scholarly journals Improving the Performance of Modular Production in the Apparel Assembly: A Mathematical Programming Approach

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Xiaoqing Wang ◽  
Chun-Hung Chiu ◽  
Wei Guo
Keyword(s):  
Optimal Allocation ◽  
Programming Approach ◽  
Time Interval ◽  
Work In Process ◽  
Target Values ◽  
Modular Production ◽  
Mathematical Programming Approach ◽  
Holding Cost ◽  
Assembly Operations ◽  
Assembly Tasks

We construct the mathematical models to find the optimal allocation of the module’s capacity (module members) to different assembly operations in a module for given garment assembly tasks in a modular production system. The objectives of the models are minimizing the holding cost for work in process (WIP) inventories in the module and the total deviation of the WIP inventories from their corresponding target values in the module during a specific time interval. The solutions of the models can be used as reference to achieve better allocation of the module members to different operations in a module to fulfill the given garment assembly tasks.

scholarly journals Fuzzy linear mathematical programming in agriculture

BIBECHANA ◽  
2015 ◽  
Vol 13 ◽  
pp. 72-76
Author(s):  
MA Lone ◽  
MS Puktha ◽  
SA Mir
Keyword(s):  
Decision Making ◽  
Mathematical Programming ◽  
Agricultural Land ◽  
Optimal Allocation ◽  
Optimal Solution ◽  
Fuzzy Linear Programming ◽  
Programming Approach ◽  
Fuzzy Mathematical Programming ◽  
Mathematical Programming Approach ◽  
Land Cultivation

In this paper we present a Fuzzy linear Mathematical programming approach for optimal allocation of land under cultivation. Fuzzy Mathematical programming approach is more realistic and flexible optimal solution for the agricultural land cultivation problem. In this study we have discussed how to deal with decision making problems that are described by Fuzzy linear programming (Flp) models and formulated with the elements of uncertainty. This form of approximation can be convenient and sufficient for making good decisions. BIBECHANA 13 (2016) 72-76

scholarly journals gBoost: a mathematical programming approach to graph classification and regression

2008 ◽  
Vol 75 (1) ◽  
pp. 69-89 ◽  
Author(s):  
Hiroto Saigo ◽  
Sebastian Nowozin ◽  
Tadashi Kadowaki ◽  
Taku Kudo ◽  
Koji Tsuda
Keyword(s):  
Mathematical Programming ◽  
Programming Approach ◽  
Graph Classification ◽  
Mathematical Programming Approach ◽  
Classification And Regression

Integrating Impedance Control and Learning Based Search Scheme for Robotic Assemblies Under Uncertainty

2018 ◽  
Author(s):  
Rishi K. Malhan ◽  
Yash Shahapurkar ◽  
Ariyan M. Kabir ◽  
Brual Shah ◽  
Satyandra K. Gupta
Keyword(s):  
Search Strategy ◽  
Small Volume ◽  
Impedance Control ◽  
Production Volume ◽  
Control Parameters ◽  
Robotic Arms ◽  
High Production Volume ◽  
High Production ◽  
Assembly Operations ◽  
Assembly Tasks

Using fixtures for assembly operations is a common practice in manufacturing processes with high production volume. For automated assembly cells using robotic arms, trajectories are programmed manually and robots follow the same path repeatedly. It is not economically feasible to build fixed fixtures for small volume productions as they require high accuracy and are part specific. Moreover, hand coding robot trajectories is a time consuming task. The uncertainties in part localization and inaccuracy in robot motions make it challenging to automate the task of assembling two parts with tight tolerances. Researchers in past have developed methods for automating the assembly task using contact-based search schemes and impedance control-based trajectory execution. Both of these approaches may lead to undesired collision with critical features on the parts. Our method guarantees safety for parts with delicate features during the assembly process. Our approach enables us to select optimum impedance control parameters and utilizes a learning-based search strategy to complete assembly tasks under uncertainties in bounded time. Our approach was tested on an assembly of two rectangular workpieces using KUKA IIWA 7 manipulator. The method we propose was able to successfully select the optimal control parameters. The learning-based search strategy successfully estimated the uncertainty in pose of parts and converged in few iterations.

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