Title: An approach for rush order acceptance decisions using simulation and multi-attribute utility theory

Authors: Faisal Aqlan; Abdulaziz Ahmed; Omar Ashour; Abdulrahman Shamsan; Mohammad M. Hamasha

Addresses: Department of Industrial Engineering, Pennsylvania State University, The Behrend College, Erie, PA, USA ' Business Department, University of Minnesota Crookston, Crookston, MN, USA ' Department of Industrial Engineering, Pennsylvania State University, The Behrend College, Erie, PA, USA ' Department of Systems Science and Industrial Engineering, State University of New York at Binghamton, Binghamton, NY, USA ' Department of Engineering Management, Prince Sultan University, Riyadh, Saudi Arabia

Abstract: Rush orders are orders with shorter lead times and higher operating priorities compared to regular orders. A company may accept rush order, regardless of its capacity or raw material constraints, to maintain customer satisfaction and/or increase profit. On the other hand, rush orders can cause problems in managing production systems due to the unbalanced use of system resources. In this paper, discrete event simulation (DES) and multi-attribute utility theory (MAUT) are integrated to study the impact of rush orders on the performance of a hybrid push-pull production system. The proposed approach is used to identify the best acceptance levels of rush orders. Numerical results showed that prioritising customer orders based on their associated utilities can improve the performance of a production system. In addition, the best acceptance levels of rush orders can be determined by maximising the performance of the production system while considering production constraints. [Received 25 May 2015; Revised 1 August 2016; Revised 6 September 2016; Revised 3 March 2017; Accepted 5 June 2017]

Keywords: rush orders; push-pull production system; discrete event simulation; DES; multi-attribute utility theory; MAUT.

DOI: 10.1504/EJIE.2017.087680

European Journal of Industrial Engineering, 2017 Vol.11 No.5, pp.613 - 630

Available online: 25 Oct 2017 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article