scholarly journals Group Scheduling with Learning Effect and Random Processing Time

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Dingyu Wang ◽  
Chunming Ye
Keyword(s):  
Objective Function ◽  
Completion Time ◽  
Processing Time ◽  
Learning Effect ◽  
Heuristic Algorithms ◽  
Random Variable ◽  
Group Scheduling ◽  
Learning Effects ◽  
Scheduling Model ◽  
Installation Time

In this paper, we establish a stochastic grouping scheduling model. In the model, there is no installation time between the jobs in the same group, but each group has an installation time before processing. There are learning effects between groups and within groups, and the completion time of jobs is a random variable. We take the long expected schedule and the expected total completion time as the objective function, and the noninterruptible static priority strategy is obtained. At the same time, heuristic algorithms and examples are given.

scholarly journals Group Scheduling Problems with Time-Dependent and Position-Dependent DeJong’s Learning Effect

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Li Sun ◽  
Lei Ning ◽  
Jia-zhen Huo
Keyword(s):  
Polynomial Time ◽  
Completion Time ◽  
Learning Effect ◽  
Time Dependent ◽  
Total Weighted Completion Time ◽  
Group Scheduling ◽  
Scheduling Problems ◽  
Scheduling Model ◽  
Proposed Model ◽  
Weighted Completion Time

In this paper, we introduce a group scheduling model with time-dependent and position-dependent DeJong’s learning effect. The objectives of scheduling problems are to minimize makespan, the total completion time, and the total weighted completion time, respectively. We show that the problems remain solvable in polynomial time under the proposed model.

scholarly journals Scheduling Jobs Families with Learning Effect on the Setup

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Sergio Fichera ◽  
Antonio Costa ◽  
Fulvio Cappadonna
Keyword(s):  
Completion Time ◽  
Learning Effect ◽  
Flow Shop ◽  
Group Scheduling ◽  
Scheduling Problem ◽  
Learning Effects ◽  
Anova Analysis ◽  
Hybrid Metaheuristic ◽  
Set Up ◽  
Over Time

The present paper aims to address the flow-shop sequence-dependent group scheduling problem with learning effect (FSDGSLE). The objective function to be minimized is the total completion time, that is, the makespan. The workers are required to carry out manually the set-up operations on each group to be loaded on the generic machine. The operators skills improve over time due to the learning effects; therefore the set-up time of a group under learning effect decreases depending on the order the group is worked in. In order to effectively cope with the issue at hand, a mathematical model and a hybrid metaheuristic procedure integrating features from genetic algorithms (GA) have been developed. A well-known problem benchmark risen from literature, made by two-, three- and six-machine instances, has been taken as reference for assessing performances of such approach against the two most recent algorithms presented by literature on the FSDGS issue. The obtained results, also supported by a properly developed ANOVA analysis, demonstrate the superiority of the proposed hybrid metaheuristic in tackling the FSDGSLE problem under investigation.

Scheduling Jobs with Truncated Exponential Sum-of-Logarithm-Processing-Times Based and Position-based Learning Effects

2015 ◽  
Vol 32 (04) ◽  
pp. 1550026 ◽  
Author(s):  
Yuan-Yuan Lu ◽  
Fei Teng ◽  
Zhi-Xin Feng
Keyword(s):  
Single Machine ◽  
Minimization Problem ◽  
Completion Time ◽  
Heuristic Algorithms ◽  
Exponential Sum ◽  
Total Weighted Completion Time ◽  
Learning Effects ◽  
Processing Times ◽  
Time Minimization ◽  
Weighted Completion Time

In this study, we consider a scheduling problem with truncated exponential sum-of-logarithm-processing-times based and position-based learning effects on a single machine. We prove that the shortest processing time (SPT) rule is optimal for the makespan minimization problem, the sum of the θth power of job completion times minimization problem, and the total lateness minimization problem, respectively. For the total weighted completion time minimization problem, the discounted total weighted completion time minimization problem, the maximum lateness minimization problem, we present heuristic algorithms (the worst-case bound of these heuristic algorithms are also given) according to the corresponding single machine scheduling problems without learning considerations. It also shows that the problems of minimizing the total tardiness, the total weighted completion time and the discounted total weighted completion time are polynomially solvable under some agreeable conditions on the problem parameters.

