Stochastic make-to-stock inventory deployment problem: an endosymbiotic psychoclonal algorithm based approach

Integrated steel manufacturers (ISMs) have no specific product, they just produce finished product from the ore. This enhances the uncertainty prevailing in the ISM regarding the nature of the finished product and significant demand by customers. At present low cost mini-mills are giving firm competition to ISMs in terms of cost, and this has compelled the ISM industry to target customers who want exotic products and faster reliable deliveries. To meet this objective, ISMs are exploring the option of satisfying part of their demand by converting strategically placed products, this helps in increasing the variability of product produced by the ISM in a short lead time. In this paper the authors have proposed a new hybrid evolutionary algorithm named endosymbiotic-psychoclonal (ESPC) to decide what and how much to stock as a semi-product in inventory. In the proposed theory, the ability of previously proposed psychoclonal algorithms to exploit the search space has been increased by making antibodies and antigen more co-operative interacting species. The efficacy of the proposed algorithm has been tested on randomly generated datasets and the results compared with other evolutionary algorithms such as genetic algorithms (GA) and simulated annealing (SA). The comparison of ESPC with GA and SA proves the superiority of the proposed algorithm both in terms of quality of the solution obtained and convergence time required to reach the optimal/near optimal value of the solution

Decision support for build-to-order supply chain management through multiobjective optimization

This is the post-print version of the final paper published in International Journal of Production Economics. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2010 Elsevier B.V.This paper aims to identify the gaps in decision-making support based on multiobjective optimization (MOO) for build-to-order supply chain management (BTO-SCM). To this end, it reviews the literature available on modelling build-to-order supply chains (BTO-SC) with the focus on adopting MOO techniques as a decision support tool. The literature has been classified based on the nature of the decisions in different part of the supply chain, and the key decision areas across a typical BTO-SC are discussed in detail. Available software packages suitable for supporting decision making in BTO supply chains are also identified and their related solutions are outlined. The gap between the modelling and optimization techniques developed in the literature and the decision support needed in practice are highlighted. Future research directions to better exploit the decision support capabilities of MOO are proposed. These include: reformulation of the extant optimization models with a MOO perspective, development of decision supports for interfaces not involving manufacturers, development of scenarios around service-based objectives, development of efficient solution tools, considering the interests of each supply chain party as a separate objective to account for fair treatment of their requirements, and applying the existing methodologies on real-life data sets.Brunel Research Initiative and Enterprise Fund (BRIEF

Decision support for build-to-order supply chain management through multiobjective optimization

This paper aims to identify the gaps in decision-making support based on multiobjective optimization for build-to-order supply chain management (BTOSCM). To this end, it reviews the literature available on modelling build-to-order supply chains (BTO-SC) with the focus on adopting multiobjective optimization (MOO) techniques as a decision support tool. The literature has been classified based on the nature of the decisions in different part of the supply chain, and the key decision areas across a typical BTO-SC are discussed in detail. Available software packages suitable for supporting decision making in BTO supply chains are also identified and their related solutions are outlined. The gap between the modelling and optimization techniques developed in the literature and the decision support needed in practice are highlighted and future research directions to better exploit the decision support capabilities of MOO are proposed

Inferring Robot Task Plans from Human Team Meetings: A Generative Modeling Approach with Logic-Based Prior

We aim to reduce the burden of programming and deploying autonomous systems to work in concert with people in time-critical domains, such as military field operations and disaster response. Deployment plans for these operations are frequently negotiated on-the-fly by teams of human planners. A human operator then translates the agreed upon plan into machine instructions for the robots. We present an algorithm that reduces this translation burden by inferring the final plan from a processed form of the human team's planning conversation. Our approach combines probabilistic generative modeling with logical plan validation used to compute a highly structured prior over possible plans. This hybrid approach enables us to overcome the challenge of performing inference over the large solution space with only a small amount of noisy data from the team planning session. We validate the algorithm through human subject experimentation and show we are able to infer a human team's final plan with 83% accuracy on average. We also describe a robot demonstration in which two people plan and execute a first-response collaborative task with a PR2 robot. To the best of our knowledge, this is the first work that integrates a logical planning technique within a generative model to perform plan inference.Comment: Appears in Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence (AAAI-13

TZC: Efficient Inter-Process Communication for Robotics Middleware with Partial Serialization

