Bicriteria scheduling problem for unrelated parallel machines with release dates

2015 ◽  
Vol 64 ◽  
pp. 28-39 ◽  
Author(s):  
Yang-Kuei Lin ◽  
Hao-Chen Lin
Keyword(s):  
Parallel Machines ◽  
Release Dates ◽  
Scheduling Problem ◽  
Unrelated Parallel Machines ◽  
Bicriteria Scheduling

BicriteriaHeuristic for Unrelated Parallel Machine Scheduling Problem

2014 ◽  
Vol 1016 ◽  
pp. 824-828
Author(s):  
Yang Kuei Lin ◽  
Hao Chen Lin
Keyword(s):  
Parallel Machines ◽  
Machine Scheduling ◽  
Parallel Machine Scheduling ◽  
Parallel Machine ◽  
Release Dates ◽  
Scheduling Problem ◽  
Unrelated Parallel Machines ◽  
Weighted Tardiness ◽  
Unrelated Parallel Machine Scheduling ◽  
Parallel Machine Scheduling Problem

In this research, a bi-criteria heuristic is proposed to find non-dominated solutions for scheduling unrelated parallel machines with release dates that minimizes makespan andtotal weighted tardiness.

Exact algorithms for a scheduling problem with unrelated parallel machines and sequence and machine-dependent setup times

2008 ◽  
Vol 35 (4) ◽  
pp. 1250-1264 ◽  
Author(s):  
Pedro Leite Rocha ◽  
Martín Gómez Ravetti ◽  
Geraldo Robson Mateus ◽  
Panos M. Pardalos
Keyword(s):  
Parallel Machines ◽  
Exact Algorithms ◽  
Setup Times ◽  
Scheduling Problem ◽  
Unrelated Parallel Machines ◽  
Dependent Setup Times

Minimizing Total Earliness and Total Tardiness on Single Machine with Release Dates

2011 ◽  
Vol 5 ◽  
pp. 30-43
Author(s):  
Elkanah Oyetunji ◽  
Ayodeji E. Oluleye
Keyword(s):  
Single Machine ◽  
Single Machine Scheduling ◽  
Total Tardiness ◽  
Release Dates ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Bicriteria Scheduling ◽  
Linear Composite ◽  
Effectiveness And Efficiency ◽  
Machine Scheduling Problems

This paper considers the bicriteria scheduling problem of minimizing the total earliness and the total tardiness on a single machine with release dates. In view of the fact that the problem has been characterized as NP-Hard, we propose two approximation algorithms (labeled as ETA1 and ETA2) for solving the problem. The proposed algorithms were compared with the MA heuristic selected from the literature. The two criteria (the total earliness and the total tardiness) were aggregated together into a linear composite objective function (LCOF). The performances of the algorithms were evaluated based on both effectiveness and efficiency. The algorithms were tested on a set of 1200 randomly generated single machine scheduling problems. Experimental results show that both the ETA1 and ETA2 algorithms outperformed (in terms of effectiveness and efficiency) the MA heuristic under all the considered problem sizes. Also, the ETA1 algorithm outperformed the ETA2 algorithm when the number of jobs (n) ranges between 20 and 500.

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