Simultaneous Modular Product Scheduling and Manufacturing Cell Reconfiguration Using a Genetic Algorithm

2006 ◽  
Vol 128 (4) ◽  
pp. 984-995 ◽  
Author(s):  
Hegui Ye ◽  
Ming Liang

Modular product design can facilitate the diversification of product variety at a low cost. Reconfigurable manufacturing, if planned properly, is able to deliver high productivity and quick responsiveness to market changes. Together, the two could provide an unprecedented competitive edge to a manufacturing company. The production of a family of modular products in a reconfigurable manufacturing system often requires reorganizing the manufacturing system in such a way that each configuration corresponds to one product variant in the same family. The successful implementation of this strategy lies in proper scheduling of the modular product operations and optimal selection of a configuration for producing each product variant. These two issues are closely related and have a strong impact on each other. Nevertheless, they have often been treated separately, rendering inefficient, infeasible, and conflicting decisions. As such, an integrated model is developed to address the two problems simultaneously. The objective is to minimize the sum of the manufacturing cost components that are affected by the two planning decisions. These include reconfiguration cost, machine idle cost, material handling cost, and work-in-process cost incurred in producing a batch of product variants. Due to the combinatorial nature of the problem, a genetic algorithm (GA) is proposed to provide quick and near-optimal solutions. A case study is conducted using a steering column to illustrate the application of the integrated approach. Our computational experience shows that the proposed GA substantially outperforms a popular optimization software package, LINGO, in terms of both solution quality and computing efficiency.

2005 ◽  
Vol 127 (4) ◽  
pp. 875-884 ◽  
Author(s):  
Zhonghui Xu ◽  
Ming Liang

Both modular product design and reconfigurable manufacturing have a great potential to enhance responsiveness to market changes and to reduce production cost. However, the two issues have thus far mostly been investigated separately, thereby causing possible mismatch between the modular product structure and the manufacturing or assembly system. Therefore, the potential benefits of product modularity may not be materialized due to such mismatch. For this reason, this paper presents a concurrent approach to the product module selection and assembly line design problems to provide a set of harmonic solutions to the two problems and hence avoid the mismatch between design and manufacturing. The integrated nature of the problem leads to several noncommensurable and often conflicting objectives. The modified Chebyshev goal programming approach is applied to solve the multi-objective problem. A genetic algorithm is further developed to provide quick and near-optimum solutions. The proposed approach and the solution procedure have been applied to an ABS motor problem. The performance of the genetic algorithm has also been examined.


2021 ◽  
pp. 130-139
Author(s):  
Marco Bortolini ◽  
Cristian Cafarella ◽  
Emilio Ferrari ◽  
Francesco Gabriele Galizia ◽  
Mauro Gamberi

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1194
Author(s):  
Thejus Pathmakumar ◽  
Madan Mohan Rayguru ◽  
Sriharsha Ghanta ◽  
Manivannan Kalimuthu ◽  
Mohan Rajesh Elara

The hydro blasting of metallic surfaces is an essential maintenance task in various industrial sites. Its requirement of a considerable labour force and time, calls for automating the hydro blasting jobs through mobile robots. A hydro blasting robot should be able to cover the required area for a successful implementation. If a conventional robot footprint is chosen, the blasting may become inefficient, even though the concerned area is completely covered. In this work, the blasting arm’s sweeping angle is chosen as the robot’s footprint for hydro blasting task, and a multi-objective optimization-based framework is proposed to compute the optimal sweeping arc. The genetic algorithm (GA) methodology is exploited to compute the optimal footprint, which minimizes the blasting time and energy simultaneously. Multiple numerical simulations are performed to show the effectiveness of the proposed approach. Moreover, the strategy is successfully implemented on our hydro blasting robot named Hornbill, and the efficacy of the proposed approach is validated through experimental trials.


Sign in / Sign up

Export Citation Format

Share Document