Análise do Desempenho de Técnicas de Otimização no Problema de Escalonamento

A optimização nas aplicações modernas assume um carácter fortemente interdisciplinar, relacionando-se com a necessidade de integração de diferentes técnicas e paradigmas na resolução de problemas reais complexos. O problema do escalonamento é recorrente no planeamento da produção. Sempre que uma ordem de fabrico é lançada, é necessário determinar que recursos serão utilizados e em que sequência as atividades serão executadas, para otimizar uma dada medida de desempenho. Embora ainda existam empresas a abordar o problema do escalonamento através de simples heurísticas, a proposta de sistemas de escalonamento tem-se evidenciado na literatura. Pretende-se nesta dissertação, a realização da análise de desempenho de Técnicas de Optimização, nomeadamente as meta-heurísticas, na resolução de problemas de optimização complexos – escalonamento de tarefas, particularmente no problema de minimização dos atrasos ponderados, 1||ΣwjTj. Assim sendo, foi desenvolvido um protótipo que serviu de suporte ao estudo computacional, com vista à avaliação do desempenho do Simulated Annealing (SA) e o Discrete Artificial Bee Colony (DABC). A resolução eficiente de um problema requer, em geral, a aplicação de diferentes métodos, e a afinação dos respetivos parâmetros. A afinação dos parâmetros pode permitir uma maior flexibilidade e robustez mas requer uma inicialização cuidadosa. Os parâmetros podem ter uma grande influência na eficiência e eficácia da pesquisa. A sua definição deve resultar de um cuidadoso esforço experimental no sentido da respectiva especificação. Foi usado, no âmbito deste trabalho de mestrado, para suportar a fase de parametrização das meta-heurísticas em análise, o planeamento de experiências de Taguchi. Da análise dos resultados, foi possível concluir que existem vantagem estatisticamente significativa no desempenho do DABC, mas quando analisada a eficiência é possível concluir que há vantagem do SA, que necessita de menos tempo computacional.

Heurísticas de pesquisa local para problemas de máquina única

O escalonamento é uma das decisões mais importantes no funcionamento de uma linha de produção. No âmbito desta dissertação foi realizada uma descrição do problema do escalonamento, identificando alguns métodos para a optimização dos problemas de escalonamento. Foi realizado um estudo ao caso do problema de máquina única através do teste de várias instâncias com o objectivo de minimizar o atraso pesado, aplicando uma Meta-Heurística baseada na Pesquisa Local e dois algoritmos baseados no SB. Os resultados obtidos reflectem que os algoritmos baseados no SB apresentaram resultados mais próximos do óptimo, em relação ao algoritmo baseado na PL. Os resultados obtidos permitem sustentar a hipótese de não existirem algoritmos específicos para os problemas de escalonamento. A melhor forma de encontrar uma solução de boa qualidade em tempo útil é experimentar diferentes algoritmos e comparar o desempenho das soluções obtidas.

Concurrency detection on finish-to-start activity precedence networks

We explore the finish-to-start precedence relations of project activities used in scheduling problems. From these relations, we devise a method to identify groups of activities that could execute concurrently, i.e. activities in the same group can all execute in parallel. The method derives a new set of relations to describe the concurrency. Then, it is represented by an undirected graph and the maximal cliques problem identifies the groups. We provide a running example with a project from our previous studies in resource constrained project cost minimization together with an example application on the concurrency detection method: the evaluation of the resource stress.

Automatização e otimização do escalonamento de pessoal hospitalar

Dissertação de mestrado em Engenharia de Sistemas

Infeasibility handling in genetic algorithm using nested domains for production planning

In this paper we present a genetic algorithm with new components to tackle capacitated lot sizing and scheduling problems with sequence dependent setups that appear in a wide range of industries, from soft drink bottling to food manufacturing. Finding a feasible solution to highly constrained problems is often a very difficult task. Various strategies have been applied to deal with infeasible solutions throughout the search. We propose a new scheme of classifying individuals based on nested domains to determine the solutions according to the level of infeasibility, which in our case represents bands of additional production hours (overtime). Within each band, individuals are just differentiated by their fitness function. As iterations are conducted, the widths of the bands are dynamically adjusted to improve the convergence of the individuals into the feasible domain. The numerical experiments on highly capacitated instances show the effectiveness of this computational tractable approach to guide the search toward the feasible domain. Our approach outperforms other state-of-the-art approaches and commercial solvers. (C) 2009 Elsevier Ltd. All rights reserved.

A coupling cutting stock-lot sizing problem in the paper industry

An important production programming problem arises in paper industries coupling multiple machine scheduling with cutting stocks. Concerning machine scheduling: how can the production of the quantity of large rolls of paper of different types be determined. These rolls are cut to meet demand of items. Scheduling that minimizes setups and production costs may produce rolls which may increase waste in the cutting process. On the other hand, the best number of rolls in the point of view of minimizing waste may lead to high setup costs. In this paper, coupled modeling and heuristic methods are proposed. Computational experiments are presented.

