Operational Management Framework Research Articles

Analysis of Approaches to Assessing Flight Delays Due to Technical Issues at Airline Network Airports Within the Operational Management Framework

ABSTRACT This paper analyzes approaches to developing models for the technical exploitation (TE) of aircraft within operational management framework. The operational management contour of the TE of the airline’s AC fleet is considered as a TE process consisting of a sequence of flight routes, which, in turn, comprise a sequence of flights between airports in the airline network during the calendar interval when the aircraft are in this contour. We present a model that evaluates the capacity for aircraft recovery at the network airports of a hypothetical airline (dubbed “RAF”) if the need so arises, utilizing the probabilistic and temporal characteristics of the airports used for aircraft recovery.

Integrating blockchain and ZK-ROLLUP for efficient healthcare data privacy protection system via IPFS

With the rapid development of modern medical technology and the dramatic increase in the amount of medical data, traditional centralized medical information management is facing many challenges. In recent years blockchain, which is a peer-to-peer distributed database, has been increasingly accepted and adopted by different industries and use cases. Key areas of healthcare blockchain applications include electronic medical record (EMR) management, medical device supply chain management, remote condition monitoring, insurance claims and personal health data (PHD) management, among others. Even so, there are a number of challenges in applying blockchain concepts to healthcare and its data, including interoperability, data security privacy, scalability, TPS and so on. While these challenges may hinder the development of blockchain in healthcare scenarios, they can be improved with existing technologies In this paper, we propose a blockchain-based healthcare operations management framework that is combined with the Interplanetary File System (IPFS) for managing EMRs, protects data privacy through a distributed approach while ensuring that this medical ledger is tamper-proof. Doctors act as full nodes, patients can participate in network maintenance either as light nodes or as full nodes, and the hospital acts as the endpoint database of data, i.e., the IPFS node, which saves the arithmetic power of nodes and allows the data stored in the hospitals and departments to be shared with the other organizations that have uploaded the data. Therefore, the integration of blockchain and zero-knowledge proof proposed in this paper helps to protect data privacy and is efficient, better scalable, and more throughput.

The human resources and knowledge management integrated role in Industry 4.0/5.0: a Human-Centric Operations Management framework

The human resources and knowledge management integrated role in Industry 4.0/5.0: a Human-Centric Operations Management framework

UAV-fleet management for extended NextG emergency support infrastructure with QoS and cost aware

The recent developments in Unmanned Aerial Vehicles (UAV)-assisted wireless communications demonstrated the valuable contributions UAVs could offer to support communication infrastructures under duress. However, due to the complicated and costly operation management, the use of UAVs was extremely limited to fixed pre-arranged deployment or short-term ad-hoc service support. Unfortunately, fixed deployments are not suitable for emergencies such as unexpected failures or overhead; a situation ad-hoc deployment would serve much better. The main obstacle facing ad-hoc deployment models is the increased cost of operation and complicated power management. This paper presents a reliable self and mission-aware multi-hop UAV operation management to support communication networks in case of infrastructure failure or overload for extended time and distances. The formalized optimization offers a solution to the operation management challenges for ad-hoc UAV deployment as a balanced multi-objective, multi-dimensional convex-optimization problem. The paper also provides the pre-flying numerical analysis and parameters to enable optimal mission-oriented extension coverage as a solution guiding the presented UAV operation management framework. The optimization problem is simulated for a 5G-sub 6 cellular network to assess the proposed framework’s efficiency and effectiveness in a coverage extension situation. Results showed that the proposed framework could facilitate extended flight time, overlay network support, and stable service provisioning.

Artificial neural network model for predicting CO2 heat pump behaviour in domestic hot water and space heating systems

The natural refrigerant, CO2, possesses thermophysical properties that make it highly suitable for domestic hot water (DHW) production using heat pump technology. In this study, the development and validation of an artificial neural network (ANN) model that enables efficient design and control of a CO2 heat pump is presented. The study employs experimental data from a CO2 heat pump with a nominal heat capacity of 8 kW. The fully instrumented rig includes the heat pump and a pump rig designed to generate system temperatures representative of various space heat and DHW demands. A comprehensive dataset was generated through systematic variation of inlet temperatures and setpoints. The ANN provides predictions for outlet temperatures, heat production, and electricity consumption utilizing inlet flow rates, temperatures, and setpoints as inputs. These predictions are important for condition monitoring or in a smart operation management framework that determines optimal schedules for the machine.

