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Gilbert Laporte - One of the best experts on this subject based on the ideXlab platform.

  • the fleet size and mix pollution Routing Problem
    Transportation Research Part B-methodological, 2014
    Co-Authors: Çağrı Koç, Tolga Bektaş, Ola Jabali, Gilbert Laporte
    Abstract:

    This paper introduces the fleet size and mix pollution-Routing Problem which extends the pollution-Routing Problem by considering a heterogeneous vehicle fleet. The main objective is to minimize the sum of vehicle fixed costs and Routing cost, where the latter can be defined with respect to the cost of fuel and CO emissions, and driver cost. Solving this Problem poses several methodological challenges. To this end, we have developed a powerful metaheuristic which was successfully applied to a large pool of realistic benchmark instances. Several analyses were conducted to shed light on the trade-offs between various performance indicators, including capacity utilization, fuel and emissions and costs pertaining to vehicle acquisition, fuel consumption and drivers. The analyses also quantify the benefits of using a heterogeneous fleet over a homogeneous one. Full text available at: http://www.sciencedirect.com/science/article/pii/S0191261514001623

  • the bi objective pollution Routing Problem
    European Journal of Operational Research, 2014
    Co-Authors: Emrah Demir, Tolga Bektaş, Gilbert Laporte
    Abstract:

    The bi-objective Pollution-Routing Problem is an extension of the Pollution-Routing Problem (PRP) which consists of Routing a number of vehicles to serve a set of customers, and determining their speed on each route segment. The two objective functions pertaining to minimization of fuel consumption and driving time are conflicting and are thus considered separately. This paper presents an adaptive large neighborhood search algorithm (ALNS), combined with a speed optimization procedure, to solve the bi-objective PRP. Using the ALNS as the search engine, four a posteriori methods, namely the weighting method, the weighting method with normalization, the epsilon-constraint method and a new hybrid method (HM), are tested using a scalarization of the two objective functions. The HM combines adaptive weighting with the epsilon-constraint method. To evaluate the effectiveness of the algorithm, new sets of instances based on real geographic data are generated, and a library of bi-criteria PRP instances is compiled. Results of extensive computational experiments with the four methods are presented and compared with one another by means of the hypervolume and epsilon indicators. The results show that HM is highly effective in finding good-quality non-dominated solutions on PRP instances with 100 nodes.

  • The fleet size and mix pollution-Routing Problem
    Transportation Research Part B: Methodological, 2014
    Co-Authors: Çağrı Koç, Tolga Bektaş, Ola Jabali, Gilbert Laporte
    Abstract:

    This paper introduces the fleet size and mix pollution-Routing Problem which extends the pollution-Routing Problem by considering a heterogeneous vehicle fleet. The main objective is to minimize the sum of vehicle fixed costs and Routing cost, where the latter can be defined with respect to the cost of fuel and CO2emissions, and driver cost. Solving this Problem poses several methodological challenges. To this end, we have developed a powerful metaheuristic which was successfully applied to a large pool of realistic benchmark instances. Several analyses were conducted to shed light on the trade-offs between various performance indicators, including capacity utilization, fuel and emissions and costs pertaining to vehicle acquisition, fuel consumption and drivers. The analyses also quantify the benefits of using a heterogeneous fleet over a homogeneous one.

  • an adaptive large neighborhood search heuristic for the pollution Routing Problem
    European Journal of Operational Research, 2012
    Co-Authors: Emrah Demir, Tolga Bektaş, Gilbert Laporte
    Abstract:

    The Pollution-Routing Problem (PRP) is a recently introduced extension of the classical Vehicle Routing Problem with Time Windows which consists of Routing a number of vehicles to serve a set of customers, and determining their speed on each route segment so as to minimize a function comprising fuel, emission and driver costs. This paper presents an adaptive large neighborhood search for the PRP. Results of extensive computational experimentation confirm the efficiency of the algorithm.

  • the inventory Routing Problem with transshipment
    Computers & Operations Research, 2012
    Co-Authors: Leandro C Coelho, Jeanfrancois Cordeau, Gilbert Laporte
    Abstract:

    This paper introduces the Inventory-Routing Problem with Transshipment (IRPT). This Problem arises when vehicle Routing and inventory decisions must be made simultaneously, which is typically the case in vendor-managed inventory systems. Heuristics and exact algorithms have already been proposed for the Inventory-Routing Problem (IRP), but these algorithms ignore the possibility of performing transshipments between customers so as to further reduce the overall cost. We present a formulation that allows transshipments, either from the supplier to customers or between customers. We also propose an adaptive large neighborhood search heuristic to solve the Problem. This heuristic manipulates vehicle routes while the remaining Problem of determining delivery quantities and transshipment moves is solved through a network flow algorithm. Our approach can solve four different variants of the Problem: the IRP and the IRPT, under maximum level and order-up-to level policies. We perform an extensive assessment of the performance of our heuristic.

Martin W P Savelsbergh - One of the best experts on this subject based on the ideXlab platform.

