Variable neighborhood search-based solution methods for the pollution location-inventory-routing problem
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Variable neighborhood search-based solution methods for the pollution location-inventory-routing problem Panagiotis Karakostas1
· Angelo Sifaleras2
· Michael C. Georgiadis1
Received: 23 March 2020 / Accepted: 5 August 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract This work presents efficient solution approaches for a new complex NP-hard combinatorial optimization problem, the Pollution Location Inventory Routing problem (PLIRP), which considers both economic and environmental issues. A mixed-integer linear programming model is proposed and first, small problem instances are solved using the CPLEX solver. Due to its computational complexity, General Variable Neighborhood Search-based metaheuristic algorithms are developed for the solution of medium and large instances. The proposed approaches are tested on 30 new randomly generated PLIRP instances. Parameter estimation has been performed for determining the most suitable perturbation strength. An extended numerical analysis illustrates the effectiveness and efficiency of the underlying methods, leading to high-quality solutions with limited computational effort. Furthermore, the impact of holding cost variations to the total cost is studied. Keywords Fuel consumption · Location · Inventory · Routing · Variable neighborhood search
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Michael C. Georgiadis [email protected] Panagiotis Karakostas [email protected] Angelo Sifaleras [email protected]
1
Department of Chemical Engineering, Aristotle University of Thessaloniki, University Campus, 54124 Thessaloniki, Greece
2
Department of Applied Informatics, School of Information Sciences, University of Macedonia, 156 Egnatia Str., 54636 Thessaloniki, Greece
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P. Karakostas et al.
1 Introduction The Location Inventory Routing Problem (LIRP) is a complex NP-hard combinatorial optimization problem, which simultaneously tackles strategic (location/allocation), tactical (inventory levels and replenishment rates) and operational (routing schedules) decisions [20,30,39]. The main goal of this problem is to determine an optimal schedule for achieving economic benefits, like total cost minimization [18]. However, due to the fact that the supply chain activities emit pollutants, such as carbon dioxide (CO2 ), the environmental impact of logistics should also be taken under consideration [7,25]. More specifically, CO2 emissions are considered as the main cause of the global warming, one of the major environment challenges [7,8] Freight transportation is mentioned as the main source of CO2 [27]. Especially, road transportation generates more than 20% of the total CO2 emissions in European Union [27] and 30% in the Organization for Economic Co-operation and Development (OECD) countries [31]. CO2 emissions are proportional to the amount of consumed fuel and the fuel consumption depends on speed, acceleration, distance and total weight of the vehicle (curb and freight weight) [25]. The green logistics concept has been studied in many literature contributions. The majority of them focus on rou
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