Systemwide Optimization of Safety Improvements for Resurfacing, Restoration, or Rehabilitation Projects

2003 ◽  
Vol 1840 (1) ◽  
pp. 148-157 ◽  
Author(s):  
Douglas W. Harwood ◽  
Emilia R. Kohlman Rabbani ◽  
Karen R. Richard
Keyword(s):  
Resource Allocation ◽  
Integer Programming ◽  
Structural Integrity ◽  
Software Tool ◽  
Optimization Process ◽  
Ride Quality ◽  
Allocation Process ◽  
Safety Effectiveness ◽  
Resource Allocation Process

Highway agencies face a dilemma in determining the appropriate balance of resurfacing and safety improvements in their programs to maintain the structural integrity and ride quality of highway pavements. Highway agencies currently lack a tool that would allow them to determine which sites should be resurfaced without accompanying safety improvements and which sites should be resurfaced and improved in other ways that would enhance safety. A resource allocation process that maximizes the benefits from resurfacing and safety improvements within a specified improvement budget can provide such a tool. A resource allocation process that accomplishes this goal has been developed and implemented in a software tool known as the Resurfacing Safety Resource Allocation Program (RSRAP). RSRAP uses an optimization process based on integer programming to determine which improvement alternatives (or combinations of alternatives) would optimize the benefits for a specified set of improvement projects. RSRAP incorporates the best available estimates of the safety effectiveness of specific geometric and safety improvements. RSRAP also gives consideration to the potential effects of resurfacing on vehicle speeds and on safety. The goal of the optimization process is not to optimize safety at any particular site but to optimize systemwide safety for a given set of resurfacing projects while not exceeding a user-specified improvement budget.

Formation of the schedule at the university with the application of greedy algorithms

2020 ◽  
Vol 4 ◽  
pp. 91-96
Author(s):  
Olga Lopateeva ◽  
◽  
Anatoly Popov ◽  
Alexey Ovsyankin ◽  
Mikhail Satsuk
Keyword(s):  
Resource Allocation ◽  
Greedy Algorithms ◽  
Educational Institution ◽  
Educational Process ◽  
Student Groups ◽  
Resource Allocation Algorithm ◽  
Allocation Process ◽  
The University ◽  
Resource Allocation Process

A greedy resource allocation algorithm is understood as an algorithm according to which the resource allocation process can be represented as a sequence of steps. At each step, an optimal, under certain conditions, distribution of a part of the resources occurs, which does not change in the future. The problem of improving the quality of the organization of the educational process in a higher educational institution is solved on the basis of the use of greedy algorithms. A well-designed timetable should ensure an even workload of student groups and faculty. The purpose of this work is to develop an algorithm that can improve the quality of the formation of the educational schedule based on the use of greedy algorithms.

scholarly journals Resource Allocation by Demand Based Optimization and Machine

2019 ◽  
Vol 8 (12) ◽  
pp. 1388-1395
Keyword(s):  
Resource Allocation ◽  
Channel Allocation ◽  
Scheduling Algorithm ◽  
Adaptive Resource Allocation ◽  
Allocation Process ◽  
Challenging Environment ◽  
Adaptive Resource ◽  
Resource Allocation Process

In real-time multimedia usage the resource allocation for the modern communication is very much needed in-order to overcome certain problems or degradation happening in the communication channels. The quality of the communication is reduced due to the TVWS (Television White Space), variable BER signal requires variable channel allocation procedures and Qos depends on the various applications. These problems in the OFDM should be corrected continuously by keeping track of channel situation so that to provide a long term video streaming in good QoS. The energy distribution for the video is high the application requirement is higher also the occurrence of multiple BER will leads to the challenging environment to control. The main objective of this paper is to enhance a Game theory based algorithm incorporated with demand optimization algorithm and scheduling algorithm for machine learning to take decision in nonlinear space, which results in a system with good channel awareness and an adaptive resource allocation process. The effect of interference due to this procedure is checked and accordingly allocations are done

scholarly journals Recommendation with Diversity: Mass Diffusion Model Based on Trust Network and Object Reputation

