Urban waste collection in dense and spatially constrained city centers is shaped by more than the daily routing of vehicles. Collection feasibility and cost also depend on installed bin capacity, available vehicle types, route-duration limits, intermediate-facility unloading, and workload distribution across the fleet. Amsterdam city center motivates the problem setting as an illustrative example of this broader class of constrained urban logistics problems. The computational evidence in this thesis is therefore Amsterdam-inspired and benchmark-based, rather than calibrated to the actual Amsterdam collection system.
This thesis studies these interactions through an integrated optimization framework for municipal waste collection. The problem is formulated as a Waste Collection Multi-period Inventory Routing Problem with Intermediate Facilities and Bin Configuration decisions (WC-M-IRP-IF-BC). The model reverses the classical distribution-based interpretation of the Inventory Routing Problem by representing waste accumulation at customer nodes and preventing overflow beyond installed modular bin capacity. It jointly decides bin installation, visit timing, vehicle deployment, route sequencing, vehicle-load propagation, and intermediate-facility use over a finite planning horizon. The exact MILP uses a normalized scalar planning-cost objective that combines bin investment, vehicle deployment, routing distance, and, when activated, a distance-range workload-balance term.
The model is tested on modified Archetti benchmark instances adapted to the waste collection setting. The computational study evaluates a fixed source-capacity baseline, objective-cost ratio sensitivity, homogeneous and mixed fleet configurations under route-duration restrictions, workload-balanced solutions, and staged adaptation policies under demand growth. The fixed source-capacity baseline shows that integrated modular bin optimization reduces cost relative to an inherited storage layout in every comparable case with a usable solution, with improvements of 3.8\% to 18.9\%. Cost-sensitivity experiments further show that bin and deployment coefficients affect collection frequency, routing effort, and fleet use. Low bin costs encourage storage-intensive solutions, while high bin costs increase collection frequency and routing effort. Additional vehicles are most valuable when route-duration constraints are binding, and reduced-capacity mixed fleets can improve feasibility by splitting work across vehicles.
The workload-balance experiment substantially reduces distance-based workload gaps in several instances, but diagnostic comparisons show that distance balance does not necessarily imply compre hensive balance of driver burden. Under demand growth, reliability is best understood through adaptation pathways, where fixed plans may first absorb growth through routing effort, then require bin reallocation, bin expansion, fleet expansion, or combined expansion. Overall, the thesis shows that integrated strategic and operational planning provides a clearer basis for evaluating waste collection systems than routing cost alone. Within the controlled benchmark setting, the exact MILP formulation supports detailed structural analysis for small and medium instances, while larger networks, repeated visits to the same physical intermediate facility, stochastic demand, and richer workload measures motivate future scalable solution methods. ... Read more

Urban public transportation often exhibits pronounced spatio-temporal imbalances in passenger demand, resulting in capacity shortages during peak periods and excessive idle capacity during off-peak times. Meanwhile, with the expansion of e-commerce, urban freight demand continues to grow, making the use of idle public transportation space for freight transport an ideal approach. This study proposes an optimization framework for integrated passenger-freight co-transportation using Modular Autonomous Vehicles (MAVs), formulating a Mixed-Integer Quadratically Constrained Programming (MIQCP) Path-based model based on a space-time network to address the Modular Autonomous Unit (MAU) Routing Problem. The model integrates Fixed-Route Transit (FRT) and Demand-Responsive Transit (DRT), allowing MAUs to dynamically couple/decouple across different routes to meet the spatio-temporal demands of passengers and freight, with the objective of minimizing total operational costs. To tackle the computational complexity of large-scale instances, a customized Adaptive Large Neighborhood Search (ALNS) algorithm is designed, incorporating two initial solution generation methods (GUROBI and Greedy heuristic) and iteratively optimizing solutions through destroy and repair operations. A real-world case study based on the Shanghai bus network validates the effectiveness of the proposed approach. The results demonstrate that the MAU co-transportation system can effectively utilize vehicle compartment space to simultaneously transport passengers and freight, significantly reducing empty load rates, leading to a substantial reduction in operating costs. Without using the co-transportation mode, the number of MAUs used would increase significantly, accompanied by a 3.9% cost increase. Compared to the traditional combination of public transit and delivery vans, costs are reduced by 83.4%. This co-transportation modular transit system, with its unique flexibility, can provide efficient and low-cost transportation services for both passengers and freight within cities. ... Read more