The development of automated container terminals faces the challenge of bridging the gap between the increasing volume of container operations and restricted resources, particularly at the seaside of the terminal. Recognizing the pressing need for effective scheduling and decision-making processes, the industry targets two operational challenges: 1) managing limited apron resources, including vehicles, quay cranes, and traffic lanes; and 2) addressing the operation complexities brought by handling twist-locks. To address this gap, this study proposes a seaside vehicle dispatching and resource scheduling problem, which is formulated as a mixed integer linear programming model. A Q-learning-based neighborhood search is developed along with the proposal of a matheuristic method to obtain high-quality initial solutions. The experiments demonstrate that the proposed approach consistently delivers superior solutions over the benchmarks, significantly advancing the management of automated container terminal operations.

The worldwide popularity of electric automated guided vehicles and autonomous vehicles in container terminals requires efficient vehicle scheduling management considering the capacitated battery energy and the charging station limitation. In this paper, the recharging-considered vehicle scheduling problem is formulated as a decentralized partially observable Markov decision process to maximize the cumulative reward, providing a highly adaptive decision-making mechanism for multi-agent-powered terminal transport system by supporting decentralized decisions and accommodating partial observability. Considering the limited number of charging stations and tight schedules, a novel scheduling method based on the actor-critic multi-agent deep reinforcement learning framework is developed to facilitate cooperation among vehicles and charging stations and enhance the stability and efficiency of the learning process. Furthermore, to address the challenge on algorithm convergence due to the vast state space, we employ a heterogeneous graph neural network in the proposed framework for feature extraction and a multi-agent proximal policy optimization algorithm for parameter training. Numerical results indicate that the proposed method outperforms the distributed-agent deep reinforcement learning and several benchmark heuristics, showcasing its superior performance in both solution quality and efficiency. Moreover, the well-trained model can be directly applied to various scenarios, demonstrating its high generalization capability.

Relief network design for predictable disasters is a key issue in humanitarian logistics. However, existing studies on relief network design have not simultaneously considered multiple relief decision stages and the uncertainties which are reduced by improved forecast accuracy as a disaster approaches. These aspects are critical for efficient relief activities. The present study investigates a new relief network design problem for predictable disasters, especially typhoons, which have become increasingly frequent and severe in recent years. It integrates facility location decisions before any specific disaster warnings are issued, relief supply deployment and evacuation decisions between a warning and the onset of the disaster, and relief supply distribution decisions after a disaster strikes. This study also considers time-dependent uncertainties of the disaster’s trajectory and intensity together. For the problem, a novel three-stage hybrid distributionally robust and robust optimization (3HDRO) model is proposed. To make it computationally tractable, the 3HDRO model is transformed into a deterministic equivalent (DE) model. A scenario-based decomposition heuristic algorithm is then designed to solve the DE model for large-scale instances. A case study of historical typhoons in Guangdong Province, China, is used to gain insights into the critical model parameters for disaster relief activities. Furthermore, experimental results on randomly generated instances demonstrate the effectiveness and efficiency of the proposed model and algorithm.

The petrol station replenishment problem (PSRP) refers to the process of transporting petroleum products from oil depots to petrol stations via tank trucks. It mainly consists of two parts: allocating petroleum products to tank trucks and planning the travel route of each truck. In this study, we examine a new variant of PSRP by considering a multi-depot vehicle routing problem with open inter-depot routes (MDVRPOI). Each depot can act as an intermediate replenishment facility, and each truck can be reloaded at any depot any number of times within the working period. Moreover, trucks can end their routes at any depot instead of making a long empty drive to the start depot. The trucks are heterogeneous with multiple load-specific compartments. We formulate the problem as a mixed-integer linear programming (MILP) model. Given the problem’s complexity, a tabu-based adaptive large neighborhood search (T-ALNS) algorithm is proposed, which integrates the tabu search approach into ALNS to solve the problem effectively. The T-ALNS executes multiple problem-tailored destroy/repair operators on the station, trip, and route levels. A local search procedure with problem-specific operators and an adaptive strategy is further embedded into T-ALNS. We use the real data of an oil company in China to evaluate our algorithm. Computational results show that our T-ALNS significantly outperforms the CPLEX solver and other algorithms in terms of solution quality and computation time. Further, it realizes an average reduction in transportation cost of about 45% compared to the company’s actual strategy.

