Mixed model assembly system with multiple secondary feeder lines: layout design and balancing procedure for ATO environment

Azzi, Anna; Battini, Daria; Faccio, Maurizio; Persona, Alessandro

doi:10.1080/00207543.2011.653590

Cited by 23 publications

(12 citation statements)

References 31 publications

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“…They proposed that a mixed-integer linear-programming-based approach minimizes the throughput loss by utilizing the robots' redundancy in the system. Azzi et al [22,23] introduced a mixed model assembly line balancing problem (MALBP) and mixed model sequencing problem (MMS) in unpaced assembly lines which consisted of a main line and multiple feeder lines using simulation and optimization models. Although their systems were not specified as an automotive body shop, they were worthwhile to analyze the subsystems of a body shop.…”

Section: Automotive Body Shop Designmentioning

confidence: 99%

Automotive Body Shop Design Problems Using Meta-Models Considering Product-Mix Change and Reconfiguration Strategy

2021

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The estimation of production rate (or throughput) is important in manufacturing system design. Herein, we consider the manufacturing system of an automotive body shop in which two types of car are produced, and one car (engine car) is substituted by the other car (electric car) gradually. In this body shop, two different underbody lines are installed because the underbody structures of the two types of cars differ completely; however, the side body line and main body line are shared by the two cars. Furthermore, we assume that the underbody lines are reconfigurable based on an increase in the product mix of the electric car. A simulation-based meta-model, which is in the form of a quadratic polynomial function, is developed to estimate the production rate. In the meta-modelling process, we group some buffer locations and represent them as one variable to reduce the number of variables included in the meta-model. Subsequently, the meta-models have been used to optimize two types of buffer allocation problems, and optimal solutions are obtained easily.

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Section: Automotive Body Shop Designmentioning

confidence: 99%

Automotive Body Shop Design Problems Using Meta-Models Considering Product-Mix Change and Reconfiguration Strategy

2021

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“…Chutima and Chimklai (2012) and Kucukkoc and Zhang (2014) consider twosided MALBPs. Azzi et al (2012) deal with the configuration problem of a mixed-model multi-line assembly system, in which some components are assembled in secondary lines that are connected to the main production line.…”

Section: Literature Reviewmentioning

confidence: 99%

Balancing mixed-model assembly lines with adjacent task duplication

Yang

Gao

2014

International Journal of Production Research

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When a mixed-model assembly line (MAL) is balanced, it is generally assumed that the variants of a task over different models should be assigned to an identical station. In this study, this restriction is relaxed and the variants of a task over different models can be duplicated on two adjacent stations (referred to as adjacent task duplication) to improve the MAL's efficiency. The adjacent task duplication incurs few additional training and tool duplication costs as each task is duplicated on at most two stations. Moreover, for each task, the assembly part storage is not duplicated as it can be shared by the two adjacent stations. The mathematical model of this problem is formulated and some important properties are characterised. A branch, bound and remember algorithm is then developed to solve the problem. The performance of the proposed algorithm is tested on 900 representative instances, of which 889 instances are optimally solved. The experimental results show that the use of the adjacent task duplication policy effectively reduces the number of stations, especially when the WEST ratios are small. IntroductionAssembly lines are flow-oriented production systems that are typical in the industrial production of high-quantity standardised commodities (Becker and Scholl 2006). The labour productivity of assembly lines is largely driven by how the tasks (indivisible elements of work) are assigned to stations subject to technological constraints, commonly referred to as assembly line balancing (ALB) (Battaïa and Dolgui 2013). The simple assembly line balancing problem (SALBP) aims to minimise the number of stations given the cycle time (type I) or the cycle time given the number of stations (type II) (Baybars 1986).The mixed-model assembly line (MAL) is a more complex environment in which several product models are assembled simultaneously in intermixed sequences (Thomopoulos 1967). MALs are widely used in many industries such as automobiles, white goods and consumer electronics, where demand is characterised by high variability and relatively small volume for each model (Becker and Scholl 2006). A MAL generally assembles different versions of one basic product rather than completely different items. Hence, many studies assume that a single precedence diagram can be drawn to resume the precedence relationships among different tasks over each model (Merengo, Nava, and Pozzetti 1999). However, the processing time of a task may differ over the various models, and each variant of the task over these models should be regarded as a separate work element (Boysen, Fliedner, and Scholl 2008). The MAL balancing problem (MALBP) involves assigning all the task variants to stations.A straightforward approach for modelling and solving MALBPs is to restrict all the variants of a task over different models to an identical station. This restriction is reasonable, considering that these task variants may require the same skills, tools or assembly parts (Thomopoulos 1967). With this restriction, MALBPs are reduced to some version of...

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“…The task of scheduling resources forms the key challenge to increase efficiency and optimise capacity utilisation in production environments by reducing machine running times and consequently increasing profitability (Vinod and Sridharan, 2008). Resource scheduling is found in various applications including project scheduling (Najid and Arroub, 2010), processor scheduling (Venkataramana and Srinivasa Raghavan, 2010), maintenance scheduling (Hadidi et al, 2011), mixed model assembly scheduling (Azzi et al, 2012a(Azzi et al, , 2012bFinetto et al, 2013), and production scheduling (Sharma et al, 2013). In case of different types of machines, the complexity of the problem increases due to the required sequencing of the jobs (Balin, 2012, Azzi et al, 2012c.…”

Section: Introductionmentioning

confidence: 99%

Simulated annealing approach to solve dual resource constrained job shop scheduling problems: layout impact analysis on solution quality

Faccio

Ries

Saggiorno

2015

IJMOR

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Real-world manufacturing systems are operating subject to a substantial level of resource constraints. One characteristic model that considers the combination of human and machine resource constraints is called dual resource constrained (DRC). In this context a number of machines n mach is managed by a selection of operators n op , with typically n op ≤ n mach.. A real life case study for an Italian manufacturing company is introduced that uses a set of identical parallel machines being operated by a set of operators. Each job is scheduled to one machine with corresponding loading and unloading process times. A simulated annealing approach is proposed to solve the DRC job shop scheduling problem. A sensitivity analysis is conducted for a selection of algorithm-specific parameters used to solve characteristic DRC layouts. Being characteristic for the just-in-time (JIT) production environment, the high variability in job times has also been taken into account. The results show that the selected layout n mach. /n op ratio strongly influences the production system performance. The impact of the ratio of constrained resources has been analysed for different layouts, showing that simulated annealing performs better for single resource constrained problems while also demonstrating that this trend is not symmetrical for different layouts, either operator or machine constrained.

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Mixed model assembly system with multiple secondary feeder lines: layout design and balancing procedure for ATO environment

Cited by 23 publications

References 31 publications

Automotive Body Shop Design Problems Using Meta-Models Considering Product-Mix Change and Reconfiguration Strategy

Automotive Body Shop Design Problems Using Meta-Models Considering Product-Mix Change and Reconfiguration Strategy

Balancing mixed-model assembly lines with adjacent task duplication

Simulated annealing approach to solve dual resource constrained job shop scheduling problems: layout impact analysis on solution quality

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