Integration of multi-product supply chain network design and assembly line balancing

Ramezanian, Reza; Khalesi, Sadjad

doi:10.1007/s12351-019-00453-9

Cited by 5 publications

(3 citation statements)

References 66 publications

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“…Ritt and Costa (2018) proposed an improved formulation of the precedence constraints and the station limits for the simple ALBP through which better solution results could be obtained. Besides, Ramezanian and Khalesi (2021) studied an integrated optimization problem combining a multi‐product supply chain network design problem with ALBP (Paksoy and Ozceylan, 2012; Paksoy et al., 2012) and proposed an integer nonlinear programming model for the integrated optimization problem, and the competition algorithm and genetic algorithm were applied to solve large‐scale problems while obtaining the exact solution of small‐scale problems.…”

Section: Literature Reviewmentioning

confidence: 99%

A multi‐objective hybrid evolutionary search algorithm for parallel production line balancing problem including disassembly and assembly tasks

Zhang

Li-xia

Chen

et al. 2022

Int Trans Operational Res

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This paper proposes a multi‐objective hybrid evolutionary search algorithm to simultaneously optimize the number of workstations, the idle index and the quantity of the production equipment required for the parallel production line balancing problem including disassembly and assembly tasks. The proposed algorithm uses the crossover operation and the mutation operation to generate neighborhood individuals of the current evolutionary population. Based on the mixed population composed of the current evolutionary population and the newly generated neighborhood population, a novel population update method is developed. The individuals of the new generation population are composed of Pareto dominant individuals filtered from the mixed population and several individuals randomly selected from the dominated individuals. Then the local search strategy based on simulated annealing operation is applied to the new generation of population individuals to overcome the defect of premature convergence of the proposed algorithm. By solving test examples of different scales and comparing them with a variety of classical algorithms, the advantages in the aspects of the diversity, the convergence, and the distribution of the solution results of the proposed algorithm are proved. Finally, the established mathematical model and the developed algorithm are applied to the parallel production line designed for the bogie remanufacturing production line of a railway freight car maintenance company. The advantages of the parallel production line in improving the production efficiency and reducing the production cost are identified.

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Section: Literature Reviewmentioning

confidence: 99%

A multi‐objective hybrid evolutionary search algorithm for parallel production line balancing problem including disassembly and assembly tasks

Zhang

Li-xia

Chen

et al. 2022

Int Trans Operational Res

View full text Add to dashboard Cite

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“…A mathematical model is presented to formulate this integrated decision. Supply chain management modeling approaches include economic, deterministic analytical, stochastic, and simulation models [51]. Moreover, uncertainty can be classified according to the three approaches: stochastic programming, fuzzy programming, and robust optimization.…”

Section: Cf With Operations Schedulingmentioning

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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“…In order to formulate this integrated decision, a mathematical model is presented. The modeling approaches of supply chain management include economic, deterministic analytical, stochastic, and simulation models [62]. Moreover, uncertainty in the CM could be classified according to the three approaches: stochastic programming, fuzzy programming, and robust optimization.…”

Section: The CMmentioning

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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Integration of multi-product supply chain network design and assembly line balancing

Cited by 5 publications

References 66 publications

A multi‐objective hybrid evolutionary search algorithm for parallel production line balancing problem including disassembly and assembly tasks

A multi‐objective hybrid evolutionary search algorithm for parallel production line balancing problem including disassembly and assembly tasks

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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