scholarly journals A Time-Dependent Fuzzy Programming Approach for the Green Multimodal Routing Problem with Rail Service Capacity Uncertainty and Road Traffic Congestion

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-22 ◽  
Author(s):  
Yan Sun ◽  
Martin Hrušovský ◽  
Chen Zhang ◽  
Maoxiang Lang
Keyword(s):  
Sensitivity Analysis ◽  
Exact Solution ◽  
Travel Time ◽  
Time Dependent ◽  
Mixed Integer ◽  
Programming Approach ◽  
Routing Problem ◽  
Time Uncertainty ◽  
Capacity Uncertainty ◽  
Travel Time Uncertainty

This study explores an operational-level container routing problem in the road-rail multimodal service network. In response to the demand for an environmentally friendly transportation, we extend the problem into a green version by using both emission charging method and bi-objective optimization to optimize the CO2 emissions in the routing. Two uncertain factors, including capacity uncertainty of rail services and travel time uncertainty of road services, are formulated in order to improve the reliability of the routes. By using the triangular fuzzy numbers and time-dependent travel time to separately model the capacity uncertainty and travel time uncertainty, we establish a fuzzy chance-constrained mixed integer nonlinear programming model. A linearization-based exact solution strategy is designed, so that the problem can be effectively solved by any exact solution algorithm on any mathematical programming software. An empirical case is presented to demonstrate the feasibility of the proposed methods. In the case discussion, sensitivity analysis and bi-objective optimization analysis are used to find that the bi-objective optimization method is more effective than the emission charging method in lowering the CO2 emissions for the given case. Then, we combine sensitivity analysis and fuzzy simulation to identify the best confidence value in the fuzzy chance constraint. All the discussion will help decision makers to better organize the green multimodal transportation.

scholarly journals The Electric Vehicle Routing Problem with Time Windows under Travel Time Uncertainty

2021 ◽  
Vol 2095 (1) ◽  
pp. 012032
Author(s):  
Dan Wang ◽  
Hong Zhou
Keyword(s):  
Travel Time ◽  
Vehicle Routing ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
Vehicle Routing Problem ◽  
Search Algorithm ◽  
Time Windows ◽  
Routing Problem ◽  
Time Uncertainty ◽  
Travel Time Uncertainty

Abstract Due to environmental friendliness, electric vehicles have become more and more popular nowadays in the transportation system. For many express companies, it is more and more important to meet the predetermined time window of customers. The uncertainty in travel times often causes uncertain energy consumption and uncertain recharging time, thus electric vehicles may miss the time windows of customers. Therefore, this paper addresses the electric vehicle routing problem with time windows under travel time uncertainty, which aims to determine the optimal delivery strategy under travel time uncertainty. To solve this problem, a robust optimization model is built based on the route-dependent uncertainty sets. However, considering the complexity of the problem, the robust model can only solve few instances including the small number of customers. Thus, a hybrid metaheuristic consisting of the adaptive large neighborhood search algorithm and the local search algorithm is proposed. The results show that the algorithm can obtain the optimal solution for the small-sized instances and the large-sized instances.

scholarly journals A model for the time dependent vehicle routing problem with time windows under traffic conditions with intelligent travel times

2021 ◽  
Author(s):  
Saeed Khanchehzarrin ◽  
Maral Shahmizad ◽  
Iraj Mahdavi ◽  
Nezam Mahdavi-Amiri ◽  
Peiman Ghasemi
Keyword(s):  
Travel Time ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Programming Model ◽  
Time Windows ◽  
Time Dependent ◽  
Mixed Integer ◽  
Travel Times ◽  
Routing Problem ◽  
Traffic Conditions

A new mixed-integer nonlinear programming model is presented for the time-dependent vehicle routing problem with time windows and intelligent travel times. The aim is to minimize fixed and variable costs, with the assumption that the travel time between any two nodes depends on traffic conditions and is considered to be a function of vehicle departure time. Depending on working hours, the route between any two nodes has a unique traffic parameter. We consider each working day to be divided into several equal and large intervals, termed as a scenario. Here, allowing for long distances between some of the nodes, travel time may take more than one scenario, resulting in resetting the scenario at the start of each large interval. This repetition of scenarios has been used in modeling and calculating travel time. A tabu search optimization algorithm is devised for solving large problems. Also, after linearization, a number of random instances are generated and solved by the CPLEX solver of GAMS to assess the effectiveness of our proposed algorithm. Results indicate that the initial travel time is estimated appropriately and updated properly in accordance with to the repeating traffic conditions.

scholarly journals Impact of travel time uncertainties on the solution cost of a two-echelon vehicle routing problem with synchronization

2019 ◽  
Vol 32 (4) ◽  
pp. 806-828 ◽  
Author(s):  
Alexandra Anderluh ◽  
Rune Larsen ◽  
Vera C. Hemmelmayr ◽  
Pamela C. Nolz
Keyword(s):  
Travel Time ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Optimization Procedure ◽  
Time Dependent ◽  
Computational Results ◽  
Routing Problem ◽  
Time Uncertainty ◽  
Deterministic Solution ◽  
Major Loss

AbstractTwo-echelon vehicle routing problems which contain synchronization between vehicles can be deeply impacted by time uncertainty, because one vehicle’s delay can propagate to other vehicles. In this paper, we evaluate the deterministic solution of such a problem based on simulated travel time scenarios. The information obtained by simulation is incorporated in the optimization procedure iteratively. Computational results show that the degree of synchronization in an instance is directly correlated with the potential improvements by reoptimization. We present findings on the number of travel time scenarios required to obtain a representative picture of the stochastic solutions. In addition, we demonstrate that time dependent travel times can be aggregated on a city-wide level and linearized as a function of free flow times without major loss of reliability.

scholarly journals A Robust VRPHTW Model with Travel Time Uncertainty

2014 ◽  
Vol 2 (4) ◽  
pp. 289-300 ◽  
Author(s):  
Fengmei Yang ◽  
Yakun Wang ◽  
Wenyan Yuan ◽  
Jian Li
Keyword(s):  
Travel Time ◽  
Time Window ◽  
Optimal Solution ◽  
Ant System ◽  
Routing Problem ◽  
Numerical Example ◽  
Time Uncertainty ◽  
Hard Time ◽  
Robust Model ◽  
Travel Time Uncertainty

AbstractVehicle routing problem with hard time window (VRPHTW) is extremely strict in travel time. However, the travel time is usually uncertain due to some stochastic factors such as weather and other road conditions. It is an important issue to take travel time uncertainty into consideration in VRPHTW. This paper develops a robust VRPHTW model to cope with time uncertainty. We use robustness method of Bertismas to consider the maximum change of uncertain travel time in the degree of robustness set by decision maker. The probability that the optimal solution violates constraints is derived. The violated probability shows that the robustness of VRPHTW model can reach a satisfactory level. Finally, one modified max-min ant system algorithm is proposed to solve this problem and one numerical example is conducted to illustrate the model and the algorithm. Both theory analysis and numerical example show the effectiveness of the proposed robust model.

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