Cellular manufacturing design and replenishment strategy in a capacitated supply chain system: A simulation-based analysis

Sadeghi, Azadeh; Süer, Gürsel A.; Sinaki, Roohollah Younes

doi:10.1016/j.cie.2020.106282

Cited by 14 publications

(10 citation statements)

References 29 publications

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“…The first stage is to design the system based on a layered cell production system for which a complex integer linear programming method was used. The objective of the model is to minimize the required number of cells [23]. Hong et al, discussed the problem of energy efficient planning of a multi cellular production system with eligibility constraints.…”

Section: Cell Production Systemmentioning

confidence: 99%

Cell Production System Design: A Literature Review

Ghahremani-Nahr

Bathaee

Mazloumzadeh

et al. 2021

IJIMES

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In a cell production system, a number of machines that differ in function are housed in the same cell. The task of these cells is to complete operations on similar parts that are in the same group. Determining the family of machine parts and cells is one of the major design problems of production cells. Cell production system design methods include clustering, graph theory, artificial intelligence, meta-heuristic, simulation, mathematical programming. This article discusses the operation of methods and research in the field of cell production system design. Methodology:To examine these methods, from 187 articles published in this field by authoritative scientific sources, based on the year of publication and the number of restrictions considered and close to reality, which are searched using the keywords of these restrictions and among them articles Various aspects of production and design problems, such as considering machine costs and cell size and process routing, have been selected simultaneously.Findings: Finally, the distribution diagram of the use of these methods and the limitations considered by their researchers, shows the use and efficiency of each of these methods. By examining them, more efficient and efficient design fields of this type of production system can be identified.Originality/Value: In this article, the literature on cell production system from 1972 to 2021 has been reviewed.

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Section: Cell Production Systemmentioning

confidence: 99%

Cell Production System Design: A Literature Review

Ghahremani-Nahr

Bathaee

Mazloumzadeh

et al. 2021

IJIMES

View full text Add to dashboard Cite

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“…Hence, amounts of used parameters in the mathematical model consider the stability condition of queuing system related to the manufacturer. The stabilizing equation (2) will be elaborated in this subsection. The following equation avoids the infinite queue length behind of each machine:…”

Section: Model Formulationmentioning

confidence: 99%

“…In the following, the stabilizing equation (2) is explained. The utilization factor for each machine in the manufacturer stage has to be less than 1 to avoid instability of the queuing system.…”

Section: Model Formulationmentioning

confidence: 99%

“…The purpose is connecting procurement activities, distribution network, and marketplace to manufacturing process in terms of making a unified decision. Therefore, customers are serviced at upper level and lower cost and manufacturers are getting competitive advantage through both cost reduction and product augmentation ( [2] and [3]). Supply chain scheduling is looking for coordination between different parts of a supply chain.…”

Section: Introductionmentioning

confidence: 99%

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A two-stage stochastic supply chain scheduling problem with production in cellular manufacturing environment: A case study

Esmailnezhad

Saidi‐Mehrabad

2021

Scientia Iranica

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An integrated decision in supply chain is a significant principle in order to compete in today's market. This paper proposes a novel mathematical model in a two-stage supply chain scheduling to cooperate procurement and manufacturing activities. The supply chain scheduling along with the production approach of cellular manufacturing under demand, processing time, and transportation time uncertainties makes business environment sustainably responsive to the changing needs of customers. Uncertainties are formulated by queuing theory. In this paper, a new mixed-integer nonlinear programming formulation is used to determine types of vehicles to carry raw materials, suppliers to procure, priority of each part in order to process, and cell formation to configure work centers. The goal is to minimize total tardiness. A linearization method is used to ease tractability of the model. A genetic algorithm is developed due to the NP-hard nature of the problem. The parameters of the genetic algorithm are set and estimated by Taguchi's experimental design. Numerous test problems are employed to validate the effectiveness of the modeling and the efficiency of solution approaches. Finally, a real case study and a sensitivity analysis are discussed to provide significant managerial insights and assess the applicability of the proposed model.

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“…The goal is to link procurement activities, manufacturing processes, distribution networks, and the marketplace. As a result, customers are served at a higher level and a lower cost, and manufacturers gain a competitive advantage through cost reduction and product augmentation [2][3]. In practical terms, integrated decision-making is significant in industries such as automobile, electronics, and aviation because the production activities in these industries are decentralized [4].…”

Section: Introductionmentioning

confidence: 99%

Making an integrated decision in a three-stage supply chain along with cellular manufacturing under uncertain environments: A queueing-based analysis

Esmailnezhad

Saidi‐Mehrabad

2021

RAIRO-Oper. Res.

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Today’s complicated business environment has underscored the importance of integrated decision-making in supply chains. In this paper, a novel mixed-integer nonlinear mathematical model is proposed to integrate cellular manufacturing systems into a three-stage supply chain to deal with customers' changing demands, which has been little explored in the literature. This model determines the types of vehicles to transport raw materials and final parts, the suppliers to procure, the priorities of parts to be processed, and the cell formation to configure work centers. In addition, queueing theory is used to formulate the uncertainties in demands, processing times, and transportation times in the model more realistically. A linearization method is employed to facilitate the tractability of the model. A genetic algorithm is also developed to deal with the NP-hardness of the problem. Numerous instances are used to validate the effectiveness of the modeling and the efficiency of solution procedures. Finally, a sensitivity analysis and a real case study are discussed to provide important management insights and evaluate the applicability of the proposed model.

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Cellular manufacturing design and replenishment strategy in a capacitated supply chain system: A simulation-based analysis

Cited by 14 publications

References 29 publications

Cell Production System Design: A Literature Review

Cell Production System Design: A Literature Review

A two-stage stochastic supply chain scheduling problem with production in cellular manufacturing environment: A case study

Making an integrated decision in a three-stage supply chain along with cellular manufacturing under uncertain environments: A queueing-based analysis

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