Location, Allocation and Routing of Temporary Health Centers in Rural Areas in Crisis, Solved by Improved Harmony Search Algorithm

Alinaghian, Mahdi; Goli, Alireza

doi:10.2991/ijcis.2017.10.1.60

Cited by 51 publications

(31 citation statements)

References 29 publications

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“…Metaheuristics is formally defined as an iterative process that guides a subordinate heuristic by combining intelligently different concepts to explore and exploit the search space or create a balance between the intensification and diversification of the search space [11]. The metaheuristic methods that are used to solve the LRP include simulated annealing (SA) [10], genetic algorithm (GA) [12], non-dominated sorting genetic algorithm II (NSGA-II) [13], particle swarm optimization (PSO) [14], ant colony [15], tabu search (TS) [16], large neighborhood search (LNS) [17], iterated local search (ILS) [18], memetic algorithm (MA) [19], differential evolution [20], harmony search [21], hyper-heuristic (HH) [22], cross-entropy algorithm (CEA) [23], and greedy randomized adaptive search procedure (GRASP) [24].…”

Section: Literature Reviewmentioning

confidence: 99%

Regional Location Routing Problem for Waste Collection Using Hybrid Genetic Algorithm-Simulated Annealing

Aloina

Susanto

et al. 2022

Mathematics

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Municipal waste management has become a challenging issue with the rise in urban populations and changes in people’s habits, particularly in developing countries. Moreover, government policy plays an important role associated with municipal waste management. Thus, this research proposes the regional location routing problem (RLRP) model and multi-depot regional location routing problem (MRLRP) model, which are extensions of the location routing problem (LRP), to provide a better municipal waste collection process. The model is constructed to cover the minimum number of depot facilities’ policy requirements for each region due to government policy, i.e., the large-scale social restrictions in each region. The goal is to determine the depot locations in each region and the vehicles’ routes for collecting waste to fulfill inter-regional independent needs at a minimum total cost. This research conducts numerical examples with actual data to illustrate the model and implements a hybrid genetic algorithm and simulated annealing optimization to solve the problem. The results show that the proposed method efficiently solves the RLRP and MRLRP.

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Section: Literature Reviewmentioning

confidence: 99%

Regional Location Routing Problem for Waste Collection Using Hybrid Genetic Algorithm-Simulated Annealing

Aloina

Susanto

et al. 2022

Mathematics

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“…Ant colony optimization (ACO) with stochastic demand was used to solve the stochastic uncapacitated location allocation problem with an unknown number of facilities [21]. A metaheuristic algorithm based on the harmony search algorithm was presented for the location, allocation, and routing of temporary health centers in rural areas in a crisis situation [22]. An improved artificial bee colony was proposed for the facility location allocation problem of the end-of-life vehicles recovery network [23].…”

Section: Literature Reviewmentioning

confidence: 99%

GIS-Based Niche Hybrid Bat Algorithm for Solving Optimal Spatial Search

Chen

Sun

2020

Mathematical Problems in Engineering

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Complex nonlinear optimization problems are involved in optimal spatial search, such as location allocation problems that occur in multidimensional geographic space. Such search problems are generally difficult to solve by using traditional methods. The bat algorithm (BA) is an effective method for solving optimization problems. However, the solution of the standard BA is easily trapped at one of its local optimum values. The main cause of premature convergence is the loss of diversity in the population. The niche technique is an effective method to maintain the population diversity, to enhance the exploration of the new search domains, and to avoid premature convergence. In this paper, a geographic information system- (GIS-) based niche hybrid bat algorithm (NHBA) is proposed for solving the optimal spatial search. The NHBA is able to avoid the premature convergence and obtain the global optimal values. The GIS technique provides robust support for processing a substantial amount of geographical data. A case in Fangcun District, Guangzhou City, China, is used to test the NHBA. The comparative experiments illustrate that the BA, GA, FA, PSO, and NHBA algorithms outperform the brute-force algorithm in terms of computational efficiency, and the optimal solutions are more easily obtained with NHBA than with BA, GA, FA, and PSO. Moreover, the precision of NHBA is higher and the convergence of NHBA is faster than those of the other algorithms under the same conditions.

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“…Thus, certain levels of uncertainty need to be introduced [ 6 ]. Numerous authors have employed fuzzy elements to model optimization problems [ 7 – 11 ]. In particular, there are several engaging solutions to transportation planning problems, like the numerous variants of the Vehicle Routing Problem (VRP) [ 12 – 14 ].…”

Section: Introductionmentioning

confidence: 99%

Conscious Exploration of Alpha-Cuts in the Parametric Solution of the School Bus Routing Problem with Fuzzy Walking Distance

Sánchez-Ansola

Pérez

Rosete-Suárez

et al. 2022

Computational Intelligence and Neuroscience

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Combinatorial optimization problems allow for modeling multiple situations in which proper allocation of resources is needed. For some real-world problems, the use of fuzzy elements in the models allows for incorporating certain levels of uncertainty to better approximate such real-world situations. One way to solve combinatorial optimization problems with fuzzy elements is the parametric approach, where it is necessary to define how to explore different relaxation levels using alpha-cuts. Researchers tend to select such alpha-cuts uniformly. The current investigation proposes a novel strategy for selecting alpha-cuts in the School Bus Routing Problem with fuzzy students’ maximum walking distance. This proposal bases its foundations on the number of student-bus stop pairs available according to the different levels of relaxations allowed. Results demonstrate how the proposed strategy gives attractive solutions with more diverse trade-offs, contrasted with other methods in the literature. Furthermore, it decreases the computational cost for those instances where the maximum relaxation does not provide new pairs of students-bus stops.

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Location, Allocation and Routing of Temporary Health Centers in Rural Areas in Crisis, Solved by Improved Harmony Search Algorithm

Cited by 51 publications

References 29 publications

Regional Location Routing Problem for Waste Collection Using Hybrid Genetic Algorithm-Simulated Annealing

Regional Location Routing Problem for Waste Collection Using Hybrid Genetic Algorithm-Simulated Annealing

GIS-Based Niche Hybrid Bat Algorithm for Solving Optimal Spatial Search

Conscious Exploration of Alpha-Cuts in the Parametric Solution of the School Bus Routing Problem with Fuzzy Walking Distance

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