Batch scheduling jobs with learning effect to minimize the total weighted completion time

2011 ◽  
Author(s):  
Haixia Li ◽  
Youzeng Wang ◽  
Qingfeng Liu ◽  
Sun Chuanguang
Keyword(s):  
Completion Time ◽  
Learning Effect ◽  
Batch Scheduling ◽  
Total Weighted Completion Time ◽  
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.

A SINGLE-MACHINE TWO-AGENT SCHEDULING PROBLEM BY GA APPROACH

2012 ◽  
Vol 29 (02) ◽  
pp. 1250013 ◽  
Author(s):  
SHUENN-REN CHENG
Keyword(s):  
Single Machine ◽  
Completion Time ◽  
Learning Effect ◽  
Control Parameter ◽  
Optimal Schedule ◽  
Total Weighted Completion Time ◽  
Scheduling Problem ◽  
Processing Times ◽  
Agent Scheduling ◽  
Weighted Completion Time

A single-machine two-agent scheduling problem with a truncation learning effect is being addressed in the study. The truncation learning effect means that the actual processing time of a job is a function of the sum of processing times of already scheduled jobs and a control parameter. The aim is to find an optimal schedule to minimize the total weighted completion time of jobs of the first agent under the circumstances that no tardy job is allowed for the second agent. A branch-and-bound and three heuristic-based genetic algorithms (GAs) are proposed to solve the problem. Also presented in the study are the computational results of all proposed algorithms.

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.

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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