Transportation service procurement is often realized by an auction. With the rolling horizon planning concept adopted in logistics, carriers usually plan their transportation operations of several periods (days) in advance. This implies that carriers must consider multiple periods when participating in combinatorial auctions organized by shippers. Since transportation requests in future cannot be foreseen, carriers must consider request uncertainty in such auctions. In this paper, we consider a carrier’s bid generation problem appeared in a multi-round combinatorial auction for truckload transportation service procurement with the consideration of multiple periods and request uncertainty. The problem is to maximize the total expected net profit of the carrier in a planning horizon of multiple periods by optimally determining the transportation requests to bid, the period to serve each request, and the routes to serve all requests including the carrier’s reserved requests. This problem is hard to solve because of its stochastic nature. By adopting the scenario approach of stochastic optimization, a mixed integer linear programming model is formulated for the problem. A Benders decomposition approach is then proposed to solve the model, with Pareto-optimal cuts to accelerate its solution process. The performance of the approach is evaluated by numerical experiments on randomly generated instances. The computational results demonstrate that the Bender decomposition approach is much more efficient than CPLEX solver in solving large instances of the problem. In addition, the value of considering uncertain requests and multi-period in the bid generation is evaluated.

With the increase of transport demands, more pressure and challenges are being imparted into efficient transportation. As a conventional and direct congestion alleviation strategy, constructing new roads and lanes are increasingly restricted by limited land resources and high costs. Thus, making full use of existing transport network via appropriate management is critical to realize the sustainable development of transportation systems. As a flexible management strategy, lane reservation strategy has been widely adopted in real life. The reserved lanes can improve the efficiency of special transports, while they bring negative impact such as travel delay for general-purpose transports. In addition, the setting and operating of reserved lanes require a certain amount of cost. This paper proposes a new dual-objective integer linear programming model for optimally determining reserved lanes on a network for time-guaranteed special transports in order to simultaneously maximize the benefits and minimize the negative impact brought by reserved lanes, which incorporates road residual capacity and limited budget to the actual decision. Moreover, an iterative weighted sum-based method is proposed to solve it, in which a new relax-and-optimize algorithm is developed to exactly solve the single-objective optimization problems. Results of extensive numerical experiments show the effectiveness and efficiency of the proposed model and approach.

Hoist scheduling in automated electroplating lines has been extensively studied in a static environment. However, practical electroplating lines are subject to diversified unforeseen disruptions that require frequent rescheduling to maintain or optimize system performance. This paper addresses a hoist scheduling problem, where randomly arriving jobs need to be inserted into an existing schedule without changing the sequence of hoist moves already scheduled. The objective is to minimize the total completion time of all the jobs in the existing schedule and a newly inserted job. We develop a polynomial-time heuristic that adjusts the starting times of the existing hoist moves to a limited extent but does not bring about a severe disturbance of the existing hoist moves. We compare our algorithm with two existing approaches with different rescheduling policies (i.e., partial and zero adjustment of the existing schedule). We empirically analyze the productivity and the stability of the schedules generated by the three approaches. Computational results demonstrate that our algorithm can generate more productive and stable schedules than the two existing approaches.

As a by-product of industrial development, large quantities of hazardous materials are shipped in the transportation network every day. The risk of transporting hazardous materials is an important consideration in transportation planning. Vehicle routing models in previous studies simultaneously minimized the transportation cost and the total risk. However, simply evaluating the total risk of a fleet as a whole does not consider each individual vehicle’s risk. Even if the total risk is low, the risk of a specific vehicle may still be very high. In addition, the situation of several vehicles of a logistics company shipping hazardous materials and travelling on the same road together, called vehicles travelling in echelon, may cause a chain of incidents. This situation has been ignored in previous studies. In this paper, a bi-objective vehicle routing model for hazardous materials transportation with no vehicles travelling in echelon is developed, simultaneously minimizing the maximum risk of each vehicle and the transportation cost. A two-stage exact algorithm is developed based on the$\varepsilon $-constraint method with several improvements to solve the proposed problem. An approximation approach is proposed for this two-stage algorithm for large-scale problems. Furthermore, the approximation ratio and time complexity of this approximation algorithm are analyzed. Computational experiments with randomly generated instances are reported, and several managerial insights are derived from the sensitivity analysis.