  • an optimization based heuristic for the split delivery vehicle Routing Problem
    Transportation Science, 2008
    Co-Authors: Claudia Archetti, Grazia M Speranza, Martin W P Savelsbergh
    Abstract:

    The split delivery vehicle Routing Problem is concerned with serving the demand of a set of customers with a fleet of capacitated vehicles at minimum cost. Contrary to what is assumed in the classical vehicle Routing Problem, a customer can be served by more than one vehicle, if convenient. We present a solution approach that integrates heuristic search with optimization by using an integer program to explore promising parts of the search space identified by a tabu search heuristic. Computational results show that the method improves the solution of the tabu search in all but one instance of a large test set.

  • Dynamic Programming Approximations for a Stochastic Inventory Routing Problem
    Transportation Science, 2004
    Co-Authors: Anton J. Kleywegt, Vijay S. Nori, Martin W P Savelsbergh
    Abstract:

    This work is motivated by the need to solve the inventory Routing Problem when implementing a business practice called vendor managed inventory replenishment (VMI). With VMI, vendors monitor their customers’ inventories, and decide when and how much inventory should be replenished at each customer. The inventory Routing Problem attempts to coordinate inventory replenishment and transportation in such a way that the cost is minimized over the long run. We formulate a Markov decision process model of the stochastic inventory Routing Problem, and propose approximation methods to find good solutions with reasonable computational effort. We indicate how the proposed approach can be used for other Markov decision processes involving the control of multiple resources.

  • A Decomposition Approach for the Inventory-Routing Problem
    Transportation Science, 2004
    Co-Authors: Ann Melissa Campbell, Martin W P Savelsbergh
    Abstract:

    I n this paper, we present a solution approach for the inventory-Routing Problem. The inventory-Routing prob-lem is a variation of the vehicle-Routing Problem that arises in situations where a vendor has the ability to make decisions about the timing and sizing of deliveries, as well as the Routing, with the restriction that cus-tomers are not allowed to run out of product. We develop a two-phase approach based on decomposing the set of decisions: A delivery schedule is created first, followed by the construction of a set of delivery routes. The first phase utilizes integer programming, whereas the second phase employs Routing and scheduling heuristics. Our focus is on creating a solution methodology appropriate for large-scale real-life instances. Computational experiments demonstrating the effectiveness of our approach are presented.

  • The Stochastic Inventory Routing Problem with Direct Deliveries
    Transportation Science, 2002
    Co-Authors: Anton J. Kleywegt, Vijay S. Nori, Martin W P Savelsbergh
    Abstract:

    Vendor managed inventory replenishment is a business practice in which vendors monitor their customers’ inventories, and decide when and how much inventory should be replenished. The inventory Routing Problem addresses the coordination of inventory management and transportation. The ability to solve the inventory Routing Problem contributes to the realization of the potential savings in inventory and transportation costs brought about by vendor managed inventory replenishment. The inventory Routing Problem is hard, especially if a large number of customers is involved. We formulate the inventory Routing Problem as a Markov decision process, and we propose approximation methods to find good solutions with reasonable computational effort. Computational results are presented for the inventory Routing Problem with direct deliveries.

Tolga Bektaş - One of the best experts on this subject based on the ideXlab platform.

  • the fleet size and mix pollution Routing Problem
    Transportation Research Part B-methodological, 2014
    Co-Authors: Çağrı Koç, Tolga Bektaş, Ola Jabali, Gilbert Laporte
    Abstract:

    This paper introduces the fleet size and mix pollution-Routing Problem which extends the pollution-Routing Problem by considering a heterogeneous vehicle fleet. The main objective is to minimize the sum of vehicle fixed costs and Routing cost, where the latter can be defined with respect to the cost of fuel and CO emissions, and driver cost. Solving this Problem poses several methodological challenges. To this end, we have developed a powerful metaheuristic which was successfully applied to a large pool of realistic benchmark instances. Several analyses were conducted to shed light on the trade-offs between various performance indicators, including capacity utilization, fuel and emissions and costs pertaining to vehicle acquisition, fuel consumption and drivers. The analyses also quantify the benefits of using a heterogeneous fleet over a homogeneous one. Full text available at: http://www.sciencedirect.com/science/article/pii/S0191261514001623

  • the bi objective pollution Routing Problem
    European Journal of Operational Research, 2014
    Co-Authors: Emrah Demir, Tolga Bektaş, Gilbert Laporte
    Abstract:

    The bi-objective Pollution-Routing Problem is an extension of the Pollution-Routing Problem (PRP) which consists of Routing a number of vehicles to serve a set of customers, and determining their speed on each route segment. The two objective functions pertaining to minimization of fuel consumption and driving time are conflicting and are thus considered separately. This paper presents an adaptive large neighborhood search algorithm (ALNS), combined with a speed optimization procedure, to solve the bi-objective PRP. Using the ALNS as the search engine, four a posteriori methods, namely the weighting method, the weighting method with normalization, the epsilon-constraint method and a new hybrid method (HM), are tested using a scalarization of the two objective functions. The HM combines adaptive weighting with the epsilon-constraint method. To evaluate the effectiveness of the algorithm, new sets of instances based on real geographic data are generated, and a library of bi-criteria PRP instances is compiled. Results of extensive computational experiments with the four methods are presented and compared with one another by means of the hypervolume and epsilon indicators. The results show that HM is highly effective in finding good-quality non-dominated solutions on PRP instances with 100 nodes.

