Application of computational intelligence to explore and analyze system architecture and design alternatives

Systems Engineering involves the development or improvement of a system or process from effective need to a final value-added solution. Rapid advances in technology have led to development of sophisticated and complex sensor-enabled, remote, and highly networked cyber-technical systems. These complex modern systems present several challenges for systems engineers including: increased complexity associated with integration and emergent behavior, multiple and competing design metrics, and an expansive design parameter solution space. This research extends the existing knowledge base on multi-objective system design through the creation of a framework to explore and analyze system design alternatives employing computational intelligence. The first research contribution is a hybrid fuzzy-EA model that facilitates the exploration and analysis of possible SoS configurations. The second contribution is a hybrid neural network-EA in which the EA explores, analyzes, and evolves the neural network architecture and weights. The third contribution is a multi-objective EA that examines potential installation (i.e. system) infrastructure repair strategies. The final contribution is the introduction of a hierarchical multi-objective evolutionary algorithm (MOEA) framework with a feedback mechanism to evolve and simultaneously evaluate competing subsystem and system level performance objectives. Systems architects and engineers can utilize the frameworks and approaches developed in this research to more efficiently explore and analyze complex system design alternatives --Abstract, page iv

Order picking strategies for healthcare warehouses.

Order picking is the process of collecting goods and items in specified quantities from storage locations, in response to customer orders. Since many labor resources are involved in this process, finding ways to make it more efficient have been a primary goal for researchers and practitioners. Determining a better allocation of products to the storage areas, finding the best route and sequence to pick multiple products, and choosing the best picking policies to minimize congestion in the aisles are just a few of many objectives regarding order picking process. Due to regulatory compliances and the chance of product spoilage, additional criteria should be taken into account when dealing with order picking in a healthcare warehouse. Following the first-expired, first-out (FEFO) principle and fulfilling an order from a single manufacturing batch, are two of the requirements that are considered in this research. Moreover, minimizing the traveled distance to picking locations, minimizing the penalty or cost of using the space by depleting it quicker, and maximizing the probability of a successful pick in the future by considering the order size distribution are the other objectives that have been studied in this research. The desired order picking problem is formulated and solved as a multi-objective mixed integer programming optimization model. Assigning importance weights to each objective and obtaining one single solution does not provide a full picture of all possible and potentially attractive solutions. On the other hand, providing all the solutions is not always achievable as it is computationally expensive and most of the time a set of rules and regulations drive decision makers toward the chosen solution. Finally, this research focuses on solution simplicity, generality, and practicality since the solution will be implemented by order pickers. To achieve this goal, a novel approach using association rule mining is presented. Products are classified in different groups and some order picking rules are derived based on the relations of these classes together. These rules are then compared with the results of multi-objective optimization model to evaluate their quality. The comparison results showed that surprisingly, some simple rules extracted from the preferences of the decision maker, can obtain good quality solutions. For example, in products with high order variability, it is possible to generate solutions within an average gap of less than ±8.8% from the multi-objective optimization solutions. The findings of this research leads to the following primary recommendations: Orders should be picked based on the expiration dates of the batches (FEFO principle) unless, the amount of inventory in the location is exactly equal to the order amount. In that case, regardless of the expiration date and the traveled distance to the location, it is better to pick from that location and make the space available. If product replenishment is not instantaneous, regardless of the expiration date and the traveled distance to the location, it is recommended to not pick from a location which can lead to an undesired and low amount of inventory. Due to order variability of the product, it is better to wait until the inventory in that location transfers to a desirable level. If fast replenishment of products is obtainable, then prioritizing traveled distance over the objectives linked to space usage and inventory is advisable

Advanced Forward Modeling and Inversion of Stokes Profiles Resulting from the Joint Action of the Hanle and Zeeman Effects

A big challenge in solar and stellar physics in the coming years will be to decipher the magnetism of the solar outer atmosphere (chromosphere and corona) along with its dynamic coupling with the magnetic fields of the underlying photosphere. To this end, it is important to develop rigorous diagnostic tools for the physical interpretation of spectropolarimetric observations in suitably chosen spectral lines. Here we present a computer program for the synthesis and inversion of Stokes profiles caused by the joint action of atomic level polarization and the Hanle and Zeeman effects in some spectral lines of diagnostic interest, such as those of the He I 10830 A and D_3 multiplets. It is based on the quantum theory of spectral line polarization, which takes into account all the relevant physical mechanisms and ingredients (optical pumping, atomic level polarization, Zeeman, Paschen-Back and Hanle effects). The influence of radiative transfer on the emergent spectral line radiation is taken into account through a suitable slab model. The user can either calculate the emergent intensity and polarization for any given magnetic field vector or infer the dynamical and magnetic properties from the observed Stokes profiles via an efficient inversion algorithm based on global optimization methods. The reliability of the forward modeling and inversion code presented here is demonstrated through several applications, which range from the inference of the magnetic field vector in solar active regions to determining whether or not it is canopy-like in quiet chromospheric regions. This user-friendly diagnostic tool called "HAZEL" (from HAnle and ZEeman Light) is offered to the astrophysical community, with the hope that it will facilitate new advances in solar and stellar physics.Comment: 62 pages, 19 figures, 3 tables. Accepted for publication in Ap