It is challenging to perform emergency scheduling for fighting forest fires subject to limited rescue resources (i.e., vehicles with fire engines), since extinguishing each fire point should take into account multiple factors, such as the actual fire spreading speed, distance from fire engine depot to fire points, fire-fighting speed of fire engines, and the number of dispatched vehicles. This paper investigates a bi-objective rescue vehicle scheduling problem for multi-point forest fires, which aims to optimally dispatch a limited number of fire engines to extinguish fires. The objectives are to minimize the total fire-extinguishing time and the number of dispatched fire engines. For this problem, we first develop an integer program that is an improved and simplified version of an existing one. After exploring some properties of the problem, we develop an exact dynamic programming algorithm and a fast greedy heuristic method. Computational results for a real-life instance, and benchmark and large-size randomly generated instances confirm the effectiveness and efficiency of the proposed model and algorithms. Besides, a bi-objective integer program is developed to address the multi-depot fire engine scheduling issue.

This paper addresses a new lane reservation problem called bus lane reservation problem (BLRP). The focus of the problem is on optimally selecting lanes to be reserved from an existing transport network and designing reserved lane-based bus paths, such that the rapid and station arrival-time guaranteed bus transit can be ensured, thereby achieving rapid and reliable bus transportation. However, once lanes are reserved, negative impact, such as an increase in travel time on adjacent non-reserved lanes may be caused. For this problem, an improved integer linear program is first formulated to minimize such negative impact. As the existing commercial solvers, e.g., CPLEX, can only solve small-size problems, we develop an exact enhanced cut-and-solve algorithm and an improved kernel search heuristic for solving medium- and large-size problems. Results of extensive numerical experiments confirm the effectiveness and efficiency of the proposed algorithms. In addition, a bi-objective robust BRLP is investigated to study the tradeoff between the negative impact of reserved lanes and the robustness of solution against the uncertainties in the link travel time and the bus dwell time.

This study investigates a new biobjective lanereservation problem, which is to exclusively reserve lanes from an existing transportation network for special transport tasks with given deadlines. The objectives are to minimize the total negative impact on normal traffic due to the reduction of available lanes for general-purpose vehicles and to maximize the robustness of the lane-reservation solution against the uncertainty in link travel times. We first define the robustness for the lanereservation problem and formulate a biobjective mixed-integer linear program. Then, we develop an improved exact ε-constraint and a cut-and-solve combined method to generate its Pareto front. Computational results for an instance based on a real network topology and 220 randomly generated instances with up to 150 nodes, 600 arcs, and 50 tasks demonstrate that the proposed method is able to find the Pareto front and that the proposed cut-and-solve method is more efficient than the direct use of optimization software CPLEX.

This paper studies a lane reservation problem for large sport events in big cities. Such events require organizers to deliver certain people and materials from athlete villages to geographically dispersed venues within a given travel duration. A lane reservation strategy is usually adopted in this circumstance to ensure that time-critical transportation tasks can be completed despite heavy urban traffic congestion. However, it causes negative impact on normal traffic. The problem aims to optimally select and reserve some lanes in a transportation network for the exclusive use of the tasks such that the total traffic impact is minimized. To solve the problem, we first develop an improved integer linear program. Then, its properties are analyzed and used to reduce the search space for its optimal solutions. Finally, we develop a fast and effective quantum-inspired evolutionary algorithm for large-size problems. Computational results on instances with up to 500 nodes in the network and 50 tasks show that the proposed algorithm is efficient in yielding high-quality solutions within a relatively short time.

