Multiprocessor scheduling in a genetic paradigm

In this paper, we present a technique based on the problem-space genetic algorithm (PSGA) for the static scheduling of directed acyclic graphs onto homogeneous multiprocessor systems to reduce the response-time. The PSGA based approach combines genetic algorithms, with a list scheduling heuristic to search a large solution space efficiently and effectively. Comparison of results with the genetic algorithm based scheduling technique for the Stanford manipulator and the Elbow manipulator examples shows a significant improvement in the response-time.

The task assignment problem is one of assigning tasks of a parallel program among the processors of a distributed computing system in order to reduce the job turnaround time and to increase the throughput of the system. Since the task assignment problem is known to be NP‐complete except in a few special situations, satisfactory suboptimal solutions obtainable in a reasonable amount of computation time are generally sought. In the paper we introduce a technique based on the problem‐space genetic algorithm (PSGA) for the static task assignment problem in both homogeneous and heterogeneous distributed computing systems to reduce the task turnaround time and to increase the throughput of the system by properly balancing the load and reducing the interprocessor communication time among processors. The PSGA based approach combines the power of genetic algorithms, a global search method, with a simple and fast problem‐specific heuristic to search a large solution space efficiently and effectively to find the best possible solution in an acceptable CPU time. Experimental results on test examples from the literature show considerable improvements in both the assignment cost and the CPU times over the previous work. The proposed scheme is also applied to a digital signal processing (DSP) system consisting of 119 tasks to illustrate its balancing properties and computational advantage on a large system. The proposed scheme offers 12–30% improvement in the assignment cost as compared to the previous best known results for the DSP example.

In this study, a problem space genetic algorithm (PSGA) is used to solve bicriteria tool management and scheduling problems simultaneously in flexible manufacturing systems. The PSGA is used to generate approximately efficient solutions minimizing both the manufacturing cost and total weighted tardiness. This is the first implementation of PSGA to solve a multiobjective optimization problem (MOP). In multiobjective search, the key issues are guiding the search towards the global Pareto-optimal set and maintaining diversity. A new fitness assignment method, which is used in PSGA, is proposed to find a well-diversified, uniformly distributed set of solutions that are close to the global Pareto set. The proposed fitness assignment method is a combination of a nondominated sorting based method which is most commonly used in multiobjective optimization literature and aggregation of objectives method which is popular in the operations research literature. The quality of the Pareto-optimal set is evaluated by using the performance measures developed for multiobjective optimization problems.

The paper describes investigations into multiprocessor scheduling techniques for high quality audio DSP. A DSP processor/multiprocessor simulator is introduced which allows register-level simulation of multiprocessor DSP architectures and parallel DSP algorithms. The simulator has been used to investigate the implementation of recursive filtering algorithms for audio, using multiprocessor DSP architectures. Results from these studies are presented and discussed. Also described are multiprocessor DSP algorithm mapping and scheduling techniques based on genetic algorithms. These investigate techniques using genetic algorithms to schedule audio DSP algorithms written in the form of data flow graphs onto specified DSP multiprocessor arrays. The basic techniques are described and initial results from these studies are also presented and discussed. (8 pages)

In our work, we consider the problem of load management in residential area. We adopt Genetic Algorithm (GA) and Strawberry Algorithm (SBA) for load scheduling. These algorithms are used to manage residential load between shoulder, on-peak and off-peak hours. Time of Use (ToU) pricing scheme has been used for bill calculation. Simulation results show that GA based energy optimization controller perform good than SBA based energy optimization controller in term of Peak to Average Ratio (PAR), electricity bill reduction and waiting time.

First, a multi-objective immune genetic algorithm integrating immune algorithm and genetic algorithm for flexible job shop scheduling is designed. Second, Markov chain is used to analyze quantitatively its convergence. Third, a simulation experiment of the flexible job shop scheduling is carried out. Running results show that the proposed algorithm can converge to the Pareto frontier quickly and distribute evenly along the Pareto frontier.

Hybrid ant genetic algorithm for efficient task scheduling in cloud data centers

Genetic algorithms (GAs) are optimization tools that simulate evolutionary processes in order to develop solutions for a wide range of optimization problems. In recent years, they have increasingly gained acceptance in industry, as confidence improves in their abilities to solve complex problems that have previously appeared to be unsolvable. One example of an industrial application for GAs is scheduling, an area that can present many difficulties for the manufacturing industry. Much of the advantage of GAs lies in the flexibility with which they may be implemented. In this work, two practically motivated improvements are made to the basic GA used for schedule optimization, to allow the technique to include additional complexities that arose in an industrial application. The first improvement enables the GA to deal with uncertain information in the factory, and illustrates the ability of the GA to aid scheduling decision-making. The second improvement is the application of the GA to multiobjective optimization, in the form of a multiobjective genetic algorithm (MOGA). In this way, the GA can solve problems with several conflicting or incompatible objectives, and allow the user to interact with the process as it evolves solutions. These variations permit the inclusion of specific user preferences, extending the scheduling choices available, whilst still ensuring that global optimization performance is not diminished. Thus the schedule optimization system becomes more interactive, accurate and effective for manufacturing schedule optimization.

