Pareto‐optimization of three‐agent scheduling to minimize the total weighted completion time, weighted number of tardy jobs, and total weighted late work

2020 ◽  
Author(s):  
Yuan Zhang ◽  
Jinjiang Yuan ◽  
Chi To Ng ◽  
Tai Chiu E. Cheng
Keyword(s):  
Completion Time ◽  
Pareto Optimization ◽  
Total Weighted Completion Time ◽  
Late Work ◽  
Number Of Tardy Jobs ◽  
Agent Scheduling ◽  
Weighted Number ◽  
Weighted Completion Time ◽  
Tardy Jobs

Approximation algorithms for multi-agent scheduling to minimize total weighted completion time

2009 ◽  
Vol 109 (16) ◽  
pp. 913-917 ◽  
Author(s):  
Kangbok Lee ◽  
Byung-Cheon Choi ◽  
Joseph Y.-T. Leung ◽  
Michael L. Pinedo
Keyword(s):  
Approximation Algorithms ◽  
Completion Time ◽  
Total Weighted Completion Time ◽  
Agent Scheduling ◽  
Multi Agent ◽  
Weighted Completion Time

Single Machine Two-Agent Scheduling with Deteriorating Jobs

2016 ◽  
Vol 33 (05) ◽  
pp. 1650034 ◽  
Author(s):  
Zhenyou Wang ◽  
Cai-Min Wei ◽  
Yu-Bin Wu
Keyword(s):  
Single Machine ◽  
Completion Time ◽  
Processing Time ◽  
Deteriorating Jobs ◽  
Single Machine Scheduling ◽  
Total Weighted Completion Time ◽  
Scheduling Problem ◽  
Agent Scheduling ◽  
Polynomially Solvable ◽  
Weighted Completion Time

This paper deals with the single machine scheduling problem with deteriorating jobs in which there are two distinct families of jobs (i.e., two-agent) pursuing different objectives. In this model the processing time of a job is defined as a function that is proportional to a linear function of its stating time. For the following three scheduling criteria: minimizing the makespan, minimizing the total weighted completion time, and minimizing the maximum lateness, we show that some basic versions of the problem are polynomially solvable. We also establish the conditions under which the problem is computationally hard.

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 Two-Agent Pareto-Scheduling of Minimizing Total Weighted Completion Time and Total Weighted Late Work

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 2070 ◽  
Author(s):  
Yuan Zhang ◽  
Zhichao Geng ◽  
Jinjiang Yuan
Keyword(s):  
Completion Time ◽  
Pareto Frontier ◽  
Exact Algorithms ◽  
Total Weighted Completion Time ◽  
Scheduling Problem ◽  
Due Date ◽  
Late Work ◽  
Common Due Date ◽  
Numerical Tests ◽  
Weighted Completion Time

We investigate the Pareto-scheduling problem with two competing agents on a single machine to minimize the total weighted completion time of agent A’s jobs and the total weighted late work of agent B’s jobs, the B-jobs having a common due date. Since this problem is known to be NP-hard, we present two pseudo-polynomial-time exact algorithms to generate the Pareto frontier and an approximation algorithm to generate a (1+ϵ)-approximate Pareto frontier. In addition, some numerical tests are undertaken to evaluate the effectiveness of our algorithms.

Sign in / Sign up

Export Citation Format

Share Document