Title: Investigating total earliness and tardiness costs through unrelated parallel machine scheduling in uncertain job shop environment using robust optimisation and design of experiment

Authors: Parsa Kianpour; Deepak Gupta; Krishna Krishnan; Bhaskaran Gopalakrishnan

Addresses: Department of Industrial, Systems, and Manufacturing Engineering, Wichita State University, Wichita, KS, USA ' Department of Industrial, Systems, and Manufacturing Engineering, Wichita State University, Wichita, KS, USA ' Department of Industrial, Systems, and Manufacturing Engineering, Wichita State University, Wichita, KS, USA ' Benjamin M. Statler College of Engineering and Mineral Resources, West Virginia University, Morgantown, WV, USA

Abstract: In real world production systems, uncertain events such as random machine breakdown and processing time can occur anytime. These events lead to disruption of normal activities and consequently invalidate the initial schedule. Considering uncertainty in the scheduling process enables organisations to resume their activities effectively after uncertain events occur. The focus of this paper is proactive scheduling approach with an objective of minimising the total cost (lateness/earliness penalty and tooling cost). Robust optimisation is used to solve the scheduling problem considering processing time, setup times and tooling cost as uncertain parameters. Numerous scenarios are solved using data from local job shop. Multiple performance measurement criteria are evaluated to assess the significance of results obtained using robust and deterministic models. Design of experiment (DOE) has been implemented to evaluate the effects of different factors on the total cost and computational times.

Keywords: job shop; scheduling; unrelated parallel machines; tardiness; earliness; inventory cost; penalty cost; robust optimisation; design of experiment; DOE.

DOI: 10.1504/IJOR.2022.128415

International Journal of Operational Research, 2022 Vol.45 No.4, pp.511 - 539

Received: 08 May 2019
Accepted: 09 Jan 2020
Published online: 20 Jan 2023 *

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