A hybrid genetic approach to solve real make-to-order job shop scheduling problems

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro TecnologicoProcedimentos de busca local (ex. busca tabu) e algoritmos genéticos têm apresentado excelentes resultados em problemas clássicos de programação da produção em ambientes job shop. No entanto, estas abordagens apresentam pobres habilidades de modelamento e poucas aplicações com restrições de ambientes reais de produção têm sido publicadas. Além disto, os espaços de busca considerados nestas aplicações são nomlalmente incompletos e as restrições reais são poucas e dependentes do problema em questão. Este trabalho apresenta uma abordagem genética híbrida para resolver problemas de programação em ambientes job shop com grande número de restrições reais, tais como produtos com vários níveis de submontagem, planos de processamento altemativos para componentes e recursos alternativos para operações, exigência de vários recursos para executar uma operação (ex., máquina, ferramentas, operadores), calendários para todos os recursos, sobreposição de operações, restrições de disponibilidade de matéria-prima e componentes comprados de terceiros, e tempo de setup dependente da sequência de operações. A abordagem também considera funções de avaliação multiobjetivas. O sistema usa algoritmos modificados de geração de programação, que incorporam várias heurísticas de apoio à decisão, para obter um conjunto de soluções iniciais. Cada solução inicial é melhorada por um algoritmo de subida de encosta. Então, um algoritmo genético híbrido com procedimentos de busca local é aplicado ao conjunto inicial de soluções localmente ótimas. Ao utilizar técnicas de programação de alta perfomlance (heurísticas construtivas, procedimentos de busca local e algoritmos genéticos) em problemas reais de programação da produção, este trabalho reduziu o abismo existente entre a teoria e a prática da programação da produção

Job Scheduling with Genetic Algorithm

In this paper, we have used a Genetic Algorithm (GA) approach for providing a solution to the Job Scheduling Problem (JSP) of placing 5000 jobs on 806 machines. The GA starts off with a randomly generated population of 100 chromosomes, each of which represents a random placement of jobs on machines. The population then goes through the process of reproduction, crossover and mutation to create a new population for the next generation until a predefined number of generations are reached. Since the performance of a GA depends on the parameters like population size, crossover rate and mutation rate, a series of experiments were conducted in order to identify the best parameter combination to achieve good solutions to the JSP by balancing makespan with the running time. We found that a crossover rate of 0.3, a mutation rate of 0.15 and a population size of 100 yield the best results

Satisfying flexible due dates in fuzzy job shop by means of hybrid evolutionary algorithms

This paper tackles the job shop scheduling problem with fuzzy sets modelling uncertain durations and flexible due dates. The objective is to achieve high-service level by maximising due-date satisfaction, considering two different overall satisfaction measures as objective functions. We show how these functions model different attitudes in the framework of fuzzy multicriteria decision making and we define a measure of solution robustness based on an existing a-posteriori semantics of fuzzy schedules to further assess the quality of the obtained solutions. As solving method, we improve a memetic algorithm from the literature by incorporating a new heuristic mechanism to guide the search through plateaus of the fitness landscape. We assess the performance of the resulting algorithm with an extensive experimental study, including a parametric analysis, and a study of the algorithm’s components and synergy between them. We provide results on a set of existing and new benchmark instances for fuzzy job shop with flexible due dates that show the competitiveness of our method.This research has been supported by the Spanish Government under research grant TIN2016-79190-R

Dynamic Job Shop Scheduling Using Ant Colony Optimization Algorithm Based On A Multi-Agent System

Ph.DDOCTOR OF PHILOSOPH

Sequenciamento de embarque de granéis sólidos por meio de terminais portuários utilizando algoritmos genéticos /

Orientador: Celso CarnieriCo-orientador: Marcos MasnikInclui bibliografia e anexosÁrea de concentraçăo: Programaçăo matemátic

Using genetic algorithms for practical multi-objective production schedule optimisation.