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Yun Bai ◽  
Wandong Cai
Keyword(s):  
Resource Allocation ◽  
Diffusion Model ◽  
Recommendation System ◽  
Small Degree ◽  
Mass Diffusion ◽  
Recommendation Algorithm ◽  
Trust Relationship ◽  
Positive Effects ◽  
Allocation Process ◽  
Resource Allocation Process

The traditional mass diffusion recommendation algorithm only relies on the user’s object collection relationship, resulting in poor recommendation performance for users with small purchases (i.e., small-degree user), and it is difficult to balance the accuracy and diversity of the recommendation system. This paper introduces the trust relationship into the resource allocation process of the traditional mass diffusion algorithm and proposes the Dual Wing Mass Diffusion model (DWMD), which constructs a dual wing graph based on trust relationships and object collection relationships. Implicit trust is mined according to the network structure of the trust relationship and integrated into the resource allocation process, and then merging the positive effects of object reputation on a recommendation through tunable scaling parameters. The user controls the tunable scaling parameter to achieve the best recommendation performance. The experimental results show that the DWMD method significantly improves diversity and novelty while ensuring high accuracy and effectively improves the accuracy and diversity balance. The improved recommendation performance for small-degree users proves that the trust relationship can effectively alleviate the generalized cold start problem of the recommendation algorithm for users who collect a small number of objects.

The Resource Allocation Process

2018 ◽  
pp. 79-93
Author(s):  
Richard Busulwa ◽  
Matthew Tice ◽  
Bruce Gurd
Keyword(s):  
Resource Allocation ◽  
Allocation Process ◽  
Resource Allocation Process

A Stochastic Decentralized Resource Allocation Process: Part II

Econometrica ◽  
1975 ◽  
Vol 43 (3) ◽  
pp. 363 ◽  
Author(s):  
Leonid Hurwicz ◽  
Roy Radner ◽  
Stanley Reiter
Keyword(s):  
Resource Allocation ◽  
Allocation Process ◽  
Decentralized Resource Allocation ◽  
Resource Allocation Process

Research on Multi-Project Resource Allocation Process and Application

2013 ◽  
Vol 357-360 ◽  
pp. 2267-2272
Author(s):  
Xin Li Zhang ◽  
Jie Li ◽  
Yan Fang Zhu
Keyword(s):  
Resource Allocation ◽  
Allocation Problem ◽  
Resource Allocation Problem ◽  
Interactive Process ◽  
Allocation Process ◽  
Fixed Period ◽  
Resource Allocation Process

Based on the existing research on multi-project resource allocation, this research presents the triangle relationship diagram about the objective, constraints, and algorithm during project resource allocation; and designs the interactive process for multi-project resource allocation, which combines the project objective, constraint, and algorithm. In addition, a case about fixed period - fixed resources problem is solved to verify the feasibility of the interactive process; the research develops the comprehensive concept for project resource allocation problem.

The Influence of Managerial Orientation in Decision-Making Processes

ACCRUALS ◽  
2020 ◽  
Vol 4 (01) ◽  
pp. 1-8
Author(s):  
Rusdianto Rusdianto
Keyword(s):  
Decision Making ◽  
Resource Allocation ◽  
Stakeholder Theory ◽  
Resource Availability ◽  
Individual Level ◽  
Allocation Process ◽  
Resources Distribution ◽  
Decision Making Processes ◽  
The Individual ◽  
Resource Allocation Process

This research aims to examine managerial preferences in the resource allocation process. This research used an experimental method to test whether resource availability, stakeholder claims, and managers’ affiliations to stockholders can influence the decision-making process of resource allocation. The results show that resource availability, stakeholder claims, and managers’ affiliation could influence the resource allocation process. The results of the research contribute to several things. The first is to show that stakeholder theory can test managerial preferences at the individual level. Secondly, the resources distribution is influenced by behavioral factors associated with normative stakeholder theory.

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