Skip to main content

Advertisement

SpringerLink
Log in

Multiple criteria decision-making for hospital location-allocation based on improved genetic algorithm

  • Original Paper
  • Published:
Applied Geomatics Aims and scope Submit manuscript

Abstract

Hospital site selection is an essential problem in all societies, and the allocation of the population to these centers are included as the important optimization issues to be considered in urban planning. The present paper aims at improving the genetic algorithm by using the effective and affectability rates for the combination of chromosomes. For evaluating the performance of the new algorithm, it was attempted to use the comparison of improved genetic algorithm (IGA) with GA and particle swarm optimization (PSO). For limiting the searching space, it was attempted to use the analysis capabilities of geospatial information systems (GIS) as well as the analytic hierarchy process (AHP) for selecting the candidate sites. Then, the abovementioned algorithms were implemented to determine six optimized sites and allocating the peer blocks in real data. The findings of this paper indicate that selecting proper combinations of chromosomes with high fitness function and chromosomes with low fitness function will result in an increased algorithm’s exploitation and exploration ability. As a result, the algorithm will not be involved with local minimums, the convergence process of the algorithm will improve, and the algorithm will indicate a high level of stability in different performances. Given the findings of this paper, IGA has a better performance in comparison to the other algorithms. The convergence velocity of IGA is higher than that of the GA and PSO. All algorithms showed a high level of repeatability. However, in comparison to the other algorithms, IGA has a higher level of stability. Moreover, the run time of IGA is much shorter than the other algorithms.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

References

  • Abdelmaguid TF, Elrashidy W (2019) Halting decisions for gas pipeline construction projects using AHP: a case study. Oper Res 19(1):179–199

    Google Scholar 

  • Abebe MT, Megento TL (2017) Urban green space development using GIS-based multi-criteria analysis in Addis Ababa metropolis. Applied Geomatics 9(4):247–261

    Google Scholar 

  • Ahmed AH, Mahmoud H, Aly AMM (2016) Site suitability evaluation for sustainable distribution of hospital using spatial information technologies and AHP: a case study of upper Egypt, Aswan City. J Geogr Inf Syst 8(05):578

    Google Scholar 

  • Arnaout JP (2013) Ant colony optimization algorithm for the Euclidean location-allocation problem with unknown number of facilities. J Intell Manuf 24(1):45

    Google Scholar 

  • Balasubramani K, Gomathi M, Bhaskaran G, Kumaraswamy K (2019) GIS-based spatial multi-criteria approach for characterization and prioritization of micro-watersheds: a case study of semi-arid watershed, South India. Applied Geomatics 1–19

  • Chamchali MM, Tafreshi AM, Tafreshi GM Utilizing GIS linked to AHP for landfill site selection in Rudbar County of Iran. GeoJournal 1–21

  • Changdar C, Pal RK, Mahapatra GS, Khan A (2018) A genetic algorithm based approach to solve multi-resource multi-objective knapsack problem for vegetable wholesalers in fuzzy environment. Oper Res 1–32

  • Clarke J, McLay L, McLeskey JT (2014) Comparison of genetic algorithm to particle swarm for constrained simulation-based optimization of a geothermal power plant. Adv Eng Inform 28(1):81–90

    Google Scholar 

  • Cleghorn CW, Engelbrecht AP (2018) Particle swarm stability: a theoretical extension using the non-stagnate distribution assumption. Swarm Intell 12(1):1–22

    Google Scholar 

  • de Assis Corrêa F, Chaves AA, Lorena LAN (2007) Hybrid heuristics for the probabilistic maximal covering location-allocation problem. Oper Res 7(3):323–343

    Google Scholar 

  • Dell’Ovo M, Capolongo S, Oppio A (2018) Combining spatial analysis with MCDA for the siting of healthcare facilities. Land Use Policy 76:634–644

    Google Scholar 

  • Dias J, Captivo ME, Clíma J (2007) Dynamic multi-level capacitated and uncapacitated location problems: an approach using primal-dual heuristics. Oper Res 7(3):345–379

    Google Scholar 

  • ElKady SK, Abdelsalam HM (2016) A modified particle swarm optimization algorithm for solving capacitated maximal covering location problem in healthcare systems. In: Applications of intelligent optimization in biology and medicine. Springer international publishing, pp 117–133

  • Funtowicz S, Ravetz JK (2018) Post-normal science. In: Companion to environmental studies (vol 443, no 447, pp 443–447). ROUTLEDGE in association with GSE research

  • Ghaderi A, Jabalameli MS, Barzinpour F, Rahmaniani R (2012) An efficient hybrid particle swarm optimization algorithm for solving the uncapacitated continuous location-allocation problem. Netw Spat Econ 12(3):421–439

    Google Scholar 

  • Ghodratnama A, Arbabi HR, Azaron A (2018) A bi˗objective hub location-allocation model considering congestion. Oper Res 1–40

  • Hajipour V, Fattahi P, Tavana M, Di Caprio D (2016) Multi-objective multi-layer congested facility location-allocation problem optimization with Pareto-based meta-heuristics. Appl Math Model 40(7):4948–4969

