Analysis and Optimization of a U-Shaped Production Line

Ohno, Katsuhisa; Nakade, Koichi

doi:10.15807/jorsj.40.90

Cited by 34 publications

(12 citation statements)

References 2 publications

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“…When demand pattern is prone to change, NC neither adapts nor responds to the changes adequately due to the physical grouping of machines. Instead of restraining the machines into the cells, machines with similar processing abilities are located in vicinity for effective utilization [35,36,38]. This feature enables the system to provide the product families.…”

Section: Operating Characteristicsmentioning

confidence: 99%

A Heuristic Scheduling Algorithm for Minimizing Makespan and Idle Time in a Nagare Cell

Muthukumaran

Muthu

2012

Advances in Mechanical Engineering

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Adopting a focused factory is a powerful approach for today manufacturing enterprise. This paper introduces the basic manufacturing concept for a struggling manufacturer with limited conventional resources, providing an alternative solution to cell scheduling by implementing the technique of Nagare cell. Nagare cell is a Japanese concept with more objectives than cellular manufacturing system. It is a combination of manual and semiautomatic machine layout as cells, which gives maximum output flexibility for all kind of low-to-medium-and medium-to-high-volume productions. The solution adopted is to create a dedicated group of conventional machines, all but one of which are already available on the shop floor. This paper focuses on the development of heuristic scheduling algorithm in step-by-step method. The algorithm states that the summation of processing time of all products on each machine is calculated first and then the sum of processing time is sorted by the shortest processing time rule to get the assignment schedule. Based on the assignment schedule Nagare cell layout is arranged for processing the product. In addition, this algorithm provides steps to determine the product ready time, machine idle time, and product idle time. And also the Gantt chart, the experimental analysis, and the comparative results are illustrated with five (1×8 to 5×8) scheduling problems. Finally, the objective of minimizing makespan and idle time with greater customer satisfaction is studied through.

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Section: Operating Characteristicsmentioning

confidence: 99%

A Heuristic Scheduling Algorithm for Minimizing Makespan and Idle Time in a Nagare Cell

Muthukumaran

Muthu

2012

Advances in Mechanical Engineering

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“…Since the focus of this study is the U-shaped assembly line balancing problem (UALBP) with deterministic task times our literature review covers deterministic line balancing research. For discussions on various aspects of line flow research see Aase, Olson, and Schniederjans (2004), Celano et al (2004), Chand and Zeng (2001), Cheng, Miltenburg, and Motwani (2000), Miltenburg (2000;2001a;2001b), Nakade and Ohno (1995;1999;, Nakade, Ohno, and Shanthikumar (1997), and Ohno and Nakade (1997). Miltenburg (1998) attributed the first discussion in the open literature in English concerning U-lines to Schonberger (1982) who noticed a preference among Japanese manufacturers for multiple U-lines, where workstations often spanned more than one U-line.…”

Section: U-shaped Assembly Line Balancing Literature Reviewmentioning

confidence: 99%

An empirical comparison of improvement heuristics for the mixed-model U-line balancing problem

Visich¹,

Khumawala²,

Diaz-Saiz³

2010

IJMTM

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Mixed-model assembly lines often create model imbalance due to differences in task times for the different product models. Smoothing algorithms guided by meta-heuristics that can escape local optimums can be used to reduce model imbalance. In this research we utilize the metaheuristics tabu search (TS), the great deluge algorithm (GDA) and record-to-record travel (RTR) to reduce three objective functions: the absolute deviation from cycle time, the maximum deviation from cycle time, and the sum of the cycle time violations. We found that the GDA was significantly superior to the RTR and TS algorithms across all problem sizes and objective functions. For the 19 task problems, RTR performed significantly better than TS for all three objective functions. On the other hand, for the 61 and 111 task problems TS performed significantly better than RTR for all three objective functions.

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“…For example, Nakade et al [17] and Ohno and Nakade [19] analyzed serial AAP lines in which cross-trained workers visit their assigned workstations according to a cyclic policy. (See Section 3.3 for a detailed description of a cyclic policy).…”

Section: Literature Reviewmentioning

confidence: 99%

Design and control of agile automated CONWIP production lines

Hopp

Iravani²,

Shou

et al. 2008

Naval Research Logistics

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Abstract:In this article, we study the design and control of manufacturing cells with a mix of manual and automated equipment, operating under a CONWIP pull protocol, and staffed by a single agile (cross-trained) worker. For a three-station line with one automated station, we fully characterize the structure of the optimal control policy for the worker and show that it is a static priority policy. Using analytical models and extensive simulation experiments, we also evaluate the effectiveness of practical heuristic control policies and provide managerial insights on automation configuration design of the line. This characterization of the worker control policy enables us to develop managerial insights into the design issues of how best to locate and concentrate automation in the line. Finally, we show that, in addition to ease of control and greater design flexibility, the CONWIP protocol also offers higher efficiency and robustness than does the push protocol.

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Analysis and Optimization of a U-Shaped Production Line

Cited by 34 publications

References 2 publications

A Heuristic Scheduling Algorithm for Minimizing Makespan and Idle Time in a Nagare Cell

A Heuristic Scheduling Algorithm for Minimizing Makespan and Idle Time in a Nagare Cell

An empirical comparison of improvement heuristics for the mixed-model U-line balancing problem

Design and control of agile automated CONWIP production lines

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