Heuristics for tactical time slot management: a periodic vehicle routing problem view

Hernandez, Florent; Gendreau, Michel; Potvin, Jean-Yves

doi:10.1111/itor.12403

Cited by 40 publications

(19 citation statements)

References 20 publications

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“…Thus, the multidepot VRPPD (MDVRPPD) is solved to establish a sustainable pickup and delivery service among multiple depots [31], where the open-closed mixed routes could be designed as an important component of the extended complexity of real-life logistics networks [14]. However, compared with the standard VRPPD and MDVRP, which are performed in only one-time unit, PVRP and MPVRP seek to provide services for various customer demands at multiple consecutive time periods (e.g., weekdays and weekends of a week), and vehicle routes in multiple service periods must be scheduled with a service frequency for each customer [7,[32][33][34]. Multidepot and periodic VRP (MDPVRP) incorporates PVRP and MDVRP to generate new routes originating from multiple depots to clients within a planning horizon of multiple periods [35,36].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Azad et al [36] introduced a heuristic initialized stochastic memetic algorithm to rebalance exploration and exploitation, and avoid premature convergence in the MDPVRP optimization. Hernandez et al [33] used a unified TS that evaluates each tactical routing plan in the multiperiod logistics network and obtains the minimum cost under the constraints of customer demands and service times. Estrada-Moreno et al [34] introduced a two-stage approach consisting of iterated local search and biased-randomization technologies to solve the MPVRP with optimal travel routes.…”

Section: Literature Reviewmentioning

confidence: 99%

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Collaboration and Resource Sharing in the Multidepot Multiperiod Vehicle Routing Problem with Pickups and Deliveries

Wang

Guan

et al. 2020

Sustainability

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In this work, a multidepot multiperiod vehicle routing problem with pickups and deliveries (MDPVRPPD) is solved by optimizing logistics networks with collaboration and resource sharing among logistics service providers. The optimal solution can satisfy customer demands with periodic time characteristics and incorporate pickup and delivery services with maximum resource utilization. A collaborative mechanism is developed to rearrange both the open and closed vehicle routes among multiple pickup and delivery centers with improved transportation efficiency and reduced operational costs. The effects of resource sharing strategies combining customer information sharing, facility service sharing, and vehicle sharing are investigated across multiple service periods to maximize resource utilization and refine the resource configuration. A multiobjective optimization model is developed to formulate the MDPVRPPD so that the minimum total operational costs, waiting time, and the number of vehicles are obtained. A hybrid heuristic algorithm incorporating a 3D clustering and an improved multiobjective particle swarm optimization (IMOPSO) algorithm is introduced to solve the MDPVRPPD and find Pareto optimal solutions. The proposed hybrid heuristic algorithm is based on a selective exchange mechanism that enhances local and global searching capabilities. Results demonstrate that the proposed IMOPSO outperforms other existing algorithms. We also study profit allocation issues to quantify the stability and sustainability of long-term collaboration among logistics participants, using the minimum costs remaining savings method. The proposed model and solution methods are validated by conducting an empirical study of a real system in Chongqing City, China. This study contributes to the development of efficient urban logistics distribution systems, and facilitates the expansion of intelligent and sustainable supply chains.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Collaboration and Resource Sharing in the Multidepot Multiperiod Vehicle Routing Problem with Pickups and Deliveries

Wang

Guan

et al. 2020

Sustainability

View full text Add to dashboard Cite

show abstract

“…A tabu search algorithm was proposed to minimize the sum of vehicle completion times, and a Lagrangian relaxation algorithm was used to generate the lower bounds of the problems. Hernandez et al [21] considered the periodic VRP and the periodic VRP with time windows. A tactical routing plan for a time slot schedule, based on demand and service time estimates, is devised to minimize the expected cost.…”

Section: Literature Reviewmentioning

confidence: 99%

An Enhanced Approach for the Multiple Vehicle Routing Problem with Heterogeneous Vehicles and a Soft Time Window

