Airport shuttle services are crucial for enhancing air-to-ground connections and improving airline customer satisfaction. However, operating a fleet of vehicles for timely and seamless shuttle services is challenging for airlines. This paper addresses an airport shuttle vehicle scheduling problem, providing cost-saving solutions for airlines. Airlines manage a limited fleet of heterogeneous vehicles that shuttle passengers between the airport and the city center, picking up or delivering passengers whenever and wherever. The decisions to be made include accepting, assigning, and sequencing shuttle service requests, considering passenger clustering, travel time tolerance, maximum waiting time, and flight schedule coordination. The objective is to minimize total operational costs, including vehicle hire costs, vehicle travel costs, and request outsourcing costs. We formulate the problem as a mixed-integer linear program (MILP) enhanced by valid inequalities and a trip number bounding method. We design a logic-based Benders decomposition (LBBD), in which the master problem (MP) generates a trip-chain for each vehicle and the subproblem verifies its timing feasibility. If the subproblem detects infeasibility, feasibility cuts are added to the MP to eliminate invalid trip-chains. Numerical results from a real-world case demonstrate the effectiveness of our model and algorithm. Additionally, results on random instances show that the exact method achieves optimal solutions for instances with up to 90 requests and around 270 passengers.

Container terminals face tremendous pressure to improve their throughput due to the expanding global shipping market. As a key for throughput, handling capacity requires effective coordination between various automated facilities. Observed from the operational practice of Tianjin port, a world-leading smart port, four critical facilities, namely quay cranes, lock stations, intersections, and yard cranes, are identified as bottlenecks that impact handling efficiency. Congestion at these facilities, in particular, pose significant challenges to terminal managers. To address these issues, we investigate an integrated terminal scheduling problem and formulate this novel problem as a mixed-integer linear program, from which we derive two efficient lower bounds. To tackle practical-sized problems, we propose a reinforcement learning (RL)-based algorithm with two modules. The offline module uses RL to learn from abundant historical data. When actual instance information is available, the online module enhances offline decisions using a rollout mechanism and mathematical programming. The proposed algorithm employs a pre-trained offline policy to handle extensive computations before actual decision-making and an online phase that provides a streamlined and stable method to enhance the solution. Extensive experiments validate the effectiveness of the proposed algorithm, demonstrating an 18.22% reduction in makespan compared to the rule-based heuristic used in actual port operations.

Unmanned surface vehicles (USVs) have been widely used in maritime logistics, environmental monitoring, and military fields due to their intelligence, flexibility, economy, and environmental protection advantages. However, USV applications are restricted by their limited operational capabilities. Most USVs are electrified, which require frequent charging operations, making them incapable of executing long-range tasks. Large mobile charging vessels have recently been produced to provide electric supply, enabling USVs to complete long-range tasks without returning to the port. This paper investigates an integrated mobile charging vessel location and USV scheduling problem, where multiple mobile charging vessels are deployed to offshore anchorages to provide charging services to USVs. The objective is to minimize the combined weighted sum of location costs, travel costs, and tardiness penalty costs. We propose two mixed integer linear programming models, which off-the-shelf commercial solvers can solve. Computational results on 40 random instances with up to 100 charging requests of USV, ten candidate locations, and five mobile charging vessels demonstrate the model’s effectiveness. In addition, sensitivity analysis is performed on key parameters, including time window duration and tardiness penalty coefficient, which provides useful managerial insights for practitioners.

This paper studies a new integrated delivery and installation routing problem arising from a home-furnishing delivery and installation service. A fleet of vehicles is arranged to provide both delivery and installation service for customers, and each vehicle is assigned to a team of technicians with multiple skills. Depending on worker capabilities, certain customers receive both delivery and installation service from a single vehicle, while others necessitate separate vehicles for each service. The company should make an optimal plan for this home-delivery and installation service, encompassing decisions such as order acceptance, assignment, and sequencing of delivery and installation tasks within each vehicle, along with worker allocation, balancing cost and service level. We provide two bi-objective mixed-integer linear programs that can be solved by off-the-shelf commercial solvers. The two objectives are maximizing the company’s service profit and minimizing customer waiting time between delivery and installation services. We conduct numerical experiments on both models using randomly generated cases. Additionally, sensitivity analysis is performed to provide practical managerial insights.

This study investigates a biobjective integrated parallel machine scheduling and location problem. It aims to place machines on a set of candidate locations, assign jobs dispersed in different locations to the placed machines, and sequence them while minimizing the maximum completion time, i.e., makespan, and the location cost. For the challenging NP-hard problem, we first develop an improved mixed-integer linear program. Then, several inequalities are proposed to further strengthen it. To more effectively and efficiently solve practical-size instances, a new iterative two-stage heuristic algorithm based on$\varepsilon $-constraint is proposed. Extensive experimental results demonstrate that 1) the improved model with valid inequalities can solve 78.4% of 500 benchmark instances, more than 29.8% for the state-of-the-art one and the Pareto solutions obtained by the former are much superior to that of the latter and 2) the proposed iterative two-stage heuristic algorithm can solve all benchmark instances and its performance is significantly superior to the widely adapted nondominated sorting genetic algorithm II in obtaining high-quality Pareto solutions.

