Una comparación de algoritmos basados en trayectoria granular para el problema de localización y ruteo con flota heterogénea (LRPH)

Indexación: Scopus.We consider the Location-Routing Problem with Heterogeneous Fleet (LRPH) in which the goal is to determine the depots to be opened, the customers to be assigned to each open depot, and the corresponding routes fulfilling the demand of the customers and by considering a heterogeneous fleet. We propose a comparison of granular approaches of Simulated Annealing (GSA), of Variable Neighborhood Search (GVNS) and of a probabilistic Tabu Search (pGTS) for the LRPH. Thus, the proposed approaches consider a subset of the search space in which non-favorable movements are discarded regarding a granularity factor. The proposed algorithms are experimentally compared for the solution of the LRPH, by taking into account the CPU time and the quality of the solutions obtained on the instances adapted from the literature. The computational results show that algorithm GSA is able to obtain high quality solutions within short CPU times, improving the results obtained by the other proposed approaches.https://revistas.unal.edu.co/index.php/dyna/article/view/55533/5896

A stochastic hybrid algorithm for multi-depot and multi-product routing problem with heterogeneous vehicles

Abstract. A mathematical model and heuristic method for solving multi-depot and multi-product vehicle routing problem (MD-MPVRP) with heterogeneous vehicles have been proposed in this article. Customers can order eclectic products and depots are supposed to deliver customers' orders before the lead time, using vehicles with diverse capacities, costs and velocities. Hence, mathematical model of multi-depot vehicle routing problem has been developed to mirror these conditions. This model is aimed at minimizing the serving distances which culminates in a reduction in prices and also serving time. As the problem is so complex and also solving would be too time-taking, a heuristic method has been offered. The heuristic method, at first, generates an initial solution through a three-step procedure which encompasses grouping, routing and vehicle selection, scheduling and packaging. Then it improves the solution by means of simulated annealing. We have considered the efficiency of offered algorithm by comparing its solutions with the optimum solutions and also during a case study. [V. Mahdavi Asl, S.A. Sadeghi, MR. Ostadali Makhmalbaf. A stochastic hybrid algorithm for multi-depot and multi-product routing problem with heterogeneous vehicles

An improved Ant Colony System for the Sequential Ordering Problem

It is not rare that the performance of one metaheuristic algorithm can be improved by incorporating ideas taken from another. In this article we present how Simulated Annealing (SA) can be used to improve the efficiency of the Ant Colony System (ACS) and Enhanced ACS when solving the Sequential Ordering Problem (SOP). Moreover, we show how the very same ideas can be applied to improve the convergence of a dedicated local search, i.e. the SOP-3-exchange algorithm. A statistical analysis of the proposed algorithms both in terms of finding suitable parameter values and the quality of the generated solutions is presented based on a series of computational experiments conducted on SOP instances from the well-known TSPLIB and SOPLIB2006 repositories. The proposed ACS-SA and EACS-SA algorithms often generate solutions of better quality than the ACS and EACS, respectively. Moreover, the EACS-SA algorithm combined with the proposed SOP-3-exchange-SA local search was able to find 10 new best solutions for the SOP instances from the SOPLIB2006 repository, thus improving the state-of-the-art results as known from the literature. Overall, the best known or improved solutions were found in 41 out of 48 cases.Comment: 30 pages, 8 tables, 11 figure

Charging Constrained Electric Vehicle Routing Problem with Prioritized Customers

This thesis proposes a unique Vehicle Routing Problem (VRP) focusing on charging management optimisations for electric vehicles with prioritised customers. This problem is modelled as a hybrid of the Travelling Repairman Problem (TRP) and the Electric Vehicle Routing Problem (EVRP). After a brief literature review around the scope of the problem, a base mathematical model is formulated to explain the constraints and objective function of the problem. The problem is solved using a Nearest Neighbour Based Heuristic (NNBH) and Simulated Annealing with Variable Neighbourhood Search. The Nearest Neighbour Based Heuristic (NNBH) generates an initial solution. The initial solution is used by the metaheuristic for achieving a better final solution. The base mathematical model is used to benchmark the performance of the solution approach. The algorithmic framework developed is run for smaller and larger instances to demonstrate the accuracy and scalability of the model produced, respectively. The computational results of both instances show the success of the proposed model

An efficient heuristic for the multi-vehicle one-to-one pickup and delivery problem with split loads

In this study, we consider the Multi-vehicle One-to-one Pickup and Delivery Problem with Split Loads (MPDPSL). This problem is a generalization of the one-to-one Pickup and Delivery Problem (PDP) where each load can be served by multiple vehicles as well as multiple stops by the same vehicle. In practice, split deliveries is a viable option in many settings where the load can be physically split, such as courier services of third party logistics operators. We propose an efficient heuristic that combines the strengths of Tabu Search and Simulated Annealing for the solution of MPDPSL. Results from experiments on two problems sets in the literature indicate that the heuristic is capable of producing good quality solutions in reasonable time. The experiments also demonstrate that up to 33\% savings can be obtained by allowing split loads; however, the magnitude of savings is dependent largely on the spatial distribution of the pickup and delivery points

A review of the Tabu Search Literature on Traveling Salesman Problems

The Traveling Salesman Problem (TSP) is one of the most widely studied problems inrncombinatorial optimization. It has long been known to be NP-hard and hence research onrndeveloping algorithms for the TSP has focused on approximate methods in addition to exactrnmethods. Tabu search is one of the most widely applied metaheuristic for solving the TSP. Inrnthis paper, we review the tabu search literature on the TSP, point out trends in it, and bringrnout some interesting research gaps in this literature.