This paper aims to study a new form of facility layout problem, in which the building has already been constructed and the specific room layout inside has been determined. Unlike the traditional facility layout problem, what we take into account is how to assign a certain number of rooms to a given number of departments with the purpose of maximizing the utilization rate of the rooms. This is equivalent to minimizing the total difference value between the extra area of different departments after satisfying their required area, thus reducing the space waste. To solve this special combinatorial optimization problem, we develop a Mixed-Integer Linear Programming (MILP) model. The model is solved using commercial software CPLEX12.6. Computational results on several randomly generated instances demonstrate the effectiveness of the proposed approach.

This paper addresses the single-track multi-hoist cyclic scheduling problem. In most existing studies, loaded hoist moves are implicitly or explicitly assumed to start and end within the same cycle. We give a counterexample to demonstrate that the optimal solution obtained with such an assumption is not necessarily the best one among all feasible solutions, called globally optimal solution. To obtain a globally optimal solution, we propose an improved mixed integer programming (MIP) approach for the multi-hoist cyclic scheduling problem with relaxation of the above assumption. Computational results on benchmark and randomly generated instances are reported and analyzed.

With economic development, a great amount of hazardous material is shipped in the transport network every day. Hazardous material transportation is well known for its high potential risk. An accident can cause very serious economic damage and will have a negative impact on public health and the environment over the long term. Transporting hazardous materials on special lanes can reduce the risk. However, a lane reservation strategy may worsen traffic conditions for other vehicles. This paper investigates a hazardous material transportation problem with lane reservation. The problem lies in how to choose lanes to be reserved in the network and select the path for each hazardous material shipment from the reserved lanes. The goal is to obtain the best compromise between the impact on normal traffic and the transportation risk. A multiobjective integer programming model is presented for the new problem. Then, an algorithm is developed based on the ε -constraint method and a fuzzy-logic-based approach. Pareto optimal solutions are obtained by the former, and a preferred solution is selected by the fuzzy-logic-based approach. Computational results demonstrate the efficiency of the proposed algorithm using an instance based on a real network topology and randomly generated instances.

This paper addresses multi-degree cyclic scheduling of two robots in a no-wait flowshop, where exactly r(r > 1) identical parts with constant processing times enter and leave the production line during each cycle, and transportation of the parts between machines is performed by two robots on parallel tracks. The objective is to minimize the cycle time. The problem is transformed into enumeration of pairs of overlapping moves that cannot be performed by the same robot. This enumeration is accomplished by enumerating intervals for some linear functions of decision variables. The algorithm developed is polynomial in the number of machines for a fixed r, but exponential if r is arbitrary. Computational results with benchmark instances are reported. Note to Practitioners-This paper was motivated by the problem of cyclic scheduling of a no-wait production line, where a part must be processed without any interruption either on or between machines due to characteristics of the processing technology itself or the absences of storage capacity between operations of a part. Multi-degree schedules, in which multiple parts enter and leave the line during a cycle, usually have larger throughput rate than simple ones. This paper proposes an algorithm for multi-degree cyclic scheduling of a no-wait flowshop with two robots. Computational results show that the throughput rate can be really improved by using multi-degree schedules with two robots. However, we have not addressed the decision of the optimal value of the degree of the cycle. Furthermore, since we consider that the two robots travel along parallel tracks, the collision-avoidance constraints have been relaxed in the algorithm. In future research, we will address the two problems and generalize the algorithm to multi-robot cases.

Proposes an exact algorithm for the multicyclic schedules of hoist moves in a printed circuit board (PCB) electroplating facility, where exactly r(r>1) parts enter and r parts leave the production line during each cycle, and the processing time at each production stage is a given constant. The multicyclic scheduling problem is transformed into enumeration of intervals for linear functions of decision variables. This enumeration is accomplished with a branch and bound procedure. At each node of the search tree, by solving a linear programming problem (LPP), either the corresponding partial solution is proved to be unable to lead to a feasible solution, or a lower bound is computed. Due to its particular structure, this LPP is equivalent to a cycle time evaluation problem in a bivalued graph which can be solved efficiently. The proposed algorithm is polynomial in the number of tanks for a fixed r, but exponential if r is arbitrary. Computational experience with both benchmark and randomly generated test instances is presented.