Multiobjective trajectory planner for industrial robots with payload constraints

Robotica ◽  
2008 ◽  
Vol 26 (6) ◽  
pp. 753-765 ◽  
Author(s):  
R. Saravanan ◽  
S. Ramabalan ◽  
C. Balamurugan
Keyword(s):  
Performance Measures ◽  
Industrial Robot ◽  
Industrial Robots ◽  
Fuzzy Membership Function ◽  
Pareto Optimal ◽  
Nsga Ii ◽  
Multi Objective ◽  
Objective Performance ◽  
Objective Performance Measures ◽  
Pareto Optimal Fronts

SUMMARYA general new methodology using evolutionary algorithms viz., Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-objective Differential Evolution (MODE), for obtaining optimal trajectory planning of an industrial robot manipulator (PUMA 560 robot) in the presence of fixed and moving obstacles with payload constraint is presented. The problem has a multi-criterion character in which six objective functions, 32 constraints and 288 variables are considered. A cubic NURBS curve is used to define the trajectory. The average fuzzy membership function method is used to select the best optimal solution from Pareto optimal fronts. Two multi-objective performance measures namely solution spread measure and ratio of non-dominated individuals are used to evaluate the strength of Pareto optimal fronts. Two more multi-objective performance measures namely optimiser overhead and algorithm effort are used to find computational effort of the NSGA-II and MODE algorithms. The Pareto optimal fronts and results obtained from various techniques are compared and analysed. Both NSGA-II and MODE are best for this problem.

scholarly journals Analisis Perbandingan Aggregat Of Function (AOF) dengan Non-Dominated Sorting Genetic Algorithm (NSGA-II) dalam Menentukan Optimasi Multi-Objective pada Penjadwalan Mesin Produksi Flow Shop

2018 ◽  
Vol 17 (2) ◽  
pp. 158
Author(s):  
Fifin Sonata ◽  
Dede Prabowo Wiguna
Keyword(s):  
Genetic Algorithm ◽  
Flow Shop ◽  
Total Tardiness ◽  
Pareto Optimal ◽  
Nsga Ii ◽  
Trade Off ◽  
Multi Objective

Penjadwalan mesin produksi dalam dunia industri memiliki peranan penting sebagai bentuk pengambilan keputusan. Salah satu jenis sistem penjadwalan mesin produksi adalah sistem penjadwalan mesin produksi tipe flow shop. Dalam penjadwalan flow shop, terdapat sejumlah pekerjaan (job) yang tiap-tiap job memiliki urutan pekerjaan mesin yang sama. Optimasi penjadwalan mesin produksi flow shop berkaitan dengan penyusunan penjadwalan mesin yang mempertimbangkan 2 objek yaitu makespan dan total tardiness. Optimasi kedua permasalahan tersebut merupakan optimasi yang bertolak belakang sehingga diperlukan model yang mengintegrasikan permasalahan tersebut dengan optimasi multi-objective A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimazitaion : NSGA-II. Dalam penelitian ini akan dibandingkan 2 buah metode yaitu Aggregat Of Function (AOF) dengan NSGA-II agar dapat terlihat nilai solusinya. Penyelesaian penjadwalan mesin produksi flow shop dengan algoritma NSGA-II untuk membangun jadwal dengan meminimalkan makespan dan total tardiness.Tujuan yang ingin dicapai adalah mengetahui bahwa model yang dikembangkan akan memberikan solusi penjadwalan mesin produksi flow shop yang efisien berupa solusi pareto optimal yang dapat memberikan sekumpulan solusi alternatif bagi pengambil keputusan dalam membuat penjadwalan mesin produksi yang diharapkan. Solusi pareto optimal yang dihasilkan merupakan solusi optimasi multi-objective yang optimal dengan trade-off terhadap seluruh objek, sehingga seluruh solusi pareto optimal sama baiknya.

Improving service operations: linking safety culture to hospital performance

2017 ◽  
Vol 28 (1) ◽  
pp. 57-84 ◽  
Author(s):  
Gregory N. Stock ◽  
Kathleen L. McFadden
Keyword(s):  
Patient Safety ◽  
Performance Measures ◽  
Safety Culture ◽  
Linear Regression Analysis ◽  
Patient Safety Culture ◽  
Hospital Performance ◽  
Data Set ◽  
Content Type ◽  
Objective Performance ◽  
Objective Performance Measures

Purpose The purpose of this paper is to examine the relationship between patient safety culture and hospital performance using objective performance measures and secondary data on patient safety culture. Design/methodology/approach Patient safety culture is measured using data from the Agency for Healthcare Research and Quality’s Hospital Survey on Patient Safety Culture. Hospital performance is measured using objective patient safety and operational performance metrics collected by the Centers for Medicare and Medicaid Services (CMS). Control variables were obtained from the CMS Provider of Service database. The merged data included 154 US hospitals, with an average of 848 respondents per hospital providing culture data. Hierarchical linear regression analysis is used to test the proposed relationships. Findings The findings indicate that patient safety culture is positively associated with patient safety, process quality and patient satisfaction. Practical implications Hospital managers should focus on building a stronger patient safety culture due to its positive relationship with hospital performance. Originality/value This is the first study to test these relationships using several objective performance measures and a comprehensive patient safety culture data set that includes a substantial number of respondents per hospital. The study contributes to the literature by explicitly mapping high-reliability organization (HRO) theory to patient safety culture, thereby illustrating how HRO theory can be applied to safety culture in the hospital operations context.

