This research article presents the development process of a cost-estimating model for railway renewal projects at the early stage of a project life cycle. The model comprises four main stages: creating a project structure that composes the goal, project criteria, and alternatives; collecting the necessary data in the form of pairwise comparisons made by a domain expert; producing alternative weights using a geometric mean; finally, employing an algorithmic method using the produced alternative weights and the known cost of one alternative per criteria.
The practical implications of the developed model are its ability to estimate renewal project costs of railway assets when there is a lack of quantitative data and detailed project definition. The process provides a transparent and a structured way of formalizing subjective judgments, which produces a three-point estimate as the resulting output. The model is described and validated using a switch and crossing case study.
The paper presents the model and possible application of Military Materials Supply Chain Management(MMSCM) philosophy. Firstly, authors compare the different definitions of Supply Chain Management(SCM)and discuss its evolutions, set forth the model framework of Military Materials Supply Chain Management (MMSCM). Secondly, the paper examines Internet technologies streamlining the supply chain, and analysis the current situation of MMSCM. Finally, the article addresses the measurement and possible applications of MMSCM.
As a special form of multiaccess edge computing (MEC), vehicular edge computing (VEC) plays an important role in emergency logistics by providing real-time and low-latency services for vehicles. The solution of the joint task offloading and resource allocation problem (JTORA) is the key to improving VEC efficiency. This study formulates a special model according to the multistage characteristics of the computational task in vehicular edge computing networks (VECNs) for emergency logistics. First, the JTORA problem is decomposed into three computational steps, each of which includes a task offload (TO) problem and a resource allocation (RA) problem. Then, a hybrid solution is proposed which uses a simulated annealing process to optimize the genetic algorithm (GA) and cooperate with the particle swarm optimization (PSO) algorithm, called the genetic simulated annealing and particle swarm optimization (GSA-PSO) algorithm. Furthermore, a simulation experiment is designed and the effectiveness of the GSA-PSO is verified.
Customarily modern procurement adopts a multi-purpose procurement way, so the bid evaluation and contractor selection will be a great problem. The purpose of this paper is to provide a new method to resolve the problem by using regression analysis based on facts-set. This method can evaluation all bids with some cheap computer software. By proving the scientificity of this new method, the author thinks that it can avoid the defects of the past evaluation methods efficiently. In virtue of the regression analysis to carry on the bid evaluation, this paper puts forward three kinds of different concrete ways: comparison of the residuals, using equivalent-benefit curve, setting up indexes-related evaluation system. The method can be used separately to trade off two or several factors, and can handle indexes whatever linear correlation or nonlinear correlation. This method also can be combined with other method in the procession of bid evaluation.
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