Computation of Lower Bounds for a Multiple Depot, Multiple Vehicle Routing Problem With Motion Constraints

Author(s):  
Satyanarayana G. Manyam ◽  
Sivakumar Rathinam ◽  
Swaroop Darbha

This paper considers the problem of planning paths for a collection of identical vehicles visiting a given set of targets, such that the total lengths of their paths are minimum. Each vehicle starts at a specified location (called a depot) and it is required that each target to be on the path of at least one vehicle. The path of every vehicle must satisfy the motion constraints of every vehicle. In this paper, we develop a method to compute lower bound to the minimum total path lengths by relaxing some of the constraints and posing it as a standard multiple traveling salesmen problem (MTSP). A lower bound is often important to ascertain suboptimality bounds for heuristics and for developing stopping criterion for algorithms computing an optimal solution. Simulation results are presented to show that the proposed method can be used to improve the lower bounds of instances with four vehicles and 40 targets by approximately 39%.

Author(s):  
Satyanarayana G. Manyam ◽  
Sivakumar Rathinam ◽  
Swaroop Darbha ◽  
Karl J. Obermeyer

2010 ◽  
Vol 36 ◽  
pp. 1001-1008 ◽  
Author(s):  
Luis Gouveia ◽  
Juan-José Salazar-González

2017 ◽  
Vol 21 ◽  
pp. 255-262 ◽  
Author(s):  
Mazin Abed Mohammed ◽  
Mohd Khanapi Abd Ghani ◽  
Raed Ibraheem Hamed ◽  
Salama A. Mostafa ◽  
Mohd Sharifuddin Ahmad ◽  
...  

Author(s):  
S. P. Anbuudayasankar ◽  
K. Ganesh ◽  
Tzong-Ru Lee

This chapter presents the development of simulated annealing (SA) for a health care application which is modeled as Single Depot Vehicle routing problem called Mixed Vehicle Routing Problem with Backhauls (MVRPB), an extension of Vehicle Routing Problem with Backhauls (VRPB). This variant involves both delivery and pick-up customers and sequence of visiting the customers is mixed. The entire pick-up load should be taken back to depot. The latest rapid advancement of meta-heuristics has shown that it can be applied in practice if they are personified in packaged information technology (IT) solutions along with the combination of a Supply Chain Management (SCM) application integrated with an enterprise resource planning (ERP) resulted to this decision support tool. This chapter provides empirical proof in sustain of the hypothesis, that a population extension of SA with supportive transitions leads to a major increase of efficiency and solution quality for MVRPB if and only if the globally optimal solution is located close to the center of all local optimal solutions.


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