Model and heuristic solutions for the multiple double-load crane scheduling problem in slab yards

This article studies a multiple double-load crane scheduling problem in steel slab yards. Consideration of multiple cranes and their double-load capability makes the scheduling problem more complex. This problem has not been studied previously. We first formulate the problem as a mixed-integer linear programming (MILP) model. A two-phase model-based heuristic is then proposed. To solve large problems, a pointer-based discrete differential evolution (PDDE) algorithm was developed with a dynamic programming (DP) algorithm embedded to solve the one-crane subproblem for a fixed sequence of tasks. Instances of real problems are collected from a steel company to test the performance of the solution methods. The experiment results show that the model can solve small problems optimally, and the solution greatly improves the schedule currently used in practice. The two-phase heuristic generates near-optimal solutions, but it can still only solve comparatively modest problems within reasonable (4 h) computational timeframes. The PDDE algorithm can solve large practical problems relatively quickly and provides better results than the two-phase heuristic solution, demonstrating its effectiveness and efficiency and therefore its suitability for practical use

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

Modelling of integrated vehicle scheduling and container storage problems in unloading process at an automated container terminal

Effectively scheduling vehicles and allocating storage locations for containers are two important problems in container terminal operations. Early research efforts, however, are devoted to study them separately. This paper investigates the integration of the two problems focusing on the unloading process in an automated container terminal, where all or part of the equipment are built in automation. We formulate the integrated problem as a mixed-integer programming (MIP) model to minimise ship’s berth time. We determine the detailed schedules for all vehicles to be used during the unloading process and the storage location to be assigned for all containers. A series of experiments are carried out for small-sized problems by using commercial software. A genetic algorithm (GA) is designed for solving large-sized problems. The solutions from the GA for the small-sized problems are compared with the optimal solutions obtained from the commercial software to verify the effectiveness of the GA. The computational results show that the model and solution methods proposed in this paper are efficient in solving the integrated unloading problem for the automated container terminal

Dynamic Quay Crane Allocation

We introduce simple rules for quay cranes to handle containers along a berth where vessels arrive continuously in time. We first analyze a model where workload is continuous. Our analysis shows that if the system is configured properly, it will always converge to a state with the maximum possible throughput regardless of external disruptions or changes in workload. Numerical simulations based on a discrete workload model suggest that, by following the same rules, the system can still converge to state with throughput that is very close to its upper bound

Sequence-Based Simulation-Optimization Framework With Application to Port Operations at Multimodal Container Terminals

It is evident in previous works that operations research and mathematical algorithms can provide optimal or near-optimal solutions, whereas simulation models can aid in predicting and studying the behavior of systems over time and monitor performance under stochastic and uncertain circumstances. Given the intensive computational effort that simulation optimization methods impose, especially for large and complex systems like container terminals, a favorable approach is to reduce the search space to decrease the amount of computation. A maritime port can consist of multiple terminals with specific functionalities and specialized equipment. A container terminal is one of several facilities in a port that involves numerous resources and entities. It is also where containers are stored and transported, making the container terminal a complex system. Problems such as berth allocation, quay and yard crane scheduling and assignment, storage yard layout configuration, container re-handling, customs and security, and risk analysis become particularly challenging. Discrete-event simulation (DES) models are typically developed for complex and stochastic systems such as container terminals to study their behavior under different scenarios and circumstances. Simulation-optimization methods have emerged as an approach to find optimal values for input variables that maximize certain output metric(s) of the simulation. Various traditional and nontraditional approaches of simulation-optimization continue to be used to aid in decision making. In this dissertation, a novel framework for simulation-optimization is developed, implemented, and validated to study the influence of using a sequence (ordering) of decision variables (resource levels) for simulation-based optimization in resource allocation problems. This approach aims to reduce the computational effort of optimizing large simulations by breaking the simulation-optimization problem into stages. Since container terminals are complex stochastic systems consisting of different areas with detailed and critical functions that may affect the output, a platform that accurately simulates such a system can be of significant analytical benefit. To implement and validate the developed framework, a large-scale complex container terminal discrete-event simulation model was developed and validated based on a real system and then used as a testing platform for various hypothesized algorithms studied in this work

Construction Requirements Driven Planning and Scheduling

Ph.DDOCTOR OF PHILOSOPH

A conceptual procedural framework for effective scheduling to enhance efficient use of construction resources on the jobsite

Selection of construction methods, scheduling, site layout and component procurement arrangement affect efficiency of operations on the jobsite. Efficiency has been previously measured by such parameters as; budget, on time completion and meeting specification standards. Little attention has been given to the interim processes which create these. Efficiency in man- and machine-hour management may translate to cost and time gains and enhanced quality. The study reported recognises that there are numerous aspects to the question of efficiency of operations. To focus the study and narrow the scope to a manageable size, the issues of efficiency that can be addressed in the scheduling process are those considered. Extensive and thorough literature search identified guidelines for effective construction scheduling. Empirical data were collected following these guidelines to develop a scheduling procedure aimed at making the process more effective and which may enhance efficient use of construction resources on the jobsite. The developed framework show that activity criticality based on time analysis alone is a necessary condition but not usually sufficient to declare an activity critical. Other tasks not on the critical path which have very high delay potential should be considered. Therefore though the study does not out rightly refute the idea of criticality based on time analysis alone, it adds to it that if criticality means those things that should be done so as to progress the works to a scheduled finish, criticality should be re-assessed to include several other tasks not hitherto identified on the critical path