Crew Planning at Netherlands Railways: Improving Fairness, Attractiveness, and Efficiency

The development and improvement of decision support voor crew planning at Netherlands Railways (NS

Enhanced Pump Schedule Optimization For Large Water Distribution Networks To Maximize Environmental And Economic Benefits

For more than four decades researchers tried to develop optimization method and tools to reduce electricity consumption of pump stations of water distribution systems. Based on this ongoing research trend, about a decade ago, some commercial pump operation optimization software introduced to the market. Using metaheuristic and evolutionary techniques (e.g. Genetic Algorithm) make some commercial and research tools able to optimize the electricity cost of small water distribution systems (WDS). Still reducing the environmental footprint of these systems and dealing with large and complicated water distribution system is a challenge. In this study, we aimed to develop a multiobjective optimization tool (PEPSO) for reducing electricity cost and pollution emission (associated with energy consumption) of pump stations of WDSs. PEPSO designed to have a user-friendly graphical interface besides the state of art internal functions and procedures that lets users define and run customized optimization scenarios for even medium and large size WDSs. A customized version of non-dominated sorting genetic algorithm II is used as the core optimizer algorithm. EPANET toolkit is used as the hydraulic solver of PEPSO. In addition to the EPANET toolkit, a module is developed for training and using an artificial neural network instead of the high fidelity hydraulic model to speed up the optimization process. A unique measure that is called “Undesirability” is also introduced and used to help PEPSO in finding the promising path of optimization and making sure that the final results are desirable and practical. PEPSO is tested for optimizing the detailed hydraulic model of WDS of Monroe city, MI, USA and skeletonized hydraulic model of WDS of Richmond, UK. The various features of PEPSO are tested under 8 different scenarios, and its results are compared with results of Darwin Scheduler (a well-known commercial software in this field). The test results showed that in a reasonable amount of time, PEPSO is able to optimize and provide logical results for a medium size WDS model with 13 pumps and thousands of system components under different scenarios. It also is concluded that this tool in many aspects can provide better results in comparison with the famous commercial optimization tool in the market

Distributed Linear Precoding and User Selection in Coordinated Multicell Systems

In this manuscript we tackle the problem of semi-distributed user selection with distributed linear precoding for sum rate maximization in multiuser multicell systems. A set of adjacent base stations (BS) form a cluster in order to perform coordinated transmission to cell-edge users, and coordination is carried out through a central processing unit (CU). However, the message exchange between BSs and the CU is limited to scheduling control signaling and no user data or channel state information (CSI) exchange is allowed. In the considered multicell coordinated approach, each BS has its own set of cell-edge users and transmits only to one intended user while interference to non-intended users at other BSs is suppressed by signal steering (precoding). We use two distributed linear precoding schemes, Distributed Zero Forcing (DZF) and Distributed Virtual Signal-to-Interference-plus-Noise Ratio (DVSINR). Considering multiple users per cell and the backhaul limitations, the BSs rely on local CSI to solve the user selection problem. First we investigate how the signal-to-noise-ratio (SNR) regime and the number of antennas at the BSs affect the effective channel gain (the magnitude of the channels after precoding) and its relationship with multiuser diversity. Considering that user selection must be based on the type of implemented precoding, we develop metrics of compatibility (estimations of the effective channel gains) that can be computed from local CSI at each BS and reported to the CU for scheduling decisions. Based on such metrics, we design user selection algorithms that can find a set of users that potentially maximizes the sum rate. Numerical results show the effectiveness of the proposed metrics and algorithms for different configurations of users and antennas at the base stations.Comment: 12 pages, 6 figure

Improving Design Optimization and Optimization-based Design Knowledge Discovery

The use of design optimization in the early stages of architectural design process has attracted a high volume of research in recent years. However, traditional design optimization requires a significant amount of computing time, especially when there are multiple design objectives to achieve. What’s more, there is a lack of studies in the current research on automatic generation of architectural design knowledge from optimization results. This paper presents computational methods for creating and improving a closed loop of design optimization and knowledge discovery in architecture. It first introduces a design knowledge-assisted optimization improvement method with the techniques - offline simulation and Divide & Conquer (D&C) - to reduce the computing time and improve the efficiency of the design optimization process utilizing architectural domain knowledge. It then describes a new design knowledge discovery system where design knowledge can be discovered from optimization through an automatic data mining approach. The discovered knowledge has the potential to further help improve the efficiency of the optimization method, thus forming a closed loop of improving optimization and knowledge discovery. The validations of both methods are presented in the context of a case study with parametric form-finding for a nursing unit design with two design objectives: minimizing the nurses’ travel distance and maximizing daylighting performance in patient rooms

Multi-Objective Constraint Satisfaction for Mobile Robot Area Defense

In developing multi-robot cooperative systems, there are often competing objectives that need to be met. For example in automating area defense systems, multiple robots must work together to explore the entire area, and maintain consistent communications to alert the other agents and ensure trust in the system. This research presents an algorithm that tasks robots to meet the two specific goals of exploration and communication maintenance in an uncoordinated environment reducing the need for a user to pre-balance the objectives. This multi-objective problem is defined as a constraint satisfaction problem solved using the Non-dominated Sorting Genetic Algorithm II (NSGA-II). Both goals of exploration and communication maintenance are described as fitness functions in the algorithm that would satisfy their corresponding constraints. The exploration fitness was described in three ways to diversify the way exploration was measured, whereas the communication maintenance fitness was calculated as the number of independent clusters of agents. Applying the algorithm to the area defense problem, results show exploration and communication without coordination are two diametrically opposed goals, in which one may be favored, but only at the expense of the other. This work also presents suggestions for anyone looking to take further steps in developing a physically grounded solution to this area defense problem

