A reclaimer scheduling problem arising in coal stockyard management

We study a number of variants of an abstract scheduling problem inspired by the scheduling of reclaimers in the stockyard of a coal export terminal. We analyze the complexity of each of the variants, providing complexity proofs for some and polynomial algorithms for others. For one, especially interesting variant, we also develop a constant factor approximation algorithm.Comment: 26 page

The matching relaxation for a class of generalized set partitioning problems

This paper introduces a discrete relaxation for the class of combinatorial optimization problems which can be described by a set partitioning formulation under packing constraints. We present two combinatorial relaxations based on computing maximum weighted matchings in suitable graphs. Besides providing dual bounds, the relaxations are also used on a variable reduction technique and a matheuristic. We show how that general method can be tailored to sample applications, and also perform a successful computational evaluation with benchmark instances of a problem in maritime logistics.Comment: 33 pages. A preliminary (4-page) version of this paper was presented at CTW 2016 (Cologne-Twente Workshop on Graphs and Combinatorial Optimization), with proceedings on Electronic Notes in Discrete Mathematic

Review of Technological Processes at the Container Terminal

The aim of this paper work is to explore technological processes which occur on container terminal.Each of them represents a sort of a challenge for a manager of every port, who is responsible for a work organization. The way of running technological processes needs to be organized by minimizing the time ships spend on a dock, including balanced and economical use of port capacity as well as maximum flow of containers. Tracking efficiency of port capacities is shown on an example of container terminal of Rijeka port. By applying quantitative methods, queuing theory, it is been explored whether the capacity of container terminals satisfy existing trade as well as whether the same are sufficient for future increase of trade in terms of expansion of existing or better organization of technological processes

Loading and unloading operations in container terminals

Department of Logistics, Faculty of BusinessAuthor name used in this publication: George L. Vairaktarakis2003-2004 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Sea Container Terminals

Due to a rapid growth in world trade and a huge increase in containerized goods, sea container terminals play a vital role in globe-spanning supply chains. Container terminals should be able to handle large ships, with large call sizes within the shortest time possible, and at competitive rates. In response, terminal operators, shipping liners, and port authorities are investing in new technologies to improve container handling infrastructure and operational efficiency. Container terminals face challenging research problems which have received much attention from the academic community. The focus of this paper is to highlight the recent developments in the container terminals, which can be categorized into three areas: (1) innovative container terminal technologies, (2) new OR directions and models for existing research areas, and (3) emerging areas in container terminal research. By choosing this focus, we complement existing reviews on container terminal operations

Exploring search space trees using an adapted version of Monte Carlo tree search for combinatorial optimization problems

In this article, a novel approach to solve combinatorial optimization problems is proposed. This approach makes use of a heuristic algorithm to explore the search space tree of a problem instance. The algorithm is based on Monte Carlo tree search, a popular algorithm in game playing that is used to explore game trees. By leveraging the combinatorial structure of a problem, several enhancements to the algorithm are proposed. These enhancements aim to efficiently explore the search space tree by pruning subtrees, using a heuristic simulation policy, reducing the domains of variables by eliminating dominated value assignments and using a beam width. They are demonstrated for two specific combinatorial optimization problems: the quay crane scheduling problem with non-crossing constraints and the 0-1 knapsack problem. Computational results show that the algorithm achieves promising results for both problems and eight new best solutions for a benchmark set of instances are found for the former problem. These results indicate that the algorithm is competitive with the state-of-the-art. Apart from this, the results also show evidence that the algorithm is able to learn to correct the incorrect choices made by constructive heuristics

Containership Load Planning with Crane Operations

Since the start of the containerization revolution in 1950's, not only the TEU capacity of the vessels has been increasing constantly, but also the number of fully cellular container ships has expanded substantially. Because of the tense competition among ports in recent years, improving the operational efficiency of ports has become an important issue in containership operations. Arrangement of containers both within the container terminal and on the containership play an important role in determining the berthing time. The berthing time of a containership is mainly composed of the unloading and loading time of containers. Containers in a containership are stored in stacks, making a container directly accessible only if it is on the top of one stack. The task of determining a good container arrangement to minimize the number of re-handlings while maintaining the ship's stability over several ports is called stowage planning, which is an everyday problem solved by ship planners. The horizontal distribution of the containers over the bays affects crane utilization and overall ship berthing time. In order to increase the terminal productivity and reduce the turnaround time, the stowage planning must conform to the berth design. Given the configuration of berths and cranes at each visiting port, the stowage planning must take into account the utilization of quay cranes as well as the reduction of unnecessary shifts to minimize the total time at all ports over the voyage. This dissertation introduces an optimization model to solve the stowage planning problem with crane utilization considerations. The optimization model covers a wide range of operational and structural constraints for containership load planning. In order to solve real-size problems, a meta-heuristic approach based on genetic algorithms is designed and implemented which embeds a crane split approximation routine. The genetic encoding is ultra-compact and represents grouping, sorting and assignment strategies that might be applied to form the stowage pattern. The evaluation procedure accounts for technical specification of the cranes as well as the crane split. Numerical results show that timely solution for ultra large size containerships can be obtained under different scenarios

Combined quay crane assignment and quay crane scheduling with crane inter-vessel movement and non-interference constraints

Integrated models of the quay crane assignment problem (QCAP) and the quay crane scheduling problem (QCSP) exist. However, they have shortcomings in that some do not allow movement of quay cranes between vessels, others do not take into account precedence relationships between tasks, and yet others do not avoid interference between quay cranes. Here, an integrated and comprehensive optimization model that combines the two distinct QCAP and QCSP problems which deals with the issues raised is put forward. The model is of the mixed-integer programming type with the objective being to minimize the difference between tardiness cost and earliness income based on finishing time and requested departure time for a vessel. Because of the extent of the model and the potential for even small problems to lead to large instances, exact methods can be prohibitive in computational time. For this reason an adapted genetic algorithm (GA) is implemented to cope with this computational burden. Experimental results obtained with branch-and-cut as implemented in CPLEX and GA for small to large-scale problem instances are presented. The paper also includes a review of the relevant literature