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

Abstract

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.

Comparative Study of Swarm‐Based Algorithms for Location‐Allocation Optimization of Express Depots

“…e solution of CLAP may bene t from a geospatial information system (GIS) since the regional division and distance between two points can be more precise. Vafaeinejad et al [15] developed a vector assignment ordered capacitated median problem (VAOCMP) model to describe the re station location and allocation problem. In the VAOCMP model, the arrival time of the re engine to demand points and the capacity of the re facility are considered.…”

New Approach to Selecting Civil Defense Centers in Al-Riyadh City (KSA) Based on Multi-Criteria Decision Analysis and GIS

“…Finally, Section 4 summarizes the main conclusions. AHP Groundwater potential Saudi Arabia [31] AHP Land Use Suitability Saudi Arabia [26] AHP Developing a fire risk map Turkey [32] AHP Agricultural land use suitability Turkey [33] AHP Boolean logic Waste landfill sites Iran [34] Buffer zones Accessibility of fire hydrants Lithuania [35] Euclidean distance Health care facilities Saudi Arabia [36] Euclidean distance Emergency department accessibility United States [37] Fuzzy AHP Analysis of accessibility Brazil [25] Fuzzy AHP Optimal location for fire stations Turkey [38] Location-allocation Emergency evacuation planning Bangladesh [39] Location-allocation Locating fire stations Belgium [40] Location-allocation Available sites for fire stations India [41] Location-allocation Fire station allocation Iran Urban fire risk locations China [44] Service areas Fire response systems Ghana [45] Weighted overlay Site layout planning Turkey [46] WLC Best practice approach for WLC - [47] WLC Regional hazard map United States [16] MCE Viticulture suitability using MCE Syria [17] MCDA, MCDS Land suitability assessment Syria…”

Where to place emergency ambulance vehicles? Using a capacitated maximum covering location model with real call data

“…Location-allocation problems deal with identifying optimal facility locations for addressing specific demands on service use (14). Due to the importance and sensitivity of the EMS setting, a large portion of the location-allocation literature is devoted to this domain (15).…”

“…These models assign an unavailability probability to facilities that take into account the finite capacity of services in some way (24). Another approach, introduced by Current and Storbeck (25), is to develop the capacitated version of the MCLP which has been applied by researchers in various fields (14,(26)(27)(28). This model adds a maximum capacity constraint into the mathematical formulations of the MCLP to ensure that the demands allocated to a facility will not exceed the maximum capacity of that facility (28).…”