Decomposition-Based Assembly Synthesis of Multiple Structures for Minimum Manufacturing Cost

2004 ◽  
Vol 127 (4) ◽  
pp. 572-579 ◽  
Author(s):  
Onur L. Cetin ◽  
Kazuhiro Saitou
Keyword(s):  
Genetic Algorithm ◽  
Early Identification ◽  
Economies Of Scale ◽  
Structural Strength ◽  
Manufacturing Cost ◽  
Manufacturing Costs ◽  
Multiobjective Genetic Algorithm ◽  
Trade Offs ◽  
Multiple Structures

An extension of decomposition-based assembly synthesis for structural modularity is presented where the early identification of shareable components within multiple structures is posed as an outcome of the minimization of estimated manufacturing costs. The manufacturing costs of components are estimated under given production volumes considering the economies of scale. Multiple structures are simultaneously decomposed, and the types of welded joints at component interfaces are selected from a given library, in order to minimize the overall manufacturing cost and the reduction of structural strength due to the introduction of joints. A multiobjective genetic algorithm is used to allow effective examination of trade-offs between manufacturing cost and structural strength. A new joint-oriented representation of structures combined with a “direct” crossover is introduced to enhance the efficiency of the search. A preliminary case study with two simplified aluminum space frame automotive bodies is presented to demonstrate that not all types of component sharing are economically justifiable under a certain production scenario.

scholarly journals Decomposition-Based Assembly Synthesis of Multiple Structures for Minimum Production Cost

2003 ◽  
Author(s):  
Onur L. Cetin ◽  
Kazuhiro Saitou
Keyword(s):  
Production Cost ◽  
Economies Of Scale ◽  
Structural Strength ◽  
Production Costs ◽  
Manufacturing Cost ◽  
Manufacturing Costs ◽  
Multiobjective Genetic Algorithm ◽  
Trade Offs ◽  
Multiple Structures

An extension of decomposition-based assembly synthesis for structural modularity is presented where the early identification of shareable components within multiple structures is posed as an outcome of the minimization of estimated production costs. The manufacturing costs of components are estimated under given production volumes considering the economies of scale. Multiple structures are simultaneously decomposed and the types of welded joints at component interfaces are selected from a given library, in order to minimize the overall production cost and the reduction of structural strength due to the introduction of joints. A multiobjective genetic algorithm is utilized to allow effective examination of trade-offs between manufacturing cost and structural strength. A new joint-oriented representation of structures combined with a “direct” crossover is introduced to enhance the efficiency of the search. A case study with two aluminum space frame automotive bodies is presented to demonstrate that not all types of component sharing are economically justifiable under a certain production scenario.

scholarly journals A Multiobjective Genetic Algorithm Based on a Discrete Selection Procedure

2015 ◽  
Vol 2015 ◽  
pp. 1-17 ◽  
Author(s):  
Qiang Long ◽  
Changzhi Wu ◽  
Xiangyu Wang ◽  
Lin Jiang ◽  
Jueyou Li
Keyword(s):  
Genetic Algorithm ◽  
Multiobjective Optimization ◽  
Optimization Problems ◽  
Selection Procedure ◽  
Population Based ◽  
Pareto Solution ◽  
Multiobjective Genetic Algorithm ◽  
Trade Offs ◽  
Multiobjective Optimization Problems ◽  
Optimum Order

Multiobjective genetic algorithm (MOGA) is a direct search method for multiobjective optimization problems. It is based on the process of the genetic algorithm; the population-based property of the genetic algorithm is well applied in MOGAs. Comparing with the traditional multiobjective algorithm whose aim is to find a single Pareto solution, the MOGA intends to identify numbers of Pareto solutions. During the process of solving multiobjective optimization problems using genetic algorithm, one needs to consider the elitism and diversity of solutions. But, normally, there are some trade-offs between the elitism and diversity. For some multiobjective problems, elitism and diversity are conflicting with each other. Therefore, solutions obtained by applying MOGAs have to be balanced with respect to elitism and diversity. In this paper, we propose metrics to numerically measure the elitism and diversity of solutions, and the optimum order method is applied to identify these solutions with better elitism and diversity metrics. We test the proposed method by some well-known benchmarks and compare its numerical performance with other MOGAs; the result shows that the proposed method is efficient and robust.

scholarly journals Which future path to pick? A contingency approach to omnichannel warehouse configuration

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Joakim Hans Kembro ◽  
Andreas Norrman
Keyword(s):  
Economies Of Scale ◽  
Task Complexity ◽  
Contextual Factors ◽  
Future Research ◽  
Research Issues ◽  
Content Type ◽  
Secondary Sources ◽  
Trade Offs ◽  
Contingency Approach

