Multi-Objective and Multi-Attribute Optimisation for Sustainable Development Decision Aiding

Optimization is considered as a decision-making process for getting the most out of available resources for the best attainable results. Many real-world problems are multi-objective or multi-attribute problems that naturally involve several competing objectives that need to be optimized simultaneously, while respecting some constraints or involving selection among feasible discrete alternatives. In this Reprint of the Special Issue, 19 research papers co-authored by 88 researchers from 14 different countries explore aspects of multi-objective or multi-attribute modeling and optimization in crisp or uncertain environments by suggesting multiple-attribute decision-making (MADM) and multi-objective decision-making (MODM) approaches. The papers elaborate upon the approaches of state-of-the-art case studies in selected areas of applications related to sustainable development decision aiding in engineering and management, including construction, transportation, infrastructure development, production, and organization management

Using CBR to improve the usability of numerical models

In this thesis we show that CBR systems can be constructed from numerical models, so as to improve their usability. It is shown that CBR models may be queried in a flexible manner, and that the user may formulate queries consisting of constraints over both “input” and “output” variables of the numerical model. It is also shown that the constraints may be formulated using either nominal or continuous variables. A generalization of the CBR retrieval process to include constraints over unified “input-output” space is formulated as a framework for the method. The method is illustrated with practical engineering models: the pneumatic conveyor problem and the projectile problem. Comparisons are made on usability of CBR and numerical models for specific problems. It is shown that CBR models can answer questions difficult or impossible to formulate using numerical models, and that CBR models can be faster. The thesis also addresses a latent problem with the general method, which is of importance generally. This is to do with interpolation over nominal values in unified space. A novel method is proposed for interpolation over nominal values, termed Generalised Shepard Nearest Neighbour method (GSNN). GSNN can utilise distance metrics defined on the solution space of a CBR system. The properties and advantages of GSNN are examined in the thesis. A comparison is made with other CBR retrieval methods, using several examples, including the travel domain case base. It is shown that GSNN can out-perform conventional nearest neighbour methods. It is shown that GSNN has advantages in that it can find solutions not in the case base and it can find solutions not in the retrieval set. It is also shown that the performance of GSNN can be improved further by using it in conjunction with a diversity algorithm. The merit of using GSNN as a case selection component is examined, and it is shown that it can give good results in sparse case bases. Finally the thesis concludes with a survey of numerical models where CBR construction can be useful, and where benefits can be expected

CNN-enabled Visual Data Analytics and Intelligent Reasoning for Real-time Optimization and Simulation: An Application to Occupancy-aware Elevator Dispatching Optimization

For most operational systems, the optimization problem is a combinatorial optimization problem, and the optimization performance largely determines the solution quality. Moreover, there exists a trade-off between the computing time of the decision-making process and the optimization performance, which is particularly evident in a system that conducts real-time operations. To obtain better solutions to the decision-making problem in a shorter time, many optimization algorithms are proposed to improve the searching efficiency in the search space. However, information extraction from the environment is also essential for problem-solving. The environment information not only includes the optimization model inputs, but also contains details of the current situation that may change the problem formulation and optimization algorithm parameter values. Due to the time constraint and the computation time of visual processing algorithms, most conventional operational systems collect environment data from sensor platforms but do not analyze image data, which contains situational information that can assist with the decision-making process. To address this issue, this thesis proposes CNN-enabled visual data analytics and intelligent reasoning for real-time optimization, and a closed-loop optimization structure with discrete event simulation to fit the use of situational information in the optimization model. In the proposed operational system, CNNs are used to extract context information from image data, like the type and the number of objects at the scene. Then reasoning techniques and methodologies are applied to deduct knowledge about the current situation to adjust problem formulation and parameter settings. Discrete event simulation is conducted to test the optimization performance of the system, and adjustments can be made to better fit situational information in the optimization process. To validate the feasibility and effectiveness, an application to occupancy-aware elevator dispatching optimization is presented.M.S

Artificial Intelligence and Cognitive Computing

Artificial intelligence (AI) is a subject garnering increasing attention in both academia and the industry today. The understanding is that AI-enhanced methods and techniques create a variety of opportunities related to improving basic and advanced business functions, including production processes, logistics, financial management and others. As this collection demonstrates, AI-enhanced tools and methods tend to offer more precise results in the fields of engineering, financial accounting, tourism, air-pollution management and many more. The objective of this collection is to bring these topics together to offer the reader a useful primer on how AI-enhanced tools and applications can be of use in today’s world. In the context of the frequently fearful, skeptical and emotion-laden debates on AI and its value added, this volume promotes a positive perspective on AI and its impact on society. AI is a part of a broader ecosystem of sophisticated tools, techniques and technologies, and therefore, it is not immune to developments in that ecosystem. It is thus imperative that inter- and multidisciplinary research on AI and its ecosystem is encouraged. This collection contributes to that

