Driving Route Recommendation With Profit Maximization in Ride Sharing

2019 ◽  
Vol 63 (11) ◽  
pp. 1607-1623
Author(s):  
Longji Huang ◽  
Jianbin Huang ◽  
Yueshen Xu ◽  
Zhiqiang Zhao ◽  
Zhenghao Zhang
Keyword(s):  
Positive Impact ◽  
Search Algorithm ◽  
Profit Maximization ◽  
Urban Traffic ◽  
Utilization Rate ◽  
Research Attention ◽  
Matching Algorithm ◽  
Price Function ◽  
Ride Sharing ◽  
Group Search

Abstract Due to the positive impact of ride sharing on urban traffic and environment, it has attracted a lot of research attention recently. However, most existing researches focused on the profit maximization or the itinerary minimization of drivers, only rare work has covered on adjustable price function and matching algorithm for the batch requests. In this paper, we propose a request matching algorithm and an adjustable price function that benefits drivers as well as passengers. Our request-matching algorithm consists of an exact search algorithm and a group search algorithm. The exact search algorithm consists of three steps. The first step is to prune some invalid groups according to the total number of passengers and the capacity of vehicles. The second step is to filter out all candidate groups according to the compatibility of requests in same group. The third step is to obtain the most profitable group by the adjustable price function, and recommend the most profitable group to drivers. In order to enhance the efficiency of the exact search algorithm, we further design an improved group search algorithm based on the idea of original simulated annealing. Extensive experimental results show that our method can improve the income of drivers, and reduce the expense of passengers. Meanwhile, ride sharing can also keep the utilization rate of seats 80%, driving distance is reduced by 30%.

Context-aware Adaptive Surgery

2021 ◽  
Vol 5 (3) ◽  
pp. 1-22
Author(s):  
Hongli Wang ◽  
Bin Guo ◽  
Jiaqi Liu ◽  
Sicong Liu ◽  
Yungang Wu ◽  
...  
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Resource Constraints ◽  
Search Algorithm ◽  
Search Time ◽  
State Graph ◽  
Context Aware ◽  
Optimal Partition ◽  
Research Attention ◽  
Neighbor Effect

Deep Neural Networks (DNNs) have made massive progress in many fields and deploying DNNs on end devices has become an emerging trend to make intelligence closer to users. However, it is challenging to deploy large-scale and computation-intensive DNNs on resource-constrained end devices due to their small size and lightweight. To this end, model partition, which aims to partition DNNs into multiple parts to realize the collaborative computing of multiple devices, has received extensive research attention. To find the optimal partition, most existing approaches need to run from scratch under given resource constraints. However, they ignore that resources of devices (e.g., storage, battery power), and performance requirements (e.g., inference latency), are often continuously changing, making the optimal partition solution change constantly during processing. Therefore, it is very important to reduce the tuning latency of model partition to realize the real-time adaption under the changing processing context. To address these problems, we propose the Context-aware Adaptive Surgery (CAS) framework to actively perceive the changing processing context, and adaptively find the appropriate partition solution in real-time. Specifically, we construct the partition state graph to comprehensively model different partition solutions of DNNs by import context resources. Then "the neighbor effect" is proposed, which provides the heuristic rule for the search process. When the processing context changes, CAS adopts the runtime search algorithm, Graph-based Adaptive DNN Surgery (GADS), to quickly find the appropriate partition that satisfies resource constraints under the guidance of the neighbor effect. The experimental results show that CAS realizes adaptively rapid tuning of the model partition solutions in 10ms scale even for large DNNs (2.25x to 221.7x search time improvement than the state-of-the-art researches), and the total inference latency still keeps the same level with baselines.

scholarly journals Optimizing Urban Distribution Routes for Perishable Foods Considering Carbon Emission Reduction

2019 ◽  
Vol 11 (16) ◽  
pp. 4387 ◽  
Author(s):  
Lin ◽  
Zhang ◽  
Wang ◽  
Yang ◽  
Shi ◽  
...  
Keyword(s):  
Carbon Emissions ◽  
Emission Reduction ◽  
Carbon Emission ◽  
Carbon Tax ◽  
Search Algorithm ◽  
Urban Traffic ◽  
Low Carbon ◽  
Carbon Emission Reduction ◽  
Urban Distribution ◽  
The Impact