PurposeRecent studies have highlighted the importance of adopting a contingency approach to configuring omnichannel warehouses. Nonetheless, research on how various contextual factors influence the selection of warehouse configuration is scarce. This study fills this knowledge gap by exploring how and why certain configurations fit in different omnichannel contexts.Design/methodology/approachA case study is conducted with six leading Swedish omnichannel retailers. Focusing on outbound warehouse configurations, data are collected through interviews, on-site observations, and secondary sources. A multistep analysis is made, including both pattern matching and explanation building.FindingsThe qualitative analysis reveals 16 contextual factors, of which assortment range, requested online order fulfillment times, goods size and total transactions are the most influential. The study shows how contextual factors create different challenges, thereby influencing the choice of the configurations. In addition to market dynamics and task complexity, the study describes four categories of the factors and related challenges that are particularly important in omnichannels: speed, space, economies of scale and tied-up capital.Research limitations/implicationsThe findings highlight the importance of understanding context and imply that multiple challenges may require trade-offs when selecting configurations, for example, regarding what storage, processes and resources to integrate or separate. To confirm, extend, challenge and further operationalize the ideas and observations put forward in this paper, an agenda with future research issues is given for this accelerating, contemporary phenomenon.Practical implicationsManagers could leverage the frameworks proposed for the contextual profiling of their current and future positions. The frameworks provide support for understanding the important challenges and potential trade-offs and developing aligned configurations.Originality/valueThis study is original in the way it provides in-depth, case study findings about contextual factors and their influence on omnichannel warehouse configuration.

Optimization of Ships’ Diagonal Ballast Water Exchange Sequence Using a Multiobjective Genetic Algorithm

2010 ◽  
Vol 54 (04) ◽  
pp. 257-267
Author(s):  
Jing Chen ◽  
Yan Lin ◽  
Junzhou Huo ◽  
Mingxia Zhang ◽  
Zhuoshang Ji
Keyword(s):  
Genetic Algorithm ◽  
Ballast Water ◽  
Structural Strength ◽  
Mathematic Model ◽  
Pareto Solutions ◽  
Sequential Method ◽  
Exchange Method ◽  
Pareto Solution ◽  
Multiobjective Genetic Algorithm ◽  
Intact Stability

Ballast water management and the method chosen to achieve it is a key issue and concerns key technologies in ship design. If the sequential exchange method is the chosen method, the sequence chosen to perform the exchange is very important and affects many aspects including ships' stability, structural strength, maneuverability, operational expenses and building cost, and so forth. In this paper, based on the multiple risk assessment criteria of the sequential method, the problem of finding a feasible and optimum exchange sequence is boiled down to a multiobjective combinatorial optimization problem with multiple nonlinear constraints. The diagonal exchange strategy was adopted, and the diagonal exchange mathematical model was built, taking into consideration the ship's intact stability, structural strength, trims, draughts, and bridge vision. In order to find a set of Pareto solutions, a multiobjective genetic algorithm (MOGA) was used. In the algorithm, a constraint-domination principle was adopted to handle the multiple constraints, and a nondominated sorting method was used to perform the selection of the Pareto solutions. Using the proposed mathematic model and the MOGA, a Pareto solution set that met all the design criteria could be efficiently and accurately obtained for the engineers to choose from within short running times. Compared with the traditional symmetrical exchanging method, the simulation results showed that the proposed method can produce more and better solutions with smaller trims, smaller bridge blind vision range, and better structural strength performances.

Optimum tolerance synthesis of simple assemblies with nominal dimension selection using genetic algorithm

2016 ◽  
Vol 230 (19) ◽  
pp. 3488-3508 ◽  
Author(s):  
D Vignesh Kumar ◽  
D Ravindran ◽  
M Siva Kumar ◽  
MN Islam
Keyword(s):  
Genetic Algorithm ◽  
Vital Role ◽  
Tolerance Allocation ◽  
Manufacturing Cost ◽  
Test Results ◽  
Manufacturing Costs ◽  
Critical Dimensions ◽  
Optimal Adjustment ◽  
Selection Of ◽  
Nominal Dimension

Optimum tolerance allocation plays a vital role in minimization of the direct manufacturing cost, and it is sensitive to tolerances related to variations in manufacturing processes. However, optimal adjustment of both nominal dimensions and selection of tolerances may further reduce assembly manufacturing cost and wastage of materials during processing. Most studies in existing literature focus on optimum tolerance allocation for the assemblies without considering nominal dimension selection. The method proposed in this work uses genetic algorithm techniques to allocate tolerances to assembly components, thereby minimizing costs. The component alternate nominal dimensions are predicted based on critical dimensions and its tolerances. The effectiveness of the developed algorithms demonstrated using randomly generated problems as well as sample problems taken from the literature. Test results are compared with those obtained using the Lagrange multiplier method. It is shown that by adjusting the nominal dimensions, the proposed method yields considerable savings in manufacturing costs.