A BATCHING PROBLEM WITH LEARNING EFFECT CONSIDERATIONS

2009 ◽  
Vol 26 (02) ◽  
pp. 307-317 ◽  
Author(s):  
WEN-HUA YANG
Keyword(s):  
Dynamic Programming ◽  
Completion Time ◽  
Processing Time ◽  
Learning Effect ◽  
Polynomial Function ◽  
Optimal Number ◽  
Total Completion Time ◽  
Batch Sizing ◽  
Forward Dynamic Programming ◽  
Unit Processing

Consider a batch-sizing problem, where all jobs are identical or similar, and a unit processing time (p = 1) is specified for each job. To minimize the total completion time of jobs, partitioning jobs into batches may be necessary. Learning effect from setup repetition makes small-sized batches; on the contrary, job's learning effect results in large-sized batches. With their collaborative influence, we develop a forward dynamic programming (DP) algorithm to determine the optimal number of batches and their optimal integer sizes. The computation effort required by this DP algorithm is a polynomial function of job size.

STOCHASTIC BATCH SCHEDULING AND THE “SMALLEST VARIANCE FIRST” RULE

2007 ◽  
Vol 21 (4) ◽  
pp. 579-595 ◽  
Author(s):  
Michael Pinedo
Keyword(s):  
Completion Time ◽  
Processing Time ◽  
Random Variable ◽  
Combinatorial Problems ◽  
Set Partitioning ◽  
Batch Scheduling ◽  
Matching Problem ◽  
Batch Mode ◽  
Processing Times ◽  
Expected Makespan

Consider a single machine that can process multiple jobs in batch mode. We havenjobs and the processing time of jobjis a random variableXjwith distributionFj. Up tobjobs can be processed simultaneously by the machine. The jobs in a batch all have to start at the same time and the batch is completed when all jobs have finished their processing (i.e., at the maximum of the processing times of the jobs in that batch). We are interested in two objective functions, namely the minimization of the expected makespan and the minimization of the total expected completion time. We first show that under certain fairly general conditions, the minimization of the expected makespan is equivalent to specific deterministic combinatorial problems, namely the Weighted Matching problem and the Set Partitioning problem. We then consider the case when all jobs have the same mean processing time but different variances. We show that for certain special classes of processing time distributions theSmallest Variance Firstrule minimizes the expected makespan as well as the total expected completion time. In our conclusions we present various general rules that are suitable for the minimization of the expected makespan and the total expected completion time in batch scheduling.

scholarly journals Minimizing Total Weighted Completion Time on Single Machine with Past-Sequence-Dependent Setup Times and Exponential Time-Dependent and Position-Dependent Learning Effects

2009 ◽  
Vol 2009 ◽  
pp. 1-10 ◽  
Author(s):  
Kaibiao Sun ◽  
Hongxing Li
Keyword(s):  
Single Machine ◽  
Completion Time ◽  
Processing Time ◽  
Setup Times ◽  
Time Dependent ◽  
Total Weighted Completion Time ◽  
Learning Effects ◽  
Exponential Time ◽  
Weighted Completion Time ◽  
Dependent Learning

This paper addresses a single-machine problem in which the past-sequence-dependent (p-s-d) setup times and exponential time-dependent and position-dependent learning effects are considered. By the exponential time-dependent learning effect, it means that the processing time of a job is defined by an exponent function of the total actual processing time of the already processed jobs. The setup times are proportional to the length of the already processed jobs. The aim is to minimize the total weighted completion time, this is an NP-hard problem. Under certain conditions, it is shown that the classical WSPT rule is optimal for the problem.

scholarly journals Scheduling Jobs with Linear Model of Simultaneous Ageing and Learning Effects

