Ship scheduling optimization in restricted two-way channel ports

We present a bulk ship scheduling problem that is a combined multi-ship pickup and delivery problem with time windows (m-PDPTW) and multi-allocation problem. In contrast to other ship scheduling problems found in the literature, each ship in the fleet is equipped with a flexible cargo hold that can be partitioned into several smaller holds in a given number of ways. Therefore, multiple products can be carried simultaneously by the same ship. The scheduling of the ships constitutes the m-PDPTW, while the partition of the ships' flexible cargo holds and the allocation of cargoes to the smaller holds make the multi-allocation problem. A set partitioning approach consisting of two phases is proposed for the combined ship scheduling and allocation problem. In the first phase, a number of candidate schedules (including allocation of cargoes to the ships' cargo holds) is generated for each ship. In the second phase, we minimise transportation costs by solving a set partitioning problem where the columns are the candidate schedules generated in phase one. The computational results show that the proposed approach works, and optimal solutions are obtained on several cases of a real ship planning problem.

This research proposes a two-layered supply chain model consisting of a set of clients and suppliers for dynamic supply chain management considering production and transportation scheduling problems. The model provides a method to optimize both production schedules and transportation schedules simultaneously, in order to estimate suitable prices and delivery times of products. A prototype of simulation system for dynamic supply chain management was developed and some computational experiments were carried out in order to verify the effectiveness of the model. Experimental results show that the prototype system is available to determine suitable production schedules and transportation schedules from the viewpoint of the number of contracted orders and the cost for wasteful shipping.

The purpose of this paper is to present an exact solution method for an important planning problem faced by many shipping companies dealing with transportation of bulk cargoes. A bulk shipping company usually has a set of contract cargoes that it is committed to carry and will try to derive additional revenue from optional spot cargoes. Each cargo, either it is a contract or spot cargo, consists of a given quantity to be picked up in a given loading port and delivered in a given unloading port within specified time windows. The shipping company controls a fixed fleet for the purpose of transporting the cargoes. In most of the literature on ship routing and scheduling problems, a cargo cannot be transported by more than one ship. By introducing split loads, this restriction is removed and each cargo can be transported by several ships. The resulting planning problem can be denoted as a maritime pickup and delivery problem with time windows and split loads (PDPTWSL). We present an arc flow formulation of the PDPTWSL. We also suggest a solution method based on a priori generation of single ship schedules and two alternative path flow models that deal with the selection of ship schedules and assignment of cargo quantities to the schedules. Computational results show that the solution method can provide optimal solutions to small realistic planning instances. We also believe this paper provides an important starting point for research on other exact solution methods for the maritime PDPTWSL.

Motivated by some practical applications, we study a new integrated loading and transportation scheduling problem. Given a set of jobs, a single crane is available to load jobs, one by one, onto semitrailers with a given capacity. Loaded semitrailers are assigned to tractors for transportation tasks. Subject to limited resources (crane, semitrailers, and tractors), the problem is to determine (1) an assignment of jobs to semitrailers for loading tasks, (2) a sequence for the crane to load jobs onto semitrailers, (3) an assignment of loaded semitrailers to tractors for transportation tasks, and (4) a transportation schedule of assigned tractors such that the completion time of the last transportation task is minimized. We first formulate the problem as a mixed integer linear programming model (MILPM) and prove that the problem is strongly NP‐hard. Then, optimality properties are provided which are useful in establishing an improved MILPM and designing solution algorithms. We develop a constructive heuristic, two LP‐based heuristics, and a recovering beam search heuristic to solve this problem. An improved procedure for solutions by heuristics is also presented. Furthermore, two branch‐and‐bound (B&B) algorithms with two different lower bounds are developed to solve the problem to optimality. Finally, computational experiments using both real data and randomly generated data demonstrate that our heuristics are highly efficient and effective. In terms of computational time and the number of instances solved to optimality in a time limit, the B&B algorithms are better than solving the MILPM. © 2015 Wiley Periodicals, Inc. Naval Research Logistics 62: 416–433, 2015

Column generation heuristics for ship routing and scheduling problems in crude oil transportation with split deliveries

This paper develops an efficient variant of a Genetic Algorithm (GA) for a ship routing and scheduling problem (SRSP) with time-window in industrial shipping operation mode. This method addresses the problem of loading shipments for many customers using heterogeneous ships. Constraints relate to delivery time windows imposed by customers, the time horizon by which all deliveries must be made and ship capacities. The results of a computational investigation are presented and the solution quality and execution time are explored with respect to problem size. The proposed algorithm is compared, in terms of solution quality and computational time, with an exact method that uses Set Partitioning Problem (SPP). It is found that while the exact method solves small scale problem efficiently, treating large scale problems with the exact method becomes involved due to computational problem, a deficiency that the GA can encounter. Meantime, GA consistently returns better solution than other published work using Tabu Search method in term of solution quality.

