Improving highway accident management through patrol beat scheduling

2014 ◽  
Vol 37 (1) ◽  
pp. 108-125 ◽  
Author(s):  
Jiann-Sheng Wu ◽  
Tze-chiang Lou
Keyword(s):  
Historical Data ◽  
Response Times ◽  
Work Hours ◽  
Simulation Program ◽  
Personnel Cost ◽  
Content Type ◽  
Daily Work ◽  
Scheduling Model ◽  
Proposed Model ◽  
Accident Management

Purpose – The purpose of this paper is to improve the efficiency of accident management from the angle of reducing highway accident response times while taking into account total daily work hours. Design/methodology/approach – The authors developed a patrol beat scheduling model, which is formulated as a chance-constrained optimization model, with the objective of minimizing the sum of officer work hours. Along with the model, a simulation program was also developed to help evaluate the effectiveness of the model-generated beat schedule in terms of response times. Findings – This study concluded that, first, the current manually designed beat schedule could be improved should the National Highway Police Bureau adopt the proposed model. Second, the total daily work hours of the model-generated schedule at the confidence level of 100 percent were 64 hours, 21 hours less than the average work hours recorded in the 2006 data, or about an improvement of 24 percent. Should the model be adopted, not only response times will be improved, the 24 percent reduction in work hours could be translated into a cut in personnel cost. Research limitations/implications – The scheduling model and simulation program are both built upon one-year historical data whose accuracy and completeness is prerequisite. Practical implications – The proposed model can be adopted by other public service agencies such as fire departments, or emergency service centers. By replacing the historical data used in the study with their own data, agencies can evaluate the efficiency of their existing schedule or generate various schedules based on institutional needs. Originality/value – This model utilizes historical accident data to generate optimal beat schedule and evaluate the efficiency of such schedule. Similar models have not been found in other studies.

Data-driven approach to find the best partner for merger and acquisitions in banking industry

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Qingyuan Zhu ◽  
Xingchen Li ◽  
Feng Li ◽  
Alireza Amirteimoori
Keyword(s):  
Production Technology ◽  
Commercial Banks ◽  
Historical Data ◽  
Banking Industry ◽  
Content Type ◽  
Proposed Model ◽  
Data Driven Approach ◽  
Merger And Acquisitions ◽  
Selection For ◽  
Theoretical Side

PurposeMerger and acquisitions (M&A) is a process of restructuring two or more companies into one, a process that occurs frequently in many companies. Previous studies on M&A mainly paid attention to the potential gains from a merger, while ignored the problem of how to select the partners to merge. This paper aims to select the best partner from different candidates for a given company to merge.Design/methodology/approachEach company's historical data are used to identify each company's own production technology. With resources change, each company's new operation is restricted by its own production technology. Then, a 0–1 integer programming is proposed to select the best partner for M&A.FindingsThe banking industry involving 27 China's commercial banks is given to verify the applicability of our proposed model. The study shows the best partner selection for each bank company.Originality/valueOn the theoretical side, the study uses each company's own historical data to construct its own production technology to compressively reflect the production change after M&A. On the practical side, the study uses the proposed model to help the 27 commercial banks in China to select their best merger partner.

scholarly journals Modelling a Nurse Shift Schedule with Multiple Preference Ranks for Shifts and Days-Off

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Chun-Cheng Lin ◽  
Jia-Rong Kang ◽  
Wan-Yu Liu ◽  
Der-Jiunn Deng
Keyword(s):  
Nursing Staff ◽  
Historical Data ◽  
Nurse Scheduling ◽  
Shift Schedule ◽  
Scheduling Model ◽  
Schedule Period ◽  
Proposed Model ◽  
Shift Schedules ◽  
Priority Ordering

When it comes to nurse shift schedules, it is found that the nursing staff have diverse preferences about shift rotations and days-off. The previous studies only focused on the most preferred work shift and the number of satisfactory days-off of the schedule at the current schedule period but had few discussions on the previous schedule periods and other preference levels for shifts and days-off, which may affect fairness of shift schedules. As a result, this paper proposes a nurse scheduling model based upon integer programming that takes into account constraints of the schedule, different preference ranks towards each shift, and the historical data of previous schedule periods to maximize the satisfaction of all the nursing staff's preferences about the shift schedule. The main contribution of the proposed model is that we consider that the nursing staff’s satisfaction level is affected by multiple preference ranks and their priority ordering to be scheduled, so that the quality of the generated shift schedule is more reasonable. Numerical results show that the planned shifts and days-off are fair and successfully meet the preferences of all the nursing staff.

