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نتیجه جستجو - Multi-attribute decision making

تعداد مقالات یافته شده: 2
ردیف عنوان نوع
1 Multi-objective sustainable opened- and closed-loop supply chain under mixed uncertainty during COVID-19 pandemic situation
چندین هدف پایدار حلقه بسته و حلقه بسته تحت عدم اطمینان مخلوط در طی وضعیت همه گیر COVID-19-2021
Logistics problems play a significant role in an emergency situation. During and after a critical circumstance (like pandemic COVID-19), it is an important task to active the opened- and closed-loop system through an efficient and resilient supply chain network. This paper considers a multi-objective multi-product multi-period two-stage sustainable opened- and closed-loop supply chain planning to maintain supply among production centers and various hospitals during COVID-19 pandemic situation. To build a less contagious network, transportation problem and pick-up-delivery vehicle routing problem are designed as two stages, respectively to carry out distribution. We allow a mixed uncertain environment by considering uncertain-random parameters in the proposed model to express ambiguity in real-life data. A multi-attribute decision making approach is suggested to determine the priorities of affected areas, according to their urgency in terms of entropy weights. Moreover, a robust optimization approach for uncertain-random parameter is developed to cope with uncertainty in different scenarios, and thereafter augmented weighted Tchebycheff method is applied to solve the model. To demonstrate the practicability of the proposed model and solving approach, three test problems with reasonable sizes are considered and results are discussed through some sensitivity analyses.
Keywords: Sustainable opened- and closed-loop supply chain | Mixed uncertainty | Multi-attribute decision making | Transportation problem | Pick-up-delivery vehicle routing problem | Robust optimization.
مقاله انگلیسی
2 Discovery of resources using MADM approaches for parallel and distributed computing
کشف منابع با استفاده از روش MADM برای محاسبات موازی و توزیع شده-2017
Grid, a form of parallel and distributed computing, allows the sharing of data and computational resources among its users from various geographical locations. The grid resources are diverse in terms of their underlying attributes. The majority of the state-of-the-art resource discovery techniques rely on the static resource attributes during resource selection. However, the matching resources based on the static resource attributes may not be the most appropriate resources for the execution of user appli cations because they may have heavy job loads, less storage space or less working memory (RAM). Hence, there is a need to consider the current state of the resources in order to find the most suitable resources. In this paper, we have proposed a two-phased multi-attribute decision making (MADM) approach for dis covery of grid resources by using P2P formalism. The proposed approach considers multiple resource attributes for decision making of resource selection and provides the best suitable resource(s) to grid users. The first phase describes a mechanism to discover all matching resources and applies SAW method to shortlist the top ranked resources, which are communicated to the requesting super-peer. The second phase of our proposed methodology applies integrated MADM approach (AHP enriched PROMETHEE-II) on the list of selected resources received from different super-peers. The pairwise comparison of the resources with respect to their attributes is made and the rank of each resource is determined. The top ranked resource is then communicated to the grid user by the grid scheduler. Our proposed methodology enables the grid scheduler to allocate the most suitable resource to the user application and also reduces the search complexity by filtering out the less suitable resources during resource discovery.
Keywords: Grid computing | Resource discovery | Static attributes | Dynamic attributes | AHP | MADM | PROMETHEE-II | SAW
مقاله انگلیسی
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