REPRESENTATIVE-DAY DISCRETE-EVENT SIMULATION TO REDUCE OVERTIME IN OUTBOUND WAREHOUSE DOCKING

Pachira  Na nan; Thammawit Prasert; Peerapong Pakawanich

Authors

Pachira Na nan M.Eng. Student, Department of Industrial Engineering and Management, Faculty of Engineering and Industrial Technology, Silpakorn University, Mueang District, Nakhon Pathom, 73000, Thailand
Thammawit Prasert Lecturer, Department of Industrial Engineering and Management, Faculty of Engineering and Industrial Technology, Silpakorn University, Mueang District, Nakhon Pathom, 73000, Thailand
Peerapong Pakawanich Lecturer, Department of Industrial Engineering and Management, Faculty of Engineering and Industrial Technology, Silpakorn University, Mueang District, Nakhon Pathom, 73000, Thailand

Keywords:

discrete-event simulation, outbound logistics, overtime reduction, representative-day selection, truck dock scheduling

Abstract

Overtime in outbound warehouse docking operations is a persistent logistics challenge, yet existing studies rarely address realistic labor calendars, zoning constraints, heterogeneous truck-type processes, or extreme congestion days. This study develops a data-driven, minute-resolution discrete-event simulation (DES) in Python to evaluate five dispatching and bay-sharing policies (S0–S4) at a Thai beverage manufacturer. Operational data from 235 working days covering four truck types (EXP, MAN, CAN, BOT) across 19 bays were used to calibrate the model. A hybrid scenario-reduction approach-combining tail-based extreme-day retention and Ward's hierarchical clustering-compressed the dataset to 40 representative days (including 16 extreme days), reducing computational effort by 83% while preserving workload fidelity (NRMSE = 0.070; Pearson r = 0.873). A total of 200 DES runs (40 days × 5 policies) were conducted, with all improvements confirmed statistically significant (paired t-test, p < 0.001). FIFO dispatching with BOT-to-CAN bay sharing (S1) proved most robust, reducing weighted-average overtime by 43.34% (from 112.94 to 63.99 min/day) with the lowest truck waiting times. While S2 achieved slightly lower absolute overtime (62.79 min/day), it redistributed delays under peak conditions rather than relieving congestion. Sensitivity analysis identified a 90-minute end-of-shift activation window as optimal. S1 is recommended as the operational default, deployable via gate-level queue management without optimization infrastructure. The proposed DES framework offers an efficient, tail-aware platform for evidence-based dock policy evaluation before real-world implementation.

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