The increasing demand for urban distribution increases the number of transportation vehicles which intensifies the congestion of urban traffic and leads to a lot of carbon emissions. This paper focuses on carbon emission reduction in urban distribution, taking perishable foods as the object. It carries out optimization analysis of urban distribution routes to explore the impact of low carbon policy on urban distribution routes planning. On the basis of analysis of the cost components and corresponding constraints of urban distribution, two optimization models of urban distribution routes with and without carbon emissions cost are constructed. Fuel quantity related to cost and carbon emissions in the model is calculated based on traffic speed, vehicle fuel quantity and passable time period of distribution. Then an improved algorithm which combines genetic algorithm and tabu search algorithm is designed to solve models. Moreover, an analysis of the influence of carbon tax price is also carried out. It is concluded that in the process of urban distribution based on the actual network information, path optimization considering the low carbon factor can effectively reduce the distribution process of CO2, and reduce the total cost of the enterprise and society, thus achieving greater social benefits at a lower cost. In addition, the government can encourage low-carbon distribution by rationally adjusting the price of carbon tax to achieve a higher social benefit.

scholarly journals Re-examining the Impact of ACFTA on ASEAN's Exports of Manufactured Goods to China

2014 ◽  
Vol 13 (3) ◽  
pp. 63-82 ◽  
Author(s):  
Tham Siew Yean ◽  
Andrew Kam Jia Yi
Keyword(s):  
Positive Impact ◽  
Large Body ◽  
Trade Agreement ◽  
Estimation Methods ◽  
Utilization Rate ◽  
Production Networks ◽  
Regional Trade ◽  
Firm Level ◽  
Manufactured Goods ◽  
The Impact

There is a relatively large body of literature examining ASEAN–China relations, including assessments of the impact of the ASEAN–China Free Trade Agreement (ACFTA) on ASEAN's welfare and its trade with China. Overall, the results of these studies indicate a positive impact of ACFTA on the region's exports to China. These results differ from firm-level surveys that indicate a low utilization rate of most regional trade agreement tariff concessions, including those provided by ACFTA. Moreover, trade in manufactured goods in the region has been characterized as market-led, and governed by multinationals (MNCs) and their regional production networks. Thus, MNC decisions are the driving force influencing changes in manufactured parts and components trade in the region. This trade is also fostered by duty-free imports in the export enclaves provided by the host economies for these MNCs. In view of the conflicting empirical evidence on the trade effects of regional trade agreements, the objective of this study is to re-assess the impact of ACFTA on ASEAN's manufactured exports to China. In performing this analysis, we separately evaluate the effects of trade in parts and components (P&C) and non–parts and components (non P&C) or final manufactured goods. When we apply gravity estimation methods to individual regressions for these two forms of trade, we find that the determinants of trade are indeed different for the two sectors, and that the implementation of ACFTA had different effects on P&C versus final goods ASEAN exports to China.

Understanding the Link Between Initial ERP Systems and ERP-Enabled Adoption

2013 ◽  
Vol 26 (4) ◽  
pp. 18-39 ◽  
Author(s):  
Joseph K. Nwankpa ◽  
Yaman Roumani ◽  
Alan Brandyberry ◽  
Alfred Guiffrida ◽  
Michael Hu ◽  
...  
Keyword(s):  
Information Integration ◽  
Positive Impact ◽  
Current System ◽  
Ease Of Use ◽  
Relative Advantage ◽  
Erp Systems ◽  
Erp Implementation ◽  
Research Attention ◽  
Erp System ◽  
Significant Research

ERP systems have been identified as platform technologies that permit the adoption of subsequent technologies that leverage the information integration created by an ERP system. Although significant research attention has been directed at ERP system adoption, very little attention has been paid to understanding ERP-enabled adoption, that is, adoptions that occur and are facilitated after the initial ERP system. This paper seeks to fill this void. Synthesizing existing works, the authors construct a model that examines the link between initial ERP system and ERP-enabled adoption. The results indicate that initial ERP system factors (Extent of ERP Implementation and Current System Performance) act as antecedences to organizational ease of use and relative advantage of subsequent technologies. Moreover, the authors’ findings implicate that organizational ease of use and relative advantage of subsequent technologies have a positive impact on ERP-enabled adoption. These results have important implications for ERP system evaluation and justification. First, the findings highlight the importance of recognizing the potential additions and inherent benefits created by an initial ERP system. Second, the findings also underscore the importance of considering ERP-enabled adoption of subsequent technologies.