Formulações fortes para o problema integrado de dimensionamento e sequenciamento da produção

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Método heurístico para criação de linhas de trabalho em problemas de escalonamento de pessoal

Pós-graduação em Engenharia Mecânica - FEG

Combinatorial and Robust Optimisation Models and Algorithms for Railway Applications

This thesis deals with an investigation of combinatorial and robust optimisation models to solve railway problems. Railway applications represent a challenging area for operations research. In fact, most problems in this context can be modelled as combinatorial optimisation problems, in which the number of feasible solutions is finite. Yet, despite the astonishing success in the field of combinatorial optimisation, the current state of algorithmic research faces severe difficulties with highly-complex and data-intensive applications such as those dealing with optimisation issues in large-scale transportation networks. One of the main issues concerns imperfect information. The idea of Robust Optimisation, as a way to represent and handle mathematically systems with not precisely known data, dates back to 1970s. Unfortunately, none of those techniques proved to be successfully applicable in one of the most complex and largest in scale (transportation) settings: that of railway systems. Railway optimisation deals with planning and scheduling problems over several time horizons. Disturbances are inevitable and severely affect the planning process. Here we focus on two compelling aspects of planning: robust planning and online (real-time) planning.

Resource allocation with project management software

We present results of a benchmark test evaluating the resource allocation capabilities of the project management software packages Acos Plus.1 8.2, CA SuperProject 5.0a, CS Project Professional 3.0, MS Project 2000, and Scitor Project Scheduler 8.0.1. The tests are based on 1560 instances of precedence– and resource–constrained project scheduling problems. For different complexity scenarios, we analyze the deviation of the makespan obtained by the software packages from the best feasible makespan known. Among the tested software packages, Acos Plus.1 and Project Scheduler show the best resource allocation performance. Moreover, our numerical analysis reveals a considerable performance gap between the implemented methods and state–of–the–art project scheduling algorithms, especially for large–sized problems. Thus, there is still a significant potential for improving solutions to resource allocation problems in practice.

Optimization methods for the operating room management under uncertainty: stochastic programming vs. decomposition approach

The operating theatres are the engine of the hospitals; proper management of the operating rooms and its staff represents a great challenge for managers and its results impact directly in the budget of the hospital. This work presents a MILP model for the efficient schedule of multiple surgeries in Operating Rooms (ORs) during a working day. This model considers multiple surgeons and ORs and different types of surgeries. Stochastic strategies are also implemented for taking into account the uncertain in surgery durations (pre-incision, incision, post-incision times). In addition, a heuristic-based methods and a MILP decomposition approach is proposed for solving large-scale ORs scheduling problems in computational efficient way. All these computer-aided strategies has been implemented in AIMMS, as an advanced modeling and optimization software, developing a user friendly solution tool for the operating room management under uncertainty.

Realization of an Optimal Schedule for the Two-machine Flow-Shop with Interval Job Processing Times

Non-preemptive two-machine flow-shop scheduling problem with uncertain processing times of n jobs is studied. In an uncertain version of a scheduling problem, there may not exist a unique schedule that remains optimal for all possible realizations of the job processing times. We find necessary and sufficient conditions (Theorem 1) when there exists a dominant permutation that is optimal for all possible realizations of the job processing times. Our computational studies show the percentage of the problems solvable under these conditions for the cases of randomly generated instances with n ≤ 100 . We also show how to use additional information about the processing times of the completed jobs during optimal realization of a schedule (Theorems 2 – 4). Computational studies for randomly generated instances with n ≤ 50 show the percentage of the two- machine flow-shop scheduling problems solvable under the sufficient conditions given in Theorems 2 – 4.

Sequencing Jobs with Uncertain Processing Times and Minimizing the Weighted Total Flow Time

We consider an uncertain version of the scheduling problem to sequence set of jobs J on a single machine with minimizing the weighted total flow time, provided that processing time of a job can take on any real value from the given closed interval. It is assumed that job processing time is unknown random variable before the actual occurrence of this time, where probability distribution of such a variable between the given lower and upper bounds is unknown before scheduling. We develop the dominance relations on a set of jobs J. The necessary and sufficient conditions for a job domination may be tested in polynomial time of the number n = |J| of jobs. If there is no a domination within some subset of set J, heuristic procedure to minimize the weighted total flow time is used for sequencing the jobs from such a subset. The computational experiments for randomly generated single-machine scheduling problems with n ≤ 700 show that the developed dominance relations are quite helpful in minimizing the weighted total flow time of n jobs with uncertain processing times.

Decision support system for production control: Multiple criteria decision making in practice

This paper presents a development of decision support systems for solving scheduling problems. It consists of two parts — the first describing the production processes which can be handled by the system and the second describing how the system works.

An Estimation of Distribution Algorithm with Intelligent Local Search for Rule-based Nurse Rostering

This paper proposes a new memetic evolutionary algorithm to achieve explicit learning in rule-based nurse rostering, which involves applying a set of heuristic rules for each nurse's assignment. The main framework of the algorithm is an estimation of distribution algorithm, in which an ant-miner methodology improves the individual solutions produced in each generation. Unlike our previous work (where learning is implicit), the learning in the memetic estimation of distribution algorithm is explicit, i.e. we are able to identify building blocks directly. The overall approach learns by building a probabilistic model, i.e. an estimation of the probability distribution of individual nurse-rule pairs that are used to construct schedules. The local search processor (i.e. the ant-miner) reinforces nurse-rule pairs that receive higher rewards. A challenging real world nurse rostering problem is used as the test problem. Computational results show that the proposed approach outperforms most existing approaches. It is suggested that the learning methodologies suggested in this paper may be applied to other scheduling problems where schedules are built systematically according to specific rules.