Possibility/Necessity-Based Probabilistic Expectation Models for Linear Programming Problems with Discrete Fuzzy Random Variables

This paper considers linear programming problems (LPPs) where the objective functions involve discrete fuzzy random variables (fuzzy set-valued discrete random variables). New decision making models, which are useful in fuzzy stochastic environments, are proposed based on both possibility theory and probability theory. In multi-objective cases, Pareto optimal solutions of the proposed models are newly defined. Computational algorithms for obtaining the Pareto optimal solutions of the proposed models are provided. It is shown that problems involving discrete fuzzy random variables can be transformed into deterministic nonlinear mathematical programming problems which can be solved through a conventional mathematical programming solver under practically reasonable assumptions. A numerical example of agriculture production problems is given to demonstrate the applicability of the proposed models to real-world problems in fuzzy stochastic environments

Simplexity: A Hybrid Framework for Managing System Complexity

Knowledge management, management of mission critical systems, and complexity management rely on a triangular support connection. Knowledge management provides ways of creating, corroborating, collecting, combining, storing, transferring, and sharing the know-why and know-how for reactively and proactively handling the challenges of mission critical systems. Complexity management, operating on “complexity” as an umbrella term for size, mass, diversity, ambiguity, fuzziness, randomness, risk, change, chaos, instability, and disruption, delivers support to both knowledge and systems management: on the one hand, support for dealing with the complexity of managing knowledge, i.e., furnishing criteria for a common and operationalized terminology, for dealing with mediating and moderating concepts, paradoxes, and controversial validity, and, on the other hand, support for systems managers coping with risks, lack of transparence, ambiguity, fuzziness, pooled and reciprocal interdependencies (e.g., for attaining interoperability), instability (e.g., downtime, oscillations, disruption), and even disasters and catastrophes. This support results from the evident intersection of complexity management and systems management, e.g., in the shape of complex adaptive systems, deploying slack, establishing security standards, and utilizing hybrid concepts (e.g., hybrid clouds, hybrid procedures for project management). The complexity-focused manager of mission critical systems should deploy an ambidextrous strategy of both reducing complexity, e.g., in terms of avoiding risks, and of establishing a potential to handle complexity, i.e., investing in high availability, business continuity, slack, optimal coupling, characteristics of high reliability organizations, and agile systems. This complexity-focused hybrid approach is labeled “simplexity.” It constitutes a blend of complexity reduction and complexity augmentation, relying on the generic logic of hybrids: the strengths of complexity reduction are capable of compensating the weaknesses of complexity augmentation and vice versa. The deficiencies of prevalent simplexity models signal that this blended approach requires a sophisticated architecture. In order to provide a sound base for coping with the meta-complexity of both complexity and its management, this architecture comprises interconnected components, domains, and dimensions as building blocks of simplexity as well as paradigms, patterns, and parameters for managing simplexity. The need for a balanced paradigm for complexity management, capable of overcoming not only the prevalent bias of complexity reduction but also weaknesses of prevalent concepts of simplexity, serves as the starting point of the argumentation in this chapter. To provide a practical guideline to meet this demand, an innovative model of simplexity is conceived. This model creates awareness for differentiating components, dimensions, and domains of complexity management as well as for various species of interconnectedness, such as the aligned upsizing and downsizing of capacities, the relevance of diversity management (e.g., in terms of deviations and errors), and the scope of risk management instruments. Strategies (e.g., heuristics, step-by-step procedures) and tools for managing simplexity-guided projects are outlined

Risk Assessment of Nautical Navigational Environment Based on Grey Fixed Weight Cluster

In order to set up a mathematical model suitable for nautical navigational environment risk evaluation and systematically master the navigational environment risk characteristics of the Qiongzhou Strait in a quantitative way, a risk assessment model with approach steps is set up based on the grey fixed weight cluster (GFWC). The evaluation index system is structured scientifically through both literature review and expert investigation. The relative weight of each index is designed to be obtained via fuzzy analytic hierarchy process (FAHP); Index membership degree of every grey class is proposed to be achieved by fuzzy statistics (FS) to avoid the difficulty of building whiten weight functions. By using the model, nautical navigational environment risk of the Qiongzhou Strait is determined at a “moderate” level according to the principle of maximum membership degree. The comprehensive risk evaluation of the Qiongzhou Strait nautical navigational environment can provide theoretical reference for implementing targeted risk control measures. It shows that the constructed GFWC risk assessment model as well as the presented steps are workable in case of incomplete information. The proposed strategy can excavate the collected experts’ knowledge mathematically, quantify the weight of each index and risk level, and finally lead to a comprehensive risk evaluation result. Besides, the adoptions of probability and statistic theory, fuzzy theory, aiming at solving the bottlenecks in case of uncertainty, will give the model a better adaptability and executability.</p

Efficiency of the rail sections in Brazilian railway system, using TOPSIS and a genetic algorithm to analyse optimized scenarios