Business management in the football industry from a supply chain management perspective

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Zhaleh Memari ◽  
Abbas Rezaei Pandari ◽  
Mohammad Ehsani ◽  
Shokufeh Mahmudi
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Business Management ◽  
Relationship Management ◽  
Flow Management ◽  
Manufacturing Firm ◽  
Chain Management ◽  
Content Type ◽  
Proposed Model ◽  
Football Industry

PurposeTo understand the football industry in its entirety, a supply chain management (SCM) approach is necessary. This includes the study of suppliers, consumers and their collaborations. The purpose of this study was to present a business management model based on supply chain management.Design/methodology/approachData were collected through in-depth interviews with 12 academic and executive football experts. After three steps of open, axial and selective coding based on grounded theory with a paradigmatic approach, the data were analysed, and a football supply chain management (FSCM) was developed. The proposed model includes three managerial components: upstream suppliers, the manufacturing firm, and downstream customers.FindingsThe football industry sector has three parts: upstream suppliers, manufacturing firm/football clubs and downstream customers. We proposed seven parts for the managerial processes of football supply chain management: event/match management, club management, resource and infrastructure management, customer relationship management, supplier relationship management, cash flow management and knowledge and information flow management. This model can be used for configuration, coordination and redesign of business operations as well as the development of models for evaluation of the football supply chain's performance.Originality/valueThe proposed model of a football supply chain management, with the existing literature and theoretical review, created a synergistic outcome. This synergy is presented in the linkage of the players in this chain and interactions between them. This view can improve the management of industry productivity and improve the products quality.

An exploratory study of teacher buoyancy

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Angel Kit Yi Wong ◽  
Sylvia Yee Fan Tang ◽  
Dora Dong Yu Li ◽  
May May Hung Cheng
Keyword(s):  
Professional Growth ◽  
Pearson Correlation ◽  
Well Being ◽  
Initial Teacher Education ◽  
Enabling Factors ◽  
Content Type ◽  
Daily Work ◽  
Component Framework ◽  
Personal Strengths ◽  
New Instrument

PurposeThe purpose of this paper is threefold. Firstly, a new concept, teacher buoyancy, is introduced. Based on the significance to study how teachers bounce back from minor and frequent setbacks (vs. major adversities emphasized in resilience) in their daily work and the research on buoyancy by Martin and Marsh, a dual-component framework to conceptualize this new concept is introduced. Secondly, the development of a new instrument, the Teacher Buoyancy Scale (TBS), to measure it is presented. Thirdly, results of a study using the TBS are reported, which provide insights into how teacher buoyancy can be fostered.Design/methodology/approachThe study employed a quantitative design. A total of 258 teachers taking a part-time initial teacher education (ITE) program completed the TBS. Their responses were analyzed by exploratory factor analysis (EFA). In addition to descriptive statistics and reliability coefficients, Pearson correlation coefficients were calculated to examine the relationship among the factors.FindingsThe data analysis indicated five factors, namely, Coping with difficulties, Bouncing back cognitively and emotionally, Working hard and appraising difficulties positively, Caring for one's well-being and Striving for professional growth. These factors can be readily interpreted by the dual-component framework. Correlations among the factors further revealed that enabling factors can be subdivided into more proximal personal strengths relating to direct coping, and more distal personal assets pertaining to personal well-being. It is the latter that correlates most highly with perceived teacher buoyancy.Originality/valueThe most original contribution of this paper is the proposal of the new concept of teacher buoyancy which is teachers' capacity to deal with the everyday challenges that most teachers face in their teaching. The delineation between buoyancy and resilience sharpens the focus of the problem domain that is most relevant to teachers. The development of the TBS provides a useful and reliable instrument to examine teacher buoyancy in future studies.

