Proposal of a nonlinear multi-objective genetic algorithm using conic scalarization to the design of cellular manufacturing systems

Erozan, İhsan; Torkul, Orhan; Üstün, Özden

doi:10.1007/s10696-014-9194-y

Cited by 8 publications

(4 citation statements)

References 33 publications

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“…It means that point B will not be calculated by a scalarization method that is based on linear converting as a weighted sum scalarization. But this point B can be supported by cone K instead of a hyperplane (Erozan et al., ).…”

Section: Biobjective Cell Scheduling Modelmentioning

confidence: 99%

A flexible job shop cell scheduling with sequence‐dependent family setup times and intercellular transportation times using conic scalarization method

Deliktaş

Torkul

Üstün

2017

Int Trans Operational Res

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In this paper, we propose novel mathematical models involving both single‐ and biobjective functions that deal with a flexible job shop scheduling problem in cellular manufacturing environment by taking into consideration exceptional parts, intercellular moves, intercellular transportation times, sequence‐dependent family setup times, and recirculation. The problem has been known as NP‐hard. The proposed models have been tested and solved using Lingo 11.0 with minimization of makespan for the problems involving about 4 cells, 4 part families, 15 parts, and 12 machines. The most suitable model among the proposed single‐objective models is determined using the test results. Then, another objective function as total tardiness is added to this model. The obtained biobjective model is solved using the scalarization methods, the weighted sum method, ɛ‐constraint method, and conic scalarization method (CSM), in order to convert the mathematical model's objectives into a single‐objective function. By utilizing these scalarization methods, the Pareto effective solutions are generated for a specific test problem. The advantages of the CSM are demonstrated by considering the Pareto effective solutions.

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Section: Biobjective Cell Scheduling Modelmentioning

confidence: 99%

A flexible job shop cell scheduling with sequence‐dependent family setup times and intercellular transportation times using conic scalarization method

Deliktaş

Torkul

Üstün

2017

Int Trans Operational Res

Self Cite

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“…Constraint (9) guarantees that the mean procurement time of raw materials is larger than the mean completion time of raw materials in a supplier plus mean transportation time from the same supplier to the manufacturer for each part. Constraints ( 10) and (11) assign each priority to one part. Constraint (12) makes sure that each machine is assigned only to one cell.…”

Section: Indexmentioning

confidence: 99%

“…It will also cope with fluctuating demands by providing adequate speed of adjustment and sufficient production of goods. Group technology has gained worldwide acceptance as a manufacturing system philosophy due to its flexibility and adaptability [11]. Cellular manufacturing (CM) is the application of the concepts of group technology to shop floor layout design and reconfiguration of a firm.…”

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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“…Furthermore, it will cope with fluctuating demands via the fair speed of adaptation and sufficient production of goods. Group technology has been experienced all over the world as a manufacturing system philosophy owing to its flexibility and adaptability [12]. Cellular manufacturing (CM) is the usage of the group technology concepts to shop floor layout design and firm reconfiguration.…”

Section: Introductionmentioning

confidence: 99%

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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Proposal of a nonlinear multi-objective genetic algorithm using conic scalarization to the design of cellular manufacturing systems

Cited by 8 publications

References 33 publications

A flexible job shop cell scheduling with sequence‐dependent family setup times and intercellular transportation times using conic scalarization method

A flexible job shop cell scheduling with sequence‐dependent family setup times and intercellular transportation times using conic scalarization method

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

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

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