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 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 the resolution of the simple assembly line balancing problem type E

The simple assembly line balancing problem type E (abbreviated as SALBP-E) occurs when the number of workstations and the cycle time are variables and the objective is to maximise the line efficiency. In contrast with other types of SALBPs, SALBP-E has received little attention in the literature. In order to solve optimally SALBP-E, we propose a mixed integer liner programming model and an iterative procedure. Since SALBP-E is NP-hard, we also propose heuristics derived from the aforementioned procedures for solving larger instances. An extensive experimentation is carried out and its results show the improvement of the SALBP-E resolution

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 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

Technical note: relating to the parameter values given by Nelder and Mead in their algorithm

The popular Nelder and Mead (NM) algorithm has four parameters associated to the operations known as reflection, expansion, contraction and shrinkage. The authors set their values to 1, 2, 0.5 and 0.5, respectively, which have been universally used. Here, we propose to use NM to calibrate itself. A computational experiment is carried out and results show that the parameter values originally proposed by NM are better than those obtained with more sophisticated ways.Postprint (published version