Optimized Location-Allocation of Earthquake Relief Centers Using PSO and ACO, Complemented by GIS, Clustering, and TOPSIS

Saeidian, Bahram; Mesgari, Mehran; Pradhan, Biswajeet; Ghodousi, Mostafa

doi:10.3390/ijgi7080292

Cited by 37 publications

(33 citation statements)

References 60 publications

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“…The method ranks the advantages and disadvantages of the scheme by analyzing the distance between the best and the worst schemes, which significantly improves the scientificity, accuracy, and operability of multi-objective decision analysis. It has been successfully applied in many fields such as logistics supplier selection [41][42][43], meteorological disaster assessment [44], land use planning [45], and human resource management assessment [46]. However, in the process of dealing with problems, it requires that the preferences of decision makers must be definite, clear, and quantifiable.…”

Section: Introductionmentioning

confidence: 99%

Small and Medium-Sized Enterprises Sustainable Supply Chain Financing Decision Based on Triple Bottom Line Theory

et al. 2018

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In recent years, sustainable supply chains that balance economic development and the environment have become an inevitable focus for many businesses and industries. Supply chain finance as the core driving force for supply chain development, plays a vital role in resolving any financing difficulties that exist in many small and medium-sized enterprises (SMEs) in the upstream and downstream of the supply chain. However, most SME supply chain financing assessments currently use economic indicators as the sole measure of the evaluation system and rarely consider sustainability. While existing supply chain financing decision-making systems can resolve SME financing problems to some extent, the one-sided pursuit of maximum economic benefits is contrary to sustainable development and does not assist financial institutions in avoiding finance risks. Therefore, this paper, based on the theory of the triple bottom line (economy, environment, and society) from a sustainable development perspective, innovatively proposes an SME financing evaluation model for supply chain finance that applies a fuzzy multi-criteria evaluation method combined with Topsis. Additionally, at the end, an example is given to demonstrate model validity and evaluate the best possible SME financing model for financial institutions.

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Section: Introductionmentioning

confidence: 99%

Small and Medium-Sized Enterprises Sustainable Supply Chain Financing Decision Based on Triple Bottom Line Theory

et al. 2018

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“…The most remarkable work is Yi and Kumar [35], where the authors apply this metaheuristic to solve a distribution and evacuation problem. More recently, Saeidian et al [36] combine different techniques, including ACO, to face a location-allocation problem of relief centers after an earthquake. Therefore, this paper presents the first application of Ant Colony Optimization to a multi-criteria last mile distribution problem.…”

Section: Reference # Criteria Security Multi-criteria Approach Heurismentioning

confidence: 99%

A New Ant Colony-Based Methodology for Disaster Relief

2020

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Humanitarian logistics in response to large scale disasters entails decisions that must be taken urgently and under high uncertainty. In addition, the scarcity of available resources sometimes causes the involved organizations to suffer assaults while transporting the humanitarian aid. This paper addresses the last mile distribution problem that arises in such an insecure environment, in which vehicles are often forced to travel together forming convoys for security reasons. We develop an elaborated methodology based on Ant Colony Optimization that is applied to two case studies built from real disasters, namely the 2010 Haiti earthquake and the 2005 Niger famine. There are very few works in the literature dealing with problems in this context, and that is the research gap this paper tries to fill. Furthermore, the consideration of multiple criteria such as cost, time, equity, reliability, security or priority, is also an important contribution to the literature, in addition to the use of specialized ants and effective pheromones that are novel elements of the algorithm which could be exported to other similar problems. Computational results illustrate the efficiency of the new methodology, confirming it could be a good basis for a decision support tool for real operations.

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“…Its movement, in any iteration, is a combination of its last move, move towards its best personal location, and move towards the best previous location of the particle's group. Like similar algorithms, any location of a particle is evaluated by an objective function [61][62][63]. In PSO, a movement (a change in the location) of a particle is called velocity.…”

Section: Modelling Pso Operators In Aw Problemmentioning

confidence: 99%

Irrigation Water Allocation at Farm Level Based on Temporal Cultivation-Related Data Using Meta-Heuristic Optimisation Algorithms

Saeidian

Mesgari

Pradhan

et al. 2019

Water

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The present water crisis necessitates a frugal water management strategy. Deficit irrigation can be regarded as an efficient strategy for agricultural water management. Optimal allocation of water to agricultural farms is a computationally complex problem because of many factors, including limitations and constraints related to irrigation, numerous allocation states, and non-linearity and complexity of the objective function. Meta-heuristic algorithms are typically used to solve complex problems. The main objective of this study is to represent water allocation at farm level using temporal cultivation data as an optimisation problem, solve this problem using various meta-heuristic algorithms, and compare the results. The objective of the optimisation is to maximise the total income of all considered lands. The criteria of objective function value, convergence trend, robustness, runtime, and complexity of use and modelling are used to compare the algorithms. Finally, the algorithms are ranked using the technique for order of preference by similarity to ideal solution (TOPSIS). The income resulting from the allocation of water by the imperialist competitive algorithm (ICA) was 1.006, 1.084, and 1.098 times that of particle swarm optimisation (PSO), bees algorithm (BA), and genetic algorithm (GA), respectively. The ICA and PSO were superior to the other algorithms in most evaluations. According to the results of TOPSIS, the algorithms, by order of priority, are ICA PSO, BA, and GA. In addition, the experience showed that using meta-heuristic algorithms, such as ICA, results in higher income (4.747 times) and improved management of water deficit than the commonly used area-based water allocation method. and Huang et al. [42] used the GA-PSO hybrid algorithm for the optimal management of water resources. Davijani, Banihabib, Anvar, and Hashemi [9] compared the use of PSO and GA algorithms for allocating water to agricultural, industrial, and municipal consumptions over one year.The ICA is successfully used in a variety of applications related to industrial, civil, mechanical, electrical, and computer engineering [43]. Hosseini-Moghari et al. [44] used this algorithm for water resource management. Karamouz et al. [45] compared the suitability of the ICA and PSO algorithms for allocating reservoir water to agricultural, industrial, and urban consumers. Many studies have used the BA for various applications [46]. However, to the best of our knowledge, it is not used in AW.Considering the preceding review on the use of these algorithms in water resource management and other applications, the reasons for selecting GA, BA, ICA, and PSO algorithms in this study are as follows: (1) They are among the most commonly used meta-heuristic algorithms.(2) On similar problems, the performance of these algorithms was acceptable. (3) The solution space of this study is continuous, while the ICA and PSO are inherently continuous, and GA and BA are also applicable to continuous issues.The aim of this study is to model the AW to farms as...

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Optimized Location-Allocation of Earthquake Relief Centers Using PSO and ACO, Complemented by GIS, Clustering, and TOPSIS

Cited by 37 publications

References 60 publications

Small and Medium-Sized Enterprises Sustainable Supply Chain Financing Decision Based on Triple Bottom Line Theory

Small and Medium-Sized Enterprises Sustainable Supply Chain Financing Decision Based on Triple Bottom Line Theory

A New Ant Colony-Based Methodology for Disaster Relief

Irrigation Water Allocation at Farm Level Based on Temporal Cultivation-Related Data Using Meta-Heuristic Optimisation Algorithms

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