Hybrid of multi-car elevator system and double-deck elevator system

Multi-car elevator system is a new breakthrough in an elevator system in 2001. It has broken the traditional concept of developing only one elevator car in an elevator shaft. Multi-car elevator system can have more than one elevator car moving in an elevator shaft and it has improved a lot in minimizing the waiting time of passengers if compared with only one elevator car in an elevator shaft. The main advantage of multi-car elevator system is to reduce the construction cost where 30% of the core-tube area of the elevator system is made up of shaft. By developing multi-car elevator system, many of elevator shafts need not to be developed and it still can perform about the same efficiency in serving passengers. However, it is still not able to transport a large number of passengers efficiently if the passengers are calling from the same floor, especially during the up-peak traffic. For that reason, the feature of double-deck elevator system is integrated into multi-car elevator system to develop a new hybridized elevator system called “Hybrid of multi-car elevator system and double-deck elevator system” to solve the limited car capacity problem. The performance of both systems, the hybridized elevator system and the multi-car elevator system is simulated. The result shows that the average journey time of the hybridized elevator system is shorter than the multicar elevator system in all the three traffic modes, i.e. up-peak, down-peak and inter-floor traffics. For the up-peak traffic mode of the hybridized elevator system, it manages to achieve the best result of 33.5% shorter of the average journey time compared to the multi-car elevator system

A study on the memory schemes for genetic network programming

制度:新 ; 報告番号:甲3376号 ; 学位の種類:博士(工学) ; 授与年月日:2011/9/15 ; 早大学位記番号:新569

Containership Load Planning with Crane Operations

Since the start of the containerization revolution in 1950's, not only the TEU capacity of the vessels has been increasing constantly, but also the number of fully cellular container ships has expanded substantially. Because of the tense competition among ports in recent years, improving the operational efficiency of ports has become an important issue in containership operations. Arrangement of containers both within the container terminal and on the containership play an important role in determining the berthing time. The berthing time of a containership is mainly composed of the unloading and loading time of containers. Containers in a containership are stored in stacks, making a container directly accessible only if it is on the top of one stack. The task of determining a good container arrangement to minimize the number of re-handlings while maintaining the ship's stability over several ports is called stowage planning, which is an everyday problem solved by ship planners. The horizontal distribution of the containers over the bays affects crane utilization and overall ship berthing time. In order to increase the terminal productivity and reduce the turnaround time, the stowage planning must conform to the berth design. Given the configuration of berths and cranes at each visiting port, the stowage planning must take into account the utilization of quay cranes as well as the reduction of unnecessary shifts to minimize the total time at all ports over the voyage. This dissertation introduces an optimization model to solve the stowage planning problem with crane utilization considerations. The optimization model covers a wide range of operational and structural constraints for containership load planning. In order to solve real-size problems, a meta-heuristic approach based on genetic algorithms is designed and implemented which embeds a crane split approximation routine. The genetic encoding is ultra-compact and represents grouping, sorting and assignment strategies that might be applied to form the stowage pattern. The evaluation procedure accounts for technical specification of the cranes as well as the crane split. Numerical results show that timely solution for ultra large size containerships can be obtained under different scenarios

Advances in Supply Chain Management Decision Support Systems: Potential for Improving Decision Support Catalysed by Semantic Interoperability between Systems