Fuzzy Techniques for Decision Making 2018

Zadeh's fuzzy set theory incorporates the impreciseness of data and evaluations, by imputting the degrees by which each object belongs to a set. Its success fostered theories that codify the subjectivity, uncertainty, imprecision, or roughness of the evaluations. Their rationale is to produce new flexible methodologies in order to model a variety of concrete decision problems more realistically. This Special Issue garners contributions addressing novel tools, techniques and methodologies for decision making (inclusive of both individual and group, single- or multi-criteria decision making) in the context of these theories. It contains 38 research articles that contribute to a variety of setups that combine fuzziness, hesitancy, roughness, covering sets, and linguistic approaches. Their ranges vary from fundamental or technical to applied approaches

Uncertain Multi-Criteria Optimization Problems

Most real-world search and optimization problems naturally involve multiple criteria as objectives. Generally, symmetry, asymmetry, and anti-symmetry are basic characteristics of binary relationships used when modeling optimization problems. Moreover, the notion of symmetry has appeared in many articles about uncertainty theories that are employed in multi-criteria problems. Different solutions may produce trade-offs (conflicting scenarios) among different objectives. A better solution with respect to one objective may compromise other objectives. There are various factors that need to be considered to address the problems in multidisciplinary research, which is critical for the overall sustainability of human development and activity. In this regard, in recent decades, decision-making theory has been the subject of intense research activities due to its wide applications in different areas. The decision-making theory approach has become an important means to provide real-time solutions to uncertainty problems. Theories such as probability theory, fuzzy set theory, type-2 fuzzy set theory, rough set, and uncertainty theory, available in the existing literature, deal with such uncertainties. Nevertheless, the uncertain multi-criteria characteristics in such problems have not yet been explored in depth, and there is much left to be achieved in this direction. Hence, different mathematical models of real-life multi-criteria optimization problems can be developed in various uncertain frameworks with special emphasis on optimization problems

A hybrid model-based and memory-based short-term traffic prediction system

Short-term traffic forecasting capabilities on freeways and major arterials have received special attention in the past decade due primarily to their vital role in supporting various travelers\u27 trip decisions and traffic management functions. This research presents a hybrid model-based and memory-based methodology to improve freeway traffic prediction performance. The proposed methodology integrates both approaches to strengthen predictions under both recurrent and non-recurrent conditions. The model-based approach relies on a combination of static and dynamic neural network architectures to achieve optimal prediction performance under various input and traffic condition settings. Concurrently, the memory-based component is derived from the data archival system that encodes the commuters\u27 travel experience in the past. The outcomes of the two approaches are two prediction values for each query case. The two values are subsequently processed by a prediction query manager, which ultimately produces one final prediction value using an error-based decision algorithm. It was found that the hybrid approach produces speed estimates with smaller errors than if the two approaches employed separately. The proposed prediction approach could be used in deriving travel times more reliable as the Traffic Management Centers move towards implementing Advanced Traveler Information Systems (ATIS) applications

Binary Black Widow Optimization Algorithm for Feature Selection Problems

This thesis addresses feature selection (FS) problems, which is a primary stage in data mining. FS is a significant pre-processing stage to enhance the performance of the process with regards to computation cost and accuracy to offer a better comprehension of stored data by removing the unnecessary and irrelevant features from the basic dataset. However, because of the size of the problem, FS is known to be very challenging and has been classified as an NP-hard problem. Traditional methods can only be used to solve small problems. Therefore, metaheuristic algorithms (MAs) are becoming powerful methods for addressing the FS problems. Recently, a new metaheuristic algorithm, known as the Black Widow Optimization (BWO) algorithm, had great results when applied to a range of daunting design problems in the field of engineering, and has not yet been applied to FS problems. In this thesis, we are proposing a modified Binary Black Widow Optimization (BBWO) algorithm to solve FS problems. The FS evaluation method used in this study is the wrapper method, designed to keep a degree of balance between two significant processes: (i) minimize the number of selected features (ii) maintain a high level of accuracy. To achieve this, we have used the k-nearest-neighbor (KNN) machine learning algorithm in the learning stage intending to evaluate the accuracy of the solutions generated by the (BBWO). The proposed method is applied to twenty-eight public datasets provided by UCI. The results are then compared with up-to-date FS algorithms. Our results show that the BBWO works as good as, or even better in some cases, when compared to those FS algorithms. However, the results also show that the BBWO faces the problem of slow convergence due to the use of a population of solutions and the lack of local exploitation. To further improve the exploitation process and enhance the BBWO’s performance, we are proposing an improvement to the BBWO algorithm by combining it with a local metaheuristic algorithm based on the hill-climbing algorithm (HCA). This improvement method (IBBWO) is also tested on the twenty-eight datasets provided by UCI and the results are then compared with the basic BBWO and the up-to-date FS algorithms. Results show that the (IBBWO) produces better results in most cases when compared to basic BBWO. The results also show that IBBWO outperforms the most known FS algorithms in many cases