This paper proposes three genetic algorithms with different types of crossovers dedicated to solve the problem of short-term scheduling of batch processes. The genetic algorithms suggested are able to determine the amount of batches produced, their production sequence, machinery assignments, and a different batch size for each batch. This is a more complex variation of the batch scheduling problem where genetic algorithms are usually limited to decide the batch production sequence. A manufacturing case study is used to test the performance when minimizing the makespan, in order to compare the proposed genetic algorithms against a simulated annealing implementation, a totally random search, and a simplified genetic algorithm with no crossover (to test the crossovers’ efficiency). The results show that one of the crossovers proposed has the fastest convergence in all scenarios, finding the best solutions in short searches. This crossover is slightly outperformed by the simulated annealing only in extensive searches with more iterations. The other two crossovers suggested have a good performance in certain scenarios, but show poor results in others.

Task scheduling is one of the key technology of cloud computing, its main goal is to satisfy the fairness of the system resources and tasks more effectively, and at the same time as much as possible to realize the load balance of system resources and reduce the completion time. Combined with the advantages of genetic Algorithm, we put forward a Fair and Balanced and effective genetic Scheduling Algorithm FBEGSA (Fair Balanced Efficient Genetic Scheduling Algorithm).The experimental results show that the algorithm is useful to task scheduling.

Grid computing presents a new trend to distribute and Internet computing to coordinate large scale heterogeneous resources providing sharing and problem solving in dynamic, multi- institutional virtual organizations. Scheduling is one of the most important problems in computational grid to increase the performance. Genetic Algorithm is adaptive method that can be used to solve optimization problems, based on the genetic process of biological organisms. The objective of this research is to develop a job scheduling algorithm using genetic algorithm with high exploration processes. To evaluate the proposed scheduling algorithm this study conducted a simulation using GridSim Simulator and a number of different workload. The research found that genetic algorithm get best results when increasing the mutation and these result directly proportional with the increase in the number of job. The paper concluded that, the mutation and exploration process has a good effect on the final execution time when we have large number of jobs. However, in small number of job mutation has no effects.

© 2018 Massachusetts Institute of Technology. Designing effective dispatching rules for production systems is a difficult and timeconsuming task if it is done manually. In the last decade, the growth of computing power, advanced machine learning, and optimisation techniques has made the automated design of dispatching rules possible and automatically discovered rules are competitive or outperform existing rules developed by researchers. Genetic programming is one of the most popular approaches to discovering dispatching rules in the literature, especially for complex production systems. However, the large heuristic search space may restrict genetic programming from finding near optimal dispatching rules. This article develops a new hybrid genetic programming algorithm for dynamic job shop scheduling based on a new representation, a new local search heuristic, and efficient fitness evaluators. Experiments show that the new method is effective regarding the quality of evolved rules. Moreover, evolved rules are also significantly smaller and contain more relevant attributes.

This paper discusses the problem of scheduling a set of jobs on parallel machines considering sequence-dependent setup times. The processing sequence of jobs assigned to each machine is determined to minimize the makespan. Since it is a NP-hard problem, even for instances with small sizes, the computational effort may be prohibitively large. Then genetic algorithms with two types of assignment scheme are presented to obtain near optimal solutions. The popular scheme of random assignment is used in the genetic algorithm (GA-1). Unlike GA-1, the scheme of greedy assignment is employed in the genetic algorithm (GA-2). The computational experiments are carried out to compare the performance of GA-1 with that of GA-2. Computational results show that GA-2 has better performance than GA-1.

Many optimization problems from the industrial engineering world, in particular the manufacturing systems, are very complex in nature and quite hard to solve by conventional optimization techniques. There has been increasing interest in imitating living beings to solve such kinds of hard optimization problems. Simulating the natural evolutionary process of human beings results in stochastic optimization tech niques called evolutionary algorithms, which can often outperform conventional optimization methods when applied to difficult real-world problems. There are currently three main avenues of this research: genetic algorithms (GAs), evolutionary programming (EP) and evolution strategies (ESs). Among them, genetic algorithms are perhaps the most widely known types of evolutionary algorithms today. During the past years, several GAs for the job-shop scheduling problems have been proposed, each with different chromosome representation. In this paper, the different GAs are collected from the literature and an attempt has been made to evaluate them. The benchmark problems available in open literature are used for evaluation and the performance measure considered is makespan. The algorithms are coded in C+ +.

This study proposes a digital twin–enabled hybrid scheduling framework that integrates mixed integer linear programming (MILP) and genetic algorithms (GA) for efficient scheduling in multi-product cellular manufacturing systems. Setup time during product changeovers directly affects production efficiency, yet existing rule-based methods and single-algorithm approaches fall short of capturing the complexity of actual production environments. The proposed framework embeds a hybrid MILP–GA within a digital twin, where simulation serves a dual role: evaluating candidate schedules during GA evolution and validating the final optimized schedule. By combining the extensive solution space exploration capability of GA with the precise optimization ability of MILP, the framework addresses the single-machine scheduling problem with sequence-dependent setup times and achieves both makespan minimization and setup time reduction. Experimental results demonstrated that the proposed framework reduced makespan by up to 4.3% and setup time by up to 9.4% relative to the current system. This research contributes a practical and adaptable scheduling solution for small-batch multi-product cellular manufacturing systems, highlighting the value of integrating digital twin technology with hybrid optimization in smart manufacturing environments.