scholarly journals Operating Rooms efficiency: leveraging data to improve management of elective surgery

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
C Quercioli ◽  
G A Carta ◽  
G Cevenini ◽  
G Messina ◽  
N Nante ◽  
...  
Keyword(s):  
Historical Data ◽  
Elective Surgery ◽  
District General Hospital ◽  
Operating Rooms ◽  
Accurate Estimation ◽  
Surgical Time ◽  
Accurate Analysis ◽  
Management Efficiency ◽  
Scheduling Model ◽  
The Mean

Abstract Background Careful scheduling of elective surgery Operating Rooms (ORs) is crucial for their efficient use, to avoid low/over utilization and staff overtime. Accurate estimation of procedures duration is essential to improve ORs scheduling. Therefore analysis of historical data about surgical times is fundamental to ORs management. We analyzed the effect, in a real setting, of an ORs scheduling model based on estimated optimum surgical time in improving ORs efficiency and decreasing the risk of overtime. Methods We studied all the 2014-2019 elective surgery sessions (3,758 sessions, 12,449 interventions) of a district general hospital in Siena's Province, Italy. The hospital had3 ORs open 5 days/week 08:00-14:00. Surgery specialties were general surgery, orthopedics, gynecology and urology. Based on a pilot study conducted in 2016, which estimated a 5 times greater risk of having an OR overtime for sessions with a surgical time (incision-suture)>200 minutes, from 2017 all the ORs were scheduled using a maximum surgical time of 200 minutes calculated summing the mean surgical times for intervention and surgeon (obtained from 2014-2016 data). We carried out multivariate logistic regression to calculate the probability of ORs overtime (of 15 and 30 minutes) for the periods 2014-2016 and 2017-2019adjusting for raw ORs utilization. Results The 2017-2019 risk of an OR overtime of 15 minutes decreased by 25% compared to the 2014-2016 period (OR = 0.75, 95%CI=0.618-0.902, p = 0.003); the risk of a OR overtime of 30 minutes decreased by 33% (OR = 0.67, 95%CI= 0.543-0.831, p < 0.001). Mean raw OR utilization increase from 62% to 66% (p < 0.001). Mean number of interventions per surgery sessions increased from 3.1 to 3.5 (p < 0.001). Conclusions This study has shown that an analysis of historical data and an estimate of the optimal surgical time per surgical session could be helpful to avoid both a low and excessive use of the ORs and therefore to increase the efficiency of the ORs. Key messages An accurate analysis of surgical procedures duration is crucial to optimize operating room utilization. A data-based approach can improve OR management efficiency without extra resources.

Leader–follower narcissism and subgroup formation in teams: a conceptual model

2019 ◽  
Vol 34 (6) ◽  
pp. 429-442 ◽  
Author(s):  
Manuel London
Keyword(s):  
Team Performance ◽  
Team Identification ◽  
Goal Congruence ◽  
Content Type ◽  
Team Outcomes ◽  
Performance Pressure ◽  
Proposed Model ◽  
Leaders And Followers ◽  
Trait Activation ◽  
And Performance

Purpose Drawing on existing theory, a model is developed to illustrate how the interaction between leaders and followers similarity in narcissism and goal congruence may influence subgroup formation in teams, and how this interaction influences team identification and team performance. Design/methodology/approach The proposed model draws on dominance complementary, similarity attraction, faultline formation and trait activation theories. Findings Leader–follower similarity in narcissism and goal congruence may stimulate subgroup formation, possibly resulting in conformers, conspirators, outsiders and victims, especially when performance pressure on a team is high. Followers who are low in narcissism and share goals with a leader who is narcissistic are likely to become conformers. Followers who are high in narcissism and share goals with a narcissistic leader are likely to become confederates. Followers who do not share goals with a narcissistic leader will be treated by the leader and other members as outsiders if they are high in narcissism, and victimized if they are low in narcissism. In addition, the emergence of these subgroups leads to reduced team identification and lower team performance. Practical implications Higher level managers, coaches and human resource professions can assess and, if necessary, counteract low team identification and performance resulting from the narcissistic personality characteristics of leaders and followers. Originality/value The model addresses how and under what conditions narcissistic leaders and followers may influence subgroup formation and team outcomes.

