Abstract
Efficient container truck scheduling is essential for optimising export logistics, yet it faces complex challenges due to uncertainties such as truck punctuality variations, fluctuating service times, and broader supply chain disruptions. These challenges not only impact operational efficiency but also increase the risk of costly delays, presenting a need for adaptable and resilient scheduling models. This thesis presents a multi-perspective framework that addresses these issues through three interconnected lenses: the Third-party Logistics (3PL) provider, the exporter’s warehouse, and a synchronised scheduling model that integrates both warehousing and transportation operations to improve overall supply chain resilience.From the 3PL provider perspective, this research introduces a data-driven Arrival Time Suggestion Model (ATSM) for the 3PL platform that optimises arrival time recommendations by accounting for truck punctuality and service time variability. This enables the 3PL platform to balance the satisfaction of exporters and truck drivers by reducing both waiting times. The ATSM is validated using real-world data, with results showing that the platform can reduce total time costs for both drivers and exporters by 2.49% to 8.24% across varied scenarios. Optimal arrival time suggestion patterns vary across different required start loading times and days, driver punctuality rates, weather conditions and the locations of the warehouses. Impacts of the key parameters including allowed schedule deviations and drivers’ time tolerances are also discussed, leading to various managerial insights for implementing the model in practice.
The second perspective of the framework shifts to the exporter’s warehouse, which is able to tackle the complexities associated with multi-class truck punctuality when managing truck appointment scheduling. Three distinct models are explored to address the truck scheduling under uncertainty: Single-class Stochastic Programming (SP), Multi-class Stochastic Programming (MSP), and Error-Aware Multi-class Stochastic Programming (EMSP). To solve these models, various optimisation techniques are employed, including Deterministic Approach (DA), Sample Average Approximation (SAA), and Distributionally Robust Optimisation (DRO). Under stable conditions, the SAA-EMSP model provides a balanced and cost-effective solution. Conversely, when facing extreme variability in truck arrival times and service durations, the DRO-MSP model effectively manages risks and controls tail-end costs. Through these approaches, the exporter’s warehouse gains refined capabilities for achieving more resilient and adaptive scheduling strategies.
The third perspective integrates warehousing and transportation through a synchronised scheduling model that employs a drop-and-pull transportation strategy to enhance flexibility and coordination. This model utilises a Population-based Variable Neighbourhood Search (PVNS) algorithm, paired with a deadlock repair mechanism, to dynamically adjust both warehouse and truck dispatch schedules. The primary objective is to synchronise order picking, loading bay assignments, and truck dispatch sequences, thereby minimising overall waiting times, particularly under disrupted supply chain conditions. Results from multiple simulations indicate that synchronised decision-making can reduce total waiting times by over 50% when compared to independent scheduling of warehouse and transportation activities. This improvement is further enhanced under dynamic synchronisation, which accommodates real-time perturbations in operational times. However, the study also introduces that frequent rescheduling, while beneficial in a dynamic environment, may increase operational costs due to the interaction effects of various perturbations.
Together, these perspectives form a holistic framework for managing the inherent uncertainties in container truck scheduling. This thesis contributes actionable insights for 3PL providers and exporters and promotes more efficient, resilient, and adaptable logistics operations. Through this integrated approach, the research underscores the potential for improved operational performance amidst diverse and unpredictable logistics challenges.
Date of Award | 15 Jul 2025 |
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Original language | English |
Awarding Institution |
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Supervisor | Zhen Tan (Supervisor), Hao Luo (Supervisor) & Zhao Cai (Supervisor) |