Daniel Pandolfi

STUDS MATING IMMIGRANTS IN EVOLUTIONARY ALGORITHM TO SOLVE THE EARLINESS-TARDINESS SCHEDULING PROBLEM

Current trends in manufacturing are focused on just-in-time production which emphasizes policies discouraging earliness as well as tardiness of job completion. New trends to enhance evolutionary algorithms introduced multiple-crossovers-on-multiple-parents (MCMP) a multirecombinative approach allowing multiple crossovers on the selected pool of (more than two) parents. As a novel variant, MCMP-SRI considers the inclusion of a stud-breeding individual in a pool of random immigrant parents. Members of this mating pool subsequently undergo multiple crossover operations. This paper describes implementation and performance of MCMP-SRI when solving diverse instances of the earliness-tardiness problem in a single machine environment.

Solving dynamic tardiness problems in single machine environments

Dynamic scheduling can be classified as partial or total. In simplest partially dynamic problems the only unknown attribute of a job is its arrival time r/sub j/. In some totally dynamic problems, other job attributes such as processing time p/sub j/, due date d/sub j/, and weights w/sub j/, are also unknown until processing. This paper proposes two approaches to face dynamic tardiness problems in single machine environments. The first approach uses, as a dispatching rule the job order provided by a total schedule S generated by an evolutionary algorithm, or by conventional heuristics for a similar static problem: same job features, processing time, due dates and weights. The second approach uses conventional heuristics and a hybrid evolutionary algorithm to reorder jobs in the waiting queue. Details of implementation of the proposed algorithms and results for a group of selected instances are discussed in this work.

Multirecombined Evolutionary Algorithm Inspired in the Selfish Gene Theory to Face the Weighted Tardiness Scheduling Problem

In a production system it is usual to stress minimum tardiness to achieve higher client satisfaction. According to the client relevance, job processing cost, and many other considerations a weight is assigned to each job. An important and non-trivial objective is to minimize weighted tardiness. Evolutionary Algorithms have been successfully applied to solve scheduling problems. MCMP-SRI (Multiple Crossover Multiple Parents - Stud Random Immigrants) is a MCMP variant which considers the inclusion of a stud- breeding individual in a parents pool of random immigrants. The Selfish Gene Algorithm proposed by Corno et al. is an interpretation of Darwinian theory given by the biologist Richard Dawkins. In this work we are showing a new algorithm that combines the MCMP-SRI and Selfish Gene approaches. This algorithm is used to face the weighted tardiness problem in a single machine environment. The paper summarizes implementation details and discusses its performance for a set of problem instances taken from the OR-Library.

Algoritmo Multirecombinativo para la Planificación Dinámica del Mantenimiento de Locaciones Petroleras

The development of a computer tool named PAE (Scheduling based on an Evolutionary Algorithm) that improves the dynamic schedules of the maintenance of oil fields, is presented. This tool uses a multirecombinative evolutionary algorithm generator of multiple solutions to this problem. The results were compared with information of historical schedules facilitated by the company. The obtained results have been satisfactory concluding that the use of this tool improves the total timing of the original schedule and dynamic re-scheduling is allowed keeping the same quality level in the results. Finally, the benefit of this should be observed from two points of view: the decrease of the maintenance cost and the reduction of the probability of falling or contingencies in the production.

Hybrid estimation of Distribution Algorithms for the Flow Shop Scheduling Problem

Estimation of Distribution Algorithms (EDAs) are stochastic optimization techniques that explore the space of potential solutions by building and sampling explicit probabilistic models of promising candidate solutions. EDAs provide scalable solutions to many problems that are intractable with other techniques, solving enormously complex problems that often need additional efficiency enhancements. In this paper we present different mechanisms of hybridization based on an canonical EDA and applied to the Flow Shop Scheduling Problem (FSSP). We aim to achieve significant numerical improvements in the results compared to those obtained by a canonical EDA. We also analyze the performance of our proposed hybrid versions of EDAs using a set of different instances of the FSSP. The results obtained are quite satisfactory in efficacy and efficiency.

An experimental study of an evolutionary tool for scheduling in oil wells

The exploitation and the transport of oil are very important activities for the economic development of the industrial modern society. However, these activities are generating risks that are translated in accidental contaminations, or chronic contaminations, that directly affect the ecosystem. It is important that oil companies carry out a correct maintenance of their oil fields. In cases of scheduling maintenance of 200 or more oil wells, our so-called PAE, is a tool able to provide a maintenance visit schedule at the right moment. This tool was developed by LabTEm and uses an evolutionary algorithm to produce multiple solutions to this problem. In this work, we compare two approaches (Lamarckian versus Baldwinian) to a constrained scheduling in oil wells. Details of implementation, results, and benefits are presented.

Effect of crossover operators under multirecombination: weighted tardiness, a test case

In evolutionary algorithms based on genetics, the crossover operation creates individuals by exchange of genes. Selection mechanisms propitiate reproduction of better individuals replace worst ones. Consequently, part of the genetic material contained in these worst individuals vanishes forever. This loss of diversity can lead to a premature convergence. To prevent an early convergence to a local optimum under the same selection mechanism then, either a large population size or adequate genetic operators are needed. Multirecombination allows multiple crossover operations on two or more parents each time a new individual is created. In this work, we show the influence on genetic diversity, quality of results and required computational effort, when applying different crossover methods to a set of hard instances, selected as a test case, of the weighted tardiness scheduling problem in single machine environments under multirecombined approaches. A description of the multirecombination variant used, experiments and preliminary results are reported.