2011 ◽  
Vol 5 (1) ◽  
pp. 37-48 ◽  
Author(s):  
Adam Janiak ◽  
Maciej Lichtenstein ◽  
Agata Rusoń
Keyword(s):  
Heuristic Algorithms ◽  
Piecewise Linear ◽  
Piecewise Linear Function ◽  
Learning Effects ◽  
Aging Effects ◽  
Scheduling Model ◽  
Special Cases ◽  
Polynomially Solvable ◽  
Single Processor ◽  
Makespan Criterion

In the paper, we introduce some new scheduling model in which learning and aging effects are both considered simultaneously. In this model the actual processing time of the jobs depends only on its position in a schedule and can be described by the piecewise linear function. For single-processor problem with introduced model, we show that the problem of minimizing the makespan criterion for independent jobs with release dates is strongly NPhard, but some special cases of this problem are polynomially solvable. Based on those special cases, we propose 4 heuristic algorithms and we experimentally examine their usefulness for solving the general problem.

scholarly journals A best possible algorithm for an online scheduling problem with position-based learning effect

2021 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
Ran Ma ◽  
Lu Zhang ◽  
Yuzhong Zhang
Keyword(s):  
Single Machine ◽  
Completion Time ◽  
Processing Time ◽  
Learning Effect ◽  
Online Algorithm ◽  
Online Scheduling ◽  
Release Time ◽  
Scheduling Problem ◽  
Over Time ◽  
Actual Processing Time

<p style='text-indent:20px;'>In this paper, we focus on an online scheduling problem with position-based learning effect on a single machine, where the jobs are released online over time and preemption is not allowed. The information about each job <inline-formula><tex-math id="M1">\begin{document}$ J_j $\end{document}</tex-math></inline-formula>, including the basic processing time <inline-formula><tex-math id="M2">\begin{document}$ p_j $\end{document}</tex-math></inline-formula> and the release time <inline-formula><tex-math id="M3">\begin{document}$ r_j $\end{document}</tex-math></inline-formula>, is only available when it arrives. The actual processing time <inline-formula><tex-math id="M4">\begin{document}$ p_j' $\end{document}</tex-math></inline-formula> of each job <inline-formula><tex-math id="M5">\begin{document}$ J_j $\end{document}</tex-math></inline-formula> is defined as a function related to its position <inline-formula><tex-math id="M6">\begin{document}$ r $\end{document}</tex-math></inline-formula>, i.e., <inline-formula><tex-math id="M7">\begin{document}$ p_j' = p_j(\alpha-r\beta) $\end{document}</tex-math></inline-formula>, where <inline-formula><tex-math id="M8">\begin{document}$ \alpha $\end{document}</tex-math></inline-formula> and <inline-formula><tex-math id="M9">\begin{document}$ \beta $\end{document}</tex-math></inline-formula> are both nonnegative learning index. Our goal is to minimize the sum of completion time of all jobs. For this problem, we design a deterministic polynomial time online algorithm <i>Delayed Shortest Basic Processing Time</i> (DSBPT). In order to facilitate the understanding of the online algorithm, we present a relatively common and simple example to describe the execution process of the algorithm, and then by competitive analysis, we show that online algorithm DSBPT is a best possible online algorithm with a competitive ratio of 2.</p>

scholarly journals Single-Machine Scheduling with Accelerating Learning Effects

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
T. C. E. Cheng ◽  
Shih-Chang Tseng ◽  
Peng-Jen Lai ◽  
Wen-Chiung Lee
Keyword(s):  
Single Machine ◽  
Completion Time ◽  
Learning Effect ◽  
Single Machine Scheduling ◽  
Total Tardiness ◽  
Total Weighted Completion Time ◽  
Learning Effects ◽  
Optimal Solutions ◽  
New Model ◽  
Weighted Completion Time

Scheduling with learning effects has been widely studied. However, there are situations where the learning effect might accelerate. In this paper, we propose a new model where the learning effect accelerates as time goes by. We derive the optimal solutions for the single-machine problems to minimize the makespan, total completion time, total weighted completion time, maximum lateness, maximum tardiness, and total tardiness.

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