A genetic algorithm for online-scheduling of a multiproduct polymer batch plant

Focuses on the development of a learning-based heuristic for scheduling heterogeneous machines. Although list scheduling methods have been widely used for a large class of scheduling problems, including the heterogeneous machine scheduling problem, they involve designing priority rules, which usually require a fair amount of insights on the characteristics of the problem to be solved. Instead of elaborate design of priority rules in a single step, we propose an iterative list scheduling process, which refines priority rules while generating a number of schedules. The proposed iterative list scheduling is formulated as a reinforcement learning problem, with states and actions defined in list scheduling. Due to the large number of possible states, reinforcement learning algorithms which use value functions in constructing an optimal policy may not be suitable for scheduling problems. Thus, to directly work with policies rather than the values of states, we propose genetic reinforcement learning (GRL), in which the policies of reinforcement learning are encoded into the chromosomes of genetic algorithms and a near-optimal policy is searched for by genetic algorithms. A GRL-based scheduler, called evolutionary intracell scheduler (EVIS), has been developed and applied to various scheduling problems such as the heterogeneous machine scheduling, the processor scheduling, the job-shop scheduling, the flow-shop scheduling, and the open-shop scheduling problems. The proposed model of EVIS, which has a linear order of population-fitness convergence, is verified by computer experiments. Even without fine tuning EVIS, the quality of solutions achieved by EVIS is comparable to that of problem-tailored heuristics for most of the problem instances.

Job shop scheduling is one of the complex problems in the manufacturing industry, such as an automotive body manufacturer. This manufacturing company, located in Cikarang, Indonesia, deals with huge customer demand that leads to difficulties in production scheduling. This will cause a delay in some jobs and product deliveries. The current system is using the semi-active scheduling approach and requires 637 minutes for performing 6 jobs with 5 machines. The genetic algorithm (GA) model is proposed as an alternative solution to solve this problem. The GA parameter is set as follow: The population size expected is 30 with maximum generation can be produced in amount of 50. The crossover rate, mutation, and preservation are set to 0.3, 0.1, and 0.1, respectively. After 50 generations are obtained, the optimum solution is shown in generation 6 with a makespan of 597 minutes. Thus, the genetic algorithm model is effectively reducing the makespan of the job-shop scheduling problem by 10% compared to the current method applied at the company.

This paper innovates, within the context of preventive maintenance planning, the application of Genetic Algorithm, as a modern powerful optimization tool, in minimum downtime strategies, for optimal part replacement. Therefore, a brief account of Genetic Algorithm is given and a computer program is written to aid the solution of such problems by Genetic Algorithm. Then, the program is used to solve a numerical example, concerning an equipment with Gaussian failure characteristics. The results obtained by Genetic Algorithm and the ordinary methods are compared to show that exactly the same results can be approached by the application of Genetic Algorithm. Genetic Algorithm and maintenance planning, both being subjects of statistical nature, can be combined to provide a vast area of interesting research work and, hence, some guidelines are suggested for further research.

The Meta heuristic algorithms are the higher level technique which helps to find the best feasible solution out of all possible solution of an optimization problem. There are various different types of meta heuristic algorithms like Ant Colony Optimization (ACO), Genetic Algorithm, Simulated Annealing, Particle Swarm Optimization, etc. Genetic Algorithm is a search-based optimization technique based on the biological principle of Genetics and adaptation. It is a meta-heuristic approach which is used to solve complex combinatorial problem. The integration of Genetic algorithm with machine learning will be helpful in solving unconstrained and constrained optimization problem. The various genetic operator like selection operator, mutation and cross-over are discussed which will be helpful in knowing how these operators significantly improves State Space search. In this paper the various applications of Genetic algorithms which can be used in machine learning has been discussed. In this paper the author discussed how the significance of Genetic algorithm will be improved while solving complex optimization problem in machine learning. In this paper, flow diagram of Genetic Algorithms has been discussed which will ease the understanding of complex optimization problem like 0–1 Knapsack, Traveling Salesman Problem, etc. In this paper a comparative analysis between traditional algorithm and genetic algorithm has been done on the basis of parameters like flow of control, state space search, Complication, Preconditions, CPU utilization etc. The various limitations of Genetic Algorithms in solving problems with optimal solutions has also been discussed.

Genetic algorithm is one of the optimization techniques, successfully applied in nuclear reactor environment for a variety of applications. Nuclear fuel management optimization is a classical nuclear engineering problem, which has been studied extensively and several techniques, including genetic algorithms, have been used for its solution. The study presented in this paper addresses the overall procedures and methods developed for the application of genetic algorithms in the optimization studies of nuclear fuel management. The result obtained from a study that explores a typical application of genetic algorithm in nuclear fuel management is also presented. Finding out of optimal number of fuel subassemblies in the core of a nuclear reactor (having power generating capacity of 500 MWe) is taken up for the study. The results demonstrate the suitability and efficiency of the algorithm in generating feasible solutions for the selected optimization problem.

