Abstract
Spatial facility management (SFM) intends to improve the flexibility of use, work productivity and capital profitability by the integration of planning , control and management of buildings, installations, facilities. In view of SPM, the chapter aims at the best use of a community’s land and resources for residential, commercial, institutional and recreational purposes. The problem of multi-criteria decision-making for targeted facilities (public transport routes, water supply lines, institutional, commercial, agricultural and residential zones, developing resources like rainwater harvesting scheme, self-sustaining energy efficiency services and protecting ecologically sensitive regions) will be handled with the help of genetic algorithm (GA). The first step towards the algorithm development is facility assessment for the study area location. The proposed community facilities are examined. The detailed spatial and attribute database have been generated with existing facility information which include current use of land for residential, business and community purposes, information on the location and capacity of streets, water and sewer lines, schools, libraries, and cultural and recreational sites. It also includes data on type of industries in the community, the characteristics of the population, employment and economic trends with the aid of satellite imagery and above-specified information, along with input from citizen’s advisory committees. Geographic information systems (GIS ) are used to map land area, to overlay maps with geographic variables such as population density and to combine or manipulate geographic information to produce alternative plans for land use or development. Spatial data infrastructure has been developed at the end that will enable end-users, advisory committees and decision-makers to know that effectiveness of land-use schemes and replicate the same in other areas as well.
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References
Melanie, M.: An Introduction to Genetic Algorithm. MIT Press (1996)
Neubauer, A.: Theory of the simple genetic algorithm with α-selection. In: GECCO 2008: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation. ACM (2008)
Bajpai, P., Kumar, M.: Genetic algorithm-an approach to solve global optimization problems. Indian J. Comput. Sci. Eng. 1 (2009)
Srinivas, M., Patnaik, L.M.: Genetic algorithms: a survey. IEEE J. Comput. 27(6) (1994)
Goldberg, D.E.: Genetic and evolutionary algorithms come of age. Commun. ACM 37(3) (1994)
Jones, G.: Encyclopedia of Computational Chemistry-Genetic and Evolutionary Algorithms (2002)
Thede, S.M.: An introduction to genetic algorithms. J. Comput. Sci. Coll. 20(1) (2004)
Forrest, S.: Genetic algorithms: principles of natural selection applied to computation. Science 261(5123), 872–878 (1993)
Chaudhry, S.S., He, S., Chaudhry, P.E.: Solving a class of facility location problems using genetic algorithms. Expert Syst. 20(1) (2003)
Alba, E., Troya, J.M.: A survey of parallel distributed genetic algorithm. Complexity 4(4) (1999)
Oliveto, P.S., Witt, C.: On the analysis of the simple genetic algorithm. In: GECCO 2012: Proceedings of the Fourteenth International Conference on Genetic and Evolutionary Computation Conference. ACM (2012)
Preux, P., Talbi, E.-G.: Towards hybrid evolutionary algorithms. Int. Trans. Oper. Res. 6, 557–570 (1999)
Mitchell, M.: Genetic algorithms and Artificial Life. Sci. Am. 114–116 (1993)
Sastry, G., Kendall, G.: Search Methodologies-Introductory Tutorials in Optimization and Decision Support Techniques, 1st edn. Springer (2005)
Clark, A., Thornton, C.: Trading spaces: computation, representation and limits of uninformed learning. Behav. Brain Sci. 20, 57–90 (1997)
Pandey, H.M., Dixit, A., Mehrotra, D.: Genetic algorithms: concepts, issues and a case study of grammar induction. In: Proceedings of the CUBE International Information Technology Conference. ACM (2012)
Silveira, L.R., Tanscheit, R., Vellasco, M.: Quantum-inspired genetic algorithms applied to ordering combinatorial optimization problems. In: WCCI 2012 IEEE World Congress on Computational Intelligence, Brisbane, Australia (2012)
Reeves, C.: Genetic algorithms and neighborhood search, evolutionary computing, AISB Workshop. In: Fogarty, T.C. (ed.) Lecture Notes in Computer Science, vol. 865, pp. 115–130. Springer (1994)
Črepinšek, M., Liu, S.-H., Mernik, M.: Exploration and exploitation in evolutionary algorithms: a survey. Comput. Surv. (CSUR), ACM 45(3) (2013)
Whitley, D.: An overview of evolutionary algorithms: practical issues and common pitfalls. Inf. Softw. Technol. 43, 817–831 (2001)
Goldberg, D.E., Holland, J.H.: Genetic Algorithms and Machine Learning. Springer (1988)
Barret, P.: Facilities Management: Towards Best Practice. Blackwell Science Ltd., Oxford (1995)
Becker, F.: The Total Workplace. Van Nostrand Reinhold, New York, NY (1990)
Hinks, J.: The creation of a management-by-variance tool for facilities management performance assessment. Facilities 17(1/2), 31–53 (1999)
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Gupta, R. (2018). Spatial Facility Management: A Step to Design Smart City. In: Sarda, N., Acharya, P., Sen, S. (eds) Geospatial Infrastructure, Applications and Technologies: India Case Studies. Springer, Singapore. https://doi.org/10.1007/978-981-13-2330-0_13
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DOI: https://doi.org/10.1007/978-981-13-2330-0_13
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