MOVING FROM BUREAUCRACY TO ADHOCRACY: AN INNOVATIVE APPROACH TOWARDS TORWARDS AN IMPROVED OPERATIONS STRATEGY

This review paper seeks to analyse and elucidate on the depth and extent of public sector managers’ involvement in creating, inculcating and sustaining an innovative culture in their public spheres thus shifting from bureaucracy and adopting an adhocratic organisational culture. This process involved a systematic review, synthesis and analysis of several articles, journals and books as a n exhaustive review of literature. A selection of articles and journals articulating the concepts of bureaucracy, adhocracy and public sector innovation were analysed. This analysis revealed that there is a paradigm shift in the organisational culture, design and strategy of some public sector organisations and institutions as they seemingly incorporate bureaucracy, adhocracy and innovation as an endeavour to adapt to the dynamic nature of the public service and as a mechanism to enhance organisational functionality and capability of the state to deliver services. This paper further establishes a glaring and inherent relationship between adhocracy culture and strategy implementation as outlined and described in Quin’s theory of Competing Values Framework (CVF) supported by McKinsey 7S Framework. This paper analyses the current highly bureaucratised public sector can implement and infuse in their organisational structure, operations and culture adhocracies towards the realisation of the public sector innovation concept. The analysed articles are relevant and befitting to the topic as they link bureaucracy and adhocracy and how a possible shift can bring about an innovative and creative public service. A qualitative research approach was used where existing literature collected as research empiricism and the empirical findings of the articles were documented. This review paper is novel because it seeks to contribute to the current debate in the literature of public sector innovation through the use adhocracy and bureaucracy. This paper concludes that there is a symbiotic relationship between adhocracy, strategy implementation and innovation It recommends that the leadership and management of public sector organisations should work towards a structure of establishing a structure that accommodates adhocracy within their organisation as a recipe for effective strategy implementation and innovation. Both operational and strategic levels of leadership and management should be structured such an adhocratic culture within the organisation prevails in order to drive innovation through a paradigm shift from a bureaucratic approach to an innovative culture through adhocracy. Implications of this paper include a revision of the Department of Public Service Administration’s Operations Management Framework operations strategy to entail adhocracy as a central tenet in formulating the Service Delivery Model, Service Delivery Improvement Plan and Organisational structure design. This paper further recommends a similar survey to be undertaken in individual government departments and further pursuit of adhocracy culture in other semi government organisations to test appositeness.

Proposed museum service operations framework for addressing overcrowding: A Taipei case study

AbstractAlthough the high growth of the tourism market contributes to a record number of visitors to landmark museums, they also encounter the overcrowding problem. This study reviewed the case of National Palace Museum (NPM) in Taipei to discuss the overcrowding problem and provide suggestions to alleviate this problem. This study adopted document analysis to summarize NPM's policies regarding the overcrowding problem that were identified by performing text mining of visitors' reviews for the NPM. And a content analysis involving semantic analysis and coding of negative reviews for NPM retrieved from TripAdvisor was performed for extracting complaint categories regarding the overcrowding problem. On the basis of the results, a service operation management framework in response to museum overcrowding is proposed. This study suggests that a museum can manage the overcrowding problem on the basis of visitor characteristics by adjusting visitor demand, optimizing the service design, diverting visitors' flow, elaborating the floor plan and exhibition design, or forming a decentralized museum hub to move must‐see exhibits for tourists to a stand‐alone pavilion or branch to disperse the crowd.

FAST: A Forecasting Model With Adaptive Sliding Window and Time Locality Integration for Dynamic Cloud Workloads

The workload predictor has attracted attention as a key component of the proactive service operation management framework. However, the request and resource workloads of cloud applications are highly dynamic. Existing approaches decompose the original workload into trend, seasonal, and random components, establish models accordingly, and then combine all outputs to generate results. Indeed, the random component usually has significant heteroscedasticity and noise, having little or even a negative effect on model accuracy improvement. In our model, trend and seasonal components are seen as macro workload changes, and the micro workload changes are obtained by an adaptive sliding window algorithm. Therefore, we propose an ensembling model named FAST for Forecasting workloads with Adaptive Sliding window and Time locality integration. Notably, we propose an adaptive sliding window algorithm that considers trend correlation, time correlation, and random fluctuations of workload for online regression to achieve higher accuracy with lower overhead; and for the error-based integration strategy, we propose a time locality concept for local-predictor behavior and develop a multi-class regression algorithm for model integration. Finally, we conduct experiments on Google cluster trace datasets which show FAST has better accuracy than all other state-of-the-art models for dynamic workloads.

