72 research outputs found
Rescheduling Problems with Agreeable Job Parameters to Minimize the Tardiness Costs under Deterioration and Disruption
This paper considers single-machine rescheduling problems with agreeable job parameters under deterioration and disruption. Deteriorating jobs mean that the processing time of a job is defined by an increasing function of its starting time. Rescheduling means that, after a set of original jobs has already been scheduled, a new set of jobs arrives and creates a disruption. We consider four cases of minimization of the total tardiness costs with agreeable job parameters under a limit of the disruptions from the original job sequence. We propose polynomial-time algorithms or some dynamic programming algorithms under sequence disruption and time disruption
Hierarchical workflow management system for life science applications
In modern laboratories, an increasing number of automated stations and instruments are applied as standalone automated systems such as biological high throughput screening systems, chemical parallel reactors etc. At the same time, the mobile robot transportation solution becomes popular with the development of robotic technologies. In this dissertation, a new superordinate control system, called hierarchical workflow management system (HWMS) is presented to manage and to handle both, automated laboratory systems and logistics systems.In modernen Labors werden immer mehr automatisierte Stationen und Instrumente als eigenständige automatisierte Systeme eingesetzt, wie beispielsweise biologische High-Throughput-Screening-Systeme und chemische Parallelreaktoren. Mit der Entwicklung der Robotertechnologien wird gleichzeitig die mobile Robotertransportlösung populär. In der vorliegenden Arbeit wurde ein hierarchisches Verwaltungssystem für Abeitsablauf, welches auch als HWMS bekannt ist, entwickelt. Das neue übergeordnete Kontrollsystem kann sowohl automatisierte Laborsysteme als auch Logistiksysteme verwalten und behandeln
The possibility of implementing intelligent systems and the respective impact of artificial intelligence on inventory management and warehousing
The industry is in a new phase: Industry 4.0. This fourth industrial revolution focuses
on improving processes within industries. The basis for this improvement is artificial
intelligence, which, by digitizing the operations of companies operating in different sectors,
improves their productivity, innovation, reduces costs and improves their markets on the
international stage. In the context of retail companies, the sourcing and storage of products is
one of the most important operations.
Thus, this study intends to investigate the concept of artificial intelligence, that is, its
benefits, trust, and risks to, consequently, analyze the possibility of its application in stock and
storage management. The first phase of the study underwent a literature review, where all these
aspects were thoroughly analyzed. Then, to collect data and reach conclusions, two
methodologies were used: an online questionnaire and interviews.
After analyzing the outputs of the online questionnaire, it was concluded that Artificial
Intelligence has benefits and trust indicators that positively influence its implementation in
stock and warehouse management.
Regarding the interviews, these reveal that Artificial Intelligence is beginning to be
recognized for its advantages, but it is still seen as a large investment, especially for small
industrial companies. However, the applications that are taking place are considered by AI
experts as beneficial, being machine learning, robotics and computer vision technologies that
can enhance the productivity of the logistics operations addressed in this investigation.A indústria está numa nova fase: Indústria 4.0. Esta quarta revolução industrial
concentra-se em melhorar os processos dentro das indústrias. A base para essa melhoria é a
inteligência artificial, que, ao digitalizar as operações das empresas que atuam nos diversos
setores, melhora a sua produtividade, inovação, reduz custos e melhora os seus mercados no
cenário internacional. No contexto das empresas de retalho, o aprovisionamento e
armazenamento de produtos é uma das operações mais importantes.
Assim, este estudo pretende investigar o conceito de inteligência artificial, ou seja, os
seus benefícios, confiança e riscos para, consequentemente, analisar a possibilidade da sua
aplicação na gestão de stock e armazenamento. A primeira fase do estudo passou por uma
revisão de literatura, onde todos esses aspetos foram minuciosamente analisados. Em seguida,
para coletar dados e chegar a conclusões, foram utilizadas duas metodologias: um questionário
online e entrevistas.
Após a análise dos outputs do questionário online, concluiu-se que a Inteligência
Artificial tem benefícios e indicadores de confiança que influenciam positivamente a sua
implementação na gestão de stock e de armazém.
