Design of BOM configuration for reducing spare parts logistic costs

Wu, Muh-Cherng; Hsu, Yang-Kang

doi:10.1016/j.eswa.2007.04.001

Cited by 27 publications

(7 citation statements)

References 25 publications

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“…Hanafizadeh et al (2009) deal with designing a fuzzy-genetic learner model based on multi-agent systems in supply chain management. Examples in inventory management include the work of Sadeghi Moghadam et al (2008) on lot sizing with supplier selection using a hybrid intelligent algorithm that use GAs, FL, and NNs, and Wu and Hsu (2008) who use a combination of GAs and NNs to design BOM configuration for reducing spare parts logistics costs.…”

Section: Hybrid Ai In Process Planning and Controlmentioning

confidence: 99%

A survey of AI in operations management from 2005 to 2009

Kobbacy

Vadera

2011

Journal of Manufacturing Technology Management

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Purpose: The use of AI for operations management, with its ability to evolve solutions, handle uncertainty and perform optimisation continues to be a major field of research. The growing body of publications over the last two decades means that it can be difficult to keep track of what has been done previously, what has worked, and what really needs to be addressed. Hence this paper presents a survey of the use of AI in operations management aimed at presenting the key research themes, trends and directions of research.Design/methodology/approach: The paper builds upon our previous survey of this field which was carried out for the 10 year period 1995 to 2004(Kobbacy et al. 2007. Like the previous survey, it uses the Elsevier's ScienceDirect database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus the application categories adopted are: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Research on utilising neural networks, case based reasoning, fuzzy logic, knowledge based systems, data mining, and hybrid AI in the four application areas are identified. Findings:The survey categorises over 1400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: (a) The trends for Design and Scheduling show a dramatic increase in the use of GAs since 2003-04 that reflect recognition of their success in these areas, (b) A significant decline in research on use of KBS, reflecting their transition into practice, (c) an increasing trend in the use of fuzzy logic in Quality, Maintenance and Fault Diagnosis, (d) surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research.Originality/value: This is the largest and most comprehensive study to classify research on the use of AI in operations management to date. The survey and trends identified provide a useful reference point and directions for future research.

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Section: Hybrid Ai In Process Planning and Controlmentioning

confidence: 99%

A survey of AI in operations management from 2005 to 2009

Kobbacy

Vadera

2011

Journal of Manufacturing Technology Management

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“…Cai et al 135 developed a combined method of genetic algorithm and Monte Carlo to get the optimal inventory level, safety level and potential failure threshold, and proposed an appointment policy of spare parts based on the prediction of residual life. Wu et al 136 proposed an approach based on genetic algorithms to reduce the total operational cost of spare parts logistic system by appropriately designing the bill of material configuration. Dura´n et al 137 used genetic algorithm to develop an optimization model for spare parts management during the life cycle based on the principles of Activity Based Costing.…”

Section: Optimization Algorithms Of Spare Parts Configurationmentioning

confidence: 99%

“…Multi-level spare parts inventory and redundant system Spare parts reliability [107][108][109][110][111][112] Redundancy system [116][117][118][119][120][121][122][123][124] Supply chain [126][127][128][129][130] Optimization algorithms Supply volume, supply time, system guarantee rate, maintenance period, cost of spare parts in each warehouse Genetic algorithm [131][132][133][134][135][136][137] Solve fast and can be applied to highdimensional problems…”

Section: Lower Configuration Accuracymentioning

confidence: 99%

Civil Aircraft Spare Parts Prediction and Configuration Management Techniques: Review and Prospect

Feng

Chen

et al. 2021

Advances in Mechanical Engineering

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Spare parts are treated as the basis to guarantee the safe and economic operation of civil aircraft, its scientific prediction and reasonable configuration play an important role in perfecting integrated logistics support (ILS) and achieving win-win situation of stakeholders (e.g. manufacturers, operators, maintenance providers). This paper studies the existing spare parts prediction and configuration methods of civil aircraft, and discusses future development trend of spare parts from the perspective of prediction and configuration, respectively. The current development status of civil aircraft spare parts prediction techniques are firstly introduced, according to demand characteristics of spare parts; the present research status of civil aircraft spare parts configuration methods are then elaborated, based on the traditional methods and machine learning methods; the development trend of civil aircraft spare parts prediction and configuration methods is finally analyzed, combined with the advantages and disadvantages of the existing methods of civil aircraft spare parts management methods. The efforts of this work provide a reference for the comprehensive management of civil aircraft spare parts and improve the perfection degree for the ILS of civil aircraft.

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“…Based on an enhanced fuzzy neural network, Li and Kuo (2008) developed a decision support system to manage automobile spares inventory. Furthermore, Wu and Hsu (2008) proposed a GA-neural network approach to reduce spare parts logistics costs.…”

Section: Materials Planning / Inventory Managementmentioning

confidence: 99%