Please use this identifier to cite or link to this item: https://ruomo.lib.uom.gr/handle/7000/689
Title: Adaptive variable neighborhood search solution methods for the fleet size and mix pollution location-inventory-routing problem
Authors: Karakostas, Panagiotis
Sifaleras, Angelo
Georgiadis, Michael C.
Type: Article
Subjects: FRASCATI::Natural sciences::Mathematics::Applied Mathematics
FRASCATI::Natural sciences::Computer and information sciences
Keywords: Green Logistics Optimization
Metaheuristics
Location
Inventory
Routing
Fleet Composition
Issue Date: Apr-2020
Publisher: Elsevier
Source: Expert Systems with Applications
First Page: 113444
Abstract: This work introduces the Fleet-size and Mix Pollution Location-Inventory-Routing Problem with Just-in-Time replenishment policy and Capacity Planning. This problem extends the strategic-level decisions of classic LIRP by considering capacity selection decisions and heterogeneous fleet composition. An MIP formulation of this new complex combinatorial optimization problem is proposed and small-sized problem instances are solved using the CPLEX solver. For the solution of more realistic-sized problem instances, a General Variable Neighborhood Search (GVNS)-based framework is adopted. Novel adaptive shaking methods are proposed as intelligent components of the developed GVNS algorithms to further improve their performance. To evaluate the proposed GVNS schemes, several problem instances are randomly generated by following specific instructions from the literature and adopting real vehicles' parameters. Comparisons between these solutions and the corresponding ones achieved by CPLEX are made. The computational results indicate the efficiency of the proposed GVNS-based algorithms, with the best GVNS scheme to produce 7% better solutions than CPLEX for small problems. Finally, the economic and environmental impacts of using either homogeneous or heterogeneous fleet of vehicles are examined.
URI: https://doi.org/10.1016/j.eswa.2020.113444
https://ruomo.lib.uom.gr/handle/7000/689
ISSN: 0957-4174
Other Identifiers: 10.1016/j.eswa.2020.113444
Appears in Collections:Department of Applied Informatics

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