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 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

Solving the Response Time Variability Problem by means of a psychoclonal approach

The Response Time Variability Problem (RTVP) is a combinatorial scheduling problem which has recently appeared in the literature. This problem has a wide range of reallife applications in, for example, manufacturing, hard real-time systems, operating systems and network environment. Originally, the RTVP occurs whenever products, clients or jobs need to be sequenced in such a way that the variability in the time between the instants at which they receive the necessary resources is minimized. Since RTVP is hard to solve, heuristic techniques are needed for solving it. In a previous study, three metaheuristic algorithms (a multi-start, a GRASP and a PSO algorithm) were proposed to solve the RTVP. These three metaheuristic algorithms have been the most efficient to date in solving non-small instances of the RTVP. We propose solving the RTVP by means of a psychoclonal algorithm based approach. The psychoclonal algorithm inherits its attributes from the need hierarchy theory proposed by Maslow and the artificial immune system (AIS) approach, specifically the clonal selection principle. In this paper we compare the proposed psychoclonal algorithm with the other three metaheuristic algorithms previously mentioned and show that, on average, the psychoclonal algorithm strongly improves the obtained results

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 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 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