A railway system plays a significant role in countries with large territorial dimensions. The Brazilian rail cargo system (BRCS), however, is focused on solid bulk for export. This paper investigates the extreme performances of BRCS through a new hybrid model that combines TOPSIS with a genetic algorithm for estimating the weights in optimized scenarios. In a second stage, the significance of selected variables was assessed. The transport of any type of cargo, a centralized control of the operation, and sharing the railway track pushing competition, and the diversification of services are significant for high performance. Public strategies are discussed.Indisponível

Reusable modelling and simulation of flexible manufacturing for next generation semiconductor manufacturing facilities

Automated material handling systems (AMHS) in 300 mm semiconductor manufacturing facilities may need to evolve faster than expected considering the high performance demands on these facilities. Reusable simulation models are needed to cope with the demands of this dynamic environment and to deliver answers to the industry much faster. One vision for intrabay AMHS is to link a small group of intrabay AMHS systems, within a full manufacturing facility, together using what is called a Merge/Diverge link. This promises better operational performance of the AMHS when compared to operating two dedicated AMHS systems, one for interbay transport and the other for intrabay handling. A generic tool for modelling and simulation of an intrabay AMHS (GTIA-M&S) is built, which utilises a library of different blocks representing the different components of any intrabay material handling system. GTIA-M&S provides a means for rapid building and analysis of an intrabay AMHS under different operating conditions. The ease of use of the tool means that inexpert users have the ability to generate good models. Models developed by the tool can be executed with the merge/diverge capability enabled or disabled to provide comparable solutions to production demands and to compare these two different configurations of intrabay AMHS using a single simulation model. Finally, results from simulation experiments on a model developed using the tool were very informative in that they include useful decision making data, which can now be used to further enhance and update the design and operational characteristics of the intrabay AMHS

DEVELOPMENT OF AN INTEGRATED RIDE-SHARED MOBILITY-ON-DEMAND (MOD) AND PUBLIC TRANSIT SYSTEM

The Mobility-on-Demand (MOD) services, like the ones offered by Uber and Lyft, are transforming urban transportation by providing more sustainable and convenient service that allows people to access anytime and anywhere. In most U.S. cities with sprawling suburban areas, the utilization of public transit for commuting is often low due to lack of accessibility. Thereby the MOD system can function as a first-and-last-mile solution to attract more riders to use public transit. Seamless integration of ride-shared MOD service with public transit presents enormous potential in reducing pollution, saving energy, and alleviating congestion. This research proposes a general mathematical framework for solving a multi-modal large-scale ride-sharing problem under real-time context. The framework consists of three core modules. The first module partitions the entire map into a set of more scalable zones to enhance computational efficiency. The second module encompasses a mixed-integer-programming model to concurrently find the optimal vehicle-to-request and request-to-request matches in a hybrid network. The third module forecasts the demand for each station in the near future and then generates an optimized vehicle allocation plan to best serve the incoming rider requests. To ensure its applicability, the proposed model accounts for transit frequency, MOD vehicle capacity, available fleet size, customer walk-away condition and travel time uncertainty. Extensive experimental results prove that the proposed system can bring significant vehicular emission reduction and deliver timely ride-sharing service for a large number of riders. The main contributions of this study are as follows: • Design of a general framework for planning a multi-modal ride-sharing system in cities with under-utilized public transit system; • Development of an efficient real-time algorithm that can produce solutions of desired quality and scalability and redistribute the available fleet corresponding to the future demand evolution; • Validation of the potential applicability of the proposed system and quantitatively reveal the trade-off between service quality and system efficiency

A Decision Support System for the Storage Space Allocation Problem under the Effect of Disturbances: a Case of the Port of Arica (Chile)

Lo scopo del lavoro è provare a risolvere il container allocation problem: come disporre i container in maniera più efficace all'interno di un terminal? Dopo un attento esame della letteratura, sono state sviluppate diverse strategie di allocazione basate sulla fuzzy logic. Tali strategie sono poi state combinate per dare vita ad un sistema che sia in grado di reagire a eventi che possono alterare le normali operazioni all'interno del terminal.ope

Supply Chain

Traditionally supply chain management has meant factories, assembly lines, warehouses, transportation vehicles, and time sheets. Modern supply chain management is a highly complex, multidimensional problem set with virtually endless number of variables for optimization. An Internet enabled supply chain may have just-in-time delivery, precise inventory visibility, and up-to-the-minute distribution-tracking capabilities. Technology advances have enabled supply chains to become strategic weapons that can help avoid disasters, lower costs, and make money. From internal enterprise processes to external business transactions with suppliers, transporters, channels and end-users marks the wide range of challenges researchers have to handle. The aim of this book is at revealing and illustrating this diversity in terms of scientific and theoretical fundamentals, prevailing concepts as well as current practical applications