Robustness and Adaptiveness Analysis of Future Fleets

Making decisions about the structure of a future military fleet is a challenging task. Several issues need to be considered such as the existence of multiple competing objectives and the complexity of the operating environment. A particular challenge is posed by the various types of uncertainty that the future might hold. It is uncertain what future events might be encountered; how fleet design decisions will influence and shape the future; and how present and future decision makers will act based on available information, their personal biases regarding the importance of different objectives, and their economic preferences. In order to assist strategic decision-making, an analysis of future fleet options needs to account for conditions in which these different classes of uncertainty are exposed. It is important to understand what assumptions a particular fleet is robust to, what the fleet can readily adapt to, and what conditions present clear risks to the fleet. We call this the analysis of a fleet's strategic positioning. This paper introduces how strategic positioning can be evaluated using computer simulations. Our main aim is to introduce a framework for capturing information that can be useful to a decision maker and for defining the concepts of robustness and adaptiveness in the context of future fleet design. We demonstrate our conceptual framework using simulation studies of an air transportation fleet. We capture uncertainty by employing an explorative scenario-based approach. Each scenario represents a sampling of different future conditions, different model assumptions, and different economic preferences. Proposed changes to a fleet are then analysed based on their influence on the fleet's robustness, adaptiveness, and risk to different scenarios

Robustness and Adaptability Analysis of Future Military Air Transportation Fleets

Making decisions about the structure of a future military fleet is challenging. Several issues need to be considered, including multiple competing objectives and the complexity of the operating environment. A particular challenge is posed by the various types of uncertainty that the future holds. It is uncertain what future events might be encountered and how fleet design decisions will influence these events. In order to assist strategic decision-making, an analysis of future fleet options needs to account for conditions in which these different uncertainties are exposed. It is important to understand what assumptions a particular fleet is robust to, what the fleet can readily adapt to, and what conditions present risks to the fleet. We call this the analysis of a fleet’s strategic positioning. Our main aim is to introduce a framework that captures information useful to a decision maker and defines the concepts of robustness and adaptability in the context of future fleet design. We demonstrate our conceptual framework by simulating an air transportation fleet problem. We account for uncertainty by employing an explorative scenario-based approach. Each scenario represents a sampling of different future conditions and different model assumptions. Proposed changes to a fleet are then analysed based on their influence on the fleet’s robustness, adaptability, and risk to different scenarios

Evolutionary fleet sizing in static and uncertain environments with shuttle transportation tasks - the case studies of container terminals

This paper aims to identify the optimal number of vehicles in environments with shuttle transportation tasks. These environments are very common industrial settings where goods are transferred repeatedly between multiple machines by a fleet of vehicles. Typical examples of such environments are manufacturing factories, warehouses and container ports. One very important optimisation problem in these environments is the fleet sizing problem. In real-world settings, this problem is highly complex and the optimal fleet size depends on many factors such as uncertainty in travel time of vehicles, the processing time of machines and size of the buffer of goods next to machines. These factors, however, have not been fully considered previously, leaving an important gap in the current research. This paper attempts to close this gap by taking into account the aforementioned factors. An evolutionary algorithm was proposed to solve this problem under static and uncertain situations. Two container ports were selected as case studies for this research. For the static cases, the state-of-the-art CPLEX solver was considered as the benchmark. Comparison results on real-world scenarios show that in the majority of cases the proposed algorithm outperforms CPLEX in terms of solvability and processing time. For the uncertain cases, a high-fidelity simulation model was considered as the benchmark. Comparison results on real-world scenarios with uncertainty show that in most cases the proposed algorithm could provide an accurate robust fleet size. These results also show that uncertainty can have a significant impact on the optimal fleet size

Scheduling and Resource Efficiency Balancing. Discrete Species Conserving Cuckoo Search for Scheduling in an Uncertain Execution Environment

The main goal of a scheduling process is to decide when and how to execute each of the project’s activities. Despite large variety of researched scheduling problems, the majority of them can be described as generalisations of the resource-constrained project scheduling problem (RCPSP). Because of wide applicability and challenging difficulty, RCPSP has attracted vast amount of attention in the research community and great variety of heuristics have been adapted for solving it. Even though these heuristics are structurally different and operate according to diverse principles, they are designed to obtain only one solution at a time. In the recent researches on RCPSPs, it was proven that these kind of problems have complex multimodal fitness landscapes, which are characterised by a wide solution search spaces and presence of multiple local and global optima. The main goal of this thesis is twofold. Firstly, it presents a variation of the RCPSP that considers optimisation of projects in an uncertain environment where resources are modelled to adapt to their environment and, as the result of this, improve their efficiency. Secondly, modification of a novel evolutionary computation method Cuckoo Search (CS) is proposed, which has been adapted for solving combinatorial optimisation problems and modified to obtain multiple solutions. To test the proposed methodology, two sets of experiments are carried out. Firstly, the developed algorithm is applied to a real-life software development project. Secondly, the performance of the algorithm is tested on universal benchmark instances for scheduling problems which were modified to take into account specifics of the proposed optimisation model. The results of both experiments demonstrate that the proposed methodology achieves competitive level of performance and is capable of finding multiple global solutions, as well as prove its applicability in real-life projects

Evolutionary Algorithms and Simulation for Intelligent Autonomous Vehicles in Container Terminals

The study of applying soft computing techniques, such as evolutionary computation and simulation, to the deployment of intelligent autonomous vehicles (IAVs) in container terminals is the focus of this thesis. IAVs are a new type of intelligent vehicles designed for transportation of containers in container terminals. This thesis for the first time investigates how IAVs can be effectively accommodated in container terminals and how much the performance of container terminals can be improved when IAVs are being used. In an attempt to answer the above research questions, the thesis makes the following contributions: First, the thesis studies the fleet sizing problem in container terminals, an important design problem in container terminals. The contributions include proposing a novel evolutionary algorithm (with superior results to the state-of-the-art CPLEX solver), combining the proposed evolutionary algorithm with Monte Carlo simulation to take into account uncertainties, validating results of the uncertain case with a high fidelity simulation, proposing different robustness measures, comparing different robust solutions and proposing a dynamic sampling technique to improve the performance of the proposed evolutionary algorithm. Second, the thesis studies the impact of IAVs on container terminals’ performance and total cost, which are very important criteria in port equipment. The contributions include developing simulation models using realistic data (it is for the first time that the impact of IAVs on containers terminals is investigated using simulation models) and applying a cost model to the results of the simulation to estimate and compare the total cost of the case study with IAVs against existing trucks. Third, the thesis proposes a new framework for the simulations of container terminals. The contributions include developing a flexible simulation framework, providing a user library for users to create 3D simulation models using drag-and-drop features, and allowing users to easily incorporate their optimisation algorithms into their simulations