2021
DOI: 10.30526/34.3.2677
|View full text |Cite
|
Sign up to set email alerts
|

Novel Heuristic Approach for Solving Multi-objective Scheduling Problems

Abstract: In this paper, we studied the scheduling of  jobs on a single machine.  Each of n jobs is to be processed without interruption and becomes available for processing at time zero. The objective is to find a processing order of the jobs, minimizing the sum of maximum earliness and maximum tardiness. This problem is to minimize the earliness and tardiness values, so this model is equivalent to the just-in-time production system. Our lower bound depended on the decomposition of the problem into two subprograms. We … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2

Citation Types

0
2
0

Year Published

2024
2024
2024
2024

Publication Types

Select...
1

Relationship

0
1

Authors

Journals

citations
Cited by 1 publication
(2 citation statements)
references
References 10 publications
0
2
0
Order By: Relevance
“…Nagar et al [2] presented a survey of multiple and binary problems in scheduling. In general, there are two structures for dealing with conflicting criteria, namely hierarchical minification and concurrent minification [3]. The first one is the primary criterion, and the other is the secondary criterion.…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…Nagar et al [2] presented a survey of multiple and binary problems in scheduling. In general, there are two structures for dealing with conflicting criteria, namely hierarchical minification and concurrent minification [3]. The first one is the primary criterion, and the other is the secondary criterion.…”
Section: Introductionmentioning
confidence: 99%
“…Given the schedule ( ( ) ( ) ( )), then for each job , we calculate the completion time by and for . The earliness of the job is defined by , the two problems 1// , 1// , and 1// are NP-hard [6], [3], [7], [8], [9], [10]. Any problem including cost functions as sub-problems is NP-hard.…”
Section: Introductionmentioning
confidence: 99%