In recent times, the areas of VRP categorization has been further discussed, the common constraints have been summarized and model algorithms have been developed.
Earman and solomon problem with frequency interpretation windows#
Capacitated Vehicle Routing Problem (CVRP), Vehicle Routing Problem with Time Windows (VRPTW), Vehicle Routing Problem with Multi-Depot (MDVRP) and other variants are integral components of VRP. Both in operational research and computer science, VRP is a combinatorial optimization issue researched at length.
The ultimate objective of this paper is to initiate a thought provoking discussion on Vehicle Routing Problems (VRP) along with its modifications or changes which incorporates recent model developments and improvements. In introducing and designing innovative solutions to the problems related to transportation and distribution systems is a contemporary area in logistics. Experimental results and comparisons indicate the effectiveness and efficiency of our proposed intelligent routing approach. The performance of this proposed MODLEM for the VRPTW is assessed against the state-of-the-art approaches in terms of both the quality of solutions and the computational time.
Our proposed MODLEM is tested on the problem instances of Solomon’s VRPTW benchmark. Moreover, a new heuristic operator is employed in the instantiating process to confront incomplete chromosome formation. To improve the quality and the diversity of the initial population, we propose a novel heuristic manner which leads to a good approximation of the Pareto fronts within a reasonable computational time. A new priority-based encoding scheme for chromosome representation in the LEM as well as corresponding routing scheme is introduced. We incorporate a robust strength Pareto evolutionary algorithm in the LEM presented here to govern the multi-objective property of this approach.
Learnable evolution model (LEM) includes a machine learning algorithm, like the decision trees, that can discover the correct directions of the evolution leading to significant improvements in the fitness of the individuals. This paper presents a new multi-objective discreet learnable evolution model (MODLEM) to address the vehicle routing problem with time windows (VRPTW).
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