We review the recent work done by our group on theoretically analyzing the behavior of genetic algorithms(GAs) and searching for new ideas and strategies to design more efficient GAs. By introducing the concept of degree of population diversity and using the tools from finite Markov chain, we presented fairly clear characterizations of premature convergence in GAs and proved some results on the relationships between premature convergence and GA’s control parameters such as the population size, the mutation and crossover probabilities, and some population statistics. In the mean time, we have also obtained a result that depicts the search ability of the crossover operators in GAs. An upper bound on the convergence rate for the GAs has been established. Based on the theoretical analyses, we have proposed a novel GA called Genetic Algorithm with No Genetic Operators (GANGO), which needs neither to maintain a population nor to use the conventional genetic operators, and yet has the same search mechanisms as the classical GAs. As another development in improving the performance of GAs, a new concept called Dual Fitness Function was proposed which, when incorporated into the classical GAs will enable the algorithms to judge whether the current population is in the local or global attractive basins. And in this way, the control parameters of GAs can be adjusted adaptively. Some applications of our results to fields such as fuzzy systems, multi-destination routing problem (MDR) are briefly

Genetic algorithms to solve the cover printing problem

This paper focuses on the development of a learning-based heuristic for the machine scheduling problem, which automatically captures the search control knowledge or the common features of good schedules while generating a number of schedules. Defining states and actions of the machine shop, the machine scheduling problem is transformed into a problem of reinforcement learning (RL) in which a learner or a scheduler will learn to select the right action at each state of the machine shop, using the reward from a schedule evaluator for executing the action. Implementing the proposed reinforcement learning with a genetic algorithm results in the genetic reinforcement learning (GRL) approach to the machine scheduling problem. Although the learning-based heuristic has the overhead of acquiring knowledge on the problem, it can be easily adapted for a wide variety of machine scheduling problems due to the weak dependence on the problem structures and objectives. A GRL-based scheduler, called EVIS (Evolutionary Intracell Scheduler), has been developed and applied to various classes of machine scheduling problems, such as the job-shop scheduling, the flow-shop scheduling and the open-shop scheduling problems, and even the processor scheduling problem, the performance evaluation of EVIS with a number of different problem instances has shown that the learning-based heuristic is robust and its performance is comparable with that of other problem-specific heuristics or search-oriented heuristics in the quality of solutions.

This study aims to improve the efficiency of logistics distribution scheduling in the supply chain and reduce logistics costs. In the current research, the time and cost problems of logistics distribution always have some limitations to meet the needs of practical applications; therefore, this paper designed genetic algorithm-based method to solve the vehicle scheduling problem. The analysis of arithmetic examples was carried out. The analysis found that the method effectively reduced the cost of distribution and had higher computing speed, showing a high performance in solving the problem of logistics distribution scheduling. The method can further optimize the logistics distribution of the supply chain. This work provides a new approach for the study of logistics distribution scheduling model and is conducive to the better application of genetic algorithm in this field.

This article presents a way to solve Resource-Constrained Project Scheduling Problem with genetic algorithm and demonstrates the high relevance of metaheuristics for solution this type problem. Also shows various benchmarks with others heuristics. The benchmark performed compares directly to a relatively new heuristic and to several other works performed. The genetic algorithm together with the approach and the form of resolution applied to solve the Resource-Constrained Project Scheduling Problem obtained better results than other approaches. This work reinforced that, even with new metaheuristics, the genetic algorithm remains in the lead to solve this problem.

An investigation into the application of neural networks, fuzzy logic, genetic algorithms, and rough sets to automated knowledge acquisition for classification problems

The application of genetic algorithms in behavioural ecology, illustrated with a model of anti-predator vigilance

Genetic Algorithm (GA) may be attributed as method for optimizing the search tool for difficult problems based on genetics selection principle. In additions to Optimization it also serves the purpose of machine learning and for Research and development. It is analogous to biology for chromosome generation with variables such as selection, crossover and mutation together constituting genetic operations which would be applicable on a random population initially. GA aims to yield solutions for the consecutive generations. The extent of success in individual production is directly in proportion to fitness of solution which is represented by it, thereby ensuring that quality in successive generations will be better. The process is concluded once an GA is most suitable for the issues that need optimization associated with some computable system.. John Holland may be regarded as funding father of original genetic algorithm and is attributed to year 1970's as funding date. Additionally a random search method represented by Charles Darwin for a defined search space in order to effetely solve a problem. In this paper, what is genetic algorithm and its basic workflow is discussed how a genetic algorithm work and what are the process is included in this is also discussed. Further, the features and application of genetic algorithm are mentioned in the paper.

The following article deals with up-to-date field of optimization and genetic algorithms use in the production process control. Theory of optimization is mentioned in the first part of this paper. The second part of this article is dedicated to genetic algorithms. Genetic algorithms are characterized by their robustness – the ability to solve difficult optimization or the tasks in which we need to decide on something.The third part of the paper focuses on practical demonstrations of two examples. The first example is on determining the optimal production. The second example is on application of genetic algorithms in process planning. Matlab and Evolver software tools were used for the data compution. Data preparation for Evolver was done in MS Excel. But the model building is only one step in knowledge discovery. The best model is often found after building models of several different types, or by trying different technologies or algorithms.

This paper considers genetic algorithm to process logistics path optimization. Genetic algorithms are significant in location issues, distribution issues, scheduling issues, transportation issues, and layout issues. A genetic algorithm for solving this problem is constructed base on the mathematical model of logistics distribution path optimization problem. The calculation results show that using genetic algorithm to optimize the logistics distribution path can easily and effectively obtain the optimal solution or approximate optimal solution.