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Session Overview
SES 5.2: Production Planning and Scheduling
Wednesday, 28/Jun/2017:
11:20am - 1:00pm

Session Chair: Rita Gamberini
Location: Aula N (first floor)

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132. A New Performance Indicator of Material Flow for Production Systems

Chi-Shuan Liu1, Luo-Yan Lin1, Ming-Chih Chen2, Horng-Chyi Horng1

1Chaoyang University of Technology, Taiwan; 2Graduate Institute of Business Adminstration, Fu Jen Catholic University, 510 Zhongzheng Rd., Xinzhuang, New Taipei City 24205, Taiwan

This research develops a new performance indicator for material flow effectiveness in production systems. To develop the so-called flow value, the first step is performing work sampling on Work in Processes (WIP) of the current production. These WIPs are then given different weightings in the calculations of flow value, based on their job types, locations in the system, and so on. This flow value can represent the smoothness of material flow in a production system, thus can be used as real time indicator to instantly reveal the performance of a production system. A simulation study on a machining equipment manufacturing company in Taiwan validates the usefulness of using flow value as an indicator of material flow performance. This flow value is also capable of revealing areas where improvement can be made to effectively improve total system’s performance in terms of material flow.

251. A framework for task sequencing for redundant robotic remote laser processing equipment based on redundancy space sampling

Sigurd Lazic VIllumsen, Morten Kristiansen

Aalborg Universitet, Denmark

This paper presents a framework for task sequencing of redundant remote laser processing equipment. The task sequencing algorithm is based on generating sets of robot configuration samples on all processing contours. The sequencing is done by using a solver for the equality constrained generalized traveling salesman problem (E- GTSP) to find the shortest path between these sets, while taking process constraints, robot redundancy and cluster connectivity into consideration. The algorithm was implemented in Matlab with interfaces to the robot simulation program V-rep and the GLKH E-GTSP solver. A test was carried out with 38 contours on a work piece with a set of angled plateaus.

382. Balanced Adaptive Tabu Search Algorithm to Optimize Dual-Gantry Pick-and-Place Assembly

Debiao Li1, Rui Qiang1, Sang Won Yoon2

1Fuzhou Uuniversity, China, People's Republic of; 2Department of Systems Science and Industrial Engineering, State University of New York at Binghamton, Binghamton, NY, 13902, US

This paper presents an optimization study of dual-gantry high-speed rotary-type pick-and-place surface mount device (SMD) machine. In the dual-gantry SMD machine, there are two feeder bases and gantry heads, such that two placement heads can pick-and-place synchronously in one PCB, which has an advantage in assembly PCB with more component types. The dual-gantry optimization problem is decomposed into nozzle arrangement, feeder assignment, pick-and-place sequence, component allocation, and gantry schedule problems. To balance the workload between gantries and reduce the wait time in the gantry's synchronous movement, a balanced adaptive tabu search (BATS) algorithm are proposed. Based on the 16 industrial data tests, the proposed algorithm yields a 7.04% improvement on average compared to the industrial package.

214. Stencil printing optimization using a hybrid of support vector regression and mixed-integer linear programming

Nourma Khader1, Sang Won Yoon1, Debiao Li2

1State University of New York at Binghamton, United States of America; 2School of Economics and Management, Fuzhou University, Fuzhou, China

This research proposes an optimization approach to enhance the stencil printing process (SPP) in surface mount printed circuit board (PCB) assembly. Stencil printing behavior is affected by many variables including stencil design, solder paste composition, squeegee speed and pressure, and other environmental conditions. In this research, support vector regression (SVR) model is trained to capture the complex relationships among these variables, based on historical data. A mixed-integer linear programming (MILP) model is proposed to minimize the total absolute predicted deviation of average volume transfer from target. The optimal printing settings are retrieved for different sample problems with low computational cost.


Rita Gamberini, Luca Galloni, Francesco Lolli, Bianca Rimini

University of Modena and Reggio Emilia, Italy

OEE (Overall Equipment Effectiveness) is a widely used indicator in the evaluation of effectiveness of manufacturing systems. However, several authors published alternative approaches for its computation, complicating the implementation step for practitioners. This study analyses the literature regarding OEE, selects four main methodologies for its evaluation and examines the underlying differences between them. A real life case study is analysed to illustrate problems arising during data collection and the differences in results obtained, together with traceable conclusions for improving the performance of production systems, both in traditional and in innovative industrial plants, following Industry 4.0 principles.

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