Service level and profit maximisation in order acceptance and scheduling problem with weighted tardiness

Mohammad Yavari and Amir Hosein Akbari

International Journal of Industrial and Systems Engineering, 2023, vol. 43, issue 3, 331-362

Abstract: Traditional order acceptance and scheduling (OAS) problem focused on profit optimisation and the number of accepted orders has been only regarded as a constraint in the OAS model in a few research studies. The current paper investigates a bi-objective OAS problem to maximise profit and service level. There are two categories of regular and special orders in a single-machine environment. We have proposed a mixed integer linear program using goal programming. Due to the NP-hard nature of the problem, we have developed a simulated annealing-based heuristic to solve the problem, and a lower bound to assess its performance. Both single objective and bi-objective versions of the problem have been studied. Computational experiments demonstrate the ability of the proposed heuristic. The advantages and disadvantages of the proposed bi-objective OAS problem are discussed. Also, the relation between service level and profit objectives is studied in both problems with and without special orders.

Keywords: order acceptance and scheduling; OAS; service level; simulated annealing-based heuristic; mixed-integer linear programming; MILP; goal programming; bi-objective; lower bound. (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=129138 (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:ijisen:v:43:y:2023:i:3:p:331-362

Access Statistics for this article

More articles in International Journal of Industrial and Systems Engineering from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().