Multiobjective supply chain design under uncertainty

Guillén, Gonzalo; Mele, Fernando D.; Bagajewicz, Miguel J.; Espuña, Antonio; Puigjaner, Luís

doi:10.1016/j.ces.2004.10.023

Cited by 259 publications

(109 citation statements)

References 25 publications

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“…(Chen et al 2003;Chen and Lee 2004;Guilléna et al 2004;Hung 2011). In all these mentioned methods, multiple objectives are combined and form a single objective problem and then the optimal solution is obtained.…”

Section: Multi-objective Optimization Problem In Scmentioning

confidence: 99%

A meta-heuristic approach supported by NSGA-II for the design and plan of supply chain networks considering new product development

et al. 2017

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There are many reasons for the growing interest in developing new product projects for any firm. The most embossed reason is surviving in a highly competitive industry which the customer tastes are changing rapidly. A well-managed supply chain network can provide the most profit for firms due to considering new product development. Along with profit, customer satisfaction and production of new products are goals which lead to a more efficient supply chain. As new products appear in the market, the old products could become obsolete, and then phased out. The most important parameter in a supply chain which considers new and developed products is the time that developed and new products are introduced and old products are phased out. With consideration of the factors noted above, this study proposes to design a tri-objective multi-echelon multiproduct multi-period supply chain model, which incorporates product development and new product production and their effects on supply chain configuration. The supply chain under consideration is assumed to consist of suppliers, manufacturers, distributors and customer groups. In terms of overcoming NP-hardness of the proposed model and in order to solve the complicated problem, a non-dominated sorting genetic algorithm is employed. As there is no benchmark available in the literature, the non-dominated ranking genetic algorithm is developed to validate the results obtained and some test problems are provided to show the applicability of the proposed methodology and evaluate the performance of the algorithms.

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Section: Multi-objective Optimization Problem In Scmentioning

confidence: 99%

A meta-heuristic approach supported by NSGA-II for the design and plan of supply chain networks considering new product development

et al. 2017

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“…The best solution can be reached by analyzing different scenarios using the DEM model, nonanticipativity constraints, and BFC. Guillén et al (2005) studied the interactions between conflicting objectives, namely net present value, customer satisfaction level, and financial risk, by using a multiobjective stochastic model. They obtained the answer using Pareto-optimal solutions and e-constraint.…”

Section: Deterministic Scn Modelsmentioning

confidence: 99%

A case study on sample average approximation method for stochastic supply chain network design problem

Wang¹,

Shou²,

Lee³

et al. 2017

Front. Eng

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This study aims to solve a typical long-term strategic decision problem on supply chain network design with consideration to uncertain demands. Existing methods for these problems are either deterministic or limited in scale. We analyze the impact of uncertainty on demand based on actual large data from industrial companies. Deterministic equivalent model with nonanticipativity constraints, branch-and-fix coordination, sample average approximation (SAA) with Bayesian bootstrap, and Latin hypercube sampling were adopted to analyze stochastic demands. A computational study of supply chain network with front-ends in Europe and back-ends in Asia is presented to highlight the importance of stochastic factors in these problems and the efficiency of our proposed solution approach.

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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%

“…In [10], the SCD problem is formulated as a multiobjective stochastic MIP model in which the uncertainty is represented by means of a demand forecast. This model is solved by ε-constraint method, and branch and bound techniques.…”

Section: Introductionmentioning

confidence: 99%

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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Multiobjective supply chain design under uncertainty

Cited by 259 publications

References 25 publications

A meta-heuristic approach supported by NSGA-II for the design and plan of supply chain networks considering new product development

A meta-heuristic approach supported by NSGA-II for the design and plan of supply chain networks considering new product development

A case study on sample average approximation method for stochastic supply chain network design problem

Supply chain design using a modified IWD algorithm

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