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The design and construction of metro networks are typically conducted in stages due to the ongoing growth of urbanization and investment in public transportation systems. Designing new metro lines within an existing metro network is challenging as passenger flow patterns will be significantly updated and change. This study investigates new line designing in an existing metro network, also known as the metro network expansion design problem, and develops two optimization models to maximize the origin-destination (OD) demand capture under a limited budget. The first is a deterministic binary integer linear programming model, while the second is a robust optimization model that considers the demand uncertainty. The paper proposes a section-based modeling strategy to make the issue tractable and consider mode competition with other transport modes to capture the attracted demand by new metro lines. The models are applied to a real-world case in Wuxi, China, to validate their applicability and computational efficiency. The results showed satisfactory metro network expansion patterns with good connections between new lines and existing networks. The deterministic model can identify corridors and sections with higher priorities under tight budget conditions. A sensitivity analysis indicates that passengers' concerns about travel time and fares are conducive to constructing the metro network. Furthermore, the advantage of the robust solution becomes critical as the demand variation increases. The robust optimization model can provide a more reliable and competitive solution than the deterministic model under large demand variation scenarios. [ABSTRACT FROM AUTHOR]

To reduce emissions, shipping companies are deploying eco-friendly ships, like those powered by LNG or methanol. Unlike traditional fuels, LNG bunkering is available at a limited number of ports. At the same time, tramp or bulk ships have fewer ports of call on their shipping lines, which may necessitate LNG-fueled ships sailing to alternative bunkering ports for refueling. The selection of bunkering ports impacts the operating costs of shipping lines due to differences in fuel prices and geographical locations. Moreover, it must factor in ship speed limits to meet loading and unloading schedules. To optimize the shipping route, speeds, and bunkering plan for LNG-fueled tramp ships, we develop an arc-based mixed-integer linear programming model with the objective of minimizing total operation costs. Subsequently, we propose a leg-based formulation based on the predetermined port call orders. Finally, case studies are conducted to demonstrate the effectiveness and efficiency of the models. Experimental results indicate that the bunkering mode with alternative bunkering ports effectively reduces total operating costs without altering the total voyage time, particularly with substantial differences in fuel prices between ports. • A novel optimization problem for the LNG-fueled tramp shipping line is introduced. • Route, speed, and bunkering for the tramp shipping line are jointly optimized. • An arc-based and a leg-based mixed-integer programming model are proposed. • Case studies using instances derived from a tramp shipping line are conducted. [ABSTRACT FROM AUTHOR]

The burden of first-mile connection to public transit stations is a key barrier that discourages riders from taking public transportation. Public transit agencies typically operate a modest fleet of vehicles to provide first-mile services due to the high operating costs, thus failing to adequately meet the first-mile travel demands, especially during peak hours. At the same time, private cars are underutilized and have a lot of idle time. With the emergence of self-driving vehicles, new opportunities for addressing the current dilemma arise, such as integrating idle private self-driving vehicles to provide first-mile services, which is beneficial for public transportation agencies to provide high-quality services at low costs. This study investigates the first-mile ridesharing problem in which public transit agencies utilize idle privately-owned autonomous vehicles to dynamically inflate their fleet. This problem is more challenging in decision-making than conventional first-mile problems, as it involves decisions on heterogeneous fleet scheduling, vehicle routing, and time scheduling, all while taking into account the service quality for riders. To address this problem, an arc-based mixed-integer linear programming (MILP) model and a trip-based set-partitioning model are developed, both aiming to minimize total operational costs. To identify promising trips, we propose a tailored labeling algorithm with a novel dominance rule, along with a time window shift algorithm to determine the best schedule. To yield high-quality solutions in a short computation time, a tailored column-generation matheuristic algorithm is introduced. A branch-and-price exact algorithm and an adaptive large neighborhood search algorithm are developed to assess the matheuristic algorithm. Numerical experiments are conducted to demonstrate the effectiveness and applicability of the proposed models and algorithms. Experiments also show that this kind of ridesharing service can provide low-cost and high-quality services for the first-mile problem. • The first-mile ridesharing problem with public and private autonomous vehicles is introduced. • Passengers' requirements for service quality, such as the latest arrival time and the maximum ride time, are considered. • An arc-based mixed-integer programming model and a trip-based set-partitioning model are proposed. • A branch-and-price algorithm and a tailored column-generation matheuristic algorithm are designed. • A tailored labeling algorithm with a novel dominance rule and a time schedule algorithm for routes are proposed. [ABSTRACT FROM AUTHOR]

This paper studies the daily storage yard manage problem arising in maritime container terminals, which integrates the space allocation and yard crane deployment decisions together with the consideration of container traffic congestion in the storage yard. The integrated problem is formulated as an integer linear programming model with the objective of minimizing the yard crane operating cost and the yard crane interblock movement cost. A divide-and-conquer solution approach is designed to solve the problem in an efficient manner in which harmony search and constraint satisfaction techniques are employed. Numerical experiments are conducted to validate the performance of the solution approach and the improvement from the integrated optimization method. [ABSTRACT FROM AUTHOR]

Abstract: This paper investigates the Train Network Design Optimization problem arising from railroad industry which involves the integration of three inter-related decision sub-problems: train routing which is to identify origin, destination and itinerary for individual trains; block-to-train assignment detailing the block movements with trains and swaps between trains; and crew-to-train assignment specifying the crew services for train routes. A column generation based hierarchical approach with two stages is designed: the first stage generates a pool of promising train routes iteratively based on the crew segments by the column generation technique; and the second stage develops an integer linear programming model for the subsequent decisions including train route selection and block-to-train assignment. Numerical experiments with realistic test instances are conducted and the outcome demonstrates the capability of the proposed approach in solving the Train Network Design Optimization problem competently. [Copyright &y& Elsevier]