Spare parts stocking analysis using genetic programming

Ghaddar, Bissan; Sakr, Nizar; Asiedu, Yaw

doi:10.1016/j.ejor.2015.12.041

Cited by 28 publications

(10 citation statements)

References 14 publications

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“…The result shows that the bullwhip effect contributes to neutralize the impact of uncertainties. Adopting a genetic programming-based symbolic regression method, Ghaddar et al 46 investigate the spare parts stocking problems with the level of repair analysis. It is worth noting that there is a fast growing literature on optimizing maintenance policies and spare parts jointly, such as those by Panagiotidou, 47 Wang and Liu, 48 and Zhang and Zeng.…”

Section: Psr Managementmentioning

confidence: 99%

Status review and research strategies on product-service supply chain

Elomri

Zhang

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part B:

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This study draws a holistic view of the supply chain with three service strategies – namely, after-sales service; maintenance, repair and operations; and the product-service system – by analysing over 71 articles in this field. In this investigation, the content analysis is used to scrutinize the research to establish the knowledge, reveal the research opportunities and propose research strategies. The supply chain for product-service system depends more heavily on the implementation of the cooperation, collaboration and integration principles in operation. However, a complete analysis reveals that those crucial principles did not embody in current literature. Current research also failed to discuss the service operation decisions by modelling the intricate relations among them in a broad product-service supply chain context. Thus, the future research directions include developing service demand forecasting models; combining the time-series methods and the causal methods; establishing service resource planning models; addressing the relations between service deliverables, service capacity and the service resource; and strengthening the quantitative evaluation of the product-service supply chain performance with a systemic view. Moreover, by addressing the principles of cooperation, collaboration and integration, the added values of this review are the proposed research strategies for integrated forecasting of product services, integrated product-service resource allocation and scheduling, and systematic performance evaluation of the product-service supply chain. The findings and the proposed research strategies develop an in-depth understanding of product-service supply chain applications and serve as a basis for future research.

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Section: Psr Managementmentioning

confidence: 99%

Status review and research strategies on product-service supply chain

Elomri

Zhang

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part B:

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show abstract

“…Indeed, solving a bi-objective problem with one qualitative dimension and one quantitative dimension is computationally better than solving a pure multi-objective problem, as has been proven in [2]. Another variation, with respect to optimization, it is to use other advanced techniques such as genetic programming [38] or joint optimization such as in [87].…”

Section: Reclassification Of Theinitialspareparts Into the Classes Amentioning

confidence: 99%

“…The authors in [27], for instance, emphasize the role of the lead time criterion in analysing the competitiveness of companies. In terms of criticality, one can also argue that it is a function of the criticality of the spare parts for the machine as well as the criticality of the machine in the whole operational system [33] [38] [57]. Accordingly, the use of multiple criteria for spare parts classification has better justification as it attempts to consider all the operations management/control requirements of different types of items.…”

Section: Introductionmentioning

confidence: 99%

Spare parts classification in industrial manufacturing using the dominance-based rough set approach

Chakhar

Siraj

et al. 2017

European Journal of Operational Research

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Classification is one of the critical issues in the operations management of spare parts. The issue of managing spare parts involves multiple criteria to be taken into consideration, and therefore, a number of approaches exists that consider criteria such as criticality, price, demand, lead time, and obsolescence, to name a few. In this paper, we first review proposals to deal with inventory control. We then propose a three-phase multicriteria classification framework for spare parts management using the dominance-based rough set approach (DRSA). In the first phase, a set of 'if-then' decision rules is generated from historical data using the DRSA. The generated rules are then validated in the second phase by using both the automated and manual approaches, including cross-validation and feedback assessments by the decision maker. The third and final phase is to classify an unseen set of spare parts in a real setting. The proposed approach has been successfully applied to data collected from a manufacturing company in China. The proposed framework was practically tested on different spare parts and, based on the feedback received from the industry experts, 96% of the spare parts were correctly classified. Furthermore, the crossvalidation results show that the proposed approach significantly outperforms other well-known classification methods. The proposed approach has several important characteristics that distinguish it from existing ones: (i) it is a learning-set based analysis approach; (ii) it uses a powerful multicriteria classification method, namely the DRSA; (iii) it validates the generated decision rules with multiple strategies; and (iv) it actively involves the decision maker during all the steps of the decision-making process.

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“…This operator transforms chromosome i based on a randomly generated number1 | | r I   . Again, the demand of new chromosomes must remain within the range defined in Step 1; otherwise, the objective function will be penalized (Ghaddar et al, 2016): 11. Loop and stop conditions: This loop continues until the difference between the best solutions of two consecutive iterations becomes lower than a specific value.…”

Section: Solution Methods For Scheduling Projectmentioning

confidence: 99%

Determination of start times and ordering plans for two-period projects with interdependent demand in project-oriented organizations: A case study on molding industry

Lotfi¹,

Nayeri²,

Sajadifar³

et al. 2017

10.5267/j.jpm

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The objective of the present study is to determine the start times and ordering plans for twoperiod projects in project-oriented organizations. The following assumptions are made: orders can be placed in either of a project's two consecutive periods; and the same raw materials are used, which are ordered at the start of the periods according to their interdependent demands. The main innovation of the present research is to develop a model that is mainly based on the two-period newsvendor problem, but assumes that demand is interdependent, i.e. it is actually a combination of inventory and project management. After presenting the model, numerical methods are used to approximate the order quantities for each period. Near-optimal solutions of a numerical example, in a case study on molding industry, are obtained using a genetic algorithm. Results show that the proposed model with interdependent demand provides a better solution than independent demand. The model is applicable to large-scale industrial and construction projects where the majority of raw materials have of the same nature.

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Spare parts stocking analysis using genetic programming

Cited by 28 publications

References 14 publications

Status review and research strategies on product-service supply chain

Status review and research strategies on product-service supply chain

Spare parts classification in industrial manufacturing using the dominance-based rough set approach

Determination of start times and ordering plans for two-period projects with interdependent demand in project-oriented organizations: A case study on molding industry

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