What good soldiers are made of: the role of personality similarity

2015 ◽  
Vol 30 (8) ◽  
pp. 1003-1018 ◽  
Author(s):  
Jennifer YM Lai ◽  
Simon SK Lam ◽  
Cheris WC Chow
Keyword(s):  
Social Integration ◽  
Social Exchange ◽  
Organizational Citizenship Behavior ◽  
Positive Impact ◽  
Future Research ◽  
Research Attention ◽  
Content Type ◽  
Personality Similarity ◽  
The Impact ◽  
Role Behaviors

Purpose – Previous studies have often yielded mixed results in relation to the similar-to-me effect on extra-role behaviors. Based on social exchange theory, the purpose of this paper is to uncover the contribution of personality similarity to organizational citizenship behavior (OCB), a type of extra-role behaviors. Design/methodology/approach – Questionnaire surveys were conducted in a multinational bank in Hong Kong. The participants were matched sample of 403 customer service representatives from 81 teams and their corresponding team supervisors. Findings – Personality similarity to peers and supervisors had a positive impact on individual OCB (OCB-I) and organizational OCB (OCB-O), respectively, through better communication and social integration. Moreover, personality similarity to peers was related only to OCB-I, whereas personality similarity to supervisor was associated only with OCB-O. Research limitations/implications – Subordinates perform different facets of OCB depending on the subjects they are similar to in terms of personality. Being similar to others could facilitate communication and social integration, which in turn promote OCB targeted at similar individuals. The importance of distinguishing the similarity effects of peers and supervisors separately as well as scrutinizing the effects of different forms of OCB warrants future research attention. Practical implications – Managers can promote OCB by enhancing communication and social integration among employees and supervisors. Social implications – As OCB enhances organizational effectiveness, the economic development of society in general will eventually benefit from having more effective organizations. Originality/value – This study addresses the inconsistent findings of previous studies regarding the impact of similarity on OCB. It demonstrates the contribution of personality similarity to OCB beyond the confounding effects of judgment bias. It also advances theory by studying personality similarity to peers in addition to the commonly studied similarity to supervisors.

scholarly journals A pragmatic study of the impact of a brief mindfulness intervention on prisoners and staff in a Category B prison and men subject to community-based probation supervision

2019 ◽  
Author(s):  
Jason Davies ◽  
Pamela Ugwudike ◽  
Hayley Anne Young ◽  
Chloe Hurrell ◽  
Peter Raynor
Keyword(s):  
Positive Impact ◽  
Self Report ◽  
Offender Rehabilitation ◽  
Research Attention ◽  
Physiological Measurement ◽  
Mindfulness Skills ◽  
Probation Supervision ◽  
Computer Based ◽  
Staff Wellbeing ◽  
The Impact

Background: Mindfulness has received increased research attention in recent years and has been shown to have a positive impact on stress and impulsivity. This paper presents two studies assessing the impact of mindfulness in prison and non-custodial settings.Method: Study 1 - prisoners (n=44) and staff (n=25) in a UK prison attended a mindfulness programme; 16 individuals acted as a single time point comparison. Data were collected using self-report, computer based and physiological measurement. Study 2 - men under community probation supervision were allocated to mindfulness (n=44) or TAU (n=27). Data were collected using self-report mindfulness measures.Results: Study 1 - statistically significant increases in mindfulness skills and cognitive control along with significant decreases in stress were found. In study 2, the mindfulness group showed non significant improvements in mindfulness skills. Conclusions: The findings suggest brief mindfulness interventions could make an important contribution to offender rehabilitation and custodial staff wellbeing.

scholarly journals Collaborative Optimization of Car-flow Organization for Freight Trains Based on Adjacent Technical Stations

2021 ◽  
Vol 33 (1) ◽  
pp. 117-128
Author(s):  
Yijing Yang ◽  
Xu Wu ◽  
Haonan Li
Keyword(s):  
Genetic Algorithm ◽  
Dwell Time ◽  
Search Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Average Dwell Time ◽  
Collaborative Optimization ◽  
Utilization Rate ◽  
Organization Problem ◽  
Scheduling Method ◽  
Taboo Search Algorithm

This paper proposes a collaborative optimization model of car-flow organization for freight trains based on adjacent technical stations to minimize the average dwell time of train cars in a yard. To solve the car-flow organization problems, a priority-based hump sequence, which depends on the cars available in two adjacent technical stations, is adopted. Furthermore, a meta-heuristic algorithm based on the genetic algorithm and the taboo search algorithm is adopted to solve the model, and the introduction of the active scheduling method improves the efficiency of the algorithm. Finally, the model is applied to the car-flow organization problem of two adjacent technical stations, and the results are compared with those from a single technical station without collaboration. The results demonstrate that collaborative car-flow organization between technical stations significantly reduces the average dwell time at the stations, thereby improving the utilization rate of railroad equipment. In addition, the results indicate that the hybrid genetic algorithm can rapidly determine the train hump and marshalling schemes.

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