At the time of commonplace automation, robotization and the rapid development of IT, high qualifications of employees have become the critical element of every industry system. This follows from their limited availabi...
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At the time of commonplace automation, robotization and the rapid development of IT, high qualifications of employees have become the critical element of every industry system. This follows from their limited availability, frequently high costs of procurement and possible employee absenteeism. Moreover, the concept of Industry 4.0 will transform current industry employees into knowledge employees. This is due to the fact that hard and routine tasks will be executed by robots and computers. This constitutes change in the required employee competences. Unfortunately, the aspect of management and configuration of employee competences is often overlooked in industrial practice. In response to the existing problem, the article puts forward the original model of employee competence configuration which is a basis for responses to numerous questions of managers of production processes, both general ones, e.g., Do we have a sufficient set of competences to execute a production schedule? as well as detailed ones, e.g., Which and how many competences are missing? etc. An important novelty of the presented model is the possibility of its application with both proactive and reactive questions. Due to the discrete and combinatorial nature of the problem under consideration, the use of mathematical programming methods was limited only to small data instances. Therefore, a proprietary dedicated genetic algorithm was proposed to solve this problem, which turned out to be extremely effective. The use of this genetic algorithm has enabled finding a solution depending on the instance data up to 70 times faster than by use of the mathematical programming.
This paper proposes an algorithm based on VNS metaheuristcs for k-medoids clustering, which is a NP-hard optimization problem. The VNS algorithm was applied in fifty data bases (instances) with small, medium, and larg...
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This paper proposes an algorithm based on VNS metaheuristcs for k-medoids clustering, which is a NP-hard optimization problem. The VNS algorithm was applied in fifty data bases (instances) with small, medium, and large sizes, considering the number of clusters between 2 and 7. The obtained results from these experiments show the effectiveness of this approach, comparing it with nine other related clustering algorithms and an optimization formulation. Furthermore, we found that our algorithm obtained the optimal solutions for the vast majority of the cases.
Recently, many works seek to exploit the negligible transmission cost of backscattering radio frequency (RF) signals, and also demonstrated its feasibility in allowing passive or batteryless tags to communicate over m...
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Recently, many works seek to exploit the negligible transmission cost of backscattering radio frequency (RF) signals, and also demonstrated its feasibility in allowing passive or batteryless tags to communicate over multiple hops. In this context, this article studies data collection in a multihop Internet of Things (IoT) wireless network consisting of tags equipped with sensor(s). These tags forward data via tag-to-tag communications to a gateway. Our aim is for the gateway to collect the maximum amount of data from tags over a given time frame. To do so, we optimize the time used by tags to sample their environment and data transmission, which involves solving an NP-hard link scheduling problem. We present a mixed integer nonlinear program (MINLP) to determine the transmitting tags in each time slot as well as the sensing duration of tags. We also propose a heuristic, called Max-L, that aims to maximize the number of links in each transmission set in order to reduce the transmission time of samples. Our results show that Max-L collects 85% of the optimal amount of samples.
Remanufacturing is the process to restore the functionality of high-value end-of-life (EOL) products, which is considered a substantial link in reverse logistics systems for value recovery. However, due to the uncerta...
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Remanufacturing is the process to restore the functionality of high-value end-of-life (EOL) products, which is considered a substantial link in reverse logistics systems for value recovery. However, due to the uncertainty of the reverse material flow, the planning of a remanufacturing reverse logistics system is complex. Furthermore, the increasing adoption of disruptive technologies in Industry 4.0/5.0, e.g., the Internet of things (IoT), smart robots, cloud-based digital twins, and additive manufacturing, has shown great potential for a smart paradigm transition of remanufacturing reverse logistics operations. In this paper, a new mixed-integer program is modeled for supporting several tactical decisions in remanufacturing reverse logistics, i.e., remanufacturing setups, production planning and inventory levels, core acquisition and transportation, and remanufacturing line balancing and utilization. The model is further extended by incorporating utilization-dependent nonlinear idle time cost constraints and stochastic takt time to accommodate different real-world scenarios. Through a set of numerical experiments, the influences of different demand patterns and idle time constraints are revealed. The potential impacts of disruptive technology adoption in remanufacturing reverse logistics are also discussed from managerial perspectives, which may help remanufacturing companies with a smart and smooth transition in the Industry 4.0/5.0 era.
Formal language theory constitutes the theoretical support for programming languages and is closely related to computability theory. Formalizing of economic processes using mathematical programming is a first step in ...
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Formal language theory constitutes the theoretical support for programming languages and is closely related to computability theory. Formalizing of economic processes using mathematical programming is a first step in the implementation of these processes in models that are easy to understand and optimize for achieving optimum performances.
This work proposes two optimization models for the optimal management (generation, conversion and distribution) of electricity in a macroscopic system divided into geo-graphical regions;the resulting model is applied ...
