A multicriteria facility location model for municipal solid waste management in North Greece

Erkut, Erhan; Karagiannidis, A.; Perkoulidis, G.; Tjandra, Stevanus A.

doi:10.1016/j.ejor.2006.09.021

Cited by 175 publications

(79 citation statements)

References 18 publications

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“…Neto et al [22] investigate the environmental impact factors along the supply chain and propose a multi objective programming based framework in modeling and optimizing the design of logistic networks. Erkut et al [23] present a multi-criteria mixed-integer linear programming formulation to solve a locationallocation problem regarding municipal waste management.…”

Section: Related Literaturementioning

confidence: 99%

EOQ Revisited with Sustainability Considerations

Arslan¹,

Türkay²

2013

Foundations of Computing and Decision Sciences

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Abstract. The economic order quantity (EOQ) model is a pure economic model in classical inventory control theory. The model is designed to find the order quantity so as to minimize total cost under a deterministic setting. In this study, we revise the standard EOQ model to incorporate sustainability considerations that include environmental and social criteria in addition to the conventional economic considerations. We propose models for a number of different policies and analyze these revised models. Based on our analysis, we show how the triple bottom line considerations of sustainability be appended to traditional cost accounting in EOQ model. We also provide a number of useful insights for decision and policy making practices.

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Section: Related Literaturementioning

confidence: 99%

EOQ Revisited with Sustainability Considerations

Arslan¹,

Türkay²

2013

Foundations of Computing and Decision Sciences

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“…Multiplying all maximization objective functions by −1, we obtain the multi-objective minimization model [24]. …”

Section: Methodsmentioning

confidence: 99%

The Ordered Capacitated Multi-Objective Location-Allocation Problem for Fire Stations Using Spatial Optimization

Bolouri

Vafaeinejad

Alesheikh

et al. 2018

IJGI

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Determining the positions of facilities, and allocating demands to them, is a vitally important problem. Location-allocation problems are optimization NP-hard procedures. This article evaluates the ordered capacitated multi-objective location-allocation problem for fire stations, using simulated annealing and a genetic algorithm, with goals such as minimizing the distance and time as well as maximizing the coverage. After tuning the parameters of the algorithms using sensitivity analysis, they were used separately to process data for Region 11, Tehran. The results showed that the genetic algorithm was more efficient than simulated annealing, and therefore, the genetic algorithm was used in later steps. Next, we increased the number of stations. Results showed that the model can successfully provide seven optimal locations and allocate high demands (280,000) to stations in a discrete space in a GIS, assuming that the stations' capacities are known. Following this, we used a weighting program so that in each repetition, we could allot weights to each target randomly. Finally, by repeating the model over 10 independent executions, a set of solutions with the least sum and the highest number of non-dominated solutions was selected from among many non-dominated solutions as the best set of optimal solutions.

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“…For most case studies, the generic model is simplified in order to adapt the characteristics of the problems with less objectives and/or smaller network. Only two exceptions are given by Coruso et al (1993) and Erkut et al (2008), in which additional objectives have to be introduced in order to improve the utilization of resource. Besides, it is also observed that the introduction of adjustment parameters , and improves the flexibility and adaptability of the environmental impact objective function, as compared with Coruso et al (1993), Eiselt and Marianov (2014) and He et al (2006).…”

Section: Comparison Of the Case Studies In Previous Research Workmentioning

confidence: 99%

A multi-objective location-allocation optimization for sustainable management of municipal solid waste

Solvang

2017

Environ Syst Decis

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Abstract:The location problem of treatment and service facilities in municipal solid waste (MSW) management system is of significant importance due to the socio-economic and environmental concerns. The consideration of waste treatment costs, environmental impact, greenhouse gas (GHG) emissions, social fairness as well as other relevant aspects should be simultaneously taken into account when a MSW management system is planned. Development of sophisticated decision support tools for planning MSW management system in an economic-efficient and environmental friendly manner is therefore important. In this paper, a general multi-objective location-allocation model for optimally managing the interactions among those conflicting factors in MSW management system is proposed. The model is comprised of a three-stage conceptual framework and a mixed integer mathematical programming. The inclusion of environmental impact and GHG emission objectives push the output of the model tightening towards more environmentally friendly and sustainable solutions in MSW management. The application of this model is demonstrated through an illustrative example and the computational efficiency of the programming is also tested through a set of incremental parameters. Latter in this paper, a comparison with previous case studies of MSW system design is presented in order to show the applicability and adaptability of the generic model in practical decision-making process, and the perspectives of future study are also discussed.

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A multicriteria facility location model for municipal solid waste management in North Greece

Cited by 175 publications

References 18 publications

EOQ Revisited with Sustainability Considerations

EOQ Revisited with Sustainability Considerations

The Ordered Capacitated Multi-Objective Location-Allocation Problem for Fire Stations Using Spatial Optimization

A multi-objective location-allocation optimization for sustainable management of municipal solid waste

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