RoboTSP - A Fast Solution to the Robotic Task Sequencing Problem

In many industrial robotics applications, such as spot-welding, spray-painting or drilling, the robot is required to visit successively multiple targets. The robot travel time among the targets is a significant component of the overall execution time. This travel time is in turn greatly affected by the order of visit of the targets, and by the robot configurations used to reach each target. Therefore, it is crucial to optimize these two elements, a problem known in the literature as the Robotic Task Sequencing Problem (RTSP). Our contribution in this paper is two-fold. First, we propose a fast, near-optimal, algorithm to solve RTSP. The key to our approach is to exploit the classical distinction between task space and configuration space, which, surprisingly, has been so far overlooked in the RTSP literature. Second, we provide an open-source implementation of the above algorithm, which has been carefully benchmarked to yield an efficient, ready-to-use, software solution. We discuss the relationship between RTSP and other Traveling Salesman Problem (TSP) variants, such as the Generalized Traveling Salesman Problem (GTSP), and show experimentally that our method finds motion sequences of the same quality but using several orders of magnitude less computation time than existing approaches.Comment: 6 pages, 7 figures, 1 tabl

Identification of Patterns in Genetic-Algorithm-Based Solutions for Optimization of Process-Planning Problems Using a Data Mining Tool

This paper presents a novel use of data mining algorithms for extraction of knowledge from a set of process plans. The purpose of this paper is to apply data mining methodologies to explore the patterns in data generated by genetic-algorithm-generating process plans and to develop a rule set planner, which helps to make decisions in odd circumstances. Genetic algorithms are random-search algorithms based on the mechanics of genetics and natural selection. Because of genetic inheritance, the characteristics of the survivors after several generations should be similar. The solutions of a genetic algorithms for process planning consists of the operation sequence of a job, the machine on which each operation is performed, the tool used for performing each operation, and the tool approach direction. Among the optimal or near-optimal solutions, similar relationships may exist between the characteristics of the operation and sequential order. Data mining software known as See5 has been used to explore the relationship between the operation’s sequence and its attributes, and a set of rules has been developed. These rules can predict the positions of operations in the sequence of process planning

Multiobjective strategies for New Product Development in the pharmaceutical industry

New Product Development (NPD) constitutes a challenging problem in the pharmaceutical industry, due to the characteristics of the development pipeline. Formally, the NPD problem can be stated as follows: select a set of R&D projects from a pool of candidate projects in order to satisfy several criteria (economic profitability, time to market) while coping with the uncertain nature of the projects. More precisely, the recurrent key issues are to determine the projects to develop once target molecules have been identified, their order and the level of resources to assign. In this context, the proposed approach combines discrete event stochastic simulation (Monte Carlo approach) with multiobjective genetic algorithms (NSGAII type, Non-Sorted Genetic Algorithm II) to optimize the highly combinatorial portfolio management problem. In that context, Genetic Algorithms (GAs) are particularly attractive for treating this kind of problem, due to their ability to directly lead to the so-called Pareto front and to account for the combinatorial aspect. This work is illustrated with a study case involving nine interdependent new product candidates targeting three diseases. An analysis is performed for this test bench on the different pairs of criteria both for the bi- and tricriteria optimization: large portfolios cause resource queues and delays time to launch and are eliminated by the bi- and tricriteria optimization strategy. The optimization strategy is thus interesting to detect the sequence candidates. Time is an important criterion to consider simultaneously with NPV and risk criteria. The order in which drugs are released in the pipeline is of great importance as with scheduling problems

Multiobjective strategies for New Product Development in the pharmaceutical industry

New Product Development (NPD) constitutes a challenging problem in the pharmaceutical industry, due to the characteristics of the development pipeline. Formally, the NPD problem can be stated as follows: select a set of R&D projects from a pool of candidate projects in order to satisfy several criteria (economic profitability, time to market) while coping with the uncertain nature of the projects. More precisely, the recurrent key issues are to determine the projects to develop once target molecules have been identified, their order and the level of resources to assign. In this context, the proposed approach combines discrete event stochastic simulation (Monte Carlo approach) with multiobjective genetic algorithms (NSGAII type, Non-Sorted Genetic Algorithm II) to optimize the highly combinatorial portfolio management problem. In that context, Genetic Algorithms (GAs) are particularly attractive for treating this kind of problem, due to their ability to directly lead to the so-called Pareto front and to account for the combinatorial aspect. This work is illustrated with a study case involving nine interdependent new product candidates targeting three diseases. An analysis is performed for this test bench on the different pairs of criteria both for the bi- and tricriteria optimization: large portfolios cause resource queues and delays time to launch and are eliminated by the bi- and tricriteria optimization strategy. The optimization strategy is thus interesting to detect the sequence candidates. Time is an important criterion to consider simultaneously with NPV and risk criteria. The order in which drugs are released in the pipeline is of great importance as with scheduling problems

Real-time energy optimization of irrigation scheduling by parallel multi-objective genetic algorithms

[EN] The present work is motivated by the need to reduce the energy costs arising from the pressure demands of drip and sprinkling irrigation, compounded by the increase in the energy price in recent years. Researchers have demonstrated that proper operation of the irrigation network reduces associated pumping costs. The main challenge was to obtain the optimal operation parameters on near real-time due to the fact that the high complexity of the optimization problem requires a great computational effort. The classic approach to the problem imposes a strict fulfilment of minimum pressures as a restriction. This study, however, presents a new methodology for the reordering of irrigation scheduling, incorporating the constraint of daily volume requests for each hydrant. The methodology is capable of minimizing the cost of energy while maximizing pressures at the critical hydrants. Cost reductions of about 6¿7% were reached for scenarios without pressure deficit for the case study. Greater computational efficiency was achieved by posing the problem from a multi-objective approach, on the one hand, and by establishing the parallel evaluation of the objective function, on the other. The speed-up obtained by combining a reduction in the number of function evaluations thanks to the faster convergence of the multi-objective approach and the reduction of the computational time due to the parallelization of the algorithm achieved results about 10 times faster. This improvement allowed the tool to be implemented for the daily optimization of irrigation requests.This work has been supported by the VALi+D R&D Program of the Generalitat Valenciana (Spain).Alonso-Campos, J.; Jiménez Bello, MA.; Martínez Alzamora, F. (2020). Real-time energy optimization of irrigation scheduling by parallel multi-objective genetic algorithms. Agricultural Water Management. 227:1-8. https://doi.org/10.1016/j.agwat.2019.105857S1822