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Related Experiment Videos

A multiobjective optimization model and an orthogonal design-based hybrid heuristic algorithm for regional urban

Hao Wu1,2, Zhong Wan1

  • 1a School of Mathematics and Statistics , Central South University , Changsha , People's Republic of China.

Journal of the Air & Waste Management Association (1995)
|October 17, 2017
PubMed
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This study optimizes urban mining systems by maximizing profit and environmental benefits while minimizing social costs. A novel algorithm efficiently solves complex recycling management problems, offering practical insights for decision-makers.

Area of Science:

  • Operations Research
  • Environmental Science
  • Materials Science

Background:

  • Urban mining is crucial for resource security, utilizing societal metal stocks as viable mineral sources.
  • Existing recycling systems require optimization to meet growing demands and resource challenges.

Purpose of the Study:

  • To develop a multiobjective mixed-integer piecewise nonlinear programming model (MOMIPNLP) for urban mining system management.
  • To jointly maximize total recycling profit and environmental utility while minimizing social negative effects.
  • To provide decision-makers with actionable strategies for optimizing recycling systems.

Main Methods:

  • Formulation of a MOMIPNLP model incorporating buy-back pricing, site selection, transportation, and capacity adjustment.
  • Transformation of the MOMIPNLP model into a standard mixed-integer nonlinear programming model.

Related Experiment Videos

  • Development of a hybrid heuristic algorithm using genetic algorithms and specialized branching/search techniques for Pareto-optimal solutions.

    • Main Results:

    • The developed hybrid heuristic algorithm demonstrates superior efficiency in solving large-scale urban mining management problems compared to existing methods.
    • Numerical experiments show reduced CPU time for complex regional management scenarios.
    • Case studies and sensitivity analyses reveal practical managerial implications for system design and policy.

    Conclusions:

    • The study provides a robust mathematical framework and an efficient algorithmic solution for optimizing urban mining operations.
    • The findings offer valuable guidance for policymakers and industry professionals on enhancing recycling system design, encouraging participation, and adjusting production capacity.
    • This research contributes to the strategic development of urban mining in an era of resource scarcity.