Minimizing the Total Completion Time and Total Earliness Time Functions for a Machine Scheduling Problem Using Local Search Methods (original) (raw)
Related papers
Job Shop Scheduling Using Modified Simulated Annealing Algorithm
Timely and cost factor is increasingly important in today’s global competitive market. The key problem faced by today’s industries are feasible allocation of various jobs to available resources i.e., machines (Scheduling) and optimal utilization of the available resources. Among the various problems in scheduling, the job shop scheduling is the most complicated and requires a large computational effort to solve it. A typical job shop scheduling problem has a set of jobs to be processed in a set of machines, with certain constraints and objective function to be achieved. The most commonly considered objectives are the minimization of make span, minimization of tardiness which leads to minimization of penalty cost, and to maximize machine utilization. Machine shop scheduling can be done using various techniques like standard dispatching rules, heuristic techniques like Simulated annealing, Tabu Search, Genetic algorithm, etc,.here a typical job shop shop scheduling problem is solved using simulated annealing(SA) technique, a heuristic search algorithm. SA is generic neighbourhood search algorithm used to locate optimal solution very nearer to global optimal solution. A software based program is developed in VB platform for a typical job shop problem and test instances were performed over it. Experimental results obtained were further tuned by varying parameters and optimal results were obtained
WSEAS Transactions on Information Science and Applications archive, 2017
Memetic algorithms (MAs) are hybrid evolutionary algorithms (EAs) that combine global and local search by using an EA to perform exploration while the local search method performs exploitation. Combining global and local search is a strategy used by many successful global optimization approaches, and MAs have in fact been recognized as a powerful algorithmic paradigm for evolutionary computing. This paper presents a hybrid heuristic model that combines particle swarm optimization (PSO) and simulated annealing (SA). This PSO/SA hybrid was applied on the multiprocessor scheduling problem to perform static allocation of tasks in a heterogeneous distributed computing system in a manner that is designed to minimize the cost. Additionally, this paper also focuses on the design and implementation of several enhancements to PSO based on diversity and efficient initialization using different distributions. The results show the effectiveness and superiority of the hybrid algorithms.
The International Journal of Advanced Manufacturing Technology, 2011
Generating schedules such that all operations are repeated every constant period of time is as important as generating schedules with minimum delays in all cases where a known discipline is desired or obligated by stakeholders. In this paper, a periodic job shop scheduling problem (PJSSP) based on the periodic event scheduling problem (PESP) is presented, which deviates from the cyclic scheduling. The PESP schedules a number of recurring events as such that each pair of event fulfills certain constraints during a given fixed time period. To solve such a hard PJSS problem, we propose a hybrid algorithm, namely PSO-SA, based on particle swarm optimization (PSO) and simulated annealing (SA) algorithms. To evaluate this proposed PSO-SA, we carry out some randomly constructed instances by which the related results are compared with the proposed SA and PSO algorithms as well as a branch-and-bound algorithm. In addition, we compare the results with a hybrid algorithm embedded with electromagnetic-like mechanism and SA. Moreover, three lower bounds (LBs) are studied, and the gap between the found LBs and the best found solutions are reported. The outcomes prove that the proposed hybrid algorithm is an efficient and effective tool to solve the PJSSP.
Discrete Dynamics in Nature and Society, 2010
The Job-shop scheduling problem JSSP is a branch of production scheduling, which is among the hardest combinatorial optimization problems. Many different approaches have been applied to optimize JSSP, but for some JSSP even with moderate size cannot be solved to guarantee optimality. The original particle swarm optimization algorithm OPSOA , generally, is used to solve continuous problems, and rarely to optimize discrete problems such as JSSP. In OPSOA, through research I find that it has a tendency to get stuck in a near optimal solution especially for middle and large size problems. The local and global search combine particle swarm optimization algorithm LGSCPSOA is used to solve JSSP, where particle-updating mechanism benefits from the searching experience of one particle itself, the best of all particles in the swarm, and the best of particles in neighborhood population. The new coding method is used in LGSCPSOA to optimize JSSP, and it gets all sequences are feasible solutions. Three representative instances are made computational experiment, and simulation shows that the LGSCPSOA is efficacious for JSSP to minimize makespan.
