A combined ship scheduling and allocation problem

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.

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.

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.

In order to improve the port service level and the efficiency of ships entering and leaving the port in restricted two-way channels, the ship scheduling problem under the type of restricted two-way channels is studied; considering the influence of tides, ship avoidance and safety distance of single two-way channels and other factors on the ship scheduling in the port, a mixed-integer planning model is constructed by taking the time of the ship entering and leaving the port as the decision variable, and minimising the total delay cost of the entering and leaving ships as the objective function; based on the features of the ship scheduling problem, the model is solved by using the Gurobi solver. Through numerical experiments, it can be seen that comparing the results of the model with the current scheduling rules, the average optimization rates under different case sizes are 15.57%, 12.15%, and 19.07%, respectively, which can significantly reduce the cost of delay of ships in the port and improve the efficiency of ship services in the port, thus verifying the validity of the scheme.

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

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.

In this paper we address the in-port ship scheduling and tug assignment problem. This problem aims to determine a schedule of ship movements, and their escorting tugs, within a canal harbor. We formulate the problem as a Boolean satisfiability problem. In particular, we deal with canal-harbors, as this kind of harbors present strict constraints, e.g., on safety distance. We consider the Port of Venice, a medium size Italian harbor, as a case study.

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

This paper presents an enhanced guided ejection search (GES) to minimize the number of vehicles in the NP-hard pickup and delivery problem with time windows. The proposed improvements decrease the convergence time of the GES, and boost the quality of results. Extensive experimental study on the benchmark set shows how the enhancements influence the GES capabilities. It is coupled with the statistical tests to verify the significance of the results. We give a guidance on how to select a proper algorithm variant based on test characteristics and objectives. We report one new world’s best result obtained using the enhanced GES.

This paper introduces an adaptive large neighborhood search metaheuristic to solve the multi-trip multi-traffic pickup and delivery problem with time windows and synchronization (MTT-PDTWS). With adaptive destroy and repair operators, it learns over time which destroy and repair operators are the most effective and governs the operator selection biased toward highly effective one. The computational experiments display the impacts of these operators on the solution quality and the performance of the ALNS in comparison to the only existing tabu search methodology addressing the MTT-PDTWS.

This paper compares the performance of the classic genetic algorithm with the more recently proposed ant colony optimisation for solving real-world pickup and delivery problems with hard time windows. A real-world problem that is present worldwide - waste collection - is used to evaluate the algorithms. As in most real-world waste collection problems, many of the waste bins have time windows. The time windows stem from such things as safety regulations and customer agreements, and must be strictly adhered to. The optimisation showed that the genetic algorithm is better than the ant colony optimisation when utilising standard implementations of both algorithms. However, when the algorithms are enhanced with a local search procedure, the ant colony optimisation immediately becomes superior and achieves improved results. Local search seems to be a drawback for the genetic algorithm when hard time windows are involved. Various implementations of the local search procedure are evaluated in this paper using five different test sets. Recommendations for future implementations are given as well as additional enhancements which could improve the performance of the ant colony optimisation.

The pickup and delivery problem with time windows (PDPTW) is an NP-hard discrete optimization problem of serving transportation requests using a fleet of homogeneous trucks. Its main objective is to minimize the number of vehicles, and the secondary objective is to minimize the distance traveled during the service. In this paper, we propose the longest common subsequence based selective route exchange crossover (LCS-SREX), and apply this operator in the memetic algorithm (MA) for the PDPTW. Also, we suggest the new solution representation which helps handle the crossover efficiently. Extensive experimental study performed on the benchmark set showed that using LCS-SREX leads to very high-quality feasible solutions. The analysis is backed with the statistical tests to verify the importance of the elaborated results. Finally, we report one new world’s best routing schedule found using a parallel version of the MA exploiting LCS-SREX.

In this chapter, two kinds of intertwined decisions regarding the movement of vehicles in an in-plant milk-run delivery system are considered: routing decisions, which determine the set of sequences of stations visited by each tugger train, and scheduling decisions, which plan congestion free movement of the tugger trains. The problem under study, called the Multi Trip and Multi Cycle Pick-up and Delivery Problem with Time Windows and Congestion Free Traffic, can be viewed as an extension of the pick-up and delivery problem with time windows in which multiple tugger trains travel along closed-loop congestion-free routes in different cycles. A declarative model of the investigated milk-run delivery principle makes it possible to formulate a vehicle routing and scheduling problem the solution to which determines the route, the time schedule, and the type and number of parts that the different trucks must carry to fulfill orders from various customers/recipients. Due to the requirement of congestion-free milk-run traffic, a scheduling period slicing principle allowing to synchronize cyclic flows of different periods is applied. Its implementation, resulting in a cyclic schedule composed of quasi cyclic sub-schedules, implies a recursive formulation of a well-known constraint satisfaction problem. The goal is to find solutions that can minimize both vehicle downtime and the takt time of the production flow. Computer experiments illustrate the possibility of using the present approach in real-life systems.

Solving large-scale vehicle routing problems with complex additional constraints such as the problem of combined vehicle routing and break scheduling is very difficult. Even large-scale vehicle routing problems with time windows without additional constraints can only be solved to optimality for problems with tight time windows and a structure which allows only few possible solutions (Pisinger and Ropke, 2007). Therefore, heuristics and metaheuristics are mainly applied to solve these problems (Funke et al., 2005). In the following the restricted dynamic programming (DP) heuristic developed by Kok et al. (2010) is presented and utilized for solving the problem of combined vehicle routing and break scheduling both from a centralized and from a distributed decision making perspective.KeywordsRest PeriodVehicle Route ProblemVehicle RouteDriving TimeTotal Travel DistanceThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.