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

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 time interval of traffic. Here, allowing for long distances between some of the nodes, travel time may take more than one time interval of traffic, resulting in resetting the time interval of traffic at the start of each large interval. This repetition of time interval of traffics 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.

Automated summary

A new mixed-integer nonlinear programming model is proposed for the time-dependent vehicle routing problem, using a tabu search optimization algorithm to solve large problems and evaluating the effectiveness of the algorithm through modeling and calculations.