Supply chain simulation tools and techniques: a survey

Kleijnen, J.P.C.

doi:10.1504/ijspm.2005.007116

Cited by 198 publications

(101 citation statements)

References 29 publications

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“…According to Figure 2, the size of the set of vertices is 17, thus V={1,2,…,17} and the set of edges is: E ={(1,5), (2,5), (3,5), (4,5), (5,10),$ $ (6,10), (7,10), (8,10), (9,10), (11,13), (10,13), (10,14), (12,14), (13,15), (13,16), (14,17)}. The subset of delivering stages is D={15, 16, 17}.…”

Section: Figure 2 Notebook Sc Structurementioning

confidence: 99%

“…Moreover, the model in this paper considers more than one option to perform a stage, contrary to what is done in the aforementioned proposal. In relation to the application of IWD to the SCD problem, there is no published attempt to solve the bi-objective SC configuration by means of IWD as shown in recent surveys specialised in SC design problem [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

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Supply chain design using a modified IWD algorithm

Moncayo–Martínez

2017

Rev. Fac. Ing. Univ. Antioquia

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ABSTRACT:The Intelligent Water Drop (IWD) algorithm is inspired by the movement of real water drops in a river. A water drop could find an optimum path to a lake or sea by interacting with the conditions of its surroundings. In the process of reaching such destination, the water drops interact with the river bed while they move through it. Similarly, the supply chain problem can be modelled as a flow of supply, manufacturing, and delivery stages that must be completed to produce a finished product and then to deliver it to the end user. The problem is to select one option that carries out the stage, e.g. for a supply stage, many suppliers could supply the component represented by it. As each stage is characterised by its time and cost, multi--objective optimisation algorithm is used to minimise the time to market and production cost, simultaneously. Focusing on this analogy, this paper proposes an approach to the supply chain problem using a multi--objective extension to the intelligent water drops algorithm. Artificial water drops, flowing through the supply chain, will simultaneously minimise the production cost and the time to market of every product in a generic BOM by using the concept of Pareto optimality. A widely-used notebook supply chain in literature is solved. We provide some performance metrics of the solution and compare the Pareto set computed by the proposed algorithm with the one returned by exhaustive enumeration. RESUMEN:El Algoritmo Inteligente de Agua está inspirado en el movimiento de las gotas de agua en un río. Una gota de agua puede encontrar una ruta óptima desde un lago hacia el mar interactuando con su entorno. En el proceso de llegar a tal destino, las gotas de agua interactúan con el lecho del río mientras se mueven a través de él. Del mismo modo, el problema de la cadena de suministro puede ser modelado como un flujo de etapas de suministro, fabricación y entrega para producir un artículo terminado y luego entregarlo al usuario final. El problema es seleccionar la opción que realizará la etapa, por ejemplo en una etapa de aprovisionamiento, muchos proveedores podrían suministrar el componente. Como cada etapa tiene asociado un costo y un tiempo, un algoritmo multi-objetivo es usado para minimizar el tiempo de entrega y el costo de producción, simultáneamente. Basados en esta analogía, este trabajo propone una aproximación al problema de la cadena de suministro utilizando una extensión multi-objetivo al algoritmo de gotas de agua. Las gotas de agua artificiales que fluyen a través de la cadena de suministro minimizarán simultáneamente el costo de producción y el tiempo de entrega de cada producto utilizando el concepto de optimización de Pareto. Se soluciona una cadena de suministro de computadoras ampliamente utilizada en la literatura. Así mismo, algunas métricas de desempeño son calculadas y se compara el conjunto de Pareto calculado por el algoritmo propuesto con el obtenido por enumeración exhaustiva.

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Section: Figure 2 Notebook Sc Structurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Supply chain design using a modified IWD algorithm

Moncayo–Martínez

2017

Rev. Fac. Ing. Univ. Antioquia

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“…The supply chain is modelled through first-order nonlinear differential equations (Riddalls et al 2000;Kleijnen 2005). This approach is commonly known as (1) K-stage production-distribution serial system.…”

Section: Mathematical Modelmentioning

confidence: 99%

Current economic downturn and supply chain: the significance of demand and inventory smoothing

Cannella

Ashayeri

Miranda

et al. 2013

International Journal of Computer Integrated Manufacturing

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The aim of this article is to analyse and quantify the effects of demand and inventory smoothing into supply-chain performance, facing the extreme volatility and impetuous alteration of the market produced by the current economic recession. To do so, we model a traditional serial three-stage supply chain and we test five settings of order smoothing under two shocks in the market demand, and we measure effects in terms of internal process benefits and customer service level of all supply chain partners. Results show that the implementation of this inventory strategy should be based on reward schemes; in fact a higher level of smoothing can generally improve the performance of the upstream stages. On the contrary, this approach cannot be always beneficial for the retailer as the decrease in service level can outweigh the decrease in bullwhip.

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“…Simulation is defined as a method to reproduce dynamic processes in a virtual model which makes it possible to analyze the behavior of complex systems (VDI 3633 2000). Especially for decision support and planning activities for industrial processes, the type of discrete event simulation has evolved as a key methodology (Kleijnen 2005). In discrete event simulation, the state of a model changes during the simulation at discrete points of time.…”

Section: Discrete Event Simulation For Supporting Industrial Process mentioning

confidence: 99%

Simulation Based Decision Support System For Cut-To-Size Plants

Robert¹,

Schmid²,

Hillbrand³

2011

ECMS 2011 Proceedings Edited By: T. Burczynski, J. Kolodziej, A. Byrski, M. Carvalho

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Cut-to-size plants are very complex systems, which are used to process several kinds of panels. The acquisition, planning, implementation and operation of these systems impose lots of challenges. To support the process from retail to operation of cut-to-size plants, a new simulation based decision support tool has been developed. This planning tool offers entirely new opportunities for system experts to model, animate, simulate and emulate cut-to-size processes. In this paper, we discuss centrally and hierarchically controlled real time systems and derive a conceptual design to explain the implemented reproduction of the virtual system. We introduce the system architecture for our simulation based decision support tool and examine our approach to model largescale plant systems. Apart from this, we present our method to process the orders which are generated on the control system and to handle them as discrete event tasks in the virtual system. Finally, based on the findings of the realization and testing of the tool, this paper discusses the opportunities arising from this approach as well as its future potential.

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Supply chain simulation tools and techniques: a survey

Cited by 198 publications

References 29 publications

Supply chain design using a modified IWD algorithm

Supply chain design using a modified IWD algorithm

Current economic downturn and supply chain: the significance of demand and inventory smoothing

Simulation Based Decision Support System For Cut-To-Size Plants

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