160 research outputs found

    Improving the Flexibility and Robustness of Machine Tending Mobile Robots

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    While traditional manufacturing production cells consist of a fixed base robot repetitively performing tasks, the Industry 5.0 flexible manufacturing cell (FMC) aims to bring Autonomous Industrial Mobile Manipulators (AIMMs) to the factory floor. Composed of a wheeled base and a robot arm, these collaborative robots (cobots) operate alongside people while autonomously performing tasks at different workstations. AIMMs have been tested in real production systems, but the development of the control algorithms necessary for automating a robot that is a combination of two cobots remains an open challenge before the large scale adoption of this technology occurs in industry. Currently popular docking based methods require a mount point for the docking station and considerable time for the robot to locate and park. These limitations necessitate the consideration and implementation of more modern robot control and path planning techniques. This work proposes and implements a simulation testbed that uses a contemporary whole-body control, OCS2, to perform more flexible pick-and-place tasks. Within this testbed, an Industry 5.0 based pick-and-place framework is deployed, fine-tuned and tested. This system supports the one-shot lead-through based assignment of a prepick position by an operator, thus enabling the cobot to drive to this position and successfully pick up the part agnostic of base orientation and/or position. The proposed system allows robot path planning experimentation and assessment against a variety of cost and constraint values, and is capable of being modified to support various vision based part locating algorithms

    Project portfolio evaluation and selection using mathematical programming and optimization methods

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    Project portfolio selection is an essential process for portfolio management and plays an important role in accomplishing organizational goals. This research explores the feasibility of developing a project portfolio selection tool by using mathematical programming and optimization models, specifically 0-1 integer programming (one objective portfolio) and goal programming (multiple objectives portfolio). These methods select the set of projects which deliver the maximum benefit (e.g., net present value, profit, etc.) represented for objective functions subjected to a series of constraints (e.g., technical requirements and/or resources availability) considering the scheduling of selected projects in a planning horizon, interdependence relationship among projects (e.g., complementary projects and mutually exclusive projects) and especial cases like mandatory and ongoing projects. ^ Based on the proposed model, a Decision Support System (DSS) will be developed and tested for accuracy, flexibility and ease of use. This computational tool will be designed for decision makers and users that are not familiar with mathematical programming models

    A Review on Optimal Operation of Distributed Network Embedded to Wind-Battery Storage System

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    Energy is a vital requirement for today's socio-economic welfare and development. But due to the continuous increase of the demand the conventional energy resources are depleting day by day and on the verge of extinction. Hence more renewable generation units are emphasised to integrate to the power network to supply the required demand. This incorporation of the distributed generation into the distributed network has the advantages of controllability, flexibility and tremendous potential if it can be exploited properly. However, due to their intermittent and unpredictable nature, there is a need for energy storages to ensure continuous operations, i.e., to meet the load all the time. There are many possible options for energy storage, but the most popular and technologically sound option is battery storage. Along with this battery storage system (BSS), a power conditioning system (PCS) has to be connected for generation of both active and reactive power from the BSS which in turn increases the overall installation cost of BSS. Moreover, the energy storage cost is a function of the storage device power, energy capacities and their specific costs depending on the chosen technology of optimization. Thus, profit from those renewable energy producers have to be maximized, and losses are to be minimized by using dynamic optimization techniques. But along with the advantages there comes the complexities due to the inclusion of distributed generation and the additional energy storages in the power system network. Moreover, it is highly critical to operate the vast system optimally. Hence there are lots of research had been done or are in process for finding the proper approach of optimization of the system. This paper presents a review of the current state of the optimization methods applied to renewable and sustainable energy source embedded with the Energy storage for maximization of the revenue obtained from the power trading to the network

    A Review on Optimal Operation of Distributed Network Embedded to Wind-Battery Storage System

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    Energy is a vital requirement for today’s socio-economic welfare and development. But due to the continuous increase of the demand the conventional energy resources are depleting day by day and on the verge of extinction. Hence more renewable generation units are emphasised to integrate to the power network to supply the required demand. This incorporation of the distributed generation into the distributed network has the advantages of controllability, flexibility and tremendous potential if it can be exploited properly. However, due to their intermittent and unpredictable nature, there is a need for energy storages to ensure continuous operations, i.e., to meet the load all the time. There are many possible options for energy storage, but the most popular and technologically sound option is battery storage. Along with this battery storage system (BSS), a power conditioning system (PCS) has to be connected for generation of both active and reactive power from the BSS which in turn increases the overall installation cost of BSS. Moreover, the energy storage cost is a function of the storage device power, energy capacities and their specific costs depending on the chosen technology of optimization. Thus, profit from those renewable energy producers have to be maximized, and losses are to be minimized by using dynamic optimization techniques. But along with the advantages there comes the complexities due to the inclusion of distributed generation and the additional energy storages in the power system network. Moreover, it is highly critical to operate the vast system optimally. Hence there are lots of research had been done or are in process for finding the proper approach of optimization of the system. This paper presents a review of the current state of the optimization methods applied to renewable and sustainable energy source embedded with the Energy storage for maximization of the revenue obtained from the power trading to the network

