logistics

This study addresses the challenge of integrated logistics and production scheduling in flexible, personalized pharmaceutical manufacturing by proposing a unified optimization framework that simultaneously considers bin packing, equipment layout, task scheduling, and path planning for automated production lines based on planar transport systems. The approach leverages drug co-occurrence patterns and Hamiltonian path-based neighborhood optimization to determine equipment placement, formulates bin packing and layout as a mixed-integer quadratic program, employs constraint programming for task scheduling, and generates conflict-free vehicle routes through directed acyclic graph reasoning coupled with iterative conflict resolution. Experimental results on 40 real-world prescriptions demonstrate that the system efficiently handles up to 500 daily orders across diverse facility layouts, achieving high performance while maintaining computational tractability.

This paper addresses the last-truck scheduling problem in e-commerce middle-mile transportation, aiming to maximize next-day delivery order fulfillment under fixed inventory locations and sufficient last-mile delivery capacity. We formulate this problem as an NP-hard submodular optimization problem with coverage constraints—the first such formulation in the literature. To solve it, we propose three scenario-adaptive algorithms integrating greedy submodular maximization, pipage rounding, and Lagrangian relaxation heuristics, achieving both theoretical guarantees (worst-case approximation bounds) and scalability. Extensive experiments on real-world logistics networks and datasets demonstrate that our solutions closely approximate the global optimum, significantly improving next-day delivery coverage while maintaining computational efficiency. The proposed framework is production-ready and demonstrates strong industrial deployability.

This work addresses the tightly coupled decisions in "person-to-goods" warehouse order fulfillment—such as item allocation, order batching, and picker routing—and the absence of a general mechanism to automatically compose and evaluate optimization algorithms tailored to specific operational contexts. To bridge this gap, the authors propose CASOP, a novel framework that enables, for the first time, context-aware automatic synthesis of end-to-end warehouse optimization pipelines. CASOP integrates a modular algorithm library, semantically annotated algorithm cards, a problem taxonomy, a pipeline synthesizer, and an automated evaluator to construct and validate customized solutions for given warehouse settings. Empirical evaluation across seven benchmark datasets yields 1,063,044 valid pipelines, and the accompanying open-source toolkit provides researchers and practitioners with robust support for high-performance pipeline design and selection.

Existing food delivery platforms neglect environmental sustainability, resulting in excessive carbon emissions. Method: This paper proposes a three-tier collaborative optimization framework for urban food delivery, integrating demand forecasting, rider route planning, and order assignment. It formulates order assignment as a capacitated network flow problem—a novel modeling approach—and designs a greedy algorithm leveraging submodularity and monotonicity to minimize vehicle utilization. Contribution/Results: The framework ensures timeliness and spatial matching while significantly reducing fleet requirements and per-order carbon emissions. Experiments demonstrate an 18.7% reduction in scheduled vehicles and a 22.3% decrease in carbon emissions compared to baseline methods. The approach provides a scalable algorithmic paradigm and practical pathway toward efficient, low-carbon urban instant delivery systems.

The Inventory Routing Problem (IRP) jointly optimizes inventory and delivery decisions from a supplier to multiple retailers across multiple periods, exhibiting strong coupling and high combinatorial complexity. To address this, we propose a customized Large Neighborhood Search (LNS) framework for IRP, introducing the novel “single-retailer full-visit removal and reinsertion” operator—capable of simultaneously optimizing routing and inventory costs. Our approach integrates an efficient dynamic programming procedure (featuring preprocessing and pruning strategies) with a Hybrid Genetic Search (HGS) mechanism. Evaluated on large-scale standard benchmark instances, the method achieves state-of-the-art solution quality, significantly outperforming existing heuristics. It establishes the first scalable paradigm for IRP that balances both computational efficiency and solution accuracy.

This study addresses the service network design challenge in intermodal logistics networks arising from uncertain travel times and limited truck fleet availability. The authors propose a two-stage optimization framework that jointly handles tactical decisions—such as transport request acceptance and capacity reservation—and operational decisions, including dynamic vehicle dispatching and route replanning. Innovatively integrating an adaptive machine learning surrogate model with a simulated annealing algorithm, the approach effectively mitigates cascading disruptions caused by uncertainty-induced perturbations. Computational experiments demonstrate that the method achieves up to a 20-fold reduction in solution time while maintaining solution quality within a 5% deviation from the optimal objective value, significantly outperforming existing benchmark approaches.

This study addresses three critical challenges in e-commerce supply chain planning—model fragmentation, computational non-scalability, and low decision credibility—by proposing the SPORD framework. SPORD introduces a novel decoupled simulation-and-optimization architecture that transforms simulation from a monitoring tool into a planning engine: feasible logistics paths are first generated via simulation and then an integer programming model selects the globally optimal subset, enabling end-to-end collaborative planning. The method integrates matrix-vectorized CPU/GPU-accelerated simulation, list scheduling, and integer programming to efficiently solve large-scale instances with numerous SKUs and network nodes. Furthermore, a closed-loop intelligent diagnostic mechanism enhances solution interpretability and reusability. Since its deployment in 2025, SPORD has served over 20,000 suppliers, reducing cross-regional fulfillment rates from 6.1% to 4.9% and achieving an average monthly carbon reduction of approximately 5,745 metric tons of CO₂ equivalent.

研究通过结合图神经网络、变邻域搜索和集合划分重组的方法，优化了两层备件网络设计，解决了高成本评估问题，并提高了节约率。

This study addresses the challenge of jointly optimizing order preparation and delivery under dynamic stochastic order arrivals. The authors propose a novel policy decomposition paradigm that models the fulfillment process as a Markov decision process with synchronization constraints, uniquely treating the preparation phase as a state-constrained filter to decouple the two stages. Through policy-level decomposition, the problem is split into a primary delivery scheduling problem and a preparation compatibility subproblem, which are iteratively refined via a feedback mechanism. The resulting DDF-VFA solution framework integrates large neighborhood search, neural network-based value function approximation, and rolling horizon optimization. Evaluated on real-world datasets, the approach significantly outperforms existing baselines, achieving lower fulfillment costs and demonstrating strong scalability.

Retail demand data are often plagued by strong seasonality, irregular spikes, and noise, which undermine the accuracy of traditional forecasting methods and hinder effective supply chain decision-making. To address this challenge, this work proposes an end-to-end three-stage framework: it begins with exploratory data analysis, followed by a systematic evaluation of deep time series models—specifically N-BEATS and N-HiTS—to identify the best-performing predictor. The superior forecast from N-BEATS is then integrated into an integer linear programming (ILP) model that generates feasible delivery plans minimizing total distribution time under constraints on budget, capacity, and service level. By combining high-accuracy deep learning forecasts with interpretable constrained optimization, the approach successfully translates four-week demand predictions for 1,918 units into cost-optimal, executable logistics plans, substantially enhancing operational efficiency.