Optimal CPU Jobs Scheduling Method Based on Simulated Annealing Algorithm
Iraqi Journal of Science
Task scheduling in an important element in a distributed system. It is vital how the jobs are correctly assigned for each computer’s processor to improve performance. The presented approaches attempt to reduce the expense of optimizing the use of the CPU. These techniques mostly lack planning and in need to be comprehensive. To address this fault, a hybrid optimization scheduling technique is proposed for the hybridization of both First-Come First-Served (FCFS), and Shortest Job First (SJF). In addition, we propose to apply Simulated Annealing (SA) algorithm as an optimization technique to find optimal job’s execution sequence considering both job’s entrance time and job’s execution time to balance them to reduce the job’s waiting time to be executed. As a result, this research proves that the proposed technique achieves an optimization efficiency with a percentage average 45.5 % according to the FCFS algorithm and 54.5% according to SJF method.
2020
In this paper, two of the local search algorithms are used (genetic algorithm and particle swarm optimization), in scheduling number of products (n jobs) on a single machine to minimize a multi-objective function which is denoted as (total completion time, total tardiness, total earliness and the total late work). A branch and bound (BAB) method is used for comparing the results for (n) jobs starting from (5-18). The results show that the two algorithms have found the optimal and near optimal solutions in an appropriate times.
A Hybrid Simulated Annealing for Job Shop Scheduling Problem
Int. J. Comb. Optim. Probl. Informatics, 2019
The Job Shop Scheduling Problem (JSSP) arises in the context of high-performance computing and belongs to the NP-hard combinatorial optimization problems. The purpose of JSSP is to find the order of execution of a set of jobs on a group of machines, subject to certain precedence and resource availability constraints. The objective in this problem is minimizing the makespan that is the time elapsed from the starting time of the first job until the completion time of the last job. In this paper, a novel hybrid algorithm named AntGenSA for solving JSSP is proposed. AntGenSA uses Ant Colony System (ACS), Simulated Annealing (SA), and Genetic Algorithm (GA). To assess the performance of this algorithm, it is executed in a parallel computer, using a set of instances proposed by Fisher-Thompson, Yamada-Nakano, Taillard, Lawrence, and Applegate-Cook. The evaluation of this algorithm was performed mainly by the quality of the solution but the execution time was measuring as well. The experim...
Optimization of job scheduling on parallel machines by simulated annealing algorithms
Expert Systems with Applications, 1992
ln this paper, we consider the problem of scheduling a set of simultaneously available jobs on several parallel machines. Specifically, the minimization of the time to finish all the jobs assigned to all machines under job deadline constraints for n jobs, m machines problem is formulated in this paper. The simulated annealing and fast-simulated annealing algorithms are reviewed and adopted for the scheduling problem. Large numbers of simulations are carried out that provide an empirical basis for comparing the application of classical simulated annealing and fast-simulated annealing algorithms to the scheduling problem.
In this paper we propose a modification to the Simulated Annealing (SA) basic algorithm that includes an additional local search cycle after finishing every Metropolis cycle. The added search finishes when it improves the current solution or after a predefined number of tries. We applied the algorithm to minimize the Maximum Tardiness objective for the Unrestricted Parallel Identical Machines Scheduling Problem for which no benchmark have been found in the literature. In previous studies we found, by using Genetic Algorithms, solutions for some adapted instances corresponding to Weighted Tardiness problem taken from the OR-Library. The aim of this work is to find improved solutions (if possible) to be considered as the new benchmark values and make them available to the community interested in scheduling problems. Evidence of the improvement obtained with proposed approach is also provided.
Using Local Search methods to solve Unrelated Parallel Machine scheduling problem
The problem of scheduling of unrelated parallel machines is considered. In this environment, a set of n jobs has to be scheduled on m unrelated parallel machines. Each job is available for processing at time zero and each machine can process at most one job at a time and a job can be processed by at most one machine at a time. A case study is considered to schedule jobs in a cutting workshop to minimize the sum of total completion time and total weighted tardiness. Six heuristics are proposed and two local search methods are Descent method(DM) and Simulated annealing (SA). That can find nearly optimal solutions with in a reasonable amount of computation time.The performance of exact and approximation algorithms above are tested on large class of test problems. From our computational results, we found that the approximation algorithms solve problem up to (7500) jobs and (50) machines in reasonable amount of time.