Loyalty program in Africa: risk-seeking and risk-averse adventurers

2014 ◽  
Vol 69 (2) ◽  
pp. 137-157 ◽  
Author(s):  
Shogo Mlozi
Keyword(s):  
Consumer Behavior ◽  
Consumer Products ◽  
Loyalty Programs ◽  
Risk Averse ◽  
Loyalty Program ◽  
Content Type ◽  
Risk Seeking ◽  
Proposed Model ◽  
First Time ◽  
The Relationship

Purpose – This article aims to test the relationship between expected attractiveness-satisfaction-loyalty for international adventure tourists visiting Tanzania. The proposed model is based on travel consumer behavior theoretical constructs extracted from the literature. Design/methodology/approach – This article aims to test the relationship between expected attractiveness-satisfaction-loyalty for international adventure tourists visiting Tanzania. The proposed model is based on travel consumer behavior theoretical constructs extracted from the literature. Findings – The findings for overall model differed from the moderating factors of high risk, low risk, first-time visit and repeat visit. Also, the results are interesting when satisfaction is tested as a mediator. Practical implications – Practitioners could consider the fact that repeat visits may change tourists’ perceptions toward destination and may even increase their inclination to take on risks. This may impact innovation of consumer products in tourism. Also, policy makers could benefit on how loyalty programs can be developed to increase performance. Originality/value – The study offers specific strategic recommendations toward different groups of tourists (i.e. first-time, repeat visitors, risk averse, risk seeking) and proposes logic for setting up a loyalty program as a long-term strategy for success.

scholarly journals How to evaluate sustainability of supply chains? A dynamic network DEA approach

2017 ◽  
Vol 117 (9) ◽  
pp. 1866-1889 ◽  
Author(s):  
Vahid Shokri Kahi ◽  
Saeed Yousefi ◽  
Hadi Shabanpour ◽  
Reza Farzipoor Saen
Keyword(s):  
Big Data ◽  
Supply Chains ◽  
Real World ◽  
Performance Optimization ◽  
Network Dynamic ◽  
Network Dea ◽  
Content Type ◽  
Dea Model ◽  
Proposed Model ◽  
First Time

Purpose The purpose of this paper is to develop a novel network and dynamic data envelopment analysis (DEA) model for evaluating sustainability of supply chains. In the proposed model, all links can be considered in calculation of efficiency score. Design/methodology/approach A dynamic DEA model to evaluate sustainable supply chains in which networks have series structure is proposed. Nature of free links is defined and subsequently applied in calculating relative efficiency of supply chains. An additive network DEA model is developed to evaluate sustainability of supply chains in several periods. A case study demonstrates applicability of proposed approach. Findings This paper assists managers to identify inefficient supply chains and take proper remedial actions for performance optimization. Besides, overall efficiency scores of supply chains have less fluctuation. By utilizing the proposed model and determining dual-role factors, managers can plan their supply chains properly and more accurately. Research limitations/implications In real world, managers face with big data. Therefore, we need to develop an approach to deal with big data. Practical implications The proposed model offers useful managerial implications along with means for managers to monitor and measure efficiency of their production processes. The proposed model can be applied in real world problems in which decision makers are faced with multi-stage processes such as supply chains, production systems, etc. Originality/value For the first time, the authors present additive model of network-dynamic DEA. For the first time, the authors outline the links in a way that carry-overs of networks are connected in different periods and not in different stages.

scholarly journals Whole-Genome Relationships among Francisella Bacteria of Diverse Origins Define New Species and Provide Specific Regions for Detection

2016 ◽  
Vol 83 (3) ◽  
Author(s):  
Jean F. Challacombe ◽  
Jeannine M. Petersen ◽  
La Verne Gallegos-Graves ◽  
David Hodge ◽  
Segaran Pillai ◽  
...  
Keyword(s):  
New Species ◽  
16S Rrna ◽  
Response Times ◽  
Amino Acid Sequences ◽  
Clinical Samples ◽  
Whole Genome ◽  
Multiple Gene ◽  
Content Type ◽  
Environmental Surveys ◽  
Genome Comparisons