Kang

Lee

2018

Symmetry

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The vehicle routing problem (VRP) is a challenging combinatorial optimization problem. This research focuses on the problem under which a manufacturer needs to outsource materials from other suppliers and to ship the materials back to the company. Heterogeneous vehicles are available to ship the materials, and each vehicle has a limited loading capacity and a limited travelling distance. The purpose of this research is to study a multiple vehicle routing problem (MVRP) with soft time window and heterogeneous vehicles. Two models, using mixed integer programming (MIP) and genetic algorithm (GA), are developed to solve the problem. The MIP model is first constructed to minimize the total transportation cost, which includes the assignment cost, travelling cost, and the tardiness cost, for the manufacturer. The optimal solution can present multiple vehicle routing and the loading size of each vehicle in each period. The GA is next applied to solve the problem so that a near-optimal solution can be obtained when the problem is too difficult to be solved using the MIP. A case of a food manufacturing company is used to examine the practicality of the proposed MIP model and the GA model. The results show that the MIP model can obtain the optimal solution under a short computational time when the scale of the problem is small. When the problem becomes non-deterministic polynomial hard (NP-hard), the MIP model cannot find the optimal solution. On the other hand, the GA model can obtain a near-optimal solution within a reasonable amount of computational time. This paper is related to several important topics of the Symmetry journal in the areas of mathematics and computer science theory and methods. In the area of mathematics, the theories of linear and non-linear algebraic structures and information technology are adopted. In the area of computer science, theory and methods, and metaheuristics are applied.Unit of measure: min.Symmetry 2018, 10, 650 9 of 20 Table 6 lists the problem configurations for the three cases, including the number of periods, number of suppliers, number of vehicles, size of the transportation network, number of variables for the MIP, and number of constraints for the MIP. In the first case, there are five periods, the number of suppliers is five, the number of vehicles is three, the transportation network is 6 × 6, and the numbers of variables and constraints for the MIP model are 931 and 1171, respectively. In the second case, there are seven periods, the number of suppliers is nine, the number of vehicles is four, the transportation network is 10 × 10, and the numbers of variables and constraints for the MIP model are 3903 and 3977, respectively. In the third case, there are nine periods, the number of suppliers is 12, the number of vehicles is five, the transportation network is 13 × 13, and the numbers of variables and constraints for the MIP model are 9758 and 9919, respectively.

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“…As a crucial kind of combinational optimization problem, the path planning problem has been widely studied such like recent research works on traveling salesman problem (TSP) [6], traffic assignment/vehicle routing problem [7][8][9], and evolutionary optimization problems, e.g., general transportation planning problems, facility location problem, and roadway repair problem [10]. Based on the existing theoretical achievements, the research on evacuation path planning can be classified as static planning and dynamic planning.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Route Network Planning Problem for Emergency Evacuation in Restricted-Space Scenarios

Hong

et al. 2018

Journal of Advanced Transportation

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We investigate a dynamic route planning problem in restricted-space evacuation, namely, the Multiobjective Dynamic Route Network Planning (MODRNP) problem. It models the multisource to multidestination evacuation in restricted-space scenarios, with the objectives of minimizing the whole evacuation delay and maximizing the evacuation efficiency. We study the problem in 3D scenarios, which can provide intuition vision for the geographic space and contribute to the evacuation plan and implementation. Based on the auxiliary graph transformation, we propose a heuristic algorithm referred to the classical problem, Minimum Weighted Set Cover. We finally conduct extensive experiments to evaluate the performance of the proposed algorithm and give an application instance on a typical kind of restricted-space scenarios. The results indicate that the proposed algorithm outperforms the existing alternatives in terms of the utilization as well as timeliness.

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Heuristics for tactical time slot management: a periodic vehicle routing problem view

Cited by 40 publications

References 20 publications

Collaboration and Resource Sharing in the Multidepot Multiperiod Vehicle Routing Problem with Pickups and Deliveries

Collaboration and Resource Sharing in the Multidepot Multiperiod Vehicle Routing Problem with Pickups and Deliveries

An Enhanced Approach for the Multiple Vehicle Routing Problem with Heterogeneous Vehicles and a Soft Time Window

Dynamic Route Network Planning Problem for Emergency Evacuation in Restricted-Space Scenarios

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