The Ordered Capacitated Multi-Objective Location-Allocation Problem for Fire Stations Using Spatial Optimization

Bolouri, Samira; Vafaeinejad, Alireza; Alesheikh, Ali Asghar; Aghamohammadi, Hossein

doi:10.3390/ijgi7020044

Cited by 35 publications

(13 citation statements)

References 29 publications

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“…In 2018, Bolouri et al examined two Genetic and Simulated Annealing algorithms to solve the multi-objective location-allocation problem of fire stations in GIS environment. The results showed the efficiency of the Genetic algorithm with high demand (Bolouri et al, 2018). Because in the various studies, Tabu and Genetic algorithm in solving location-allocation problems presented better results compared to other algorithms in less time and escaped from trapping in local optima, these two algorithms will be used to solve the location-allocation raised in this research.…”

Section: Literature Reviewmentioning

confidence: 90%

Investigating the Effect of Capacity Criterion on the Optimal Allocation of Emergency Facilities in Gis Environment

Bolouri

Vafaeinejad

Alesheikh

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Location-allocation analysis is one of the most GIS useful analysis, especially in allocating demands to facilities. One of these facilities is the fire stations, which the correct locations and optimal demand allocations to those have most importance. Each facility has a specific capacity that should be considered in locating the facilities and allocating the demand to those. In recent years, the use of unified models in solving allocation problems is too common because these models can solve a variety of problems, but in most of these models, the capacity criterion for facilities has been ignored. The present study tries to investigate the location-allocation problem of the fire stations with the aid of two Tabu and Genetic algorithms with the goal of maximizing the coverage using the (Vector Assignment Ordered Median Problem) VAOMP model, taking into account the capacity criterion and regardless of it. The results of using two algorithms in problem-solving show that the Genetic algorithm produces better quality solutions over a shorter time. Also, considering the capacity criterion that brings the problem closer to real-world space, in the study area, 59,640 demands will not be covered by any station within a 5-minute radius and will be highly vulnerable to potential hazards. Also, by adding 3 stations to the existing stations and re-allocating, the average of allocated demands with the help of Genetic was 93.39% and 92.74% for the Tabu algorithm. So it is necessary to consider the capacity of the facilities for optimal services.

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Section: Literature Reviewmentioning

confidence: 90%

Investigating the Effect of Capacity Criterion on the Optimal Allocation of Emergency Facilities in Gis Environment

Bolouri

Vafaeinejad

Alesheikh

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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show abstract

“…Tang Jian et al, Have proposed a rescue solution using simulated annealing optimization (Tang et al 2018) and also Bolouri et al, Using simulated annealing optimization to locate fire stations. (Bolouri et al 2018); also Hongman Wang et al, Improved emergency transportation using multi-purpose ant-community algorithm (Wang et al 2018). Mousanejad et al, Using geographic information system and simulated annealing for optimizing the railway design (Mousanejad et al, 2018) and Aghakhani et al, Using Geospatial Inforrmation System Assessment of the effects of land use scenarios on watershed surface runoff (Aghakhani et al 2018).…”

Section: Literature Reviewmentioning

confidence: 99%

Designing a Context-Aware Recommender System in the Optimization of the Relief and Rescue

Bahrami

Argany

Samani

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The context-aware is the knowledge that leads to better cognition and recognition of the environment, objects and factors, and the way of communication and interactions between them. As a result, it can have a great impact in providing appropriate solutions to various problems. It is possible to integrate consciousness into relief and rescue discussions and to take steps to improve and make realistic solutions. In this study, this issue was addressed in the earthquake crisis, due to a large number of seismic faults in Iran, is one of the major crises in Iran and many parts of the world. Hence, the contexts of rescuers, teams, and environment as the main textures in the above-mentioned issue are investigated and their relationship with each other and the priorities of activities and locations by identifying specialties and the physical and situational conditions of the relief workers, and an algorithm was designed and optimized to optimize the allocation of the relief workers to the affected areas and the necessary activities. Finally, the improvement of the 2.4 fold results of the algorithm and the proposed structure of this research resulted in the ratio of non-use of this algorithm.

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“…If they are not well positioned, they cannot satisfy the needs of customers or offer some services to them. In crowded cities where there is insufficient space for positioning these facilities, the issue becomes even more important (Bolouri et al , 2018).…”

Section: Introductionmentioning

confidence: 99%

A maximal covering facility location model for emergency services within an M _(t)/M/m/m queuing system

Bahrami

Ahari

Asadpour

2020

JM2

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Purpose In emergency services, maximizing population coverage with the lowest cost at the peak of the demand is important. In addition, due to the nature of services in emergency centers, including hospitals, the number of servers and beds is actually considered as the capacity of the system. Hence, the purpose of this paper is to propose a multi-objective maximal covering facility location model for emergency service centers within an M (t)/M/m/m queuing system considering different levels of service and periodic demand rate. Design/methodology/approach The process of serving patients is modeled according to queuing theory and mathematical programming. To cope with multi-objectiveness of the proposed model, an augmented ε-constraint method has been used within GAMS software. Since the computational time ascends exponentially as the problem size increases, the GAMS software is not able to solve large-scale problems. Thus, a NSGA-II algorithm has been proposed to solve this category of problems and results have been compared with GAMS through random generated sample problems. In addition, the applicability of the proposed model in real situations has been examined within a case study in Iran. Findings Results obtained from the random generated sample problems illustrated while both the GAMS software and NSGA-II almost share the same quality of solution, the CPU execution time of the proposed NSGA-II algorithm is lower than GAMS significantly. Furthermore, the results of solving the model for case study approve that the model is able to determine the location of the required facilities and allocate demand areas to them appropriately. Originality/value In the most of previous works on emergency services, maximal coverage with the minimum cost were the main objectives. Hereby, it seems that minimizing the number of waiting patients for receiving services have been neglected. To the best of the authors’ knowledge, it is the first time that a maximal covering problem is formulated within an M (t)/M/m/m queuing system. This novel formulation will lead to more satisfaction for injured people by minimizing the average number of injured people who are waiting in the queue for receiving services.

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The Ordered Capacitated Multi-Objective Location-Allocation Problem for Fire Stations Using Spatial Optimization

Cited by 35 publications

References 29 publications

Investigating the Effect of Capacity Criterion on the Optimal Allocation of Emergency Facilities in Gis Environment

Investigating the Effect of Capacity Criterion on the Optimal Allocation of Emergency Facilities in Gis Environment

Designing a Context-Aware Recommender System in the Optimization of the Relief and Rescue

A maximal covering facility location model for emergency services within an M _(t)/M/m/m queuing system

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The Ordered Capacitated Multi-Objective Location-Allocation Problem for Fire Stations Using Spatial Optimization

Cited by 35 publications

References 29 publications

Investigating the Effect of Capacity Criterion on the Optimal Allocation of Emergency Facilities in Gis Environment

Investigating the Effect of Capacity Criterion on the Optimal Allocation of Emergency Facilities in Gis Environment

Designing a Context-Aware Recommender System in the Optimization of the Relief and Rescue

A maximal covering facility location model for emergency services within an M (t)/M/m/m queuing system

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A maximal covering facility location model for emergency services within an M _(t)/M/m/m queuing system