Multiagent cooperation for solving global optimization problems: an extendible framework with example cooperation strategies

This paper proposes the use of multiagent cooperation for solving global optimization problems through the introduction of a new multiagent environment, MANGO. The strength of the environment lays in itsflexible structure based on communicating software agents that attempt to solve a problem cooperatively. This structure allows the execution of a wide range of global optimization algorithms described as a set of interacting operations. At one extreme, MANGO welcomes an individual non-cooperating agent, which is basically the traditional way of solving a global optimization problem. At the other extreme, autonomous agents existing in the environment cooperate as they see fit during run time. We explain the development and communication tools provided in the environment as well as examples of agent realizations and cooperation scenarios. We also show how the multiagent structure is more effective than having a single nonlinear optimization algorithm with randomly selected initial points

Knowledge acquisition for effective and efficient use of engineering software

The problem of effective and efficient use of engineering software can be thought of as a Pareto optimal problem. However, the complexity of modern engineering software precludes the possibility of acquiring complete knowledge of the software's Pareto optimal set. Instead, heuristic knowledge must be acquired. The thesis proposes that heuristic knowledge be acquired via a knowledge acquisition procedure. The use of a knowledge acquisition system, which may be computerised, forms an integral part of this procedure. Two examples of knowledge acquisition illustrate the use of the knowledge acquisition procedure

Spatially optimised sustainable urban development

PhD ThesisTackling urbanisation and climate change requires more sustainable and resilient cities, which in turn will require planners to develop a portfolio of measures to manage climate risks such as flooding, meet energy and greenhouse gas reduction targets, and prioritise development on brownfield sites to preserve greenspace. However, the policies, strategies and measures put in place to meet such objectives can frequently conflict with each other or deliver unintended consequences, hampering long-term sustainability. For example, the densification of cities in order to reduce transport energy use can increase urban heat island effects and surface water flooding from extreme rainfall events. In order to make coherent decisions in the presence of such complex multi-dimensional spatial conflicts, urban planners require sophisticated planning tools to identify and manage potential trade-offs between the spatial strategies necessary to deliver sustainability. To achieve this aim, this research has developed a multi-objective spatial optimisation framework for the spatial planning of new residential development within cities. The implemented framework develops spatial strategies of required new residential development that minimize conflicts between multiple sustainability objectives as a result of planning policy and climate change related hazards. Five key sustainability objectives have been investigated, namely; (i) minimizing risk from heat waves, (ii) minimizing the risk from flood events, (iii) minimizing travel costs in order to reduce transport emissions, (iv) minimizing urban sprawl and (v) preventing development on existing greenspace. A review identified two optimisation algorithms as suitable for this task. Simulated Annealing (SA) is a traditional optimisation algorithm that uses a probabilistic approach to seek out a global optima by iteratively assessing a wide range of spatial configurations against the objectives under consideration. Gradual ‘cooling’, or reducing the probability of jumping to a different region of the objective space, helps the SA to converge on globally optimal spatial patterns. Genetic Algorithms (GA) evolve successive generations of solutions, by both recombining attributes and randomly mutating previous generations of solutions, to search for and converge towards superior spatial strategies. The framework works towards, and outputs, a series of Pareto-optimal spatial plans that outperform all other plans in at least one objective. This approach allows for a range of best trade-off plans for planners to choose from. ii Both SA and GA were evaluated for an initial case study in Middlesbrough, in the North East of England, and were able to identify strategies which significantly improve upon the local authority’s development plan. For example, the GA approach is able to identify a spatial strategy that reduces the travel to work distance between new development and the central business district by 77.5% whilst nullifying the flood risk to the new development. A comparison of the two optimisation approaches for the Middlesbrough case study revealed that the GA is the more effective approach. The GA is more able to escape local optima and on average outperforms the SA by 56% in in the Pareto fronts discovered whilst discovering double the number of multi-objective Pareto-optimal spatial plans. On the basis of the initial Middlesbrough case study the GA approach was applied to the significantly larger, and more computationally complex, problem of optimising spatial development plans for London in the UK – a total area of 1,572km2. The framework identified optimal strategies in less than 400 generations. The analysis showed, for example, strategies that provide the lowest heat risk (compared to the feasible spatial plans found) can be achieved whilst also using 85% brownfield land to locate new development. The framework was further extended to investigate the impact of different development and density regulations. This enabled the identification of optimised strategies, albeit at lower building density, that completely prevent any increase in urban sprawl whilst also improving the heat risk objective by 60% against a business as usual development strategy. Conversely by restricting development to brownfield the ability of the spatial plan to optimise future heat risk is reduced by 55.6% against the business as usual development strategy. The results of both case studies demonstrate the potential of spatial optimisation to provide planners with optimal spatial plans in the presence of conflicting sustainability objectives. The resulting diagnostic information provides an analytical appreciation of the sensitivity between conflicts and therefore the overall robustness of a plan to uncertainty. With the inclusion of further objectives, and qualitative information unsuitable for this type of analysis, spatial optimization can constitute a powerful decision support tool to help planners to identify spatial development strategies that satisfy multiple sustainability objectives and provide an evidence base for better decision making