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This work proposes two optimization models for the optimal management (generation, conversion and distribution) of electricity in a macroscopic system divided into geo-graphical regions;the resulting model is applied to the national system for the distribu-tion of electricity in Mexico. The formulations are based on the concept of an energy hub. The first model is a linear programming model which represents the operation of the system during one hour. It provides the optimal decisions to satisfy demands at minimum cost. Such a basic model is then extended into a mixed-integer linear multiperiod model (MILP) which is applied to a 24-hour scenario. Further, the MILP multiperiod model also considers the interactions among transmission regions existing in each of the geo-graphical regions. In particular, this last model considers the regions of the National Transmission Network in Mexico as well as the available storage and the electricity generation facilities owned by the private sector. The MILP model consists of 21,773 constraints;its optimal results suggest that power plants operating with combined cycle contribute with 50 % of the power generation;hydroelectric power plants contribute with approximately 18 %. The current practical goal of 30 % of renewable energy generation can be achieved through the installed capacity.(c) 2022 Published by Elsevier Ltd on behalf of Institution of Chemical Engineers.
Vehicle assignment is a significant challenge faced by chemical logistics companies. The chemical industry typically outsources the delivery of chemicals to professional logistics companies to meet customers' comp...
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Vehicle assignment is a significant challenge faced by chemical logistics companies. The chemical industry typically outsources the delivery of chemicals to professional logistics companies to meet customers' complex and demanding needs. In this regard, this research explores the case study of a logistics company that selects its delivery modes and formulates its vehicle assignment plans based on its personnel's experience rather than any formal system and plans its vehicle routes on the day before the delivery. This study seeks to improve the efficiency and immediacy of logistics companies' vehicle assignments, understand vehicle route planning, maximize space utilization, and minimize transportation costs. This paper presents both a mathematical programming model and a heuristic algorithm. The algorithm development process comprises four steps: constructing the initial condition, improving transportation cost, reducing customers, and splitting vehicles. The case study company's transportation costs and vehicle distribution results indicate that the proposed heuristic algorithm can effectively save transportation costs and the time cost involved in delivery route planning.
An optimal zero-sequence voltage injection-based common-mode voltage reduction pulse-width modulation (CMVRPWM) for the reduction of common-mode voltages (CMVs) in both amplitude and third-order component is proposed ...
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An optimal zero-sequence voltage injection-based common-mode voltage reduction pulse-width modulation (CMVRPWM) for the reduction of common-mode voltages (CMVs) in both amplitude and third-order component is proposed in this paper. On the basis of general symmetrical switching patterns of CMVRPWM, a constrained mathematical programming model is established with the objective of optimizing the volt-second value of CMV in each switching period. By solving the mathematical model, the limitation of the CMV amplitude and the reduction of third-order harmonic in CMV can be achieved simultaneously, contributing to better CMV suppression and common-mode filter design in the applications of two-level voltage source inverters (2L-VSIs). The optimal zero-sequence voltage under the given target is obtained, and the calculation method of the switching pattern and modulation wave is studied. Simulation and experimental results show that the proposed method can restrict the CMV amplitude within one-sixth of the DC bus voltage, and the third-order component in CMV is effectively reduced compared with the conventional methods.
Given the current state of battery technology, applying electric trucks for long-haul transportation is a cumbersome task. Either the driving ranges are too short or high-capacity batteries are so heavy that payloads ...
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Given the current state of battery technology, applying electric trucks for long-haul transportation is a cumbersome task. Either the driving ranges are too short or high-capacity batteries are so heavy that payloads are limited. An old, yet recently revitalized charging infrastructure, currently evaluated on multiple test tracks all around the globe, allows to charge electric trucks while driving. However, installing the charging infrastructure along highways, namely, overhead wiring or road-embedded power lines both accessed by trucks equipped with a movable contact arm, is costly. This paper provides a detailed modeling approach based on continuous variables to minimize the installation costs for electrified highway kilometers while still providing sufficient energy for a given set of representative tours of electric vehicles. We apply our solution approach to show that investment costs can considerably be reduced along a European highway mainline while still allowing electrified transport among most adjacent major cities. Furthermore, we quantify the loss in precision, if instead of our detailed approach a more aggregate model based on discrete decision variables is applied.
Optimization for Learning and Control Comprehensive resource providing a masters’ level introduction to optimization theory and algorithms for learning and control Optimization for Learning and Control describes how ...
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ISBN:
(数字)9781119809173;9781119809142
ISBN:
(纸本)9781119809135
Optimization for Learning and Control Comprehensive resource providing a masters’ level introduction to optimization theory and algorithms for learning and control Optimization for Learning and Control describes how optimization is used in these domains, giving a thorough introduction to both unsupervised learning, supervised learning, and reinforcement learning, with an emphasis on optimization methods for large-scale learning and control problems. Several applications areas are also discussed, including signal processing, system identification, optimal control, and machine learning. Today, most of the material on the optimization aspects of deep learning that is accessible for students at a Masters’ level is focused on surface-level computer programming; deeper knowledge about the optimization methods and the trade-offs that are behind these methods is not provided. The objective of this book is to make this scattered knowledge, currently mainly available in publications in academic journals, accessible for Masters’ students in a coherent way. The focus is on basic algorithmic principles and trade-offs. Optimization for Learning and Control covers sample topics such as:
Optimization theory and optimization methods, covering classes of optimization problems like least squares problems, quadratic problems, conic optimization problems and rank optimization.
First-order methods, second-order methods, variable metric methods, and methods for nonlinear least squares problems.
Dynamic programming for solving optimal control problems and its generalization to reinforcement learning.
How optimization theory is used to develop theory and tools of statistics and learning, e.g., the maximum likelihood method, expectation maximization, k-means clustering, and support vector machines.
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