An application of possibilistic programming to the fuzzy location–allocation problems

Abiri, M. B.; Yousefli, Amir

doi:10.1007/s00170-010-2896-8

Cited by 10 publications

(4 citation statements)

References 23 publications

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“…Different sources of uncertainty, such as customer demands, exchange rates, travel times, amount of returns in reverse logistics, supply lead times, transportation costs, and holding costs, have been discussed throughout the literature. (Bar-Lev et al, 1993;Halidias & Michta, 2007;Abiri & Yousefli, 2011;Bartke, 2011;Carmichael & Balatbat, 2011).…”

Section: Uncertainty In Facility Location Modelsmentioning

confidence: 99%

A multi-objective robust optimization model for a facility location-allocation problem in a supply chain under uncertainty

Arabzad

Ghorbani

Zolfani

2015

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The final purpose of this study is presentation a mathematical model for a facility location-allocation problem so as to design an integrated supply chain. A supply chain with multiple suppliers, multiple products, multiple plants, multiple transportation alternatives and multiple customers is taken into account for this purpose. The problem is to specify a number and capacity level of plants, allocation of customers demand, and selection and order allocation of suppliers. A scenario approach is considered to deal effectively with the uncertainty of demand and cost parameters. The formulation is a robust multi-objective mixed-integer linear programming (MOMILP), in the context of which two conflicting objectives is taken into account simultaneously: (1) minimizing the total costs of a supply chain including raw material costs, transportation costs and establishment costs of plants, and (2) minimizing the total deterioration rate occurred by transportation alternatives. Then, the problem can be reduced to a linear one. Finally, by applying the LP-metric method, the model is solved as a single objective mixed-integer programming model. An experiment study corroborates that this procedure can be proposed to design an effective supply chain.

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Section: Uncertainty In Facility Location Modelsmentioning

confidence: 99%

A multi-objective robust optimization model for a facility location-allocation problem in a supply chain under uncertainty

Arabzad

Ghorbani

Zolfani

2015

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“…It has been studied for fifty years because of its widely practical applications [3].The problem is to locate a set of new facilities in specified area to satisfy the customs' demand and minimize the transportation cost from the location of customers to the facilities [4]. It was firstly studied by Cooper [5].…”

Section: Introductionmentioning

confidence: 99%

Particle swarm optimization based spatial location allocation of urban parks — A case study in Baoshan District, Shanghai, China

Chen

et al. 2014

2014 the Third International Conference on Agro-Geoinformatics

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This paper introduces a spatial location allocation (SLA) method for urban parks based on Particle Swarm Optimization (PSO). PSO is an effective optimization method on the basis of swarm intelligence. The algorithms of it are population based random search algorithms inspired by the social behavior of bird flocks. Compared with the other artificial intelligence (AI) algorithms, PSO is simple, easy to implement, needs fewer parameters. In the problem of SLA for urban park, three factors: population density, accessibility and competitiveness, were considered to configure a specified number of parks in this study. To find the locations of parks which satisfy these requirements, the calculation of SLA using the traditional overlaying method is with high complexity. The PSO method can decrease the complexity of computation and locate a set of parks in reasonable time. A case study in Baoshan District of Shanghai, China was proposed. The service area analysis of the simulation result of urban parks convinced that the result can confirm the fairness of public green-space service and the PSO method is a practicable and efficient approach in SLA problem. The method can easily be extended for other service facilities, for instance, the location allocation of water-saving irrigation systems, agriculture service centers, hospitals, supermarkets and cinemas, etc.

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“…In such situations the first thing needs treatment is the location-allocation of the problematic event and it follows the disaster recovery. Specifically, in [ 19 ] it is defined the location allocation problem in the context of municipality spatial area with regards to crowdsourcing and crowdsensing technologies, while in [ 20 ] it is presented a system that provides a possibilistic programming approach to treat location allocation of an event with the incorporation of fuzzy decision-making techniques. Subsequently, in [ 21 ] it is presented a location allocation approach that focuses on a multiple criteria decision-making system to treat facility location problems.…”

Section: Introductionmentioning

confidence: 99%

A Smartphone Crowdsensing System Enabling Environmental Crowdsourcing for Municipality Resource Allocation with LSTM Stochastic Prediction

Anagnostopoulos

Xanthopoulos

Psaromiligkos

2020

Sensors

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Resource allocation of the availability of certain departments for dealing with emergency recovery is of high importance in municipalities. Efficient planning for facing possible disasters in the coverage area of a municipality provides reassurance for citizens. Citizens can assist with such malfunctions by acting as human sensors at the edge of an infrastructure to provide instant feedback to the appropriate departments fixing the problems. However, municipalities have limited department resources to handle upcoming emergency events. In this study, we propose a smartphone crowdsensing system that is based on citizens’ reactions as human sensors at the edge of a municipality infrastructure to supplement malfunctions exploiting environmental crowdsourcing location-allocation capabilities. A long short-term memory (LSTM) neural network is incorporated to learn the occurrence of such emergencies. The LSTM is able to stochastically predict future emergency situations, acting as an early warning component of the system. Such a mechanism may be used to provide adequate department resource allocation to treat future emergencies.

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An application of possibilistic programming to the fuzzy location–allocation problems

Cited by 10 publications

References 23 publications

A multi-objective robust optimization model for a facility location-allocation problem in a supply chain under uncertainty

A multi-objective robust optimization model for a facility location-allocation problem in a supply chain under uncertainty

Particle swarm optimization based spatial location allocation of urban parks — A case study in Baoshan District, Shanghai, China

A Smartphone Crowdsensing System Enabling Environmental Crowdsourcing for Municipality Resource Allocation with LSTM Stochastic Prediction

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An application of possibilistic programming to the fuzzy location–allocation problems

Cited by 10 publications

References 23 publications

A multi-objective robust optimization model for a facility location-allocation problem in a supply chain under uncertainty

A multi-objective robust optimization model for a facility location-allocation problem in a supply chain under uncertainty

Particle swarm optimization based spatial location allocation of urban parks &#x2014; A case study in Baoshan District, Shanghai, China

A Smartphone Crowdsensing System Enabling Environmental Crowdsourcing for Municipality Resource Allocation with LSTM Stochastic Prediction

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Particle swarm optimization based spatial location allocation of urban parks — A case study in Baoshan District, Shanghai, China