A predictive-reactive cross-dock rescheduling system under truck arrival uncertainty. (February 2022)

• Record Type: Journal Article
• Authors: Nasiri, Mohammad Mahdi; Ahmadi, Naeime; Konur, Dinçer; Rahbari, Ali
• Is Part Of: Expert systems with applications. Volume 188(2022)
• Publication Date: 2022-02
• Subjects: Cross-docking -- Rescheduling -- Uncertainty -- Truck Scheduling; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2021.115986
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: A periodic predictive-reactive system is developed for cross-dock rescheduling. The system consists of rescheduling optimization and short interval repair policy. Rescheduling model and three different repair policies are discussed. Numerical studies are presented to analyze rescheduling and repair policies. Abstract: In a cross-docking system, products are transferred from the suppliers to the customers without being held as inventory. One of the critical operational problems in cross-docking is scheduling inbound and outbound trucks. In practice, the inbound trucks can arrive late; consequently, the timing of the unloading/loading operations may be affected. This hinders the planned schedule's efficiency and may result in the infeasibility of this schedule. Even though the cross-dock scheduling problem is studied under operational uncertainties, the focus was on taking a proactive approach and creating a master schedule against such uncertainties. Nevertheless, from an operational perspective, any master schedule would require modifications as uncertainties unfold, i.e., rescheduling is needed. While rescheduling is studied in manufacturing, cross-dock rescheduling is not investigated. This study contributes to cross-dock scheduling research by investigating rescheduling at a cross-dock facility. Particularly, a periodic predictive-reactive rescheduling system in response to truck arrival uncertainty is proposed. This system consists of two main components: a rescheduling optimization model and a short interval repair policy. The rescheduling optimization model creates updated schedules at discrete review times and the short interval repair policy specifies how to handle trucks considering the delays. First, we present the rescheduling optimization model to minimize the deviation from the master schedule, which is created at the beginning of the planning horizon based on the scheduled truck arrivals. Then, three short interval repair policies are introduced. The rescheduling model is integrated with each of the three different short interval repair policies, resulting in three variations of the rescheduling system. Computational experiments are performed to analyse and compare the performance of the short interval repair policies under various settings, and managerial insights are presented.