A new hybrid method for buffer sizing and machine allocation in unreliable production and assembly lines with general distribution time-dependent parameters

This paper proposes a multi-objective mathematical formulation and a hybrid approach to solve buffer sizing and machine allocation problems simultaneously in unreliable production and assembly lines. This paper unlike prior researches assumes that time-dependent parameters of production systems are...

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Veröffentlicht in:International journal of advanced manufacturing technology Jg. 74; H. 9-12; S. 1577 - 1593
1. Verfasser: Mohtashami, Ali
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Springer London 01.10.2014
Springer Nature B.V
Schlagworte:
Assembly lines
Buffers
CAE) and Design
Classification
Computer-Aided Engineering (CAD
Decision making
Engineering
Genetic algorithms
Industrial and Production Engineering
Mathematical models
Mechanical Engineering
Media Management
Multiple objective analysis
Original Article
Parameters
Sizing
Sorting algorithms
Time dependence
Hybrid genetic algorithm (HGA)
Buffer storage
Simulation
Machine
Multi-objective mixed binary integer non-linear programming (MOMBINP)
ISSN:0268-3768, 1433-3015
Online-Zugang:Volltext
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Zusammenfassung:This paper proposes a multi-objective mathematical formulation and a hybrid approach to solve buffer sizing and machine allocation problems simultaneously in unreliable production and assembly lines. This paper unlike prior researches assumes that time-dependent parameters of production systems are generally distributed (e.g., uniform, normal, gamma, etc.) and not only deterministic or exponential. This paper proposes a multi-objective mixed binary integer non-linear mathematical model to solve the problem of buffer sizing and machine allocation. The proposed mathematical model is capable of purchasing new machines (candidate) and also selling old machines (current available). In other words, this model compares the candidate machines to current available machines in each station based on different aspects and is capable to replace the current machines with candidate machines or to sell some of the current machines without replacement. To solve the mentioned problem, a new formulation for dealing with multi-objectiveness of the problem is proposed. This formulation generates a series of non-dominated solutions, and also, it is capable of generating a non-dominated solution between two adjacent non-dominated solutions determined by decision maker. A hybrid genetic algorithm (HGA) with a new dynamic mutation probability is proposed to solve the model. Since the proposed mathematical model and the proposed solution method are novel, the proposed HGA is compared to simple genetic algorithm and non-dominated sorting genetic algorithm (NSGA-II). The computational results indicate the effectiveness of the proposed HGA.
Bibliographie:ObjectType-Article-1
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ISSN:0268-3768
1433-3015
DOI:10.1007/s00170-014-6098-7