Intraroute Resource Replenishment with Mobile Depots

2021 ◽  
Author(s):  
Julian Hof ◽  
Michael Schneider
Keyword(s):  
Vehicle Routing ◽  
Distribution Systems ◽  
Time Windows ◽  
Computation Time ◽  
Integer Program ◽  
Mixed Integer ◽  
Neighborhood Search ◽  
Mixed Integer Program ◽  
Routing Problem ◽  
Commercial Solver

In numerous practical vehicle-routing applications, larger vehicles are employed as mobile depots to support a fleet of smaller vehicles that perform certain tasks. The mobile depots offer the possibility of keeping the task vehicles operational by supplying them en route with certain resources. For example, in two-echelon distribution systems, small task vehicles are used to navigate narrow streets and to deliver/collect goods or to collect waste, and larger vehicles serve as mobile depots to replenish the goods to be delivered or to receive collected goods or waste at the outskirts of the urban area. Accessibility constraints may also be imposed by regulations on emissions, which make some areas only accessible for environmentally friendly vehicles such as, for example, battery-powered electric vehicles. Especially if the respective refueling infrastructure is sparse, mobile refueling stations seem to be an interesting alternative. In this paper, we introduce the vehicle-routing problem with time windows and mobile depots (VRPTWMD) to capture the routing decisions of the described applications in a generalized fashion. The VRPTWMD is characterized by fleets of task vehicles (TVs) and support vehicles (SVs). The SVs may serve as mobile depots to restore either the load or the fuel capacity of the TVs that are used to fulfill the customer requests. We present a mixed-integer program for the VRPTWMD with which small instances can be solved using a commercial solver. Moreover, we develop a high-quality hybrid heuristic composed of an adaptive large neighborhood search and a path relinking approach to provide solutions on larger problem instances. We use a newly generated set of large VRPTWMD instances to analyze the effect of different problem characteristics on the structure of the identified solutions. In addition, our approach shows very convincing performance on benchmark instances for the related two-echelon multiple-trip VRP with satellite synchronization, which can be viewed as a special case of the VRPTWMD. Our heuristic is able to significantly improve a large part of the previous best-known solutions while spending notably less computation time than the comparison algorithm from the literature.

A Two-Echelon Neighborhood Search Algorithm for a Forwarder's Job Assignment in a Multi-Agent Logistics Network

2015 ◽  
Vol 32 (03) ◽  
pp. 1550018
Author(s):  
Yong Wang ◽  
Wen He ◽  
Yer Van Hui ◽  
Lawrence C. Leung
Keyword(s):  
Search Algorithm ◽  
Computation Time ◽  
Integer Program ◽  
Mixed Integer ◽  
Neighborhood Search ◽  
Mixed Integer Program ◽  
Nonlinear Program ◽  
Upper Echelon ◽  
Logistics Network ◽  
Unit Costs

Freight forwarders plan shipping logistics for client shipments based on available transport networks where logistics agents move commodities from origins to destinations. Forwarders typically have the option of assigning shipments to in-house agents or to sub-contracting agents. When making such assignment decisions, consolidation of shipments is a plausible cost-saving consideration. In this paper, we consider assignments of shipments to agents as well as shipment routing choices on a network. We formulate the problem as a nonlinear program where unit costs charged by agents are described as nonlinear functions. The special case with piecewise constant unit costs is formulated as a mixed integer program. We then develop a two-echelon heuristic algorithm to solve the nonlinear program. The upper echelon of the heuristic assigns shipments to suitable agents by adopting a set of neighborhood policies, while the lower echelon improves the routing plan by consolidating jobs along (sub) paths. The feasibility and validity of the heuristic are examined based on randomly generated instances. Computational results show that the heuristic is able to obtain good solutions in manageable computation time. The effects of network density and iteration limits on the performance of the heuristic are also characterized.

scholarly journals A Two-Echelon Electric Vehicle Routing Problem with Time Windows and Battery Swapping Stations

2021 ◽  
Vol 11 (22) ◽  
pp. 10779
Author(s):  
Dan Wang ◽  
Hong Zhou
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
High Efficiency ◽  
Programming Model ◽  
Time Windows ◽  
Transportation Cost ◽  
Mixed Integer ◽  
Neighborhood Search ◽  
Fixed Cost ◽  
Routing Problem

Driven by the new laws and regulations concerning the emission of greenhouse gases, it is becoming more and more popular for enterprises to adopt cleaner energy. This research proposes a novel two-echelon vehicle routing problem consisting of mixed vehicles considering battery swapping stations, which includes one depot, multiple satellites with unilateral time windows, and customers with given demands. The fossil fuel-based internal combustion vehicles are employed in the first echelon, while the electric vehicles are used in the second echelon. A mixed integer programming model for this proposed problem is established in which the total cost, including transportation cost, handling cost, fixed cost of two kinds of vehicles, and recharging cost, is minimized. Moreover, based on the variable neighborhood search, a metaheuristic procedure is developed to solve the problem. To validate its effectiveness, extensive numerical experiments are conducted over the randomly generated instances of different sizes. The computational results show that the proposed metaheuristic can produce a good logistics scheme with high efficiency.

scholarly journals Optimal vehicle route schedules in picking up and delivering cargo containers considering time windows in logistics distribution networks: A case study

2020 ◽  
Vol 26 (4) ◽  
pp. 174-184
Author(s):  
Thi Diem Chau Le ◽  
Duy Duc Nguyen ◽  
Judit Oláh ◽  
Miklós Pakurár
Keyword(s):  
Mathematical Model ◽  
Simulated Annealing ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Distribution Networks ◽  
Mixed Integer ◽  
Vehicle Group ◽  
Real Problem ◽  
Routing Problem

AbstractThis study describes a pickup and delivery vehicle routing problem, considering time windows in reality. The problem of tractor truck routes is formulated by a mixed integer programming model. Besides this, three algorithms - a guided local search, a tabu search, and simulated annealing - are proposed as solutions. The aims of our study are to optimize the number of internal tractor trucks used, and create optimal routes in order to minimize total logistics costs, including the fixed and variable costs of an internal vehicle group and the renting cost of external vehicles. Besides, our study also evaluates both the quality of solutions and the time to find optimal solutions to select the best suitable algorithm for the real problem mentioned above. A novel mathematical model is formulated by OR tools for Python. Compared to the current solution, our results reduced total costs by 18%, increased the proportion of orders completed by internal vehicles (84%), and the proportion of orders delivered on time (100%). Our study provides a mathematical model with time constraints and large job volumes for a complex distribution network in reality. The proposed mathematical model provides effective solutions for making decisions at logistics companies. Furthermore, our study emphasizes that simulated annealing is a more suitable algorithm than the two others for this vehicle routing problem.

Sign in / Sign up

Export Citation Format

Share Document