Attitudes of Directors towards Governance Practices in Government Institutions and Companies in Jordan

This research paper aimed to examine the attitudes of directors towards governance practices in government institutions and companies in Jordan. The investigated governance practices included the following pillars: legal and institutional frameworks, strategy and performance management frameworks, human capital management frameworks, resource management frameworks and operational management frameworks. The examined pillars of governance practices were measured by developing and distributing a 5-point scale questionnaire to directors who are responsible for institutional development in government institutions and companies listed in the Budgets Law of Government Units for the fiscal year 2019, the number of which was 57 government institutions and companies. The response rate was 79%. The results of one-sample t-test indicate that the attitudes of directors towards governance practices and related pillars are well above the average level. Results of correlation test indicate that there are statistically significant correlations between the five pillars of governance practices at the 0.01 significance level. The research concluded that governance practices include adherence to specifications and compliance with frameworks, guidelines and standards, thus improving the quality of service delivery and at the same time improving the use of resources, which enhances people's trust in government institutions and companies.

Payment outages: Experiences and issues

Major operational incidents in payment systems suggest the need to improve their resiliency. As payment infrastructures become more digital, integrated and interdependent, with users demanding real-time and uninterrupted services, a higher degree of resilience is required. Risks that could trigger major disruptions have become more acute given the rise in power outages, cyber incidents and natural disasters. International experiences suggest the need to strengthen (1) reliability objectives, (2) redundancies, (3) assessment of critical service providers, (4) endpoint security and (5) alternative arrangements. A comprehensive restructuring of resiliency plans — across the entire payment value chain — is also needed. This paper draws lessons from recent operational incidents to suggest ways in which operational risk management frameworks could be strengthened given evolving payment systems, increasing user expectations, developing technologies and varying country circumstances. The objective is to share observations from international experiences and explore operational and developmental issues to facilitating the stronger operational resilience of existing and future payment systems.

Rhythmic Control of Automated Traffic—Part II: Grid Network Rhythm and Online Routing

Connected and automated vehicle (CAV) technology is providing urban transportation managers tremendous opportunities for better operation of urban mobility systems. However, there are significant challenges in real-time implementation as the computational time of the corresponding operations optimization model increases exponentially with increasing vehicle numbers. Following the companion paper (Chen et al. 2021), which proposes a novel automated traffic control scheme for isolated intersections, this study proposes a network-level, real-time traffic control framework for CAVs on grid networks. The proposed framework integrates a rhythmic control method with an online routing algorithm to realize collision-free control of all CAVs on a network and achieve superior performance in average vehicle delay, network traffic throughput, and computational scalability. Specifically, we construct a preset network rhythm that all CAVs can follow to move on the network and avoid collisions at all intersections. Based on the network rhythm, we then formulate online routing for the CAVs as a mixed integer linear program, which optimizes the entry times of CAVs at all entrances of the network and their time–space routings in real time. We provide a sufficient condition that the linear programming relaxation of the online routing model yields an optimal integer solution. Extensive numerical tests are conducted to show the performance of the proposed operations management framework under various scenarios. It is illustrated that the framework is capable of achieving negligible delays and increased network throughput. Furthermore, the computational time results are also promising. The CPU time for solving a collision-free control optimization problem with 2,000 vehicles is only 0.3 second on an ordinary personal computer.

An integrated user‐system approach for shelter location and evacuation routing

AbstractDisasters are catastrophic events that can severely affect the life conditions of entire communities. Disaster‐related issues are usually dealt with according to the Disaster Operations Management framework, which is composed of four phases: mitigation, preparedness, response, and recovery. This work focuses on two crucial operations belonging to the response phase: shelter location and evacuation of endangered populations. Specifically, the ultimate scope of this paper is to present some applications of a scenario‐based mixed‐integer two‐stage linear program which integrates shelter location with two different types of evacuation, self‐ (or car‐based) evacuation and supported‐ (or bus‐based) evacuation, namely the Scenario‐Indexed Shelter Location and Evacuation Routing (SISLER) model. The SISLER model is solved using an off‐the‐shelf optimization software, whose performance is improved through the addition of some valid inequalities which are added at the root node of the solution tree to improve the lower bound. Computational results are reported for both testbed instances and a realistic case study (Sioux Falls network). The analysis of the solutions provides some useful managerial insights for relevant stakeholders working within the shelter location and evacuation planning area, such as emergency management practitioners and public service providers.