We investigate the discrete parallel machine scheduling and location problem, which consists of locating multiple machines to a set of candidate locations, assigning jobs from different locations to the located machines, and sequencing the assigned jobs. The objective is to minimize the maximum completion time of all jobs, that is, the makespan. Though the problem is of theoretical significance with a wide range of practical applications, it has not been well studied as reported in the literature. For this problem, we first propose three new mixed-integer linear programs that outperform state-of-the-art formulations. Then, we develop a new logic-based Benders decomposition algorithm for practical-sized instances, which splits the problem into a master problem that determines machine locations and job assignments to machines and a subproblem that sequences jobs on each machine. The master problem is solved by a branch-and-cut procedure that operates on a single search tree. Once an incumbent solution to the master problem is found, the subproblem is solved to generate cuts that are dynamically added to the master problem. A generic no-good cut is first proposed, which is later improved by some strengthening techniques. Two optimality cuts are also developed based on optimality conditions of the subproblem and improved by strengthening techniques. Numerical results on small-sized instances show that the proposed formulations outperform state-of-the-art ones. Computational results on 1,400 benchmark instances with up to 300 jobs, 50 machines, and 300 locations demonstrate the effectiveness and efficiency of the algorithm compared with current approaches. Summary of Contribution: This paper employs operations research methods and computing techniques to address an NP-hard combinatorial optimization problem: the parallel discrete machine scheduling and location problem. The problem is of practical significance but has not been well studied in the literature. For the problem, we formulate three novel mixed-integer linear programs that outperform state-of-the-art formulations and develop a new logic-based Benders decomposition algorithm. Extensive computational experiments on 1,400 benchmark instances with up to 300 jobs, 50 machines, and 300 locations are conducted to evaluate the performance of the proposed models and algorithms.

This paper investigates a new multi-objective order assignment and scheduling problem for personal protective equipment (PPE) production and distribution during the outbreak of epidemics like COVID-19. The objective is to simultaneously minimize the total cost and maximize the PPE supply timeliness. For the problem, we first develop a bi-objective mixed-integer linear program (MILP). Then an$\epsilon $-constraint combined with logic-based Benders decomposition method is proposed based on some explored properties. We then extend the proposed model to handle dynamics and randomness. In particular, we design a predictive reactive rescheduling approach to address random order arrivals and manufacturer disruptions. Computational experiments on a real case from China and 100 randomly generated instances are conducted. Results show that the proposed algorithm significantly outperforms an adapted$\epsilon $-constraint method combined with the proposed MILP and the widely used non-dominated sorting geneticalgorithm II(NSGA-II) in obtaining high-quality Pareto solutions.Note to Practitioners—The unprecedented outbreak of COVID-19 and its rapid spread caught numerous national and local governments unprepared. Healthcare systems faced a vital scarcity of PPEs. The urgency of producing and delivering PPEs increases as the number of infected cases rapidly increases. A key challenge in response to the epidemic is effectively and efficiently matching the demands and needs. Performing practical and efficient order assignment and scheduling for PPE production during the COVID-19 outbreak is critical to curbing the COVID-19 pandemic. This work first proposes a bi-objective mixed-integer linear program for optimal order assignment and scheduling for PPE production. The aim is to achieve an economical and timely PPE production and supply. A novel method that combines the$\epsilon $-constraint framework and the logic-based Benders decomposition is proposed to yield high-quality Pareto solutions for practical-sized problems. Computational results indicate that the proposed approaches are practical and feasible, which can help decision-makers to perform acceptable order assignment and scheduling decisions.

An intelligent logistics system is an important branch of intelligent transportation systems. It is a great challenge to develop efficient technologies and methodologies to improve its performance in meeting customer requirements while this is highly related to people's life quality. Its high efficiency can reduce food waste, improve food quality and safety, and enhance the competitiveness of food companies. In this paper, we investigate a new integrated planning problem for intelligent food logistics systems. Two objectives are considered: minimizing total production, inventory, and transportation cost and maximizing average food quality. For the problem, a bi-objective mixed integer linear programming model is formulated first. Then, a new method that combines an ϵ-constraint-based two-phase iterative heuristic and a fuzzy logic method is developed to solve it. Computational results on a case study and on 185 randomly generated instances with up to 100 retailers and 12 periods show the effectiveness and efficiency of the proposed method.