Metro systems in megacities such as Beijing and Tokyo face substantial differences in passenger demand between peak and off-peak periods. During peak periods, demand can exceed train capacity, causing denied boardings and long waiting times, while capacity is underutilized off-peak. Since metro systems often operate close to their maximum capacity, expanding infrastructure through additional tunnels is costly and time-consuming. Therefore, demand-management measures can provide an alternative to supply-side expansion.

This thesis integrates a supply-side measure, timetable optimization, with a demand-side measure, time-dependent pre-peak discounts. These measures should be optimized jointly, because pricing changes the temporal distribution of passenger demand and therefore affects the optimal timetable. The study investigates whether integrated timetabling and pre-peak discounts can redistribute demand across the morning peak and improve the utilization of existing metro capacity. The main research question is how integrated train timetabling and time-dependent pricing can be formulated and solved to minimize passenger waiting times at the network level.

To answer the research question, we develop a mixed-integer nonlinear programming (MINLP) model in which train headways and fare discounts are decision variables. The model links train departures to pricing stages and represents passenger demand shifts through elasticity parameters. It incorporates time-dependent pricing, train movements, and passenger flows through the network, including waiting, boarding, in-vehicle travel, transfers, and alighting. Oversaturated conditions resulting from limited train capacity are also modeled. The objective minimizes passenger waiting time, including waiting for newly arriving and transferring passengers, stranded passengers caused by capacity constraints, and a penalty for passengers remaining unserved at the end of the study period.

For small instances, the MINLP can be solved directly with Gurobi, but computation times increase rapidly with problem size. Therefore, a decomposition framework and hybrid matheuristic combining Adaptive Large Neighborhood Search (ALNS) with Gurobi are developed. The model is decomposed into a network-level train timetabling subproblem and a time-dependent pricing subproblem with integrated passenger assignment. ALNS searches for promising timetables using tailored destroy and repair operators, while the resulting pricing subproblem is linearized into a mixed-integer linear programming (MILP) model and solved exactly with Gurobi.

The algorithm is evaluated on synthetic instances using Gurobi as a benchmark. The matheuristic obtains comparable solutions while substantially reducing computation time for larger instances. For the largest instance, it reaches a solution within 0.1% of the best Gurobi solution after approximately 550 seconds, compared with 6,565 seconds for Gurobi. However, both solutions have an optimality gap of around 14%, limiting conclusions about solution quality. Overall, the matheuristic clearly improves computational efficiency, while ALNS converges faster than standard LNS.

The approach is applied to the central Beijing metro network, consisting of six bidirectional lines, 87 stations, and eight transfer stations during the morning peak. Four scenarios are compared: the initial timetable without pricing, the initial timetable with pricing, an optimized timetable without pricing, and joint optimization. Pricing alone reduces the objective by 1.65%, timetable optimization by 12.06%, and joint optimization by 13.61%. The joint pricing policy offers a 60% discount from 06:30–07:00, with approximately 3.8% revenue loss. Pricing mainly reduces stranded passengers, decreasing them from 26,024 to 20,677 compared with timetable optimization alone. Joint optimization also improves the objective by 0.32% compared with sequential optimization.

The results are subject to limitations, including headway-dependent demand, homogeneous price elasticities, fixed passenger routes, and predefined pricing stages. Nevertheless, the study demonstrates that integrated timetable and fare optimization is computationally feasible and offers a promising strategy for managing capacity-constrained metro networks. ... 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

Meal delivery platforms operate in dynamic environments, facing complex challenges including fluctuating customer demand, deciding where and which restaurants to offer, and efficient rider allocation. This thesis introduces the Restaurant Selection and Rider Dimensioning Problem (RSRDP) by proposing an integrated optimization model that jointly determines the optimal restaurant assortments and rider allocations to maximize the platform's expected profitability, while maintaining high service quality standards.

A nested logit model captures customer purchasing behavior on the platform to model how customers choose a restaurant to order from, where customers initially choose a cuisine type and subsequently select a specific restaurant within that type. Rider operations are modeled through a spatial-temporal network, enabling optimized management of rider flows across urban zones. We formulate the problem as a Mixed-Integer Linear Program (MILP) and research advanced solution methods including Benders decomposition, column generation, branch-and-price, and heuristics. Finally, we propose our novel Iterative Assortment Generation (IAG) algorithm to solve the problem, ensuring computational feasibility for large-scale scenarios.

Computational experiments based on simulated urban delivery data illustrate that integrating restaurant selection with rider dimensioning consistently increases profitability and maintains high service quality compared to traditional methods that separate these decisions. Furthermore, we explore the impact of two distinct rider compensation policies: commission-based (payment per delivery) and fixed employment (hourly wages). Our findings reveal significant trade-offs between these compensation structures. Commission-based compensation offers greater operational flexibility, adaptability to fluctuating demands, and cost-effectiveness; however, it also leads to increased rider relocations and potentially reduced rider satisfaction due to uncertainty in workload. Conversely, fixed employment provides stable rider availability, promoting better workforce management, but potentially incurs higher operational costs and decreased responsiveness to short-term demand fluctuations.

Based on these insights, we recommend meal delivery platforms adopt integrated optimization models as part of their tactical planning to effectively balance service quality and profitability. Platforms should carefully evaluate their rider compensation policies, recognizing that the ideal choice may depend on the specific context of their operational environment, such as demand variability, competitive landscape, and workforce preferences.

Future research could build on this study by investigating dynamic compensation schemes that adapt in real-time to observed demand conditions. Additionally, incorporating hybrid compensation schemes, combining salaried and commission-based riders, and utilizing the strengths of both policies, provides a promising research direction. Developing multi-objective models that balance profitability against service quality measures, such as timely deliveries or rider satisfaction, would further guide operational decisions and customer and rider retention. Furthermore, future research could improve the way service districts are designed. Instead of using fixed boundaries, districts could be adjusted dynamically and tailored more precisely to specific customer segments and changing demand patterns. This approach would mean that restaurant offerings can also change throughout the day to closely match customers' preferences, potentially increasing overall profitability. Furthermore, modeling couriers individually, rather than as aggregated flows, allows more precise scheduling that better accounts for realistic travel times, shift durations, and rider constraints. Although this approach increases modeling complexity and computational demands, it offers more accurate operational insights, leading to better-informed management decisions. Finally, employing heuristic-driven column-generation methods and comparing solution approaches systematically can improve scalability and accuracy. ... Read more