Inter-process communication (IPC) is one of the core functions of modern robotics middleware. We propose an efficient IPC technique called TZC (Towards Zero-Copy). As a core component of TZC, we design a novel algorithm called partial serialization. Our formulation can generate messages that can be divided into two parts. During message transmission, one part is transmitted through a socket and the other part uses shared memory. The part within shared memory is never copied or serialized during its lifetime. We have integrated TZC with ROS and ROS2 and find that TZC can be easily combined with current open-source platforms. By using TZC, the overhead of IPC remains constant when the message size grows. In particular, when the message size is 4MB (less than the size of a full HD image), TZC can reduce the overhead of ROS IPC from tens of milliseconds to hundreds of microseconds and can reduce the overhead of ROS2 IPC from hundreds of milliseconds to less than 1 millisecond. We also demonstrate the benefits of TZC by integrating with TurtleBot2 that are used in autonomous driving scenarios. We show that by using TZC, the braking distance can be shortened by 16% than ROS

An ESPC algorithm based approach to solve inventory deployment problem

Global competitiveness has enforced the hefty industries to become more customized. To compete in the market they are targeting the customers who want exotic products, and faster and reliable deliveries. Industries are exploring the option of satisfying a portion of their demand by converting strategically placed products, this helps in increasing the variability of product produced by them in short lead time. In this paper, authors have proposed a new hybrid evolutionary algorithm named Endosymbiotic-Psychoclonal (ESPC) algorithm to determine the amount and type of product to stock as a semi product in inventory. In the proposed work the ability of previously proposed Psychoclonal algorithm to exploit the search space has been increased by making antibodies and antigen more cooperative interacting species. The efficacy of the proposed algorithm has been tested on randomly generated datasets and the results obtained, are compared with other evolutionary algorithms such as Genetic Algorithm (GA) and Simulated Annealing (SA). The comparison of ESPC with GA and SA proves the superiority of the proposed algorithm both in terms of quality of the solution obtained, and convergence time required to reach the optimal /near optimal value of the solution

Smart Asset Management for Electric Utilities: Big Data and Future

This paper discusses about future challenges in terms of big data and new technologies. Utilities have been collecting data in large amounts but they are hardly utilized because they are huge in amount and also there is uncertainty associated with it. Condition monitoring of assets collects large amounts of data during daily operations. The question arises "How to extract information from large chunk of data?" The concept of "rich data and poor information" is being challenged by big data analytics with advent of machine learning techniques. Along with technological advancements like Internet of Things (IoT), big data analytics will play an important role for electric utilities. In this paper, challenges are answered by pathways and guidelines to make the current asset management practices smarter for the future.Comment: 13 pages, 3 figures, Proceedings of 12th World Congress on Engineering Asset Management (WCEAM) 201

Scalable Approach to Uncertainty Quantification and Robust Design of Interconnected Dynamical Systems

Development of robust dynamical systems and networks such as autonomous aircraft systems capable of accomplishing complex missions faces challenges due to the dynamically evolving uncertainties coming from model uncertainties, necessity to operate in a hostile cluttered urban environment, and the distributed and dynamic nature of the communication and computation resources. Model-based robust design is difficult because of the complexity of the hybrid dynamic models including continuous vehicle dynamics, the discrete models of computations and communications, and the size of the problem. We will overview recent advances in methodology and tools to model, analyze, and design robust autonomous aerospace systems operating in uncertain environment, with stress on efficient uncertainty quantification and robust design using the case studies of the mission including model-based target tracking and search, and trajectory planning in uncertain urban environment. To show that the methodology is generally applicable to uncertain dynamical systems, we will also show examples of application of the new methods to efficient uncertainty quantification of energy usage in buildings, and stability assessment of interconnected power networks

An Integrated Semantic Web Service Discovery and Composition Framework

In this paper we present a theoretical analysis of graph-based service composition in terms of its dependency with service discovery. Driven by this analysis we define a composition framework by means of integration with fine-grained I/O service discovery that enables the generation of a graph-based composition which contains the set of services that are semantically relevant for an input-output request. The proposed framework also includes an optimal composition search algorithm to extract the best composition from the graph minimising the length and the number of services, and different graph optimisations to improve the scalability of the system. A practical implementation used for the empirical analysis is also provided. This analysis proves the scalability and flexibility of our proposal and provides insights on how integrated composition systems can be designed in order to achieve good performance in real scenarios for the Web.Comment: Accepted to appear in IEEE Transactions on Services Computing 201