  • The fleet size and mix pollution-Routing Problem
    Transportation Research Part B: Methodological, 2014
    Co-Authors: Çağrı Koç, Tolga Bektaş, Ola Jabali, Gilbert Laporte
    Abstract:

    This paper introduces the fleet size and mix pollution-Routing Problem which extends the pollution-Routing Problem by considering a heterogeneous vehicle fleet. The main objective is to minimize the sum of vehicle fixed costs and Routing cost, where the latter can be defined with respect to the cost of fuel and CO2emissions, and driver cost. Solving this Problem poses several methodological challenges. To this end, we have developed a powerful metaheuristic which was successfully applied to a large pool of realistic benchmark instances. Several analyses were conducted to shed light on the trade-offs between various performance indicators, including capacity utilization, fuel and emissions and costs pertaining to vehicle acquisition, fuel consumption and drivers. The analyses also quantify the benefits of using a heterogeneous fleet over a homogeneous one.

  • an adaptive large neighborhood search heuristic for the pollution Routing Problem
    European Journal of Operational Research, 2012
    Co-Authors: Emrah Demir, Tolga Bektaş, Gilbert Laporte
    Abstract:

    The Pollution-Routing Problem (PRP) is a recently introduced extension of the classical Vehicle Routing Problem with Time Windows which consists of Routing a number of vehicles to serve a set of customers, and determining their speed on each route segment so as to minimize a function comprising fuel, emission and driver costs. This paper presents an adaptive large neighborhood search for the PRP. Results of extensive computational experimentation confirm the efficiency of the algorithm.

  • the pollution Routing Problem
    Transportation Research Part B-methodological, 2011
    Co-Authors: Tolga Bektaş, Gilbert Laporte
    Abstract:

    The amount of pollution emitted by a vehicle depends on its load and speed, among other factors. This paper presents the Pollution-Routing Problem (PRP), an extension of the classical Vehicle Routing Problem (VRP) with a broader and more comprehensive objective function that accounts not just for the travel distance, but also for the amount of greenhouse emissions, fuel, travel times and their costs. Mathematical models are described for the PRP with or without time windows and computational experiments are performed on realistic instances. The paper sheds light on the tradeoffs between various parameters such as vehicle load, speed and total cost, and offers insight on economies of 'environmental-friendly' vehicle Routing. The results suggest that, contrary to the VRP, the PRP is significantly more difficult to solve to optimality but has the potential of yielding savings in total cost.

W Y Szeto - One of the best experts on this subject based on the ideXlab platform.

  • an artificial bee colony algorithm for the capacitated vehicle Routing Problem
    European Journal of Operational Research, 2011
    Co-Authors: W Y Szeto
    Abstract:

    This paper introduces an artificial bee colony heuristic for solving the capacitated vehicle Routing Problem. The artificial bee colony heuristic is a swarm-based heuristic, which mimics the foraging behavior of a honey bee swarm. An enhanced version of the artificial bee colony heuristic is also proposed to improve the solution quality of the original version. The performance of the enhanced heuristic is evaluated on two sets of standard benchmark instances, and compared with the original artificial bee colony heuristic. The computational results show that the enhanced heuristic outperforms the original one, and can produce good solutions when compared with the existing heuristics. These results seem to indicate that the enhanced heuristic is an alternative to solve the capacitated vehicle Routing Problem.

P T Vanathi - One of the best experts on this subject based on the ideXlab platform.

  • nested particle swarm optimisation for multi depot vehicle Routing Problem
    International Journal of Operational Research, 2013
    Co-Authors: S Geetha, G Poonthalir, P T Vanathi
    Abstract:

    Vehicle Routing Problem (VRP) is a well-known non-deterministic polynomial hard Problem in operations research. VRP is more suited for applications having one warehouse. A variant of VRP called as multi-depot vehicle Routing Problem (MDVRP) has more than one warehouse. Cluster first and route second is the methodology used for solving MDVRP. An improved k-means algorithm is proposed for clustering that reduces the MDVRP to multiple VRP. In this work, MDVRP is considered with more than one objective and nested particle swarm optimisation with genetic operators is proposed for solving each VRP. Master particle swarm optimisation forms the group within each cluster. Slave particle swarm optimisation generates the route for each group. The objective of MDVRP is to minimise the total travel length along with route and load balance among the depots and vehicles. The results obtained are better in balancing load, route length and the number of vehicles, rather than minimisation of total cost.