Scheduling problems with rejection and piece-rate maintenance to minimise the total weighted completion time

2022 ◽  
Vol 16 (1) ◽  
pp. 64
Author(s):  
Xiuzhi Sang ◽  
Kai Huang ◽  
Dehua Xu ◽  
Zhenq Wang ◽  
Xianyu Yu
Keyword(s):  
Completion Time ◽  
Total Weighted Completion Time ◽  
Scheduling Problems ◽  
Piece Rate ◽  
Weighted Completion Time

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.

SCHEDULING PROBLEMS WITH THE EFFECTS OF DETERIORATION AND LEARNING

2007 ◽  
Vol 24 (02) ◽  
pp. 245-261 ◽  
Author(s):  
JI-BO WANG ◽  
T. C. EDWIN CHENG
Keyword(s):  
Performance Measures ◽  
Single Machine ◽  
Completion Time ◽  
Flow Shop ◽  
Machine Scheduling ◽  
Total Weighted Completion Time ◽  
Scheduling Problems ◽  
Processing Times ◽  
Weighted Completion Time ◽  
Machine Scheduling Problems

This paper deals with the machine scheduling problems with the effects of deterioration and learning. In this model the processing times of jobs are defined as functions of their starting times and positions in a sequence. We introduce polynomial solutions for some single machine problems and flow shop problems. The performance measures include makespan, total completion time, total weighted completion time, and maximum lateness.

scholarly journals Some Single-Machine Scheduling Problems with Learning Effects and Two Competing Agents

2014 ◽  
Vol 2014 ◽  
pp. 1-7
Author(s):  
Hongjie Li ◽  
Zeyuan Li ◽  
Yunqiang Yin
Keyword(s):  
Single Machine ◽  
Completion Time ◽  
Single Machine Scheduling ◽  
Total Weighted Completion Time ◽  
Scheduling Problems ◽  
Maximum Cost ◽  
Two Agents ◽  
Solution Algorithms ◽  
Weighted Completion Time ◽  
Machine Scheduling Problems

This study considers a scheduling environment in which there are two agents and a set of jobs, each of which belongs to one of the two agents and its actual processing time is defined as a decreasing linear function of its starting time. Each of the two agents competes to process its respective jobs on a single machine and has its own scheduling objective to optimize. The objective is to assign the jobs so that the resulting schedule performs well with respect to the objectives of both agents. The objective functions addressed in this study include the maximum cost, the total weighted completion time, and the discounted total weighted completion time. We investigate three problems arising from different combinations of the objectives of the two agents. The computational complexity of the problems is discussed and solution algorithms where possible are presented.

scholarly journals Multiple-Machine Scheduling with Learning Effects and Cooperative Games

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Yiyuan Zhou ◽  
Qiang Zhang
Keyword(s):  
Completion Time ◽  
Optimal Schedule ◽  
Cost Savings ◽  
Machine Scheduling ◽  
Total Weighted Completion Time ◽  
Learning Effects ◽  
Scheduling Problems ◽  
Weighted Completion Time ◽  
The Difference ◽  
The Cost

Multiple-machine scheduling problems with position-based learning effects are studied in this paper. There is an initial schedule in this scheduling problem. The optimal schedule minimizes the sum of the weighted completion times; the difference between the initial total weighted completion time and the minimal total weighted completion time is the cost savings. A multiple-machine sequencing game is introduced to allocate the cost savings. The game is balanced if the normal processing times of jobs that are on the same machine are equal and an equal number of jobs are scheduled on each machine initially.

scholarly journals A Heuristic’s Job Order Gain in Pyramidal Preemptive Job Scheduling Problems for Total Weighted Completion Time Minimization

2019 ◽  
Vol 22 ◽  
pp. 1-8
Author(s):  
Vadim Romanuke
Keyword(s):  
Completion Time ◽  
Job Scheduling ◽  
Relative Difference ◽  
Total Weighted Completion Time ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Release Date ◽  
Time Minimization ◽  
Weighted Completion Time ◽  
Processing Period

A possibility of speeding up the job scheduling by a heuristic based on the shortest processing period approach is studied in the paper. The scheduling problem is such that the job volume and job priority weight are increasing as the job release date increases. Job preemptions are allowed. Within this model, the input for the heuristic is formed by either ascending or descending job order. Therefore, an estimator of relative difference in duration of finding an approximate schedule by these job orders is designed. It is ascertained that the job order results in different time of computations when scheduling at least a few hundred jobs. The ascending-order solving becomes on average by 1 % to 2.5 % faster when job volumes increase steeply. As the steepness of job volumes decreases, this gain vanishes and, eventually, the descending-order solving becomes on average faster by up to 4 %. The gain trends of both job orders slowly increase as the number of jobs increases.

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.

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