Article

designing a multi-objective nonlinear cross-docking location allocation model using genetic algorithm

Abstract

in this study, a cross-docking system is designed at strategic and tactical levels. for making the strategic decisions, a multi-objective nonlinear location allocation model for cross-docks is presented based on a distri-bution location allocation model by andreas klose and andreas drexl. the model is further developed to in-clude the whole supply chain members and the objective functions are weighted by implementing ahp. a ge-netic algorithm solution is designed for sample cross-dock location allocation problems. in the tactical stage, model was further simulated under two different distribution strategies to decide on the tactical parameters. as an example, the performance of the model is verified.

Similar items

  • Designing a multi-objective nonlinear cross-docking location allocation model using genetic algorithm

    Article2006Journal of Industrial Engineering, InternationalAhmad Makui, Laleh Haerian, Mahyar Eftekhar

    In this study, a cross-docking system is designed at strategic and tactical levels. For making the strategic decisions, a multi-objective nonlinear location allocation model for cross-docks is presented based on a distri-bution location allocation model by Andreas Klose and Andre

  • Strategic design and multi-objective optimisation of distribution networks based on genetic algorithms

    Scholarly record2012Int. J. Computer Integrated ManufacturingVitoantonio Bevilacqua, Nicola Costantino, Mariagrazia Dotoli, Marco Falagario

    Strategic design and multi-objective optimisation of distribution networks based on genetic algorithms Vitoantonio Bevilacqua a , Nicola Costantino b , Mariagrazia Dotoli a , Marco Falagario b & Fabio Sciancalepore b a Dipartimento di Elettrotecnica ed Elettronica, Politecnico di

  • Multi-Objective Pareto Optimization of Centrifugal Pump Using Genetic Algorithms

    Scholarly record2007N. NARIMAN-ZADEH, N. AMANIFARD

    Multi-objective genetic algorithm (GAs) is used for pump design pareto optimization, competing objectives for centrifugal pump design are total head (H), input power (Ps), hydraulic efficiency ( H η ), and input parameter are capacity (Q), and the outer radius of the impeller ( )

  • Multi-objective pattern and feature selection by a genetic algorithm

    Scholarly record2000GECCOHisao Ishibuchi, Tomoharu Nakashima

    This paper discusses a genetic-algorithm-based approach for selecting a small number of representative instances from a given data set in a pattern classification problem. The genetic algorithm also selects a small number of significant features. That is, instances and features a

  • Multi-Objective Optimization of Squeeze Casting Process using Genetic Algorithm and Particle Swarm Optimization

    Scholarly record2016G.C.M. Patel, M. B. Parappagoudar

    The near net shaped manufacturing ability of squeeze casting process requiresto set the process variable combinations at their optimal levels to obtain both aesthetic appearance and internal soundness of the cast parts. The aesthetic and internal soundness of cast parts deal with

  • Multi-objective Optimization of Mcml Circuits Using a Genetic Algorithm

    Scholarly record2005Roberto Pereira-Arroyo, Pablo Alvarado-Moya, Wolfgang H. Krautschneider

    This paper introduces the Pareto front as a useful analysis tool to explore the design space of MOS Current Mode Logic (MCML) circuits. A genetic algorithm (GA) is employed to automatically detect this front in a process that efficiently finds optimal parameterizations and their