References of "Tessaro Lunardi, Willian 50023639"
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See detailAn Effective Hybrid Imperialist Competitive Algorithm and Tabu Search for an Extended Flexible Job Shop Scheduling Problem
Tessaro Lunardi, Willian UL; Voos, Holger UL; Cherri, Luiz Henrique

in 34th ACM/SIGAPP Symposium On Applied Computing, Limassol, Cyprus April 8-12, 2019 (2019, April 08)

An extended version of the flexible job shop problem is tackled in this work. The investigated extension of the classical flexible job shop problem allows the precedences between the operations to be ... [more ▼]

An extended version of the flexible job shop problem is tackled in this work. The investigated extension of the classical flexible job shop problem allows the precedences between the operations to be given by an arbitrary directed acyclic graph instead of a linear order. The problem consists of designating the operations to the machines and sequencing them in compliance with the supplied precedences. The goal in the present work is the minimization of the makespan. In order to produce reasonable outcomes in acceptable time, a hybrid imperialist competitive algorithm and tabu search is proposed to solve the problem. Numerical experiments assess the efficiency of the proposed method and compare it with well-known scheduling algorithms. [less ▲]

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See detailA parallel multi-population biased random-key genetic algorithm for electric distribution network reconfiguration
de Faria Junior, Haroldo UL; Tessaro Lunardi, Willian UL; Voos, Holger UL

in The Genetic and Evolutionary Computation Conference - GECCO'19 (2019)

This work presents a multi-population biased random-key genetic algorithm (BRKGA) for the electric distribution network reconfiguration problem (DNR). DNR belongs to the class of network design problems ... [more ▼]

This work presents a multi-population biased random-key genetic algorithm (BRKGA) for the electric distribution network reconfiguration problem (DNR). DNR belongs to the class of network design problems which include transportation problems, computer network restoration and telecommunication network design and can be used for loss minimization and load balancing, being an important tool for distribution network operators. A BRKGA is a class of genetic algorithms in which solutions are encoded as vectors of random keys, i.e. randomly generated real numbers from a uniform distribution in the interval [0, 1). A vector of random keys is translated into a solution of the optimization problem by a decoder. The decoder used generates only feasible solutions by using an efficient codification based upon the fundamentals of graph theory, restricting the search space. The parallelization is based on the single program multiple data paradigm and is executed on the cores of a multi-core processor. Time to target plots, which characterize the running times of stochastic algorithms for combinatorial optimization, are used to compare the performance of the serial and parallel algorithms. The proposed method has been tested on two standard distribution systems and the results show the effectiveness and performance of the parallel algorithm. [less ▲]

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See detailAn Imperialist Competitive Algorithm for a Real-World Flexible Job Shop Scheduling Problem
Tessaro Lunardi, Willian UL; Voos, Holger UL; Cherri, Luiz Henrique

in 23rd IEEE International Conference on Emerging Technologies and Factory Automation, Torino, Italy, September 4-7, 2018 (2018, September)

Traditional planning and scheduling techniques still hold important roles in modern smart scheduling systems. Realistic features present in modern manufacturing systems need to be incorporated into these ... [more ▼]

Traditional planning and scheduling techniques still hold important roles in modern smart scheduling systems. Realistic features present in modern manufacturing systems need to be incorporated into these techniques. The real-world problem addressed here is an extension of flexible job shop scheduling problem and is issued from the modern printing and boarding industry. The precedence between operations of each job is given by an arbitrary directed acyclic graph rather than a linear order. In this paper, we extend the traditional FJSP solutions representation to address the parallel operations. We propose an imperialist competitive algorithm for the problem. Several instances are used for the experiments and the results show that, for the considered instances, the proposed algorithm is faster and found better or equal solutions compared to the state-of-the-art algorithms. [less ▲]

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See detailA Mathematical Model and a Firefly Algorithm for an Extended Flexible Job Shop Problem with Availability Constraints
Tessaro Lunardi, Willian UL; Cherri, Luiz Henrique; Voos, Holger UL

in 17th International Conference on Artificial Intelligence and Soft Computing, Zakopane, Poland, June 3-7, 2018 (2018, June)

Manufacturing scheduling strategies have historically ignored the availability of the machines. The more realistic the schedule, more accurate the calculations and predictions. Availability of machines ... [more ▼]

Manufacturing scheduling strategies have historically ignored the availability of the machines. The more realistic the schedule, more accurate the calculations and predictions. Availability of machines will play a crucial role in the Industry 4.0 smart factories. In this paper, a mixed integer linear programming model (MILP) and a discrete firefly algorithm (DFA) are proposed for an extended multi-objective FJSP with availability constraints (FJSP-FCR). Several standard instances of FJSP have been used to evaluate the performance of the model and the algorithm. New FJSP-FCR instances are provided. Comparisons among the proposed methods and other state-of-the-art reported algorithms are also presented. Alongside the proposed MILP model, a Genetic Algorithm is implemented for the experiments with the DFA. Extensive investigations are conducted to test the performance of the proposed model and the DFA. The comparisons between DFA and other recently published algorithms shows that it is a feasible approach for the stated problem. [less ▲]