Trajectory Generation of an Industrial Robot With Constrained Kinematic and Dynamic Variations for Improving Positional Accuracy

2021 ◽  
Vol 12 (3) ◽  
pp. 163-179
Author(s):  
Amruta Rout ◽  
Deepak BBVL ◽  
Bibhtui Bhusan Biswal ◽  
Golak B. Mahanta
Keyword(s):  
Travel Time ◽  
Optimal Trajectory ◽  
Pareto Front ◽  
Industrial Robot ◽  
Trajectory Generation ◽  
Fuzzy Membership Function ◽  
Positional Accuracy ◽  
Nsga Ii ◽  
Dynamic Variations ◽  
Total Travel Time

The joint trajectory of the robot needs to be computed in an optimal manner for proper torch orientation, smooth travel of the robot along the trajectory path. This can be achieved by limiting the travel time, kinematic and dynamic variations of the robot joints like the jerks, and torque induced in the joints in the travel of the robot. As the objectives of total travel time and joint jerk and torque rate are contradictory functions, non-dominated sorting genetic algorithm-II (NSGA-II) approach has been used to obtain the pareto front consisting of optimal solutions. The fuzzy membership function has been used to obtain the optimal solution from the pareto front with best trade-off between objectives for further optimal trajectory generation. From the simulation results, it can be concluded that the proposed approach can be effectively used for optimal trajectory planning of Kawasaki RS06L industrial manipulator with minimal jerk, torque rate, and total travel time for smooth travel of robot with higher positional accuracy.

The Journey From Development to Appraisal

2019 ◽  
pp. 327-342
Author(s):  
Tracy M. Maylett
Keyword(s):  
Performance Management ◽  
Performance Appraisal ◽  
Performance Measures ◽  
Past Performance ◽  
360 Feedback ◽  
Objective Performance ◽  
Objective Performance Measures ◽  
Performance Appraisal Process ◽  
Measures Of Performance

This case study describes an initiative to change a long-standing performance management process at a large manufacturing facility within General Mills that emphasized the attainment of objective performance measures (the “what” of performance) to one that also included the “how” of goal achievement. The organization embarked on a 3-year pilot evaluation of the use of 360 Feedback as a possible solution to replace or supplement their traditional single-source (supervisor) performance appraisal process. The two systems ran in parallel using 140 randomly selected employees. Results showed little correlation between the what measures of performance from the traditional appraisals and the how data collected using the 360 Feedback, supporting the view that job performance should be viewed as requiring both aspects of evaluation, using different methods of assessment. Ultimately, the organization maintained both systems but integrated 360 Feedback into the traditional appraisals as well, creating complementary processes that looked “forward” (development) and “past” (performance).

Evaluating the ε-Domination Based Multi-Objective Evolutionary Algorithm for a Quick Computation of Pareto-Optimal Solutions

2005 ◽  
Vol 13 (4) ◽  
pp. 501-525 ◽  
Author(s):  
Kalyanmoy Deb ◽  
Manikanth Mohan ◽  
Shikhar Mishra
Keyword(s):  
Steady State ◽  
Evolutionary Algorithm ◽  
Optimization Problems ◽  
Computational Time ◽  
Test Problems ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Nsga Ii ◽  
Multi Objective

Since the suggestion of a computing procedure of multiple Pareto-optimal solutions in multi-objective optimization problems in the early Nineties, researchers have been on the look out for a procedure which is computationally fast and simultaneously capable of finding a well-converged and well-distributed set of solutions. Most multi-objective evolutionary algorithms (MOEAs) developed in the past decade are either good for achieving a well-distributed solutions at the expense of a large computational effort or computationally fast at the expense of achieving a not-so-good distribution of solutions. For example, although the Strength Pareto Evolutionary Algorithm or SPEA (Zitzler and Thiele, 1999) produces a much better distribution compared to the elitist non-dominated sorting GA or NSGA-II (Deb et al., 2002a), the computational time needed to run SPEA is much greater. In this paper, we evaluate a recently-proposed steady-state MOEA (Deb et al., 2003) which was developed based on the ε-dominance concept introduced earlier (Laumanns et al., 2002) and using efficient parent and archive update strategies for achieving a well-distributed and well-converged set of solutions quickly. Based on an extensive comparative study with four other state-of-the-art MOEAs on a number of two, three, and four objective test problems, it is observed that the steady-state MOEA is a good compromise in terms of convergence near to the Pareto-optimal front, diversity of solutions, and computational time. Moreover, the ε-MOEA is a step closer towards making MOEAs pragmatic, particularly allowing a decision-maker to control the achievable accuracy in the obtained Pareto-optimal solutions.

A pareto optimal study for the multi-objective oil platform location problem with NSGA-II

2018 ◽  
Vol 169 ◽  
pp. 258-268 ◽  
Author(s):  
Marcus Vinicius Oliveira Camara ◽  
Glaydston Mattos Ribeiro ◽  
Marielce de Cássia Ribeiro Tosta
Keyword(s):  
Location Problem ◽  
Pareto Optimal ◽  
Nsga Ii ◽  
Multi Objective ◽  
Platform Location
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