Abordagens de otimização para o planeamento e escalonamento integrado de operações

Dissertação de mestrado em Engenharia de SistemasNeste projeto consideramos o problema integrado de planeamento e escalonamento de operações em máquinas paralelas e idênticas, descrito em Kis e Kovács (2012). O problema é composto por duas partes que são resolvidas, simultaneamente, de uma forma integrada. A primeira parte consiste em determinar as tarefas que serão processadas em cada período de tempo. Esta é a parte de planeamento do problema que tem de ser resolvido para o horizonte de tempo determinado. A segunda parte consiste em atribuir as tarefas às máquinas disponíveis em cada período de tempo, de acordo com as suas datas de lançamento correspondentes e de modo a que todas as tarefas sejam processadas até ao final do horizonte de planeamento. Sempre que uma tarefa é realizada antes ou após a sua data esperada, incorre numa penalização. O objetivo global do problema consiste em determinar o planeamento e o escalonamento associados que minimizem os custos totais dessas penalizações. Tendo em conta as caraterísticas particulares do problema em estudo, foi feita uma breve análise a problemas de escalonamento de operações existentes na literatura e onde se percebe a forte ligação entre os problemas de máquinas paralelas idênticas e os problemas de corte e empacotamento de 1-dimensão, mais especificamente, de bin-packing. Pelo que foram seguidas técnicas associadas a este tipo de problemas. Esta dissertação apresenta novas abordagens de otimização com base em métodos heurísticos de pesquisa local e meta-heurísticos, baseados na pesquisa de vizinhança variável (VNS – Variable Neighborhood Search) usando duas estruturas de vizinhança. Foram implementados dois algoritmos diferentes na construção das soluções inicias conjugados com quinze variantes da sequência inicial das tarefas, alcançando, naturalmente, resultados distintos. Para a obtenção de resultados e de modo a avaliar o desempenho dos mesmos, foram realizadas experiências computacionais com instâncias de referência descritas na literatura. Assim, para além da comparação entre os diferentes resultados obtidos neste projeto foi também possível comparar os resultados conseguidos com outros já existentes para as mesmas instâncias.In this project, we consider the integrated planning and scheduling problem on parallel identical machines as in Kis and Kovács (2012). The problem is composed by two parts that are solved simultaneous in an integrated form. The first part consists in assessing which should be processed in each period of time. This is the part of the problem planning that has to be solved in a certain planning horizon. The second part consists in allocating jobs to the available machines in each time period according to their corresponding release date in a way that every job is processed until the end of the time space. Every time a job is allocated before or after its due date it incurs in a penalty. The overall objective of the problem consists in establishing the integrated planning and scheduling that minimizes the total costs of these penalties. Taking into consideration the specifics of the problem under study a brief analysis to the scheduling operation problems in literature was made in which a strong connection can be correlated between the problems of identical parallel machine and the problems of cutting and packing 1-dimension, more specifically bin-packing. Therefore, techniques associated with this type of problem were followed. This dissertation introduces new approaches of optimization based in heuristics methods of local search and metaheuristics based on Variable Neighborhood Search (VNS) using two neighborhood structures. Two different algorithms were implemented in the construction of initial solutions combining with fifteen different initial sequence of jobs, reaching distinct results. To achieve results and in order to evaluate their performance, computational experiences were performed with reference instances described in the literature. Thus, in addition to the comparison between different obtained results in this project was also possible to compare with the results achieved in other already existing projects for the same instances

Fast heuristics for integrated planning and scheduling

In this paper, we address the integrated planning and scheduling problem on parallel machines in which a set of jobs with release and due-dates have to be assigned first to consecutive time periods within the planning horizon, and then scheduled on the available machines. We explore in particular different alternative low complexity heuristics. The importance of job sequencing in the performance of these heuristics is analyzed, and a new property characterizing the optimal solutions of the problem is described. We also present a heuristic that yields optimal solutions for specific instances of the problem, and local exchange procedures that proved to be effective. To the best of our knowledge, these are the first contributions concerning the heuristic solution of this integrated planning and scheduling problem through low complexity procedures. To evaluate performance of these heuristics, we report on extensive computational experiments on benchmark instances of the literature.This work was supported by FEDER funding through the Programa Operacional Factores de Competitividade - COMPETE and by national funding through the Portuguese Science and Technology Foundation (FCT) in the scope of the project PTDC/EGE-GES/116676/2010 (FCOMP-01-0124-FEDER-020430), and by FCT within the project scope: UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio