Scheduling Jobs on Dedicated Parallel Machines

2013 ◽  
Vol 433-435 ◽  
pp. 2363-2366 ◽  
Author(s):  
Sang Oh Shim ◽  
Seong Woo Choi
Keyword(s):  
Completion Time ◽  
Parallel Machines ◽  
Computation Time ◽  
Computational Experiments ◽  
Test Problems ◽  
Scheduling Problem ◽  
Suggested Algorithm ◽  
Reasonable Amount ◽  
Scheduling Method

This paper considers scheduling problem on dedicated parallel machines where several types of machines are grouped into one process. The dedicated machine is that a job with a specific recipe should be processed on the dedicated machine even though the job can be produced on any other machine originally. In this process, a setup is required when different jobs are done consecutively. To minimize the completion time of the last job, a scheduling method is developed. Computational experiments are performed on a number of test problems and results show that the suggested algorithm give good solutions in a reasonable amount of computation time.

scholarly journals Single Machine Predictive Scheduling Using Inserted Idle Times

2014 ◽  
Vol 2014 ◽  
pp. 1-5 ◽  
Author(s):  
Hongli Zhu ◽  
Hong Zhou
Keyword(s):  
Single Machine ◽  
Completion Time ◽  
Primary Objective ◽  
Computational Experiments ◽  
Scheduling Problem ◽  
High Predictability ◽  
Surrogate Measure ◽  
Predictive Scheduling ◽  
Completion Times

A single machine predictive scheduling problem is considered. The primary objective is to minimize the total completion times. The predictability of the schedule is measured by the completion time deviations between the predictive schedule and realized schedule. The surrogate measure of predictability is chosen to evaluate the completion time deviations. Both of the primary objective and predictability are optimized. In order to absorb the effects of disruptions, the predictive schedule is generated by inserting idle times. Right-shift rescheduling method is used as the rescheduling strategy. Three methods are designed to construct predictive schedules. The computational experiments show that these algorithms provide high predictability with minor sacrifices in shop performance.

Petri Net and GA Based Method for JSP Optimization with Dual-Objective

2011 ◽  
Vol 121-126 ◽  
pp. 4547-4551
Author(s):  
Li Xin Qi ◽  
Ze Tao
Keyword(s):  
Petri Net ◽  
Supervisory Control ◽  
Completion Time ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Linear Constraint ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Scheduling Method

A new dual-objective scheduling method based on the controlled Petri net and GA is proposed to the job-shop scheduling problem (JSP) constrained by machines, workers. Firstly, a detailed analysis of supervisory control for Petri net with uncontrollable transitions, especially important, for OR-logics linear constraint, a new method for constructing a Petri net feedback controller based on monitor and inhibitor arcs is presented. The Petri net model is constructed based on above method in flexible JSP. Then, the genetic algorithm (GA) is applied based on the controlled Petri net model and Pareto. Function objectives of the proposed method are to minimize the completion time and the total expense of machines and workers. Finally, Scheduling example is employed to illustrate the effectiveness of the method.

Petri Net with Uncontrollable Transition and GA Based Approach for JSP

2011 ◽  
Vol 48-49 ◽  
pp. 824-829
Author(s):  
Tao Ze ◽  
Xiao Xia Liu
Keyword(s):  
Petri Net ◽  
Completion Time ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Flexible Job Shop ◽  
Scheduling Problem ◽  
Flexible Job Shop Scheduling ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Scheduling Method

A new dual-objective scheduling method based on the controlled Petri net and GA is proposed to the job-shop scheduling problem (JSP) with urgent orders constrained by machines, workers. Firstly, a controller designed method for Petri net with uncontrollable transition is introduced, and based on the method, the Petri net model is constructed for urgent jobs in flexible job shop scheduling problem. Then, the genetic algorithm (GA) is applied based on the controlled Petri net model and Pareto. Function objectives of the proposed method are to minimize the completion time and the total expense of machines and workers. Finally, Scheduling example is employed to illustrate the effectiveness of the method.

Scheduling Problem for Parallel Machines with Limited Processing Capacities

2011 ◽  
Vol 382 ◽  
pp. 110-113
Author(s):  
Jing Fan
Keyword(s):  
Approximation Algorithms ◽  
Objective Function ◽  
Completion Time ◽  
Parallel Machines ◽  
Industrial Engineering ◽  
Scheduling Problem ◽  
Np Hard ◽  
The Novel

In the actual industrial engineering, machines used for processing need to be checked periodically to ensure that they can work efficiently. Thus, the novel scheduling problem for parallel machines with limited capacities is worth to study. The objective function is to maximize the last completion time of jobs. We show the problem is NP-hard at least. Furthermore, two approximation algorithms are presented, and algorithms' performances are considered through the experiments with large amounts of data.

ONLINE AND SEMI-ONLINE SCHEDULING ON CAPACITATED TWO-PARALLEL MACHINES

2011 ◽  
Vol 28 (02) ◽  
pp. 163-182
Author(s):  
AN ZHANG ◽  
YIWEI JIANG ◽  
ZHIYI TAN
Keyword(s):  
Lower Bound ◽  
Completion Time ◽  
Processing Time ◽  
Parallel Machines ◽  
Online Version ◽  
Special Period ◽  
Scheduling Problem ◽  
Subset Sum ◽  
Parallel Machines Scheduling ◽  
One Machine

In this paper, we investigate the capacitated two-parallel machines scheduling problem, where one machine is only available for a special period of time after which it can no longer process any job while the other machine is continuously available. Our objective is to minimize the completion time of the machine which is continuously available. The offline version of the problem is equivalent to the minimization version of the Subset-Sum problem. We first show the lower bound of the online version is infinite. We also consider the semi-online version with known the total job processing time in advance, for which both lower bound and semi-online algorithms are given.

scholarly journals Three-Machine Flowshop Scheduling Problem to Minimize Total Completion Time with Bounded Setup and Processing Times

2007 ◽  
Vol 1 (2) ◽  
pp. 5-23 ◽  
Author(s):  
Ali Allahverdi
Keyword(s):  
Completion Time ◽  
Computational Experiments ◽  
Total Completion Time ◽  
Scheduling Problem ◽  
Flowshop Scheduling ◽  
Dominance Relations ◽  
Processing Times ◽  
Flowshop Scheduling Problem ◽  
Global And Local ◽  
Local Dominance

The three-machine flowshop scheduling problem to minimize total completion time is studied where setup times are treated as separate from processing times. Setup and processing times of all jobs on all machines are unknown variables before the actual occurrence of these times. The lower and upper bounds for setup and processing times of each job on each machine is the only information that is available. In such a scheduling environment, there may not exist a unique schedule that remains optimal for all possible realizations of setup and processing times. Therefore, it is desired to obtain a set of dominating schedules (which dominate all other schedules) if possible. The objective for such a scheduling environment is to reduce the size of dominating schedule set. We obtain global and local dominance relations for a three-machine flowshop scheduling problem. Furthermore, we illustrate the use of dominance relations by numerical examples and conduct computational experiments on randomly generated problems to measure the effectiveness of the developed dominance relations. The computational experiments show that the developed dominance relations are quite helpful in reducing the size of dominating schedules.

scholarly journals Multi-objective multi-factory scheduling

2020 ◽  
Author(s):  
J. Behnamian ◽  
S.M.T. Fatemi Ghomi
Keyword(s):  
Heuristic Algorithm ◽  
Parallel Machines ◽  
Mixed Integer ◽  
Test Problems ◽  
Scheduling Problem ◽  
Production Network ◽  
Mixed Integer Linear Program ◽  
Multi Objective ◽  
Transportation Times ◽  
Factory Scheduling

This paper introducesa multi-factory scheduling problem with heterogeneous factories and parallel machines. This problem, as a major part of supply chain planning, includes the finding of suitable factory for each job and the scheduling of the assigned jobs at each factory, simultaneously. For the first time, this paper studies multi-objective scheduling in the production network in which each factory has its customers and demands can be satisfied by itself or other factories. In other words, this paper assumes that jobs can transfer from the overloaded machine in the origin factory to the factory which has fewer workloads by imposing some transportation times. For simultaneous minimization of the sum of the earliness and tardiness of jobs and total completion time, after modeling the scheduling problem as a mixed-integer linear program, the existing multi-objective techniques are analyzed and a new one is applied to our problem. Since this problem is NP-hard, a heuristic algorithm is also proposed to generate a set of Pareto optimal solutions. Also, the algorithms are proposed to improve and cover the Pareto front. Computational experiences of the heuristic algorithm and the output of the model implemented by CPLEX over a set of randomly generated test problems are reported.

