Due-Window Assignment and Resource Allocation Scheduling with Truncated Learning Effect and Position-Dependent Weights

Shan-Shan Lin

Discrete Dynamics in Nature and Society, 2020, vol. 2020, 1-7

Abstract:

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.

Date: 2020
References: Add references at CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
http://downloads.hindawi.com/journals/DDNS/2020/9260479.pdf (application/pdf)
http://downloads.hindawi.com/journals/DDNS/2020/9260479.xml (text/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:jnddns:9260479

DOI: 10.1155/2020/9260479

Access Statistics for this article

More articles in Discrete Dynamics in Nature and Society from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().