Due-Window Assignment and Resource Allocation Scheduling with Truncated Learning Effect and Position-Dependent Weights
Author
Source
Discrete Dynamics in Nature and Society
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-10-08
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
This paper studies single-machine due-window assignment scheduling problems with truncated learning effect and resource allocation simultaneously.
Linear and convex resource allocation functions under common due-window (CONW) assignment are considered.
The goal is to find the optimal due-window starting (finishing) time, resource allocations and job sequence that minimize a weighted sum function of earliness and tardiness, due window starting time, due window size, and total resource consumption cost, where the weight is position-dependent weight.
Optimality properties and polynomial time algorithms are proposed to solve these problems.
American Psychological Association (APA)
Lin, Shan-Shan. 2020. Due-Window Assignment and Resource Allocation Scheduling with Truncated Learning Effect and Position-Dependent Weights. Discrete Dynamics in Nature and Society،Vol. 2020, no. 2020, pp.1-7.
https://search.emarefa.net/detail/BIM-1153611
Modern Language Association (MLA)
Lin, Shan-Shan. Due-Window Assignment and Resource Allocation Scheduling with Truncated Learning Effect and Position-Dependent Weights. Discrete Dynamics in Nature and Society No. 2020 (2020), pp.1-7.
https://search.emarefa.net/detail/BIM-1153611
American Medical Association (AMA)
Lin, Shan-Shan. Due-Window Assignment and Resource Allocation Scheduling with Truncated Learning Effect and Position-Dependent Weights. Discrete Dynamics in Nature and Society. 2020. Vol. 2020, no. 2020, pp.1-7.
https://search.emarefa.net/detail/BIM-1153611
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1153611