M
Mahdi Bashiri
Researcher at Coventry University
Publications - 191
Citations - 2920
Mahdi Bashiri is an academic researcher from Coventry University. The author has contributed to research in topics: Supply chain & Stochastic programming. The author has an hindex of 23, co-authored 180 publications receiving 2307 citations. Previous affiliations of Mahdi Bashiri include RMIT University & Shahed University.
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A new multi-objective stochastic model for a forward/reverse logistic network design with responsiveness and quality level
TL;DR: In this article, a stochastic multi-objective model for forward/reverse logistic network design under a uncertain environment including three echelons in forward direction (i.e., suppliers, plants, and distribution centers) and two echelon in backward direction (e.g., collection centers and disposal centers).
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Selecting optimum maintenance strategy by fuzzy interactive linear assignment method
TL;DR: A new approach for selecting optimum maintenance strategy using qualitative and quantitative data through interaction with the maintenance experts has been presented in this article, which has been based on linear assignment method (LAM) with some modifications to develop interactive fuzzy linear assignment (IFLAM).
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A new approach to tactical and strategic planning in production– distribution networks
TL;DR: In this article, a new mathematical model for strategic and tactical planning in a multiple-echelon, multiple-commodity production-distribution network is presented, in which different time resolutions are considered for strategic decisions and expansion of the network is planned based on cumulative net incomes.
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Integrated strategic and tactical planning in a supply chain network design with a heuristic solution method
TL;DR: A new mathematical model for multiple echelon, multiple commodity Supply Chain Network Design (SCND), based on a Lagrangian Relaxation method, is presented and considers different time resolutions for tactical and strategic decisions.
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Tuning the parameters of an artificial neural network using central composite design and genetic algorithm
TL;DR: The designed ANN, according to the proposed procedure, has a better performance than other networks by random selected parameters and also parameters which are selected by the Taguchi method and can be used for tuning neural network parameters in solving other problems.