Globalization has catapulted ‘cycle time’ as a key indicator of operational efficiency [1] in processes such as supply chain management (SCM). Systems automation holds the promise to augment the ability of supply chain operations or supply networks to rapidly adapt to changes, with minimal human intervention, under ideal conditions. Business communities are emerging as loose federations or organization of networks that may evolve to act as infomediaries in global SCM. These changes, although sluggish, are likely to impact process knowledge and in turn may be stimulated or inhibited by the availability or lack of process interoperability, respectively. The latter will determine operational efficiencies of supply chains. Currently “community of systems” or organization of networks (aligned by industry or business focus) contribute minimally in SCM decisions because true collaboration remains elusive. Convergence and maturity of multiple advances offers the potential for a paradigm shift in interoperability. It may evolve hand-in-hand with [a] the gradual adoption of the semantic web [2] with concomitant development of ontological frameworks, [b] increase in use of multi-agent systems and [c] advent of ubiquitous computing enabling near real-time access to identification of objects and analytics [4]. This paper examines some of these complex trends and related technologies. Irrespective of the characteristics of information systems, the development of various industry-contributed ontologies for knowledge and decision layers, may spur self-organizing networks of business communities and systems to increase their ability to sense and respond, more profitably, through better enterprise and extraprise exchange. In order to transform this vision into reality, systems automation must be weaned from the syntactic web and integrated with the organic growth of the semantic web. Understanding of process semantics and incorporation of intelligent agents with access to ubiquitous near real-time data “bus” are pillars for “intelligent” evolution of decision support systems. Software as infrastructure may integrate plethora of agent colonies through improved architectures (such as, service oriented architecture or SOA) and business communities aligned by industry or service focus may emerge as hubs of such agent empires. However, the feasibility of the path from exciting “pilots” in specific areas toward an informed convergence of systemic real-world implementation remains unclear and fraught with hurdles related to gaps in knowledge transfer from experts in academia to real-world practitioners. The value of interoperability between systems that may catalyse real-time intelligent decision support is further compromised by the lack of clarity of approach and tools. The latter offers significant opportunities for development of tools that may segue to innovative solutions approach. A critical mass of such solutions may spawn the necessary systems architecture for intelligent interoperability, essential for sustainable profitability and productivity in an intensely competitive global economy. This paper addresses some of these issues, tools and solutions that may have broad applicability in several operations including the management of adaptive supply-demand networks [7]

Controlled self-organisation using learning classifier systems

The complexity of technical systems increases, breakdowns occur quite often. The mission of organic computing is to tame these challenges by providing degrees of freedom for self-organised behaviour. To achieve these goals, new methods have to be developed. The proposed observer/controller architecture constitutes one way to achieve controlled self-organisation. To improve its design, multi-agent scenarios are investigated. Especially, learning using learning classifier systems is addressed

Controlled self-organisation using learning classifier systems

The complexity of technical systems increases, breakdowns occur quite often. The mission of organic computing is to tame these challenges by providing degrees of freedom for self-organised behaviour. To achieve these goals, new methods have to be developed. The proposed observer/controller architecture constitutes one way to achieve controlled self-organisation. To improve its design, multi-agent scenarios are investigated. Especially, learning using learning classifier systems is addressed

A constraint-based approach for assessing the capabilities of existing designs to handle product variation

All production machinery is designed with an inherent capability to handle slight variations in product. This is initially achieved by simply providing adjustments to allow, for example, changes that occur in pack sizes to be accommodated, through user settings or complete sets of change parts. By the appropriate use of these abilities most variations in product can be handled. However when extreme conditions of setups, major changes in product size and configuration, are considered there is no guarantee that the existing machines are able to cope. The problem is even more difficult to deal with when completely new product families are proposed to be made on an existing product line. Such changes in product range are becoming more common as producers respond to demands for ever increasing customization and product differentiation. An issue exists due to the lack of knowledge on the capabilities of the machines being employed. This often forces the producer to undertake a series of practical product trials. These however can only be undertaken once the product form has been decided and produced in sufficient numbers. There is then little opportunity to make changes that could greatly improve the potential output of the line and reduce waste. There is thus a need for a supportive modelling approach that allows the effect of variation in products to be analyzed together with an understanding of the manufacturing machine capability. Only through their analysis and interaction can the capabilities be fully understood and refined to make production possible. This thesis presents a constraint-based approach that offers a solution to the problems above. While employing this approach it has been shown that, a generic process can be formed to identify the limiting factors (constraints) of variant products to be processed. These identified constraints can be mapped to form the potential limits of performance for the machine. The limits of performance of a system (performance envelopes) can be employed to assess the design capability to cope with product variation. The approach is successfully demonstrated on three industrial case studies.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

A constraint-based approach for assessing the capabilities of existing designs to handle product variation

All production machinery is designed with an inherent capability to handle slight variations in product. This is initially achieved by simply providing adjustments to allow, for example, changes that occur in pack sizes to be accommodated, through user settings or complete sets of change parts. By the appropriate use of these abilities most variations in product can be handled. However when extreme conditions of setups, major changes in product size and configuration, are considered there is no guarantee that the existing machines are able to cope. The problem is even more difficult to deal with when completely new product families are proposed to be made on an existing product line. Such changes in product range are becoming more common as producers respond to demands for ever increasing customization and product differentiation. An issue exists due to the lack of knowledge on the capabilities of the machines being employed. This often forces the producer to undertake a series of practical product trials. These however can only be undertaken once the product form has been decided and produced in sufficient numbers. There is then little opportunity to make changes that could greatly improve the potential output of the line and reduce waste. There is thus a need for a supportive modelling approach that allows the effect of variation in products to be analyzed together with an understanding of the manufacturing machine capability. Only through their analysis and interaction can the capabilities be fully understood and refined to make production possible. This thesis presents a constraint-based approach that offers a solution to the problems above. While employing this approach it has been shown that, a generic process can be formed to identify the limiting factors (constraints) of variant products to be processed. These identified constraints can be mapped to form the potential limits of performance for the machine. The limits of performance of a system (performance envelopes) can be employed to assess the design capability to cope with product variation. The approach is successfully demonstrated on three industrial case studies.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

3D-in-2D Displays for ATC.

