Evrim Ursavas

Optimal policies for the berth allocation problem under stochastic nature

Key purpose of container terminals is to serve container vessels. Container vessels may be of different types such as large deep-sea vessels or feeders and barges. Container terminal operators have to deploy intelligent strategies for the allocation of their limited resources to the calling vessels of those different types. The presence of uncertainty in the real processing of the operational schedules and arrival of vessels adds to the complexity of the already multifaceted problem. An inefficient decision in the berth allocation phase affects all the other applications connected to this and may increase the service period and costs. In this study, we propose a framework based on stochastic dynamic programing approach to model the berth allocation problem and characterize optimal polices under stochastic arrival and handling times for different types of calling vessels. We find that the optimal control policy is of threshold type depending on the number of vessels in a certain berth group. The derived policies can be used at container terminals for the optimal use of their berthing facilities.

A Benders Decomposition Approach for Solving the Offshore Wind Farm Installation Planning at the North Sea

Wind farm installation and particularly offshore wind farm installation is highly complex due to high dependency on weather and remarkably large components. Amongst others projects at North Sea face considerable interruptions due to severe weather conditions. The problem we refer to consists of determining the renting period of the offshore installation vessels and the scheduling of the operations for building the wind farm. Planners need to make these decisions under the uncertain wind states. A deterministic offshore wind turbine installation model is not suitable to capture the uncertainties which may leave the vessel resource unused or vessel being sent offshore in unfavorable conditions. Accordingly, this study proposes a model that considers disruptions arising from uncertain weather conditions which stand as the main challenge for such projects. As the compact formulation fails to provide solutions due to the large number of scenarios, an approach based on Benders decomposition is developed. The tool is applied to two major wind farm projects, “Bard 1” and “Borkum West” at the North Sea using real weather data over two years. The two-stage model leans to conservatively use the suitable conditions in advance to avoid huge waiting costs incurred under severe weather. Motivated by experts at Wagenborg and MPI Offshore, we extend our experiments to analyze cases where plants are build further away from the shore. The tool presented in this study is suitable for being used in the planning of wind farm projects providing competent solutions.

Scheduling cranes at an indented berth

Container terminals are facing great challenges in order to meet the shipping industry’s requirements. An important fact within the industry is the increasing vessel sizes. Actually, within the last decade the ship size in the Asia–Europe trade has effectively doubled. However, port productivity has not doubled along with the larger vessel sizes. This has led to increased vessel turn around times at ports which indeed is a severe problem. In order to meet the industry targets a game-changer in container handling is required. Indented berth structure is one important opportunity to handle this issue. This novel berth structure requires new models and solution techniques for scheduling the quay cranes serving the indented berth. Accordingly, in this paper, we approach the quay crane scheduling problem at an indented berth structure. We focus on the challenges and constraints related to the novel architecture. We model the quay crane scheduling problem under the special structure and develop a solution technique based on branch-and-price. Extensive experiments are conducted to validate the efficiency of the proposed algorithm.

Towards the Physical Internet Paradigm: A Model for Transportation Planning in Complex Road Networks with Empty Return Optimization

The Physical Internet paradigm is redesigning the logic of moving goods around the planet, with the goal of making logistics more effective, sustainable and efficient. In this paper a road transportation network devoted to the PI paradigm is designed, modeled and implemented. The problem deals with groupage transportation, including consolidation and deconsolidation centers in the network nodes where goods are loaded/unloaded in/out from containers. The goal is to serve the demand of some shipment orders belonging to different areas with the final goal of minimizing total costs, exploiting trucks capacity and reducing empty trips. A mixed integer linear programming (MILP) model is presented and an experimental analysis is provided. The results obtained have shown the effectiveness of the approach proposed.

Integrated Passenger and Freight Train Planning on Shared-Use Corridors

Our study involves the decision-making problems that railway infrastructure managers face in a rail network with dedicated tracks and shared-use corridors. We will analyze the consolidation strategy for shared-use corridors, where the track serves passenger and freight trains. In the stochastic demand case, we will provide an analytical model for the railway infrastructure manager to compute the expected long-term profit using a consolidation system. We will pinpoint the different characteristics of passenger and freight trains, and analytically derive the optimum track allocation and consolidation time, together with the optimum price, in all such cases, using two different model structures, i.e., the additive and the multiplicative forms. We will extend our model further to consider the due-date requirements and volume incentives for railway operators. Our experiments will use realistic parameter values, based on the Dutch railway system.