With the establishment of the Emission Control Areas(ECA) of China, and the huge demand of the One Belt One Road (OBOR) strategy of intermodal transport, container intermodal transport will be rapid development in the future. Meanwhile, the air pollution problem is increasingly serious, optimizing the container intermodal transport in port, reducing emissions, and improving operation efficiency, which, is an urgent problem to the port operator. In this study, three models are proposed to deal with the intermodal transport problem of container terminal in ECA. Firstly, a dynamic berth allocation problem with ship emissions consideration (EDBAP), based on an application of shore power is modeled. Then, truck dispatching problem (TDP) is presented, the third one is ship and train scheduling problem (STSP). Mixed integer non-linear programming (MINLP) models are developed for these problems, heuristic algorithm to solve these problems. Experimental results demonstrate that the intermodal transport problem of container terminal, reflected by the proposed models, is competent to reduce emissions, while simultaneously retaining the service level, thus improve operation efficiency in intermodal transport of container terminal.

The main result of the paper is a new achievable rate region for the two-phase two-way relay channel with half duplex nodes. The new region is obtained using a compress- and-forward like protocol. Even though all information passes the relay, there are channels where decoding at the relay is suboptimal. The proposed protocol establishes a virtual two-way channel by forwarding a good enough version of the MAC output at the relay to both the receivers. In effect, the side information at the receivers can be used for the decoding of the transmission in the first phase.

In traditional schedule, there are problems such as large forecast errors and low degree of decentralization. As the basic distributed database technology, the blockchain technology can improve the accuracy of schedule and the transparency of data if it's introduced into the traditional schedule. Therefore, based on the blockchain technology, a park microgrid scheduling model is established in this paper, where the minimum operating cost is the objective function, the power balance, the output limitation, the climbing limitation and the battery constraint are the constraints. Mixed integer programming algorithm and the GUROBI solver are used to solve the proposed model, which can effectively reduce the operating cost of the park microgrid and improve the transparency of the scheduling data. Finally, a typical park microgrid case is used to prove the feasibility of the model and solution.

Assignment of parcels to loading stations in robotic sorting systems

In this paper, a model that optimal daily operation(without considering peak and valley price) of single adjustable-blade pump unit in the large pumping station along the Yangtze River is established. Where the minimum cost of energy consumption of single pump unit is set as the objective function, the periods are defined as stage variables, the blade angle is expressed as a decision variable and the daily quantity of delivery water is considered as constraint. The No. 4 Jiangdu Pump Station is taken as a study case. Dynamic programming is adopted to carry out the tidal large pumping station along the Yangtze River. Through this method, under the condition of different water quantity constraint, different average pumping head and different average tidal range amplitude,the influence of the tidal variation on optimal operation of single pump unit in the pumping station is determined. The application results shows that: With the decreased of average pumping head and the increased of average tidal range, the effect of optimal operation of adjustable-blade pump unit in the pumping station along the Yangtze River is getting more and more obvious.

Modeling tides and their influence on the circulation in Prince William Sound, Alaska

A robust ant colony metaheuristic for urban freight transport scheduling using passenger rail network

Graph-based integrated production and intermodal transport scheduling with capacity restrictions

Disasters cause severe economic and human losses, and delays in rescue efforts can lead to more deaths due to damaged infrastructure. To enhance post-disaster response efficiency, this study proposes a collaborative truck-and-drone system that leverages the high capacity of trucks and the speed and flexibility of drones to overcome road accessibility challenges. Although truck-drone collaborations have been studied in commercial logistics, their application in disaster relief remains understudied, particularly for simultaneous delivery and surveillance tasks, which are critical for saving lives and assessing damage. Unlike cost- or time-focused commercial logistics, humanitarian operations require prioritizing urgency, where tasks with higher mortality risks should be prioritized. This study addresses the truck-and-drone routing problem with flexible collaboration strategies for delivery and surveillance tasks to minimize the priority and penalty costs in the disaster response. We model the problem as a mixed integer linear programming model and develop an exact method based on the branch-and-bound algorithm and the Benders decomposition approach to solve the problem. Several valid inequalities are derived based on problem properties to improve the computational efficiency. Numerical experiments demonstrate that the proposed algorithm outperforms the state-of-the-art solver Gurobi in terms of objective function value, optimality gap, and computational time. The proposed flexible truck-and-drone collaboration can significantly enhance rescue efficiency in disaster response. Funding: The work described in this paper was partially supported by the National Natural Science Foundation of China [Grants 72371216 and 72301230] and the Research Grants Council of Hong Kong, China [Grants 17205124, 25223223, and T32-707-22]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/trsc.2024.0987 .