Near-optimal solutions and tight lower bounds for the parallel machines scheduling problem with learning effect

2020 ◽  
Vol 54 (2) ◽  
pp. 507-527
Author(s):  
Lotfi Hidri ◽  
Mahdi Jemmali
Keyword(s):  
Lower Bounds ◽  
Learning Effect ◽  
Parallel Machines ◽  
Optimal Schedule ◽  
Practical Interest ◽  
Test Problems ◽  
Scheduling Problem ◽  
Parallel Machines Scheduling ◽  
Hard Problems ◽  
Limit Position

In this paper, the parallel machines scheduling problem with Dejong’s learning effect is addressed. The considered problem has a practical interest since it models real-world situations. In addition, this problem is a challenging one because of its NP-Hardness. In this work, a set of heuristics are proposed. The developed heuristics are categorized into two types. The first category is based on the dispatching methods, with new enhancement variants. The second type is more sophisticated and requires solving NP-Hard problems. Furthermore, several lower bounds are developed in order to assess the performance of the proposed heuristics. These lower bounds are based on solving the problem of the determination of the minimum average load under taking into account some observations. Among these observations, the existence of a limit position that the jobs are not allowed to exceed in any optimal schedule. Finally, an extensive experimental study is conducted over benchmark test problems, with up to 1500 jobs and 5280 instances. The obtained results are outperforming those proposed in the literature.

A Heuristic Method for Minimizing Makespan in M-Stage Hybrid Flowshop

2012 ◽  
Author(s):  
Kazuko Morizawa ◽  
Naoki Hirabayashi
Keyword(s):  
Numerical Experiments ◽  
Parallel Machines ◽  
Heuristic Method ◽  
Computation Time ◽  
Second Phase ◽  
Scheduling Problem ◽  
Unrelated Parallel Machines ◽  
Two Phase ◽  
Hybrid Flowshop ◽  
Optimum Schedule

This paper deals with a scheduling problem to minimize makespan in m-stage hybrid flowshop with unrelated parallel-machines at least one stage. Since the problem is known to be NP-hard, a two-phase heuristic algorithm is proposed to obtain a near-optimum schedule efficiently. In the first phase of the proposed algorithm, some promising schedules with an identical job-sequence to all stages are generated by applying NEH algorithm in various ways, and then search better schedules by applying some heuristic job-moving strategies to these schedules in the second phase. Numerical experiments are implemented to demonstrate that the proposed method can provide a near-optimum schedule within a reasonable computation time.

Heuristics for an m-Machine Re-Entrant Permutation Flowshop with the Objective of Total Tardiness

2015 ◽  
Vol 752-753 ◽  
pp. 890-895 ◽  
Author(s):  
Seong Woo Choi
Keyword(s):  
Heuristic Algorithms ◽  
Total Tardiness ◽  
Computational Experiments ◽  
Test Problems ◽  
Scheduling Problem ◽  
Flowshop Scheduling ◽  
Permutation Flowshop ◽  
Flowshop Scheduling Problem ◽  
Flowshop Problem

We focus on an m-machine re-entrant flowshop scheduling problem with the objective of minimizing total tardiness. In the re-entrant flowshop considered here, routes of all jobs are identical as in ordinary flowshops, but the jobs must be processed multiple times on the machines. We present heuristic algorithms, which are modified from well-known existing algorithms for the general m-machine flowshop problem or newly developed in this paper. For evaluation of the performance of the algorithms, computational experiments are performed on randomly generated test problems and results are reported.

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