This research considers the time-dependent vehicle routing problem (TDVRP). The time-dependent VRP does not assume constant speeds of the vehicles. The speeds of the vehicles vary during the various times of the day, ...This research considers the time-dependent vehicle routing problem (TDVRP). The time-dependent VRP does not assume constant speeds of the vehicles. The speeds of the vehicles vary during the various times of the day, based on the traffic conditions. During the periods of peak traffic hours, the vehicles travel at low speeds and during non-peak hours, the vehicles travel at higher speeds. A survey by TCI and IIM-C (2014) found that stoppage delay as percentage of journey time varied between five percent and 25 percent, and was very much dependent on the characteristics of routes. Costs of delay were also estimated and found not to affect margins by significant amounts. This study aims to overcome such problems arising out of traffic congestions that lead to unnecessary delays and hence, loss in customers and thereby valuable revenues to a company. This study suggests alternative routes to minimize travel times and travel distance, assuming a congestion in traffic situation. In this study, an efficient GA-based algorithm has been developed for the TDVRP, to minimize the total distance travelled, minimize the total number of vehicles utilized and also suggest alternative routes for congestion avoidance. This study will help to overcome and minimize the negative effects due to heavy traffic congestions and delays in customer service. The proposed algorithm has been shown to be superior to another existing algorithm in terms of the total distance travelled and also the number of vehicles utilized. Also the performance of the proposed algorithm is as good as the mathematical model for small size problems.展开更多
The advancement of autonomous technology makes electric-powered drones an excellent choice for flexible logistics services at the last mile delivery stage.To reach a balance between green transportation and competitiv...The advancement of autonomous technology makes electric-powered drones an excellent choice for flexible logistics services at the last mile delivery stage.To reach a balance between green transportation and competitive edge,the collaborative routing of drones in the air and trucks on the ground is increasingly invested in the next generation of delivery,where it is particularly reasonable to consider customer time windows and time-dependent travel times as two typical time-related factors in daily services.In this paper,we propose the Vehicle Routing Problem with Drones under Time constraints(VRPD-T)and focus on the time constraints involved in realistic scenarios during the delivery.A mixed-integer linear programming model has been developed to minimize the total delivery completion time.Furthermore,to overcome the limitations of standard solvers in handling large-scale complex issues,a space-time hybrid heuristic-based algorithm has been developed to effectively identify a high-quality solution.The numerical results produced from randomly generated instances demonstrate the effectiveness of the proposed algorithm.展开更多
Home health care(HHC)includes a wide range of healthcare services that are performed in customers'homes to help them recover.With the constantly increasing demand for health care,HHC policymakers are eager to addr...Home health care(HHC)includes a wide range of healthcare services that are performed in customers'homes to help them recover.With the constantly increasing demand for health care,HHC policymakers are eager to address routing and scheduling problems from the perspective of optimization.In this paper,a bi-level programming model for HHC routing and scheduling problems with stochastic travel times is proposed,in which the degree of satisfaction with the visit time is simultaneously considered.The upper-level model is formulated for customer assignment with the aim of minimizing the total operating cost,and the lower-level model is formulated as a routing problem to maximize the degree of satisfaction with the visit time.Consistent with Stackelberg game decision-making,the trade-off relationship between these two objectives can be achieved spontaneously so as to reach an equilibrium state.A three-stage hybrid algorithm combining an iterated local search framework,which uses a large neighborhood search procedure as a sub-heuristic,a set-partitioning model,and a post-optimization method is developed to solve the proposed model.Numerical experiments on a set of instances including 10 to 100 customers verify the effectiveness of the proposed model and algorithm.展开更多
文摘This research considers the time-dependent vehicle routing problem (TDVRP). The time-dependent VRP does not assume constant speeds of the vehicles. The speeds of the vehicles vary during the various times of the day, based on the traffic conditions. During the periods of peak traffic hours, the vehicles travel at low speeds and during non-peak hours, the vehicles travel at higher speeds. A survey by TCI and IIM-C (2014) found that stoppage delay as percentage of journey time varied between five percent and 25 percent, and was very much dependent on the characteristics of routes. Costs of delay were also estimated and found not to affect margins by significant amounts. This study aims to overcome such problems arising out of traffic congestions that lead to unnecessary delays and hence, loss in customers and thereby valuable revenues to a company. This study suggests alternative routes to minimize travel times and travel distance, assuming a congestion in traffic situation. In this study, an efficient GA-based algorithm has been developed for the TDVRP, to minimize the total distance travelled, minimize the total number of vehicles utilized and also suggest alternative routes for congestion avoidance. This study will help to overcome and minimize the negative effects due to heavy traffic congestions and delays in customer service. The proposed algorithm has been shown to be superior to another existing algorithm in terms of the total distance travelled and also the number of vehicles utilized. Also the performance of the proposed algorithm is as good as the mathematical model for small size problems.
基金supported by the National Natural Science Foundation of China(No.61961146005)。
文摘The advancement of autonomous technology makes electric-powered drones an excellent choice for flexible logistics services at the last mile delivery stage.To reach a balance between green transportation and competitive edge,the collaborative routing of drones in the air and trucks on the ground is increasingly invested in the next generation of delivery,where it is particularly reasonable to consider customer time windows and time-dependent travel times as two typical time-related factors in daily services.In this paper,we propose the Vehicle Routing Problem with Drones under Time constraints(VRPD-T)and focus on the time constraints involved in realistic scenarios during the delivery.A mixed-integer linear programming model has been developed to minimize the total delivery completion time.Furthermore,to overcome the limitations of standard solvers in handling large-scale complex issues,a space-time hybrid heuristic-based algorithm has been developed to effectively identify a high-quality solution.The numerical results produced from randomly generated instances demonstrate the effectiveness of the proposed algorithm.
基金funded by the National Natural Science Foundation of China under Grant NSFCProj.71771070,71831006,71801065 and 71932005.
文摘Home health care(HHC)includes a wide range of healthcare services that are performed in customers'homes to help them recover.With the constantly increasing demand for health care,HHC policymakers are eager to address routing and scheduling problems from the perspective of optimization.In this paper,a bi-level programming model for HHC routing and scheduling problems with stochastic travel times is proposed,in which the degree of satisfaction with the visit time is simultaneously considered.The upper-level model is formulated for customer assignment with the aim of minimizing the total operating cost,and the lower-level model is formulated as a routing problem to maximize the degree of satisfaction with the visit time.Consistent with Stackelberg game decision-making,the trade-off relationship between these two objectives can be achieved spontaneously so as to reach an equilibrium state.A three-stage hybrid algorithm combining an iterated local search framework,which uses a large neighborhood search procedure as a sub-heuristic,a set-partitioning model,and a post-optimization method is developed to solve the proposed model.Numerical experiments on a set of instances including 10 to 100 customers verify the effectiveness of the proposed model and algorithm.