    Optimal scope of supply chain network & operations design

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    The increasingly complex supply chain networks and operations call for the development of decision support systems and optimization techniques that take a holistic view of supply chain issues and provide support for integrated decision-making. The economic impacts of optimized supply chain are significant and that has attracted considerable research attention since the late 1990s. This doctoral thesis focuses on developing manageable and realistic optimization models for solving four contemporary and interrelated supply chain network and operations design problems. Each requires an integrated decision-making approach for advancing supply chain effectiveness and efficiency. The first model formulates the strategic robust downsizing of a global supply chain network, which requires an integrated decision-making on resource allocation and network reconfiguration, given certain financial constraints. The second model also looks at the strategic supply chain downsizing problem but extends the first model to include product portfolio selection as a downsizing decision. The third model concerns the redesign of a warranty distribution network, which requires an integrated decision-making on strategic network redesign and tactical recovery process redesign. The fourth model simultaneously determines the operational-level decisions on job assignment and process sequence in order to improve the total throughput of a production facility unit

    A multiple objective optimization approach to the decommissioning and dismantling of a nuclear power plant.

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    The complexity, relevance and critical nature of the decommissioning of nuclear power plants (NPP) are of great significance in today\u27s society. Following the catastrophe in Fukushima a shift in the general public\u27s perception of NPP took place throughout the world and in Europe in particular. In this dissertation interdisciplinary methods will be discussed to identify solutions which take into account the technological complexity and organizational issues involved in the dismantling and decommissioning process of NPP. Operations research, lean management, simultaneous engineering, cost analysis, multiple-objective optimization, project management, software tools are powerful concepts and methodologies when undertaking the dismantling and decommissioning process of NPP. Besides the presentation of a wide range of terminological and methodological definitions and technical terms based on the Literature Review, in the dissertation a framework for model development of a Multiple objective optimization problem (MOOP) will discussed focusing on empirical data from a virtual NPP. The theoretical foundation of the framework is at the intersection of two successful approaches used to describe and accomplish similar complex challenges, and the integration of state-of-the-art process approaches such as lean management. The procedural conception of the model is mainly leant on the OMEGA model (International Atomic Energy Agency (IAEA) (2008)). Mathematically the model is derived from Jones et. al. (1998). Finally the application of the model using different software tools (AIMMS, MATLAB, R and SPSS) will be presented. In conclusion the work will be put into a position to venture a critical outlook and discussion for the future of the decommissioning and dismantling processes of NPP. The main goal of this dissertation is to define the requirements for the optimization of three objectives: Minimizing the total project cost, reducing the safety hazard (risk) and managing project duration. Also a description of how the programming language R and the AIMMS program interfaces with the OMEGA application and how R will be used to solve the MOOP will be given. The software Microsoft Project will be leveraged in order to model this objective

    A review of discrete-time optimization models for tactical production planning

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    This is an Accepted Manuscript of an article published in International Journal of Production Research on 27 Mar 2014, available online: http://doi.org/10.1080/00207543.2014.899721[EN] This study presents a review of optimization models for tactical production planning. The objective of this research is to identify streams and future research directions in this field based on the different classification criteria proposed. The major findings indicate that: (1) the most popular production-planning area is master production scheduling with a big-bucket time-type period; (2) most of the considered limited resources correspond to productive resources and, to a lesser extent, to inventory capacities; (3) the consideration of backlogs, set-up times, parallel machines, overtime capacities and network-type multisite configuration stand out in terms of extensions; (4) the most widely used modelling approach is linear/integer/mixed integer linear programming solved with exact algorithms, such as branch-and-bound, in commercial MIP solvers; (5) CPLEX, C and its variants and Lindo/Lingo are the most popular development tools among solvers, programming languages and modelling languages, respectively; (6) most works perform numerical experiments with random created instances, while a small number of works were validated by real-world data from industrial firms, of which the most popular are sawmills, wood and furniture, automobile and semiconductors and electronic devices.This study has been funded by the Universitat Politècnica de València projects: ‘Material Requirement Planning Fourth Generation (MRPIV)’ (Ref. PAID-05-12) and ‘Quantitative Models for the Design of Socially Responsible Supply Chains under Uncertainty Conditions. Application of Solution Strategies based on Hybrid Metaheuristics’ (PAID-06-12).Díaz-Madroñero Boluda, FM.; Mula, J.; Peidro Payá, D. (2014). A review of discrete-time optimization models for tactical production planning. International Journal of Production Research. 52(17):5171-5205. doi:10.1080/00207543.2014.899721S51715205521
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