ABSTRACT Francisella tularensis is a highly virulent zoonotic pathogen that causes tularemia and, because of weaponization efforts in past world wars, is considered a tier 1 biothreat agent. Detection and surveillance of F. tularensis may be confounded by the presence of uncharacterized, closely related organisms. Through DNA-based diagnostics and environmental surveys, novel clinical and environmental Francisella isolates have been obtained in recent years. Here we present 7 new Francisella genomes and a comparison of their characteristics to each other and to 24 publicly available genomes as well as a comparative analysis of 16S rRNA and sdhA genes from over 90 Francisella strains. Delineation of new species in bacteria is challenging, especially when isolates having very close genomic characteristics exhibit different physiological features—for example, when some are virulent pathogens in humans and animals while others are nonpathogenic or are opportunistic pathogens. Species resolution within Francisella varies with analyses of single genes, multiple gene or protein sets, or whole-genome comparisons of nucleic acid and amino acid sequences. Analyses focusing on single genes (16S rRNA, sdhA), multiple gene sets (virulence genes, lipopolysaccharide [LPS] biosynthesis genes, pathogenicity island), and whole-genome comparisons (nucleotide and protein) gave congruent results, but with different levels of discrimination confidence. We designate four new species within the genus; Francisella opportunistica sp. nov. (MA06-7296), Francisella salina sp. nov. (TX07-7308), Francisella uliginis sp. nov. (TX07-7310), and Francisella frigiditurris sp. nov. (CA97-1460). This study provides a robust comparative framework to discern species and virulence features of newly detected Francisella bacteria. IMPORTANCE DNA-based detection and sequencing methods have identified thousands of new bacteria in the human body and the environment. In most cases, there are no cultured isolates that correspond to these sequences. While DNA-based approaches are highly sensitive, accurately assigning species is difficult without known near relatives for comparison. This ambiguity poses challenges for clinical cases, disease epidemics, and environmental surveillance, for which response times must be short. Many new Francisella isolates have been identified globally. However, their species designations and potential for causing human disease remain ambiguous. Through detailed genome comparisons, we identified features that differentiate F. tularensis from clinical and environmental Francisella isolates and provide a knowledge base for future comparison of Francisella organisms identified in clinical samples or environmental surveys.

Modeling and simulation of saturated double rotor induction machine

2018 ◽  
Vol 37 (3) ◽  
pp. 1139-1165
Author(s):  
Koosha Choobdari Omran ◽  
Ali Mosallanejad
Keyword(s):  
Temperature Effects ◽  
Power Efficiency ◽  
Reactive Power ◽  
Induction Machine ◽  
Transient Behavior ◽  
State Equations ◽  
Content Type ◽  
Proposed Model ◽  
The Difference ◽  
Park Transformation

Purpose Double rotor induction machine (DRIM) is a particular type of induction machine (IM) that has been introduced to improve the parameters of the conventional IM. The purpose of this study is to propose a dynamic model of the DRIM under saturated and unsaturated conditions by using the equations obtained in this paper. Also, skin and temperature effects are considered in this model. Design/methodology/approach First, the DRIM structure and its performance will be briefly reviewed. Then, to realize the DRIM model, the mathematical equations of the electrical and mechanical part of the DRIM will be presented by state equations in the q-d axis by using the Park transformation. In this paper, the magnetizing fluxes saturation is included in the DRIM model by considering the difference between the amplitudes of the unsaturated and saturated magnetizing fluxes. The skin and temperature effects are also considered in this model by correcting the rotor and stator resistances values during operation. Findings To evaluate the effects of the saturation and skin effects on DRIM performance and validate the model, the machine is simulated with/without consideration of saturation and skin effects by the proposed model. Then, the results, including torque, speed, stator and rotor currents, active and reactive power, efficiency, power factor and torque-speed characteristic, are compared. In addition, the performance of the DRIM has been investigated at different speed conditions and load variations. The proposed model is developed in Matlab/Simulink for the sake of validation. Originality/value This paper presents an understandable model of DRIM with and without saturation, which can be used to analyze the steady-state and transient behavior of the motor in different situations.

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