Relativamente às entrevistas, estas revelam que a Inteligência Artificial começa a ser
reconhecida pelas suas vantagens, mas ainda é vista como um investimento avultado,
principalmente para pequenas empresas industriais. No entanto, as aplicações que estão a
decorrer são consideradas pelos especialistas de AI como benéficas, sendo a machine learning,
a robótica e computer vision tecnologias que podem potenciar a produtividade das operações
logísticas abordadas nesta investigação
Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes
The book documents 25 papers collected from the Special Issue “Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes”, highlighting recent research trends in complex industrial processes. The book aims to stimulate the research field and be of benefit to readers from both academic institutes and industrial sectors
A Comprehensive Review of Bio-Inspired Optimization Algorithms Including Applications in Microelectronics and Nanophotonics
The application of artificial intelligence in everyday life is becoming all-pervasive and unavoidable. Within that vast field, a special place belongs to biomimetic/bio-inspired algorithms for multiparameter optimization, which find their use in a large number of areas. Novel methods and advances are being published at an accelerated pace. Because of that, in spite of the fact that there are a lot of surveys and reviews in the field, they quickly become dated. Thus, it is of importance to keep pace with the current developments. In this review, we first consider a possible classification of bio-inspired multiparameter optimization methods because papers dedicated to that area are relatively scarce and often contradictory. We proceed by describing in some detail some more prominent approaches, as well as those most recently published. Finally, we consider the use of biomimetic algorithms in two related wide fields, namely microelectronics (including circuit design optimization) and nanophotonics (including inverse design of structures such as photonic crystals, nanoplasmonic configurations and metamaterials). We attempted to keep this broad survey self-contained so it can be of use not only to scholars in the related fields, but also to all those interested in the latest developments in this attractive area
Towards Agility: Definition, Benchmark and Design Considerations for Small, Quadrupedal Robots
Agile quadrupedal locomotion in animals and robots is yet to be fully understood, quantified
or achieved. An intuitive notion of agility exists, but neither a concise definition nor a common
benchmark can be found. Further, it is unclear, what minimal level of mechatronic complexity
is needed for this particular aspect of locomotion.
In this thesis we address and partially answer two primary questions: (Q1) What is agile
legged locomotion (agility) and how can wemeasure it? (Q2) How can wemake agile legged
locomotion with a robot a reality?
To answer our first question, we define agility for robot and animal alike, building a common
ground for this particular component of locomotion and introduce quantitative measures
to enhance robot evaluation and comparison. The definition is based on and inspired by
features of agility observed in nature, sports, and suggested in robotics related publications.
Using the results of this observational and literature review, we build a novel and extendable
benchmark of thirteen different tasks that implement our vision of quantitatively classifying
agility. All scores are calculated from simple measures, such as time, distance, angles and
characteristic geometric values for robot scaling. We normalize all unit-less scores to reach
comparability between different systems. An initial implementation with available robots and
real agility-dogs as baseline finalize our effort of answering the first question.
Bio-inspired designs introducing and benefiting from morphological aspects present in nature
allowed the generation of fast, robust and energy efficient locomotion. We use engineering
tools and interdisciplinary knowledge transferred from biology to build low-cost robots able
to achieve a certain level of agility and as a result of this addressing our second question. This
iterative process led to a series of robots from Lynx over Cheetah-Cub-S, Cheetah-Cub-AL,
and Oncilla to Serval, a compliant robot with actuated spine, high range of motion in all joints.
Serval presents a high level of mobility at medium speeds. With many successfully implemented
skills, using a basic kinematics-duplication from dogs (copying the foot-trajectories
of real animals and replaying themotion on the robot using a mathematical interpretation),
we found strengths to emphasize, weaknesses to correct and made Serval ready for future
attempts to achieve even more agile locomotion. We calculated Servalâs agility scores with the
result of it performing better than any of its predecessors. Our small, safe and low-cost robot
is able to execute up to 6 agility tasks out of 13 with the potential to reachmore after extended
development. Concluding, we like to mention that Serval is able to cope with step-downs,
smooth, bumpy terrain and falling orthogonally to the ground
Economic power dispatch solutions incorporating stochastic wind power generators by moth flow optimizer
Optimization encourages the economical and efficient operation of the electrical system. Most power system problems are nonlinear and nonconvex, and they frequently ask for the optimization of two or more diametrically opposed objectives. The numerical optimization revolution led to the introduction of numerous evolutionary algorithms (EAs). Most of these methods sidestep the problems of early convergence by searching the universe for the ideal. Because the field of EA is evolving, it may be necessary to reevaluate the usage of new algorithms to solve optimization problems involving power systems. The introduction of renewable energy sources into the smart grid of the present enables the emergence of novel optimization problems with an abundance of new variables. This study's primary purpose is to apply state-of-the-art variations of the differential evolution (DE) algorithm for single-objective optimization and selected evolutionary algorithms for multi-objective optimization issues in power systems. In this investigation, we employ the recently created metaheuristic algorithm known as the moth flow optimizer (MFO), which allows us to answer all five of the optimal power flow (OPF) difficulty objective functions: (1) reducing the cost of power generation (including stochastic solar and thermal power generation), (2) diminished power, (3) voltage variation, (4) emissions, and (5) reducing both the cost of power generating and emissions. Compared to the lowest figures provided by comparable approaches, MFO's cost of power production for IEEE-30 and IEEE-57 bus architectures is 31121.85 per hour, respectively. This results in hourly cost savings between 1.23 % and 1.92%. According to the facts, MFO is superior to the other algorithms and might be utilized to address the OPF problem
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