Vehicle Routing Problem with Time Windows: A Deterministic Annealing approach

The Vehicle Routing Problem with Time-Windows (VRPTW) is an important problem in allocating resources on networks in time and space. We present in this paper a Deterministic Annealing (DA)-based approach to solving the VRPTW with its aspects of routing and scheduling, as well as to model additional constraints of heterogeneous vehicles and shipments. This is the first time, to our knowledge, that a DA approach has been used for problems in the class of the VRPTW. We describe how the DA approach can be adapted to generate an effective heuristic approach to the VRPTW. Our DA approach is also designed to not get trapped in local minima, and demonstrates less sensitivity to initial solutions. The algorithm trades off routing and scheduling in an n-dimensional space using a tunable parameter that allows us to generate qualitatively good solutions. These solutions differ in the degree of intersection of the routes, making the case for transfer points where shipments can be exchanged. Simulation results on randomly generated instances show that the constraints are respected and demonstrate near optimal results (when verifiable) in terms of schedules and tour length of individual tours in each solution.

[1]  Michel Gendreau,et al.  An Effective Multirestart Deterministic Annealing Metaheuristic for the Fleet Size and Mix Vehicle-Routing Problem with Time Windows , 2008, Transp. Sci..

[2]  S. P. Lloyd,et al.  Least squares quantization in PCM , 1982, IEEE Trans. Inf. Theory.

[3]  Bruce L. Golden,et al.  The vehicle routing problem : latest advances and new challenges , 2008 .

[4]  Srinivasa M. Salapaka,et al.  Maximum Entropy Principle-Based Algorithm for Simultaneous Resource Location and Multihop Routing in Multiagent Networks , 2012, IEEE Transactions on Mobile Computing.

[5]  Jean-Yves Potvin,et al.  Vehicle Routing , 2009, Encyclopedia of Optimization.

[6]  P. Sharma,et al.  A scalable deterministic annealing algorithm for resource allocation problems , 2006, 2006 American Control Conference.

[7]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[8]  K. Rose Deterministic annealing for clustering, compression, classification, regression, and related optimization problems , 1998, Proc. IEEE.

[9]  Yunwen Xu,et al.  Clustering and Coverage Control for Systems With Acceleration-Driven Dynamics , 2014, IEEE Transactions on Automatic Control.

[10]  Gerrit K. Janssens,et al.  A Deterministic Annealing Algorithm for a Bi-Objective Full Truckload Vehicle Routing Problem in Drayage Operations , 2011 .

[11]  Richard Szeliski,et al.  An Analysis of the Elastic Net Approach to the Traveling Salesman Problem , 1989, Neural Computation.

[12]  Brian Roehl,et al.  Maximum-entropy principle approach to the multiple travelling salesman problem and related problems , 2012 .

[13]  Robert M. Gray,et al.  Multiple local optima in vector quantizers , 1982, IEEE Trans. Inf. Theory.

[14]  Billy E. Gillett,et al.  A Heuristic Algorithm for the Vehicle-Dispatch Problem , 1974, Oper. Res..

[15]  Yunwen Xu,et al.  Aggregation of Graph Models and Markov Chains by Deterministic Annealing , 2014, IEEE Transactions on Automatic Control.

[16]  G. Laporte,et al.  Transportation Demand , 2019, Energy: Supply and Demand.

[17]  Dimitrios Katselis,et al.  Deterministic annealing for clustering: Tutorial and computational aspects , 2015, 2015 American Control Conference (ACC).

[18]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[19]  M. A. Dahleh,et al.  Constraints on locational optimization problems , 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).

[20]  Kenneth Rose Deterministic annealing, clustering, and optimization , 1991 .