This paper reports on the efforts and accomplishments of the 3D-in-2D Displays for ATC project at the end of Year 1. We describe the invention of 10 novel 3D/2D visualisations that were mostly implemented in the Augmented Reality ARToolkit. These prototype implementations of visualisation and interaction elements can be viewed on the accompanying video. We have identified six candidate design concepts which we will further research and develop. These designs correspond with the early feasibility studies stage of maturity as defined by the NASA Technology Readiness Level framework. We developed the Combination Display Framework from a review of the literature, and used it for analysing display designs in terms of display technique used and how they are combined. The insights we gained from this framework then guided our inventions and the human-centered innovation process we use to iteratively invent. Our designs are based on an understanding of user work practices. We also developed a simple ATC simulator that we used for rapid experimentation and evaluation of design ideas. We expect that if this project continues, the effort in Year 2 and 3 will be focus on maturing the concepts and employment in a operational laboratory settings

Optimal seismic retrofitting of existing RC frames through soft-computing approaches

2016 - 2017Ph.D. Thesis proposes a Soft-Computing approach capable of supporting the engineer judgement in the selection and design of the cheapest solution for seismic retrofitting of existing RC framed structure. Chapter 1 points out the need for strengthening the existing buildings as one of the main way of decreasing economic and life losses as direct consequences of earthquake disasters. Moreover, it proposes a wide, but not-exhaustive, list of the most frequently observed deficiencies contributing to the vulnerability of concrete buildings. Chapter 2 collects the state of practice on seismic analysis methods for the assessment the safety of the existing buildings within the framework of a performancebased design. The most common approaches for modeling the material plasticity in the frame non-linear analysis are also reviewed. Chapter 3 presents a wide state of practice on the retrofitting strategies, intended as preventive measures aimed at mitigating the effect of a future earthquake by a) decreasing the seismic hazard demands; b) improving the dynamic characteristics supplied to the existing building. The chapter presents also a list of retrofitting systems, intended as technical interventions commonly classified into local intervention (also known “member-level” techniques) and global intervention (also called “structure-level” techniques) that might be used in synergistic combination to achieve the adopted strategy. In particular, the available approaches and the common criteria, respectively for selecting an optimum retrofit strategy and an optimal system are discussed. Chapter 4 highlights the usefulness of the Soft-Computing methods as efficient tools for providing “objective” answer in reasonable time for complex situation governed by approximation and imprecision. In particular, Chapter 4 collects the applications found in the scientific literature for Fuzzy Logic, Artificial Neural Network and Evolutionary Computing in the fields of structural and earthquake engineering with a taxonomic classification of the problems in modeling, simulation and optimization. Chapter 5 “translates” the search for the cheapest retrofitting system into a constrained optimization problem. To this end, the chapter includes a formulation of a novel procedure that assembles a numerical model for seismic assessment of framed structures within a Soft-Computing-driven optimization algorithm capable to minimize the objective function defined as the total initial cost of intervention. The main components required to assemble the procedure are described in the chapter: the optimization algorithm (Genetic Algorithm); the simulation framework (OpenSees); and the software environment (Matlab). Chapter 6 describes step-by-step the flow-chart of the proposed procedure and it focuses on the main implementation aspects and working details, ranging from a clever initialization of the population of candidate solutions up to a proposal of tuning procedure for the genetic parameters. Chapter 7 discusses numerical examples, where the Soft-Computing procedure is applied to the model of multi-storey RC frames obtained through simulated design. A total of fifteen “scenarios” are studied in order to assess its “robustness” to changes in input data. Finally, Chapter 8, on the base of the outcomes observed, summarizes the capabilities of the proposed procedure, yet highlighting its “limitations” at the current state of development. Some possible modifications are discussed to enhance its efficiency and completeness. [edited by author]XVI n.s