Integrating a SMT Solver based Local Search in Ant Colony Optimization for Solving RCMPSP

Sánchez, Mariam Gómez; Gil, Alejandro Fernández; Castro, Carlos

doi:10.1109/la-cci47412.2019.9036765

Cited by 2 publications

(1 citation statement)

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“…The used Hill-Climbing algorithm works with the best feasible solutions constructed by the Algorithm 2. The defined movement is inspired by [29] where two subtours are randomly selected (lines 3-4). After that, two customers are selected so that the distance between them is minimal (line 5) and a swap is performed (lines 6-7).…”

Section: Algorithm 1 Mathgrasp Pseudocodementioning

confidence: 99%

Cumulative VRP with Time Windows: A Trade-Off Analysis

Gil

Sánchez

Lalla-Ruiz

et al. 2020

Lecture Notes in Computer Science

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In this work, the Cumulative Vehicle Routing Problem (CumVRP) is studied. It is a routing optimization problem, in which the objective is to construct a set of vehicle routes with the minimum cumulative cost in terms of distance and weight over a traveled arc. The CumVRP can be defined with hard and soft time windows constraints for incorporating customer service. To tackle this problem, a matheuristic approach based on combining mathematical programming and an iterative metaheuristic algorithm Greedy Randomized Adaptive Search Procedure (GRASP) is proposed. In each step of our approach, a feasible solution (set of routes) is built using GRASP, and, afterward, the solution is optimized using a MILP optimizer. The main objective of this research is to analyze the trade-off between the environmental cost produced by the delivery of goods complying with the limits of time windows and the customer's dissatisfaction when these limits are violated at a certain time limit previously defined. The results show that the environmental cost is reduced if the violation of the upper limits of the customers' time windows is allowed. These violations generate a cost associated with penalties that are well balanced with respect to the reduction of emissions.

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Section: Algorithm 1 Mathgrasp Pseudocodementioning

confidence: 99%