Mathematical modelling and heuristic approaches to the location-routing problem of a cost-effective integrated solid waste management

H. Asefi, S. Lim, M. Maghrebi () and S. Shahparvari
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H. Asefi: The University of New South Wales
S. Lim: The University of New South Wales
M. Maghrebi: The University of New South Wales
S. Shahparvari: RMIT University

Annals of Operations Research, 2019, vol. 273, issue 1, No 3, 75-110

Abstract: Abstract Integrated solid waste management (ISWM) comprises activities and processes to collect, transport, treat, recycle and dispose municipal solid wastes. This paper addresses the ISWM location-routing problem in which different types of municipal solid wastes are factored concurrently into an integrated system with all interrelated facilities. To support a cost-effective ISWM system, the number of locations of the system’s components (i.e. transfer stations; recycling, treatment and disposal centres) and truck routing within the system’s components need to be optimized. A mixed-integer linear programming (MILP) model is presented to minimise the total cost of the ISWM system including transportation costs and facility establishment costs. To tackle the non-deterministic polynomial-time hardness of the problem, a stepwise heuristic method is proposed within the frames of two meta-heuristic approaches: (i) variable neighbourhood search (VNS) and (ii) a hybrid VNS and simulated annealing algorithm (VNS + SA). A real-life case study from an existing ISWM system in Tehran, Iran is utilized to apply the proposed model and algorithms. Then the presented MILP model is implemented in CPLEX environment to evaluate the effectiveness of the proposed algorithms for multiple test problems in different scales. The results show that, while both proposed algorithms can effectively solve the problem within practical computing time, the proposed hybrid method efficiently has produced near-optimal solutions with gaps of

Keywords: Municipal solid waste; Integrated solid waste management; Location-routing problem; Mixed-integer linear programming; Simulated annealing; Variable neighbourhood search (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (11)

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DOI: 10.1007/s10479-018-2912-1

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