Renato Resende Ribeiro de Oliveira

A hybrid multi-population genetic algorithm applied to solve the multi-level capacitated lot sizing problem with backlogging

The present paper proposes a new hybrid multi-population genetic algorithm (HMPGA) as an approach to solve the multi-level capacitated lot sizing problem with backlogging. This method combines a multi-population based metaheuristic using fix-and-optimize heuristic and mathematical programming techniques. A total of four test sets from the MULTILSB (Multi-Item Lot-Sizing with Backlogging) library are solved and the results are compared with those reached by two other methods recently published. The results have shown that HMPGA had a better performance for most of the test sets solved, specially when longer computing time is given.

Glass container production scheduling through hybrid multi-population based evolutionary algorithm

Driven by a real-world application in the capital-intensive glass container industry, this paper provides the design of a new hybrid evolutionary algorithm to tackle the short-term production planning and scheduling problem. The challenge consists of sizing and scheduling the lots in the most cost-effective manner on a set of parallel molding machines that are fed by a furnace that melts the glass. The solution procedure combines a multi-population hierarchically structured genetic algorithm (GA) with a simulated annealing (SA), and a tailor-made heuristic named cavity heuristic (CH). The SA is applied to intensify the search for solutions in the neighborhood of the best individuals found by the GA, while the CH determines quickly values for a relevant decision variable of the problem: the processing speed of each machine. The results indicate the superior performance of the proposed approach against a state-of-the-art commercial solver, and compared to a non-hybridized multi-population GA.

A hybrid heuristic approach to solve the multi level capacitated lot sizing problem

This paper presents preliminary results found by a hybrid heuristic applied to solve the Multi-Level Capacitated Lot Sizing Problem (MLCLSP). The proposed method combines a multi-population genetic algorithm and fix-and-optimize heuristic. These methods are also integrated to a mathematical programming approach. For this, a mathematical reformulation of MLCLSP model is proposed to embed the exact solution of the model in the heuristic approaches. The hybrid heuristic is evaluated in two sets of benchmark instances. The solutions found are compared with those reached by other methods from literature. The preliminary results obtained indicate that the hybrid heuristic outperforms other approaches in the majority of problems solved.

A hybrid compact genetic algorithm applied to the multi-level capacitated lot sizing problem

The present paper proposes a hybrid version of a compact genetic algorithm (cGA) as approach to solve the Multi-Level Capacitated Lot Sizing Problem. A fix and optimize heuristic is embedded with cGA to improve solutions while mathematical programming technique is used to evaluate them. This hybrid approach is tested over two sets of benchmark instances. The results achieved are compared with two time-oriented decomposition heuristics from literature and with a hybrid multi-population genetic algorithm recently proposed for the same problem. Computational results show that the hybrid cGA has a superior performance mainly for instances dealing with setup times.

A Genetic Programming based approach to automatically generate Wireless Sensor Networks applications

The development of Wireless Sensor Networks (WSNs) applications is an arduous task, since the application needs to be customized for each sensor. Thus, the automatic generation of WSNs applications is desirable to reduce costs, since it drastically reduces the human effort. This paper presents the use of Genetic Programming to automatically generate WSNs applications. A scripting language based on events and actions is proposed to represent the WSN behavior. Events represent the state of a given sensor node and actions modify these states. Some events are internal states and others are external states captured by the sensors. The genetic programming is used to automatically generate WSNs applications described using this scripting language. These scripts are executed by all networks sensors. This approach enables the application designer to define only the overall objective of the WSN. This objective is defined by means of a fitness function. An event-detection problem is presented in order to evaluate the proposed method. The results shown the capability of the developed approach to successfully solve WSNs problems through the automatic generation of applications.

Parallel genetic algorithm approaches applied to solve a synchronized and integrated lot sizing and scheduling problem

This paper evaluates different parallel approaches for multipopulation genetic algorithm. These approaches are applied to solve a synchronized and integrated lot sizing and scheduling problem. In this problem, the challenge is to simultaneously determine lot sizing and scheduling for raw materials in tanks and products in lines. First, the parallel algorithms are designed to be executed using a multicore server. The best approach is also executed by duo core computers using MPI. A set of real-world instances found in the literature are solved. Also, a new set of instances is proposed. The speedups improvements are showed as well as the quality of final solutions found.

Evaluation of a Genetic Programming Approach to Generate Wireless Sensor Network Applications

This article presents a systematic evaluation of a framework based on Genetic Programming (GP) which aims the automatic generation of Wireless Sensor Network (WSN) applications. Developing WSN applications poses a challenge due to massive distribution of the network nodes. The automatic generation of applications reduces drastically costs, since the manual development is a laborious process. In our approach, the user describes the desired global behavior as a fitness function which guides the evolution of the application by the GP. A scripting language based on events and actions is used to represent the WSN behavior and the GP generates programs in this language. In order to evaluate the framework, a problem of multiple events detection is introduced. Several problem instances were used to appraise the performance of our method under different parameters. Results evidence the feasibility of our approach for the proposed problem, highlighting the challenges posed by the large search space and the dead end routing problem.

Automatic generation and configuration of Wireless Sensor Networks applications with Genetic Programming

The development of Wireless Sensor Networks (WSNs) applications is an arduous task, since the developer has to design the behavior of the nodes and their interactions. The automatic generation of WSNs applications is desirable to reduce costs, since it drastically reduces the human effort. This paper presents the use of Genetic Programming to automatically generate WSNs applications. A scripting language based on events and actions is proposed to represent the WSN behavior. Events represent the state of a given sensor node and actions modify these states. Some events are internal states and others are external states captured by the sensors. A parallel genetic algorithm is used to automatically generate WSNs applications in this scripting language. These scripts are executed by a middleware installed on all sensors nodes. This approach enables the application designer to define only the overall objective of the WSN. This objective is defined by means of a fitness function. An event-detection problem is presented in order to evaluate the proposed method. The results showed the capability of the developed approach to successfully solve WSNs problems through the automatic generation of applications.

A hybrid cGA applied to the MLCLSP with overtime

A hybrid version of a compact genetic algorithm (cGA) is presented as approach to solve the Multi-Level Capacitated Lot Sizing Problem. The present paper extends results reported in [18]. The hybrid method combines a fix and optimize heuristic with cGA aiming to improve solutions generated by cGA. Also a linear mathematical programming model is solved to first evaluated solution provided by cGA. The performance of the hybrid compact genetic algorithm (HcGA) is evaluated over two sets of benchmark instances. The results are compared against methods from literature recently proposed for the same problem: two time-oriented decomposition heuristics and a hybrid multi-population genetic algorithm. A superior performance of HcGA is reported mainly for instances dealing with setup times and against time-oriented decomposition heuristics.