    Google Scholar 

  • Harley P, Samanta S (2018) Modeling of inland flood vulnerability zones through remote sensing and GIS techniques in the highland region of Papua New Guinea. Applied Geomatics 10(2):159–171

    Google Scholar 

  • Harrison KR, Engelbrecht AP, Ombuki-Berman BM (2018) Self-adaptive particle swarm optimization: a review and analysis of convergence. Swarm Intell 12(3):187–226

    Google Scholar 

  • Hassan R, Cohanim B, De Weck O, Venter G (2005) A comparison of particle swarm optimization and the genetic algorithm. In: 46th AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics and materials conference (p 1897)

  • Holland JH (1992) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT press

  • Kaveh M, Mesgari MS (2019) Improved biogeography-based optimization using migration process adjustment: an approach for location-allocation of ambulances. Comput Ind Eng 135:800–813

    Google Scholar 

  • Kaveh M, Khishe M, Mosavi MR (2019) Design and implementation of a neighborhood search biogeography-based optimization trainer for classifying sonar dataset using multi-layer perceptron neural network. Analog Integr Circ Sig Process 100(2):405–428

    Google Scholar 

  • Khehra BS, Pharwaha APS (2016) Comparison of genetic algorithm, particle swarm optimization and biogeography-based optimization for feature selection to classify clusters of microcalcifications. J Inst Eng (India): series B, 1–14

  • Khishe M, Mosavi MR, Kaveh M (2017) Improved migration models of biogeography-based optimization for sonar dataset classification by using neural network. Appl Acoust 118:15–29

    Google Scholar 

  • Ko YD (2019) An efficient integration of the genetic algorithm and the reinforcement learning for optimal deployment of the wireless charging electric tram system. Comput Ind Eng 128:851–860

    Google Scholar 

  • Li X, Yeh AGO (2005) Integration of genetic algorithms and GIS for optimal location search. Int J Geogr Inf Sci 19(5):581–601

    Google Scholar 

  • Liu X, Ou J, Li X, Ai B (2013) Combining system dynamics and hybrid particle swarm optimization for land use allocation. Ecol Model 257:11–24

    Google Scholar 

  • Mahar F, Ali SSA, Bhutto Z (2012) A Comparative Study on Particle Swarm Optimization and Genetic Algorithms for Fixed Order Controller Design. In: International Multi Topic Conference (pp 284–294). Springer Berlin Heidelberg

  • Meshram SG, Alvandi E, Singh VP, Meshram C Comparison of AHP and fuzzy AHP models for prioritization of watersheds. Soft Computing 1–11

  • Momayezi F, Chaharsooghi SK, Sepehri MM, Kashan AH (2018) The capacitated modular single-allocation hub location problem with possibilities of hubs disruptions: modeling and a solution algorithm. Oper Res 1–28

  • Mosavi MR, Kaveh M, Khishe M, Aghababaee M (2016a) Design and implementation a sonar data set classifier by using MLP NN trained by improved biogeography-based optimization. In proceedings of the second National Conference on marine technology (pp 1–6)

  • Mosavi, M. R., Kaveh, M., & Khishe, M. (2016b). Sonar data set classification using MLP neural network trained by non-linear migration rates BBO. In: The fourth Iranian conference on engineering electromagnetic (ICEEM 2016) (pp 1–5)

  • Mousavi SM, Pardalos PM, Niaki STA, Fügenschuh A, Fathi M (2019) Solving a continuous periodic review inventory-location allocation problem in vendor-buyer supply chain under uncertainty. Comput Ind Eng 128:541–552

    Google Scholar 

  • Müller D, Czado C (2019) Dependence modelling in ultra-high dimensions with vine copulas and the graphical lasso. Comput Stat Data Anal 137:211–232

    Google Scholar 

  • Oldewage ET, Engelbrecht AP, Cleghorn CW (2019) Degrees of stochasticity in particle swarm optimization. Swarm Intell 1–23

  • Oppio A, Buffoli M, Dell'Ovo M, Capolongo S (2016) Addressing decisions about new hospitals’ siting: a multidimensional evaluation approach. Ann Ist Super Sanita 52(1):78–87

    Google Scholar 

  • Pasandideh SHR, Niaki STA, Abdollahi R (2018) Modeling and solving a bi-objective joint replenishment-location problem under incremental discount: MOHSA and NSGA-II. Oper Res 1–32

  • Poi N, Samanta S (2019) GIS, remote sensing and MCE approach for identifying groundwater prospective zones in mountainous region of PNG. Applied Geomatics 1–14

  • Rahimi F, Goli A, Rezaee R (2017) Hospital location-allocation in shiraz using geographical information system (GIS). Shiraz E-Med J 18(8)

  • Rajmohan S, Natarajan R (2019) Group influence based improved firefly algorithm for design space exploration of datapath resource allocation. Appl Intell 1–17