Green intermodal freight transportation: bi-objective modeling and analysis

Efficient planning of freight transportation requires a comprehensive look at wide range of factors in the operation and management of any transportation mode to achieve safe, fast, and environmentally suitable movement of goods. In this regard, a combination of transportation modes offers flexible and environmentally friendly alternatives to transport high volumes of goods over long distances. In order to reflect the advantages of each transportation mode, it is the challenge to develop models and algorithms in Transport Management System software packages. This paper discusses the principles of green logistics required in designing such models and algorithms which truly represent multiple modes and their characteristics. Thus, this research provides a unique practical contribution to green logistics literature by advancing our understanding of the multi-objective planning in intermodal freight transportation. Analysis based on a case study from hinterland intermodal transportation in Europe is therefore intended to make contributions to the literature about the potential benefits from combining economic and environmental criteria in transportation planning. An insight derived from the experiments conducted shows that there is no need to greatly compromise on transportation costs in order to achieve a significant reduction in carbon-related emissions

Evolutionary Algorithms in Engineering Design Optimization

Evolutionary algorithms (EAs) are population-based global optimizers, which, due to their characteristics, have allowed us to solve, in a straightforward way, many real world optimization problems in the last three decades, particularly in engineering fields. Their main advantages are the following: they do not require any requisite to the objective/fitness evaluation function (continuity, derivability, convexity, etc.); they are not limited by the appearance of discrete and/or mixed variables or by the requirement of uncertainty quantification in the search. Moreover, they can deal with more than one objective function simultaneously through the use of evolutionary multi-objective optimization algorithms. This set of advantages, and the continuously increased computing capability of modern computers, has enhanced their application in research and industry. From the application point of view, in this Special Issue, all engineering fields are welcomed, such as aerospace and aeronautical, biomedical, civil, chemical and materials science, electronic and telecommunications, energy and electrical, manufacturing, logistics and transportation, mechanical, naval architecture, reliability, robotics, structural, etc. Within the EA field, the integration of innovative and improvement aspects in the algorithms for solving real world engineering design problems, in the abovementioned application fields, are welcomed and encouraged, such as the following: parallel EAs, surrogate modelling, hybridization with other optimization techniques, multi-objective and many-objective optimization, etc

Multi-Objective Scientific-Workflow Scheduling With Data Movement Awareness in Cloud.

Due to serving several purposes simultaneously, running scientific workflows on dynamic environments such as cloud computing, has become multi-objective scheduling. Among these purposes, Cost and Makespan are probably the most two primitive objectives. Another critical factor in a large-scale scientific workflow is tremendous amount of data during execution. Therefore, this work also includes Data Movement as an additional objective as it has a major impact on network utilization and energy consumption in network equipment in cloud data center. In considering these three objectives, this work proposes a framework for scheduling solutions which combines a new nodes clustering technique in Directed Acyclic Graph (DAG) model known as Multilevel Dependent Node Clustering (MDNC) and the multiobjective optimization, Extreme Nondominated Sorting Genetic Algorithm-III (E-NSGA-III). E-NSGAIII is the recent extension of Nondominated Sorting Genetic Algorithm (NSGA-III). Five well-known scientific workflows, CyberShake, Epigenomics, LIGO, Montage, and SIPHT are selected as testbeds, while the commonly known Hypervolume is chosen as the performance metric. In this work, MDNC is also experimented with both NSGA-III. Comparison among three approaches, E-NAGA-III alone, E-NAGA-III with Peer-to-Peer clustering and E-NAGA-III with MDNC are carried out. The superiority of the proposed framework among them and its limitation are discussed

Development of a Multi-Objective Optimization Capability for Heterogeneous Light Water Reactor Fuel Assemblies

As pressure grows on developed nations to move away from fossil fuel-based energy sources, so does the potential for nuclear energy to make its resurgence. However, the complex nature of the design process in nuclear engineering and a regulatory culture of ever-increasing safety standards create unique challenges to the nuclear industry. As in many engineering disciplines, the question is one of trade-offs between safety, performance, cost, and time required to develop the design from paper to real life operation. The possibilities facing a designer are virtually unlimited, with fuel choice, layout and operating conditions just three of the many categories which interact with one another in a highly non-linear manner, making it difficult to quantitatively define these trade-offs. Deciding upon an ‘optimal’ design is therefore traditionally done through expert judgement and an iterative design process. Mathematical optimization methods offer a more formal way to optimize designs by employing algorithms to explore the myriad of possibilities in a methodical manner which can yield increased performance over expert designs. In this thesis, an extensive review of the literature revealed gaps which present opportunities for novel research. Two new algorithms are created with the ability to solve optimization problems with multiple objectives simultaneously without requiring weighting or bias from the designer. They are then applied to a series of problems drawn from both the literature and real world designs. The results demonstrate the algorithms’ effectiveness and robustness as well as their ability to handle complex multi-physics problems with reasonably low computational requirements. This research offers an original and effective tool for performing optimization on nuclear fuel assembly design problems and has advanced the state of the art in both multi-objective optimization and its application to the nuclear engineering industry