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Measuring incineration plants’ performance using combined data envelopment analysis, goal programming and mixed integer linear programming

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  • Konstantinos Petridis

    (University of Macedonia)

  • Prasanta Kumar Dey

    (Aston University)

Abstract

Incineration plants produce heat and power from waste, reduce waste disposal to landfills, and discharge harmful emissions and bottom ash. The objective of the incineration plant is to maximize desirable outputs (heat and power) and minimize undesirable outputs (emissions and bottom ash). Therefore, studying the overall impact of incineration plants in a region so as to maximize the benefits and minimize the environmental impact is significant. Majority of prior works focus on plant specific decision making issues including performance analysis. This study proposes a hybrid data envelopment analysis (DEA), goal programming (GP) and mixed integer linear programming (MILP) model to assess the performance of incineration plants, in a specific region, to enhance overall power production, consumption of waste and reduction of emissions. This model not only helps the plant operators to evaluate the effectiveness of incineration but also facilitates the policy makers to plan for overall waste management of the region through decision-making on adding and closing plants on the basis of their efficiency. Majority of prior studies on incineration plants emphasize on how to improve their performance on heat and power production and neglect the waste management aspects. Additionally, optimizing benefits and minimizing negative outputs through fixing targets in order to make decision on shutting down the suboptimal plants has not been modeled in prior research. This research combines both the aspects and addresses the overall performance enhancement of incineration plants within a region from both policy makers and plant operators’ perspectives. The proposed combined DEA, GP and MILP model enables to optimize incineration plants performance within a region by deriving efficiency of each plant and identifying plants to close down on the basis of their performance. The proposed model has been applied to a group of 22 incineration plants in the UK using secondary data in order to demonstrate the effectiveness of the model.

Suggested Citation

  • Konstantinos Petridis & Prasanta Kumar Dey, 2018. "Measuring incineration plants’ performance using combined data envelopment analysis, goal programming and mixed integer linear programming," Annals of Operations Research, Springer, vol. 267(1), pages 467-491, August.
    • Handle: RePEc:spr:annopr:v:267:y:2018:i:1:d:10.1007_s10479-018-2809-z
    DOI: 10.1007/s10479-018-2809-z
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    References listed on IDEAS

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    1. Petridis, Konstantinos & Tampakoudis, Ioannis & Drogalas, George & Kiosses, Nikolaos, 2022. "A Support Vector Machine model for classification of efficiency: An application to M&A," Research in International Business and Finance, Elsevier, vol. 61(C).
    2. Lenka Štofová & Petra Szaryszová & Bohuslava Mihalčová, 2021. "Testing the Bioeconomic Options of Transitioning to Solid Recovered Fuel: A Case Study of a Thermal Power Plant in Slovakia," Energies, MDPI, vol. 14(6), pages 1-20, March.
    3. Fouad Ben Abdelaziz & Houda Alaya & Prasanta Kumar Dey, 2020. "A multi-objective particle swarm optimization algorithm for business sustainability analysis of small and medium sized enterprises," Annals of Operations Research, Springer, vol. 293(2), pages 557-586, October.

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