This paper presents a centralised rolling-horizon framework for dynamic freight matching in port-centric supply chains. Rather than solving one large static assignment, the framework advances through the planning horizon in short steps, splitting the dynamic many-to-many problem over time into a sequence of small per-step assignments. Within each step, a Graph Neural Network makes the real-time match or wait decisions. The Hungarian algorithm then solves that step’s assignment optimally. Vicinity-based consolidation groups nearby movements, with full and empty containers handled jointly. Many-to-many consolidation emerges as trucks are assigned further demands at later steps, and because each step solves only a small assignment, the framework stays tractable and supports scalable coordination under intermodal and operational constraints. Using throughput data from Sydney’s Port Botany, eight scenarios are simulated across fleet compositions and optimisation priorities. Results show clear trade-offs: small-truck-dominant fleets maximise timeliness and demand coverage but raise cost and emissions, whereas large-truck strategies lower cost and carbon intensity at the expense of service levels. Export flows consistently achieve the highest timeliness.
We study a location–routing problem motivated by the design of the first collection tier in biogas supply chains. A single vehicle collects organic waste from multiple pickup points and delivers it to pre-treatment facilities (drop-off points) that can be installed at candidate locations subject to a limited investment budget. The resulting problem, which we call the Drop-Off Routing Problem (DORP), jointly determines the activation of drop-off facilities, the number of visits to each pickup point, and a feasible vehicle route starting and ending at activated facilities. Demand at pickup points may be split across multiple visits, and the objective minimizes a convex combination of fixed routing costs and flow-dependent transportation costs.We propose a mixed-integer linear programming formulation defined on an extended graph that explicitly captures multiple visits to pickup points. Connectivity is enforced through cut-set inequalities separated within a tailored two-phase branch-and-cut algorithm embedded in a state-of-the-art optimization solver. Extensive computational experiments on a testbed of synthetic instances assess the effectiveness of the proposed approach. The results provide managerial and computational insights into the effects of vehicle capacity, visit limits at pickup points, and installation budgets on solution quality and computational effort.