  • Ramli L, Sam YM, Mohamed Z (2016) A comparison of particle swarm optimization and genetic algorithm based on multi-objective approach for optimal composite nonlinear feedback control of vehicle stability system. In: Asian simulation conference (pp 652–662). Springer Singapore

  • Rohaninejad M, Sahraeian R, Tavakkoli-Moghaddam R (2018) An accelerated benders decomposition algorithm for reliable facility location problems in multi-echelon networks. Comput Ind Eng 124:523–534

    Google Scholar 

  • Rostami M, Bagherpour M (2017) A lagrangian relaxation algorithm for facility location of resource-constrained decentralized multi-project scheduling problems. Oper Res 1–41

  • Roy SK, Mula P (2016) Solving matrix game with rough payoffs using genetic algorithm. Oper Res 16(1):117–130

    Google Scholar 

  • Ruiz E, Soto-Mendoza V, Barbosa AER, Reyes R (2019) Solving the open vehicle routing problem with capacity and distance constraints with a biased random key genetic algorithm. Comput Ind Eng 133:207–219

    Google Scholar 

  • Saeidian B, Mesgari MS, Ghodousi M (2016) Evaluation and comparison of genetic algorithm and bees algorithm for location–allocation of earthquake relief centers. Int J Disaster Risk Reduct 15:94–107

    Google Scholar 

  • Saha S, Paul GC, Hembram TK (2019) Classification of terrain based on geo-environmental parameters and their relationship with land use/land cover in Bansloi River basin, Eastern India: RS-GIS approach. Applied Geomatics 1–17

  • Şahin T, Ocak S, Top M (2019) Analytic hierarchy process for hospital site selection. Health Policy Technol 8(1):42–50

    Google Scholar 

  • Saljoughi BS, Hezarkhani A (2018) A comparative analysis of artificial neural network (ANN), wavelet neural network (WNN), and support vector machine (SVM) data-driven models to mineral potential mapping for copper mineralizations in the Shahr-e-Babak region, Kerman, Iran. Applied Geomatics 10(3):229–256

    Google Scholar 

  • Senvar O, Otay I, Bolturk E (2016) Hospital site selection via hesitant fuzzy TOPSIS. IFAC-PapersOnLine 49(12):1140–1145

    Google Scholar 

  • Shariff SR, Moin NH, Omar M (2012) Location allocation modeling for healthcare facility planning in Malaysia. Comput Ind Eng 62(4):1000–1010

    Google Scholar 

  • Soltani A, Marandi EZ (2011) Hospital site selection using two-stage fuzzy multi-criteria decision making process. J Urban Environ Eng 5(1):32–43

    Google Scholar 

  • Steiner MTA, Datta D, Neto PJS, Scarpin CT, Figueira JR (2015) Multi-objective optimization in partitioning the healthcare system of Parana state in Brazil. Omega 52:53–64

    Google Scholar 

  • Vahidnia MH, Alesheikh AA, Alimohammadi A (2009) Hospital site selection using fuzzy AHP and its derivatives. J Environ Manag 90(10):3048–3056

    Google Scholar 

  • Vartholomaios A (2019) A geospatial analysis of the influence of landscape and climate on the location of Greek vernacular settlements using GIS. Applied Geomatics 11(2):197–213

    Google Scholar 

  • Wong TC, Ngan SC (2013) A comparison of hybrid genetic algorithm and hybrid particle swarm optimization to minimize makespan for assembly job shop. Appl Soft Comput 13(3):1391–1399

    Google Scholar 

  • Yang X, Bostel N, Dejax P (2019) A MILP model and memetic algorithm for the hub location and routing problem with distinct collection and delivery tours. Comput Ind Eng

  • Zarrouk R, Bennour IE, Jemai A (2019) A two-level particle swarm optimization algorithm for the flexible job shop scheduling problem. Swarm Intell 1–24

  • Zeydan M, Bostancı B, Oralhan B (2018) A new hybrid decision making approach for housing suitability mapping of an urban area. Math Probl Eng 2018

  • Zhang W, Cao K, Liu S, Huang B (2016) A multi-objective optimization approach for health-care facility location-allocation problems in highly developed cities such as Hong Kong. Comput Environ Urban Syst 59:220–230

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. GIS Division, Faculty of Geomatics, K. N. Toosi University of Technology, Tehran, 19967-15433, Iran

    Mehrdad Kaveh, Mohammad Saadi Mesgari & Rahmatollah Sadeghi Paland

  2. Department of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran

    Masoud Kaveh

Authors
  1. Mehrdad Kaveh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Masoud Kaveh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohammad Saadi Mesgari
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rahmatollah Sadeghi Paland
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Mehrdad Kaveh or Mohammad Saadi Mesgari.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kaveh, M., Kaveh, M., Mesgari, M.S. et al. Multiple criteria decision-making for hospital location-allocation based on improved genetic algorithm. Appl Geomat 12, 291–306 (2020). https://doi.org/10.1007/s12518-020-00297-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12518-020-00297-5

Keywords

Search

Navigation