Production scheduling is a notoriously difficult problem. Manufacturing environments contain complex, time-critical processes, which create highly constrained scheduling problems. Genetic algorithms (GAs) are optimisation tools based on the principles of evolution. They can tackle problems that are mathematically complex, or even impossible to solve by traditional methods. They allow problem-specific implementation, so that the user can develop a technique that suits the situation, whilst still providing satisfactory schedule optimisation performance. This work tests GA optimisation on a real-life scheduling application, a chilled ready-meal factory. A schedule optimisation system is required to adapt to changing problem circumstances and to include uncertain or incomplete information. A GA was designed to allow successive improvements to its effectiveness at scheduling. Three objectives were chosen for minimisation. The GA proved capable of finding a solution that attempted to minimise the sum of the three costs. The GA performance was improved after experiments showed the effects of rules and preference modelling upon the optimisation process, allowing 'uncertain' data to be included. Multi-objective GAs (MOGAs) minimise each cost as a separate objective, rather than as part of a single-objective sum. Combining Pareto-optimality with varying emphasis on the conflicting objectives, a set of possible solutions can be found from one run of MOGA. Each MOGA solution represents a different situation within the factory, thus being well suited to a constantly changing manufacturing problem. Three MOGA implementations are applied to the problem; a standard weighted sum, two versions of a Pareto-optimal method and a parallel populations method. Techniques are developed to allow suitable comparison of MOGAs. Performance comparisons indicate which method is most effective for meeting the factory's requirements. Graphical and statistical methods indicate that the Pareto-based MOGA is most effective for this problem. The MOGA is demonstrated as being a highly applicable technique for production schedule optimisation

Efficient job scheduling for a cellular manufacturing environment

An important aspect of any manufacturing environment is efficient job scheduling. With an increase in manufacturing facilities focused on producing goods with a cellular manufacturing approach, the need arises to schedule jobs optimally into cells at a specific time. A mathematical model has been developed to represent a standard cellular manufacturing job scheduling problem. The model incorporates important parameters of the jobs and the cells along with other system constraints. With each job and each cell having its own distinguishing parameters, the task of scheduling jobs via integer linear programming quickly becomes very difficult and time-consuming. In fact, such a job scheduling problem is of the NP-Complete complexity class. In an attempt to solve the problem within an acceptable amount of time, several heuristics have been developed to be applied to the model and examined for problems of different sizes and difficulty levels, culminating in an ultimate heuristic that can be applied to most size problems. The ultimate heuristic uses a greedy multi-phase iterative process to first assign jobs to particular cells and then to schedule the jobs within the assigned cells. The heuristic relaxes several variables and constraints along the way, while taking into account the flexibility of the different jobs and the current load of the different cells. Testing and analysis shows that when the heuristic is applied to various size job scheduling problems, the solving time is significantly decreased, while still resulting in a near optimal solution. ii

On merging sequencing and scheduling theory with genetic algorithms to solve stochastic job shops.

The stochastic job shop problem was solved using two genetic algorithms. The first was a stochastic constrained genetic algorithm to minimize total tardiness and to evaluate chromosomes using probability Gantt charting. The second was a stochastic constrained genetic algorithm to minimize total tardiness and to evaluate chromosomes using simulation. In these two algorithms, the fitness function was altered to a utility function defined as follows: Probability $\{$total tardiness of a chromosome $\le$ target total tardiness$\}.$ When comparing the two chromosome evaluation methods, the probability Gantt charting deviated from the true mean for both the makespan and the average flow time by 3% and 1.7% respectively. Also, all averages estimated for both the makespan and the average flow time fall within the 90% confidence interval. Furthermore, using probability Gantt charting reduced the CPU time needed by 554.9% when compared to the CPU time needed by simulation. When the results obtained by the two stochastic constrained genetic algorithms were compared, the second algorithm reduced the actual expected total tardiness, the actual worse case total tardiness, and the risk by 30.3%, 56%, and 18% respectively.The standard genetic algorithm has been modified to address the job shop problem by constraining the genes in the chromosomes during the genetic operators implementations to match general theoretical sequencing constraints.When comparing the deterministic constrained and unconstrained genetic algorithms to minimize makespan, the constrained algorithm improved the average percentage error by 27.44%. Also, when the deterministic constrained and unconstrained genetic algorithms to minimize total tardiness were compared, the constrained algorithm improved the average percentage errors by 248.77%