A robust enhancement to the Clarke-Wright savings algorithm

We address the Clarke and Wright (CW) savings algorithm proposed for the Capacitated Vehicle Routing Problem (CVRP). We first consider a recent enhancement which uses the put first larger items idea originally proposed for the bin packing problem and show that the conflicting idea of putting smaller items first has a comparable performance. Next, we propose a robust enhancement to the CW savings formulation. The proposed formulation is normalized to efficiently solve different problems, independent from the measurement units and parameter intervals. To test the performance of the proposed savings function, we conduct an extensive computational study on a large set of well-known instances from the literature. Our results show that the proposed savings function provides shorter distances in the majority of the instances and the average performance is significantly better than previously presented enhancements

Comparison of Randomized Solutions for Constrained Vehicle Routing Problem

In this short paper, we study the capacity-constrained vehicle routing problem (CVRP) and its solution by randomized Monte Carlo methods. For solving CVRP we use some pseudorandom number generators commonly used in practice. We use linear, multiple-recursive, inversive, and explicit inversive congruential generators and obtain random numbers from each to provide a route for CVRP. Then we compare the performance of pseudorandom number generators with respect to the total time the random route takes. We also constructed an open-source library github.com/iedmrc/binary-cws-mcs on solving CVRP by Monte-Carlo based heuristic methods.Comment: 6 pages, 2nd International Conference on Electrical, Communication and Computer Engineering (ICECCE), 12-13 June 2020, Istanbul, Turke

Improving parametric Clarke and Wright algorithms by means of iterative empirically adjusted greedy heuristics

Since Clarke and Wright proposed their well-known savings algorithm for solving the Capacitated Vehicle Routing Problem, several enhancements to the original savings formula have been recently proposed, in the form of parameterisations. In this paper we first propose to use Empirically Adjusted Greedy Heuristics to run these parameterized heuristics and we also consider the addition of new parameters. This approach is shown to improve the savings algorithms proposed in the literature. Moreover, we propose a new procedure which leads to even better solutions, based on what we call Iterative Empirically Adjusted Greedy HeuristicsPeer Reviewe

Improving parametric Clarke and Wright algorithms by means of iterative empirically adjusted greedy heuristics

Since Clarke and Wright proposed their well-known savings algorithm for solving the Capacitated Vehicle Routing Problem, several enhancements to the original savings formula have been recently proposed, in the form of parameterisations. In this paper we first propose to use Empirically Adjusted Greedy Heuristics to run these parameterized heuristics and we also consider the addition of new parameters. This approach is shown to improve the savings algorithms proposed in the literature. Moreover, we propose a new procedure which leads to even better solutions, based on what we call Iterative Empirically Adjusted Greedy HeuristicsPeer Reviewe

Improving parametric Clarke and Wright algorithms by means of iterative empirically adjusted greedy heuristics

Since Clarke and Wright proposed their well-known savings algorithm for solving the Capacitated Vehicle Routing Problem, several enhancements to the original savings formula have been recently proposed, in the form of parameterisations. In this paper we first propose to use Empirically Adjusted Greedy Heuristics to run these parameterized heuristics and we also consider the addition of new parameters. This approach is shown to improve the savings algorithms proposed in the literature. Moreover, we propose a new procedure which leads to even better solutions, based on what we call Iterative Empirically Adjusted Greedy HeuristicsPeer Reviewe

Improving parametric Clarke and Wright algorithms by means of iterative empirically adjusted greedy heuristics

Since Clarke and Wright proposed their well-known savings algorithm for solving the Capacitated Vehicle Routing Problem, several enhancements to the original savings formula have been recently proposed, in the form of parameterisations. In this paper we first propose to use Empirically Adjusted Greedy Heuristics to run these parameterized heuristics and we also consider the addition of new parameters. This approach is shown to improve the savings algorithms proposed in the literature. Moreover, we propose a new procedure which leads to even better solutions, based on what we call Iterative Empirically Adjusted Greedy Heuristic

Improving parametric Clarke and Wright algorithms by means of iterative empirically adjusted greedy heuristics

Since Clarke and Wright proposed their well-known savings algorithm for solving the Capacitated Vehicle Routing Problem, several enhancements to the original savings formula have been recently proposed, in the form of parameterisations. In this paper we first propose to use Empirically Adjusted Greedy Heuristics to run these parameterized heuristics and we also consider the addition of new parameters. This approach is shown to improve the savings algorithms proposed in the literature. Moreover, we propose a new procedure which leads to even better solutions, based on what we call Iterative Empirically Adjusted Greedy Heuristics

Improving parametric Clarke and Wright algorithms by means of EAGH-1

Since Clarke and Wright proposed their well-known savings a lgorithm for solving the Capacitated Vehicle Routing Problem, several enhancements to the original savings formula have been recently proposed, in the form of parameterisations. In this paper we first propose to use Empirically Adjusted Greedy Heuristics to run these parameterized heuristics and we also consider the addition of new parameters. This approach is shown to improve the savings algorithms proposed in the literature. Moreover, we propose a new procedure which leads to even better solutions, based on what we call Iterative Empirically Adjusted Greedy HeuristicsPeer ReviewedPostprint (published version

Improving parametric Clarke and Wright algorithms by means of iterative empirically adjusted greedy heuristics

Since Clarke and Wright proposed their well-known savings algorithm for solving the Capacitated Vehicle Routing Problem, several enhancements to the original savings formula have been recently proposed, in the form of parameterisations. In this paper we first propose to use Empirically Adjusted Greedy Heuristics to run these parameterized heuristics and we also consider the addition of new parameters. This approach is shown to improve the savings algorithms proposed in the literature. Moreover, we propose a new procedure which leads to even better solutions, based on what we call Iterative Empirically Adjusted Greedy HeuristicsPeer Reviewe

THE VEHICLE ROUTING PROBLEM WITH STOCHASTIC DEMANDS IN AN URBAN AREA – A CASE STUDY

The vehicle routing problem with stochastic demands (VRPSD) is a combinatorial optimization problem. The VRPSD looks for vehicle routes to connect all customers with a depot, so that the total distance is minimized, each customer visited once by one vehicle, every route starts and ends at a depot, and the travelled distance and capacity of each vehicle are less than or equal to the given maximum value. Contrary to the classical VRP, in the VRPSD the demand in a node is known only after a vehicle arrives at the very node. This means that the vehicle routes are designed in uncertain conditions. This paper presents a heuristic and meta-heuristic approach for solving the VRPSD and discusses the real problem of municipal waste collection in the City of Niš