Integrating two-agent scheduling and order acceptance problems to maximise total revenue by bounding each agent penalty function

Mohammad Reisi-Nafchi and Ghasem Moslehi

International Journal of Services and Operations Management, 2015, vol. 20, issue 3, 358-384

Abstract: This paper integrates the two-agent scheduling problem and the order acceptance and scheduling problem, to form a single problem for further investigation. The idea originates from real life situations in which manufacturers deal with different customers, each having different requests. It is, therefore, assumed in the new problem that two customer (agent) types exist while each has their specific penalty functions. The penalty function of the first agent is the total lateness while that of the second is the number of tardy orders. The objective is maximising the total revenue of accepted orders by bounding each agent's penalty function. For this problem, a pseudo-polynomial dynamic programming algorithm is developed. It was shown that the algorithm is capable of optimally solving all the problem instances up to 50 orders in size. The algorithm is found to be capable of solving 87.15% of the instances with 90 orders.

Keywords: operations management; order acceptance; two-agent scheduling; dynamic programming; analysis of variance; ANOVA; total revenue; agent penalty function. (search for similar items in EconPapers)
Date: 2015
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.inderscience.com/link.php?id=67773 (text/html)
Access to full text is restricted to subscribers.

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:ids:ijsoma:v:20:y:2015:i:3:p:358-384

Access Statistics for this article

More articles in International Journal of Services and Operations Management from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().