Supply chain simulation with discrete–continuous combined modeling

Lee, Young Hae; Cho, Min Kwan; Kim, Seo Jin; Kim, Yun-Bae

doi:10.1016/s0360-8352(02)00080-3

Cited by 153 publications

(72 citation statements)

References 3 publications

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“…The literature is unanimous on the positive role of M&S in the study, analysis and performance evaluation of complex systems. For example, some authors (Ingalls, 1998;Lee et al, 2002;Longo and Mirabelli, 2008) highlight the features and advantages of a decision-making tool based on modeling and discrete event simulation.…”

Section: Literature Review Supply Chain Modeling Approachmentioning

confidence: 99%

A multi-agent knowledge model for SMEs mechatronic supply chains

et al. 2011

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Abstract:The main concern of this research work is to analyse and model supply chains (SCs) in the particular context of small and medium enterprises (SMEs) in the field of mechatronic. The study is based on the analysis of the organisational features, the actors' behaviour, and performance considerations. The development of the model relies on an iterative framework that progressively integrates different aspects into the model. This framework is the ArchMDE process, which is based on MDE (Model Driven Engineering). A major feature of this work lies in its contribution to two different areas of research. The first contribution of the work is to propose a generic metamodel for SCs. Based on a literature review, an incremental framework is proposed for the modelling of SCs in terms of concepts, structure and relationships. The application of the framework to the studied context is described and its result is a domain-metamodel for SCs. The second contribution of this work lies in the formalisation of the dynamic behaviour of the concepts in the metamodel. This formalisation is based on the multi-agent approach. An agentification of the metamodel is thus drawn, thanks to the natural links between multiagent theory and SC reality.This step leads to an agentified-domain-metamodel which also includes the monitoring of the SC and synchronisation protocols. By adding relationships and dynamic behavior aspects, we obtain a 2 metamodel of the domain that can be implemented, with its static and dynamic aspects. To validate this model, an industrial case study is detailed and has been instantiated and encoded in JAVA.

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Section: Literature Review Supply Chain Modeling Approachmentioning

confidence: 99%

A multi-agent knowledge model for SMEs mechatronic supply chains

et al. 2011

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“…Although it is believed that product and information flow have continuous factors in a supply chain, most supply chain problems are solved using discrete event simulation as they often are looked at with an operational focus [17]. From a programming perspective; it is easier to look at the operational side of a supply chain system as having a beginning, then a set of rules or commands that calculate discrete changes in the system over time, finalising the simulation when the predetermined time is up.…”

Section: Simulation Modellingmentioning

confidence: 99%

“…Once confidence in the base model has been established, different scenarios can be explored and measured against key performance indicators. Though product and information flow are typically considered to be continuous facets of a supply chain, most supply chain problems are solved using discrete-event simulation modelling in existing research [17].…”

Section: Simulation Modellingmentioning

confidence: 99%

The bullwhip effect in complex supply chains

Alony

Muñoz

2007

2007 International Symposium on Communications and Information Technologies

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This paper reviews the various methods of modelling the dynamics of supply chains. We then present recently documented causes of the Bullwhip effect in production supply chains, and the methodologies used to describe and measure the importance of these causes. We examine the limitations of these methodologies and suggest a combined approach discrete event-continuous simulation modelling approach to further study this phenomenon in complex production supply chains. Disciplines Physical Sciences and Mathematics Publication DetailsThis conference paper was originally published as Alony, I, Munoz, A, The Abstract-this paper reviews the various methods of modelling the dynamics of supply chains. We then present recently documented causes of the Bullwhip effect in production supply chains, and the methodologies used to describe and measure the importance of these causes. We examine the limitations of these methodologies and suggest a combined approach discrete event-continuous simulation modelling approach to further study this phenomenon in complex production supply chains.

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“…The simulation approach proposed by Lee et al [5] was based on the equation of continuous portion in the SCN architecture in modelling the problem. The architecture includes and describes how these portions can be used in SCN simulation models.…”

Section: Introductionmentioning

confidence: 99%

Multi-echelon supply chain network modelling and optimization via simulation and metaheuristic algorithms

2016

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Abstract. An important problem in todays industries is the cost issue, due to the high level of competition in the global market. This fact obliges organizations to focus on improvement of their production-distribution routes, in order to nd the best. The Supply Chain Network (SCN) is one of the, so-called, production-distribution models that has many layers and/or echelons. In this paper, a new SCN, which is more compatible with real world problems is presented, and then, two novel hybrid algorithms have been developed to solve the model. Each hybrid algorithm integrates the simulation technique with two metaheuristic algorithms, including the Genetic Algorithm (GA) and the Simulated Annealing Algorithm (SAA), namely, HSIM-META. The output of the simulation model is inserted as the initial population in tuned-parameter metaheuristic algorithms to nd near optimum solutions, which is in fact a new approach in the literature. To analyze the performance of the proposed algorithms, di erent numerical examples are presented. The computational results of the proposed HSIM-META, including hybrid simulation-GA (HSIM-GA) and hybrid simulation-SAA (HSIM-SAA), are compared to the GA and the SAA. Computational results show that the proposed HSIM-META has suitable accuracy and speed for use in real world applications.

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Supply chain simulation with discrete–continuous combined modeling

Cited by 153 publications

References 3 publications

A multi-agent knowledge model for SMEs mechatronic supply chains

A multi-agent knowledge model for SMEs mechatronic supply chains

The bullwhip effect in complex supply chains

Multi-echelon supply chain network modelling and optimization via simulation and metaheuristic algorithms

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