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See detailAn Extended Flexible Job Shop Scheduling Problem with Parallel Operations
Tessaro Lunardi, Willian UL; Voos, Holger UL

in ACM SIGAPP Applied Computing Review (2018), 18(2), 46-56

Traditional planning and scheduling techniques still hold important roles in modern smart scheduling systems. Realistic features present in modern manufacturing systems need to be incorporated into these ... [more ▼]

Traditional planning and scheduling techniques still hold important roles in modern smart scheduling systems. Realistic features present in modern manufacturing systems need to be incorporated into these techniques. Flexible job-shop scheduling problem (FJSP) is one of the most challenging combinatorial optimization problems. FJSP is an extension of the classical job shop scheduling problem where an operation can be processed by several different machines. In this paper, we consider the FJSP with parallel operations (EFJSP) and we propose and compare a discrete firefly algorithm (FA) and a genetic algorithm (GA) for the problem. Several FJSP and EFJSP instances were used to evaluate the performance of the proposed algorithms. Comparisons among our methods and state-of-the-art algorithms are also provided. The experimental results demonstrate that the FA and GA achieved improvements in terms of efficiency and efficacy. Solutions obtained by both algorithms are comparable to those obtained by algorithms with local search. In addition, based on our initial experiments, results show that the proposed discrete firefly algorithm is feasible, more effective and efficient than our proposed genetic algorithm for the considered problem. [less ▲]

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See detailComparative Study of Genetic and Discrete Firefly Algorithm for Combinatorial Optimization
Tessaro Lunardi, Willian UL; Voos, Holger UL

in 33rd ACM/SIGAPP Symposium On Applied Computing, Pau, France, April 9 - 13, 2018 (2018, April)

Flexible job-shop scheduling problem (FJSP) is one of the most challenging combinatorial optimization problems. FJSP is an extension of the classical job shop scheduling problem where an operation can be ... [more ▼]

Flexible job-shop scheduling problem (FJSP) is one of the most challenging combinatorial optimization problems. FJSP is an extension of the classical job shop scheduling problem where an operation can be processed by several different machines. The FJSP contains two sub-problems, namely machine assignment problem and operation sequencing problem. In this paper, we propose and compare a discrete firefly algorithm (FA) and a genetic algorithm (GA) for the multi-objective FJSP. Three minimization objectives are considered, the maximum completion time, workload of the critical machine and total workload of all machines. Five well-known instances of FJSP have been used to evaluate the performance of the proposed algorithms. Comparisons among our methods and state-of-the-art algorithms are also provided. The experimental results demonstrate that the FA and GA have achieved improvements in terms of efficiency. Solutions obtained by both algorithms are comparable to those obtained by algorithms with local search. In addition, based on our initial experiments, results show that the proposed discrete firefly algorithm is feasible, more effective and efficient than our proposed genetic algorithm for solving multi-objective FJSP. [less ▲]

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See detailAutomated Decision Support IoT Framework
Tessaro Lunardi, Willian UL; Amaral, Leonardo; Marczak, Sabrina et al

in IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA), Berlin, Germany, 2016 (2016, September)

During the past few years, with the fast development and proliferation of the Internet of Things (IoT), many application areas have started to exploit this new computing paradigm. The number of active ... [more ▼]

During the past few years, with the fast development and proliferation of the Internet of Things (IoT), many application areas have started to exploit this new computing paradigm. The number of active computing devices has been growing at a rapid pace in IoT environments around the world. Consequently, a mechanism to deal with this different devices has become necessary. Middleware systems solutions for IoT have been developed in both research and industrial environments to supply this need. However, decision analytics remain a critical challenge. In this work we present the Decision Support IoT Framework composed of COBASEN, an IoT search engine to address the research challenge regarding the discovery and selection of IoT devices when large number of devices with overlapping and sometimes redundant functionality are available in IoT middleware systems, and DMS, a rule-based reasoner engine allowing to set up computational analytics on device data when it is still in motion, extracting valuable information from it for automated decision making. DMS uses Complex Event Processing to analyze and react over streaming data, allowing for example, to trigger an actuator when a specific error or condition appears in the stream. The main goal of this work is to highlight the importance of a decision support system for decision analytics in the IoT paradigm. We developed a system which implements DMS concepts. However, for preliminarily tests, we made a functional evaluation of both systems in terms of performance. Our initial findings suggest that the Decision Support IoT Framework provides important approaches that facilitate the development of IoT applications, and provides a new way to see how the business rules and decision-making will be made towards the Internet of Things. [less ▲]

Detailed reference viewed: 99 (21 UL)