in a modest size problem. The implementation results for both algorithms showed that the Tabu Search algorithm performed more effectively.

scholarly journals Healthcare Facility Location-Allocation Optimization for China’s Developing Cities Utilizing a Multi-Objective Decision Support Approach

2018 ◽  
Vol 10 (12) ◽  
pp. 4580 ◽  
Author(s):  
Li Wang ◽  
Huan Shi ◽  
Lu Gan
Keyword(s):  
Heuristic Algorithms ◽  
Programming Model ◽  
Rapid Development ◽  
Supply And Demand ◽  
Healthcare Facility ◽  
Allocation Model ◽  
Location Allocation ◽  
Multi Objective ◽  
Conflicting Objectives ◽  
Allocation Problems

With rapid development of the healthcare network, the location-allocation problems of public facilities under increased integration and aggregation needs have been widely researched in China’s developing cites. Since strategic formulation involves multiple conflicting objectives and stakeholders, this paper presents a practicable hierarchical location-allocation model from the perspective of supply and demand to characterize the trade-off between social, economical and environmental factors. Due to the difficulties of rationally describing and the efficient calculation of location-allocation problems as a typical Non-deterministic Polynomial-Hard (NP-hard) problem with uncertainty, there are three crucial challenges for this study: (1) combining continuous location model with discrete potential positions; (2) introducing reasonable multiple conflicting objectives; (3) adapting and modifying appropriate meta-heuristic algorithms. First, we set up a hierarchical programming model, which incorporates four objective functions based on the actual backgrounds. Second, a bi-level multi-objective particle swarm optimization (BLMOPSO) algorithm is designed to deal with the binary location decision and capacity adjustment simultaneously. Finally, a realistic case study contains sixteen patient points with maximum of six open treatment units is tested to validate the availability and applicability of the whole approach. The results demonstrate that the proposed model is suitable to be applied as an extensive planning tool for decision makers (DMs) to generate policies and strategies in healthcare and design other facility projects.

Strategies for Solving Large Location-Allocation Problems by Heuristic Methods

1992 ◽  
Vol 24 (2) ◽  
pp. 289-304 ◽  
Author(s):  
P J Densham ◽  
G Rushton
Keyword(s):  
Spatial Structure ◽  
Heuristic Methods ◽  
Problem Size ◽  
Interpoint Distance ◽  
Allocation Algorithm ◽  
Complexity Of Algorithms ◽  
Location Allocation ◽  
Network Problems ◽  
Allocation Problems ◽  
Distance Data

Solution techniques for location-allocation problems usually are not a part of microcomputer-based geoprocessing systems because of the large volumes of data to process and store and the complexity of algorithms. In this paper, it is shown that processing costs for the most accurate, heuristic, location-allocation algorithm can be drastically reduced by exploiting the spatial structure of location-allocation problems. The strategies used, preprocessing interpoint distance data as both candidate and demand strings, and use of them to update an allocation table, allow the solution of large problems (3000 nodes) in a microcomputer-based, interactive decisionmaking environment. Moreover, these strategies yield solution times which increase approximately linearly with problem size. Tests on four network problems validate these claims.

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