Adaptive scheduling algorithm based on mixed graph model (original) (raw)
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Learning Technology in Scheduling Based on the Mixed Graphs
We propose the adaptive algorithm for solving a set of similar scheduling problems using learning technology. It is devised to combine the merits of an exact algorithm based on the mixed graph model and heuristics oriented on the real-world scheduling problems. The former may ensure high quality of the solution by means of an implicit exhausting enumeration of the feasible schedules. The latter may be developed for certain type of problems using their peculiarities. The main idea of the learning technology is to produce effective (in performance measure) and efficient (in computational time) heuristics by adapting local decisions for the scheduling problems under consideration. Adaptation is realized at the stage of learning while solving a set of sample scheduling problems using a branch-and-bound algorithm and structuring knowledge using pattern recognition apparatus.
Solving a job-shop scheduling problem by an adaptive algorithm based on learning
IFAC Proceedings Volumes (IFAC-PapersOnline), 2013
A learning stage of scheduling tends to produce knowledge about a benchmark of priority dispatching rules which allows a scheduler to improve the solution quality for a set of similar job-shop problems. Once trained on the sample job-shop problems (usually with small sizes), the adaptive algorithm solves a similar job-shop problem (with a moderate size or a large size) better than heuristics used as a benchmark at the learning stage of scheduling. Our adaptive algorithm does not guarantee to perform as an exact algorithm or better than a more sophisticated heuristic algorithm (like e.g. the shifting bottleneck one) which need a large running time. For an adaptive algorithm with a learning stage, the job-shop scheduling problem is modeled via a weighted mixed (disjunctive) graph with the conflict resolution strategy used for finding an appropriate schedule. 2 1 n J J J J = is the set of jobs to be processed. The operations on the bottleneck machine are scheduled due to solution of the NPhard problem max 2 1 n J J J J = O O = of each job J J i ∈ through the machines M is fixed. The machine routes i O may be different for different jobs . J J i ∈ The time ij p for processing operation ij O of job J J i ∈ on of the jobs J J i ∈ and , J J u ∈
Journal of Intelligent Manufacturing, 2013
The multi-machine scheduling problems with job-dependent and machine-dependent learning effects are proposed in this paper. Since it is almost impossible to obtain the analytic results for this complicated multi-machine scheduling problems with learning effects, four heuristic algorithms are used to solve this newly proposed model, where the variants of well-known genetic algorithm (GA), simulated annealing (SA), ant colony optimization (ACO) and particle swarm optimization (PSO) are coded in the commercial software MATLAB. The objective is to minimize the makespan of this new model. For this kind of scheduling problem, the numerical experiments show that the GA and SA outperform ACO and PSO.
Design and Analysis of Multi - Heuristic Based Solution for Task Graph Scheduling Problem
International Journal of Engineering and Advanced Technology, 2019
Heuristic based strategies have always been of interest for researchers to achieve sub-optimal solutions for various NP-Complete problems. Human evolution based methods have been an inspiration for research since ages. One of the many evolutionary strategies based on the principle of genetic algorithm have been able to provide much sought after sub-optimal solutions for various NP-Complete problems. One of the most sought after NP-Complete problem is Task graph scheduling i.e. optimally execute the schedule of tasks on available parallel and distributed environment so as to achieve efficient utilization of available resources. Task scheduling is a multi-objective combinatorial optimization problem, with key parameters being reduced completion time and effective load balance on the available resources. Various algorithms have been proposed by various authors to achieve the above mentioned goal with the help of various heuristics like list scheduling, task duplication and critical pat...
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A new adaptive neural network and heuristics hybrid approach for job-shop scheduling
Computers & Operations Research, 2001
A new adaptive neural network and heuristics hybrid approach for job-shop scheduling is presented. The neural network has the property of adapting its connection weights and biases of neural units while solving the feasible solution. Two heuristics are presented, which can be combined with the neural network. One heuristic is used to accelerate the solving process of the neural network and guarantee its convergence, the other heuristic is used to obtain non-delay schedules from the feasible solutions gained by the neural network. Computer simulations have shown that the proposed hybrid approach is of high speed and e$ciency. The strategy for solving practical job-shop scheduling problems is provided.
An adaptive scheduling approach in distributed systems
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A large number of scheduling algorithms for large scale distributed systems have been proposed, studied and compared. In spite of this, there are few studies comparing the performance of scheduling algorithms considering at the same time the distributed system structure on which we want to schedule the tasks; the type of directed acyclic graph (DAG), in which graph nodes represent tasks and graph edges represent data transfers; and the type of tasks, for example CPU-bound vs. I/O bound. This paper proposes a method for selecting, in a dynamic manner, the most appropriate scheduling algorithm for a particular distributed system, after a previous analysis. Choosing the best known scheduling algorithm can improve performance of an application if all the aspects previously enumerated are considered. The results of this paper consist in a comparison of the scheduling algorithms performance for the following scheduling algorithms: Modified Critical Path, Cluster ready Children First, Earliest Time First, Highest Level First with Estimated Times and Hybrid Remapper Minimum Partial Completion Time Static Priority. However, the main purpose of this investigation tests is to demonstrate the usefulness of the presented scheduling approach using these results.
Software for production scheduling based on the mixed (multi)graph approach
Computing & Control Engineering Journal, 1996
A network model, based on a weighted mixed (disjunctive) graph or multigraph, gives a suitable possibility for the representation of different constraints which usually arise in production planning and scheduling. The most complex problem to be considered in this article includes optimal choosing of machines, distributing the given set of operations in the chosen machines and sequencing the operations. Software developed for complex scheduling problems may be used separately or in the framework of the adaptive approach.
Neurocomputing (ISI-indexed)
This paper presents a general kind of flow shop scheduling problem in a manufacturing supply chain where a group of jobs can be processed on a machine simultaneously. Examples of such environment occur in chemical processes, semiconductor industries, electronics manufacturing, wafer fabrication, and pharmaceutical industries, etc. In this problem not only should the sequence of jobs be determined but also the formation of batches is considered as a new variable in the model. The problem under investigation is NP-hard for cost of total earliness; total tardiness and makespan as objectives. During recent years, the nature-inspired computational intelligent algorithms are successfully employed for achieving the optimum design of supply chain structures. Hence, three effective computational intelligence algorithms including a hybrid genetic algorithm (HGA), a hybrid simulated annealing (HSA) and an improved discrete particle swarm optimization (PSO) algorithm are developed and analyzed for solving the batch processing machine scheduling problem addressed in current paper. Furthermore, an adaptive learning approach which is inspired by the training weights in artificial neural network (ANN) environment is embedded into the algorithms so as to enhance the quality of solutions. An extensive simulation experiments is conducted and the performance of algorithms is compared with the traditional genetic algorithm, particle swarm optimization, some well known dispatching rules such as STPT, LTPT, SBMPT, LBMPT, EDD, MST and also with the powerful commercial solver LINGO. The attained results show the appropriate performance of our algorithms.