Multiobjective genetic algorithm applied to inverse finite element analysis: T-joint welding case study

2021 ◽  
Author(s):  
Marcus Vinicius Ferreira de Moura ◽  
Leomardos Santos Marques ◽  
Bruno Henrique Groenner Barbosa ◽  
Ricardo Rodrigues Magalhães
Keyword(s):  
Genetic Algorithm ◽  
Finite Element Analysis ◽  
Finite Element ◽  
Element Analysis ◽  
Multiobjective Genetic Algorithm ◽  
Inverse Finite Element Analysis

A web-based assembly planning approach

2008 ◽  
Vol 222 (3) ◽  
pp. 427-440 ◽  
Author(s):  
C Lu ◽  
Y S Wong ◽  
J Y H Fuh
Keyword(s):  
Genetic Algorithm ◽  
Assembly Planning ◽  
Working Mechanism ◽  
Web Based ◽  
Multiobjective Genetic Algorithm ◽  
Geographically Dispersed ◽  
Planning Approach ◽  
Planning Task

This paper presents a web-based assembly planning approach that enables geographically dispersed planners to carry out the assembly planning task collaboratively and concurrently based on an assembly planning approach using a multiobjective genetic algorithm. Issues in web-based assembly planning are discussed and a workflow is presented. A framework is proposed for web-based assembly planning and the working mechanism is discussed. Finally, a case study is given to verify the proposed approach.

scholarly journals Structural Strength and Reliability Analysis of Important Parts of Marine Diesel Engine Turbocharger

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Lin Lei ◽  
Ming-ze Ding ◽  
Hong-wei Hu ◽  
Yun-xiao Gao ◽  
Hai-lin Xiong ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Diesel Engine ◽  
Multiobjective Optimization ◽  
Turbine Blade ◽  
Structural Strength ◽  
Turbine Disk ◽  
Marine Diesel Engine ◽  
Nsga Ii ◽  
Multiobjective Genetic Algorithm ◽  
Marine Diesel

Supercharging is the main method to improve the output power of marine diesel engines. Nowadays, most marine diesel engines use turbocharging technology, which increases the air pressure and density into the cylinder and the amount of fuel injected correspondingly so as to achieve the purpose of improving the power. In a marine diesel engine, the turbocharger has become an indispensable part. The performance of turbochargers in a harsh working environment of high temperature and high pressure for a long time will directly affect the performance of diesel engine. Based on the market feedback data from manufacturers, the failure modes of compressor impeller, turbine blade, and turbine disk of marine diesel turbocharger are analyzed, and the statistical model of random factors is established. Using DOE design, the structural strength simulation data of 46 compressors and 62 turbines are obtained, and the response surface model is constructed. On this basis, Monte Carlo sampling is carried out to analyze the reliability of the compressor and turbine. The reliability of the compressor is good, while that of the turbine disk is 0.943 and that of the turbine blade is 0.96, which still has the potential of reliability optimization space. Therefore, a multiobjective optimization method based on the NSGA-II genetic algorithm is proposed to obtain the multiobjective optimization scheme data with the reliability and processing cost of turbine disk and blade as the objective function. After optimization, the reliability of turbine disk and blade is 1, the stress value of turbine blade is optimized by 4.7941%, the stress value of turbine disk is optimized by 3.0136%, the machining cost of the turbine blade is reduced by 15.5087%, and the machining cost of turbine disk is reduced by 3.9907%. At the same time, it is verified by simulation, the data based on NSGA-II multiobjective genetic algorithm are more accurate and have practical engineering reference value. The optimized data based on NSGA-II multiobjective genetic algorithm are used to manufacture new turbine samples, and the accelerated test of simulation samples is carried out. The cycle life of the optimized turbine can reach 101,697 cycles and 118,687 cycles, which is 51.75% and 77.11% longer than that of the unoptimized turbine. It can be seen that the optimized turbine can meet the requirements of the reliability index while reducing the manufacturing cost.

Sign in / Sign up

Export Citation Format

Share Document