The Maritime Pickup and Delivery Problem with Time Windows and Split Loads

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 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.

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

This paper presents the first optimization study of multi-site transportation in the construction industry, which allows mutualizing building material delivery and construction waste removal. This study is inspired by a real-world problem encountered in the framework of the French R&D project DILC, in which a pooling platform must centralize the delivery of building materials to the construction sites and the pickup of their waste, using a limited and heterogeneous fleet that are allowed to perform multiple trips, under time and capacity limitation constraints. The problem under study, called the Multi-Trip Pickup and Delivery Problem, with Split loads, Profits and Multiple TimeWindows is a new extension of the vehicle routing problem with pickup and delivery, that considers new realistic constraints specific to the construction industry such as each construction site may have a priority on its delivery request or its pickup request or both, with a higher priority level for delivery request, and each construction site may have several time windows. To solve this problem, we propose new insertion criteria that takes into consideration several aspects of our problem, which we have embedded in a construction heuristic. Experiments performed on new real instances have shown the efficiency of our method.

We present an optimisation-based solution approach for a real ship planning problem,which is a combination of a variant of the multi-vehicle pickup and delivery problemwith time windows (m-PDPTW), and a multi-inventory model. This problem involves thedesign of a set of minimum cost routes for a fleet of heterogeneous ships servicing a set ofproduction and consumption harbours with a single product (ammonia). The production andinventory information at each harbour, together with the ship capacities and the location ofthe harbours, determine the number of possible arrivals at each harbour during the planningperiod, the time windows for start of service and the load quantity intervals at each arrival.We call this problem the inventory pickup and delivery problem with time windows -IPDPTW. In the mathematical programming model, we duplicate some of the variables anduse a Dantzig - Wolfe decomposition approach. Then the IPDPTW decomposes into a sub-problemfor each harbour and each ship. By synchronising the solutions from both types ofsubproblems, we get extra constraints in the master problem as compared to the masterproblem for the m-PDPTW discussed in the literature. The LP-relaxation of the masterproblem is solved by column generation, where the columns represent ship routes or harbourvisit sequences. Finally, this iterative solution process is embedded in a branch-and-boundsearch to make the solution integer optimal. Our computational results indicate that theproposed method works for the real planning problem.

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.

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 recent decades, the rapid development of international economy has led to significant inter-regional trade imbalances. In marine transportation, the voluminous empty containers accumulated at ports in import dominated countries have to be re-circulated back to ports in export dominated countries for re-deployment. This repositioning of empty containers is crucial for shipping companies to provide effective transportation services. At present, empty container repositioning has proved to be highly expensive, contributing to 27% of the overall operation cost of shipping companies. Consequently, major stakeholders including the shipping companies have strived to reduce the repositioning cost. Unfortunately, few major improvements have been made over the years and there are still lingering research issues. The objective of this research, therefore, is to develop efficient and effective methodologies to obtain empty container repositioning plans that can significantly reduce transportation cost while fulfilling customer demands of empty and laden containers.
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\nIn this research, the empty container repositioning problem under two related real marine transportation scenarios has been studied. The first scenario considers multi-vessel transportation for both laden and empty containers with split vessel routing (denoted as ECRSVR). A mathematical model of ECRSVR is developed based on the formulation of the Pickup and Delivery Problem with Time Windows (PDPTW). However, a major research issue arises due to such integrated consideration of split vessel routing and transportation of empty and laden containers. The resulting model will encompass many more new decision variables and constraints. Thus, it cannot be readily solved by existing algorithms. To address this issue, a new Branch-and-Price (B&P) algorithm incorporating B&B and column generation is developed. Specifically, a new dynamic programming algorithm with new label structure and dominance rules is established to solve the pricing problem in B&P, and a new multi-layer branching scheme is developed for implementing the algorithm. The effectiveness and robustness of the proposed methodology are then tested on a set of randomly generated problems.
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\nThe second scenario extends the first by including transshipment activities in the ECRSVR (denoted as ECRSVRTS). Apparently, the inclusion of transshipment activities will significantly increase the size of the solution space of feasible routing and distribution schemes. Also, more constraints have to be included in the model to account for the interdependence between the distribution schemes of separate vessels. To tackle this problem, two mathematical models are developed. The first is a port-based model that explicitly characterizes the routing plans and distribution volumes for each vessel at each time interval as decision variables. The resulting integer program is non-linear and difficult to solve. Therefore, an equivalent event-driven node-based model inspired by the formulation of PDPTW is constructed. In this model, the pickup/delivery tasks and transshipment events are represented as nodes in a generated topology. The B&P scheme is then modified to solve the node-based model and a Hybrid Scatter Tabu Search (HSTS) is developed to solve the pricing problems. Results obtained from the numerical experiments using randomly generated test problems show that the proposed HSTS algorithm outperforms conventional scatter search and tabu search algorithms.

A genetic algorithm for the simultaneous delivery and pickup problems with time window

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.

Currently, the shipping company's statistical data on cargo is usually obtained through the complex operation of the business database. The statistical items are also very limited. To improve the competitiveness of shipping company and optimize its ship scheduling business, this paper mainly researches on technology and application of data warehouse in oceangoing ship scheduling business. According to the customer's actual and objective needs, the ship scheduling data warehouse is designed. Based on Microsoft SQL Server 2008 platform and ship scheduling data of a shipping company, the ship scheduling data warehouse for cargo loading/discharging analysis in global ports is established in this paper, which can offer data deeply analysis and multidimensional display.