arXiv subjects

Alan Erera

Publications and source records attributed to Alan Erera.

5 recordsLinked to original sources

arXiv2026

Distributed and Dynamic Hub Network Operation Planning in a Hyperconnected Less-Than-Truckload Operating System

The less-than-truckload (LTL) industry plays a vital role in enhancing the efficiency and sustainability of logistics systems, as LTL shipments offer greater consolidation opportunities than full-truckload shipments. Despite of this flexibility, the average cost of LTL shipments remains considerably higher due to less efficient operations and highly fragmented networks of small and medium-sized carriers. Building on our ongoing effort to develop a distributed and dynamic logistics hub network system grounded in the Physical Internet (PI) principles of modular containers and open resource sharing, this study focuses specifically on inter-hub and in-hub operations, with cooperation among multiple regional hub networks. Therefore, a shipment may traverse multiple cooperating hub networks. With respect to each hub network each shipment enters, it is defined by its expected arrival time at the entry hub and its latest arrival time at the exit hub. Based on the defined shipment information, we design a set of multi-hub operation planning protocols for distributed hub operators. In their operating networks, operators use our smartly designed protocol separately to plan in-hub shipments' assignments to destination-specific trailers and inter-hub trailers' dispatch schedules. With carefully designed interconnections between hub networks, the aggregated hub network system is well-positioned to achieve cooperative outcomes and fulfill shipment requests. We evaluate the effectiveness of the proposed protocol through a simulation-based experiment under multiple scenarios in an operator's multi-hub network. Overall, this research improves the practicality and robustness of PI-based networks and supports greater cooperation among hub networks toward more efficient and sustainable logistics systems.

math.OC↗

arXiv2025

Dynamic Pricing System for Physical Internet Enabled Hyperconnected Less-than-Truckload Freight Logistics Networks

Less than truckload shipping plays a critical role in modern supply chains by consolidating freight from multiple shippers into shared vehicles. Despite its operational flexibility and potential sustainability benefits, the LTL sector faces persistent challenges, including high per unit costs and financial instability, as evidenced by recent industry bankruptcies. This paper investigates two structural issues limiting LTL performance, including the constrained consolidation potential imposed by proprietary logistics networks, and the inefficiency of fixed pricing models that fail to reflect realtime network conditions. To address these, we explore a Physical Internet enabled, hyperconnected LTL logistics system based on open asset sharing and dynamic flow consolidation. We then propose a dynamic pricing framework tailored for this network. Through a simulation based study grounded in Freight Analysis Framework data and cost estimates from industry sources, we evaluate system performance across three demand and cost uncertainty scenarios in the Southeastern U.S. The results validate our system effectiveness and suggest a promising path forward for building more efficient LTL logistics operations.

math.OC↗

arXiv2025

SPOT: Spatio-Temporal Pattern Mining and Optimization for Load Consolidation in Freight Transportation Networks

Freight consolidation has significant potential to reduce transportation costs and mitigate congestion and pollution. An effective load consolidation plan relies on carefully chosen consolidation points to ensure alignment with existing transportation management processes, such as driver scheduling, personnel planning, and terminal operations. This complexity represents a significant challenge when searching for optimal consolidation strategies. Traditional optimization-based methods provide exact solutions, but their computational complexity makes them impractical for large-scale instances and they fail to leverage historical data. Machine learning-based approaches address these issues but often ignore operational constraints, leading to infeasible consolidation plans. This work proposes SPOT, an end-to-end approach that integrates the benefits of machine learning (ML) and optimization for load consolidation. The ML component plays a key role in the planning phase by identifying the consolidation points through spatio-temporal clustering and constrained frequent itemset mining, while the optimization selects the most cost-effective feasible consolidation routes for a given operational day. Extensive experiments conducted on industrial load data demonstrate that SPOT significantly reduces travel distance and transportation costs (by about 50% on large terminals) compared to the existing industry-standard load planning strategy and a neighborhood-based heuristic. Moreover, the ML component provides valuable tactical-level insights by identifying frequently recurring consolidation opportunities that guide proactive planning. In addition, SPOT is computationally efficient and can be easily scaled to accommodate large transportation networks.

cs.LG↗

arXiv2024

On Linear Threshold Policies for Continuous-Time Dynamic Yield Management

We study the finite-horizon continuous-time dynamic yield management problem with stationary arrival rates and two customer types. We consider a class of linear threshold policies proposed by Hodge (2008), in which each less-profitable customer is accepted if and only if the remaining inventory exceeds a threshold that linearly decreases over the horizon. We use a Markov chain representation to show that such policies achieve uniformly bounded regret. We then generalize this result to analogous policies for arbitrarily many customer types.

math.OC↗

arXiv2023

Optimization-based Learning for Dynamic Load Planning in Trucking Service Networks

The load planning problem is a critical challenge in service network design for parcel carriers: it decides how many trailers to assign for dispatch over time between pairs of terminals. Another key challenge is to determine a flow plan, which specifies how parcel volumes are assigned to planned loads. This paper considers the Outbound Load Planning Problem (OLPP) that considers flow and load planning challenges jointly in order to adjust loads and flows as the demand forecast changes over time before the day of operations in a terminal. The paper aims at developing a decision-support tool to inform planners making these decisions at terminals across the network. The paper formulates the OLPP as a mixed-integer programming model and shows that it admits a large number of symmetries in a network where each commodity can be routed through primary and alternate terminals. As a result, an optimization solver may return fundamentally different solutions to closely related problems, confusing planners and reducing trust in optimization. To remedy this limitation, this paper proposes a lexicographical optimization approach that eliminates those symmetries by generating optimal solutions staying close to a reference plan. Moreover, this paper designs an optimization proxy that addresses the computational challenges of the optimization model. The optimization proxy combines a machine-learning model and a repair procedure to find near-optimal solutions that satisfy real-time constraints imposed by planners in the loop. An extensive computational study on industrial instances shows that the optimization proxy is orders of magnitude faster for generating solutions that are consistent with each other. The proposed approach also demonstrates the benefits of the OLPP for load consolidation and the significant savings obtained from combining machine learning and optimization.

cs.AI↗