BIM-based 4D mobile crane simulation and onsite operation management

Heavy industrial construction, particularly when modularization is adopted, requires thorough heavy-lift studies to manage the extensive use of mobile cranes in congested and dynamic site layouts. This study introduces an integrated 4D crane simulation and onsite operation management framework for multiple concurrent mobile crane operations in the BIM environment with the support of a comprehensive database. Unlike similar recent efforts, the proposed framework is built upon previously developed crane planning and optimization systems, which enables generating accurate operation plans both in micro (objects) and macro scales (site). This framework addresses five different components regarding onsite crane operation management, including BIM-based multi-crane lift animation, scheduling and cost analyses, motion controlling in the BIM environment, safety monitoring and clash detection, and spatiotemporal site analyses. The developed framework's functionality is validated by implementing it into a portion of a real project in Alberta, Canada, through three different cases.

Performance Predictive Analytics for Operations Management of Shared Services

Shared services have been widely used in many organizations as an alternative to outsourcing. For shared services, common services are standardized and consolidated across multiple organizations to reduce the operational cost and to increase information and knowledge sharing. Two major advantages of shared services over outsourcing are long-term stable cost-saving and knowledge sharing. One important aspect of successful operations management of shared services is to ensure the quality of services delivered by a shared service provider to each individual partner organization. This paper proposes a performance predictive analytics framework for operations management of shared services. The paper presents a case study to demonstrate the usefulness and effectiveness of this framework.

Design of condition-based maintenance framework for process operations management in pharmaceutical continuous manufacturing.

Continuous manufacturing, an emerging technology in the pharmaceutical industry, has the potential to increase the efficiency, and agility of pharmaceutical manufacturing processes. To realize these potential benefits of continuous operations, effectively managing materials, equipment, analyzers, and data is vital. Developments for continuous pharmaceutical manufacturing have led to novel technologies and methods for processing material, designing and configuring individual equipment and process analyzers, as well as implementing strategies for active process control. However, limited work has been reported on managing abnormal conditions during operations to prevent unplanned deviations and downtime and sustain system capabilities. Moreover, although the sourcing, analysis, and management of real-time data have received growing attention, limited discussion exists on the continued verification of the infrastructure for ensuring reliable operations. Hence, this work introduces condition-based maintenance (CBM) as a general strategy for continually verifying and sustaining advanced pharmaceutical manufacturing systems, with a focus on the continuous manufacture of oral solid drug products (OSD-CM). Frameworks, such as CBM, benefit unified efforts towards continued verification and operational excellence by leveraging process knowledge and the availability of real-time data. A vital implementation consideration for manufacturing operations management applications, such as CBM, is a systems architecture and an enabling infrastructure. This work outlines the systems architecture design for CBM in OSD-CM and highlights sample fault scenarios involving equipment and process analyzers. For illustrative purposes, this work also describes the infrastructure implemented on an OSD-CM testbed, which uses commercially available automation systems and leverages enterprise architecture standards. With the increasing digitalization of manufacturing operations in the pharmaceutical industry, proactively using process data towards modernizing maintenance practices is relevant to a single unit operation as well as to a series of physically integrated unit operations.

Evaluation of Flood Risk Management in Japan through a Recent Case

Major disasters cause loss of life and serious disturbance to livelihoods. Integrative and innovative risk management is needed to reduce the impacts of such a disturbance. To pursue this, a better understanding of the current level of disaster risk management is indispensible. Following this line of thinking, the present work was intended to evaluate the various aspects of flood risk management in Japan using a recent case of major flooding that occurred last autumn as a result of a major storm. The focus was on the vulnerability assessment for the disaster-stricken area using various kinds of information including topographic features, land use, flood warning system, evacuation behavior, levee structure, insurance system and government support for recovery. Such an integrative assessment shed new light on vulnerability-generating mechanisms. In particular, it found that the disaster-stricken area has a high level of coping capacity related to flood disasters. However, the high level of coping capacity hinders the reduction of susceptibility to the impacts of flooding. Based on this finding, a new, simple but operational vulnerability management framework was proposed, which may help decision-makers prioritize actions for vulnerability reduction.

Preliminary Study of Web Based Decision Support System to Select Manufacturing Industry Suppliers

Industry 4.0 is an integration between automation and manufacturing industry which requires the use of information technology to implement it. In the operational management framework, there is a supply chain management function that is strongly influenced by quality suppliers. Because choosing a quality new supplier is not an easy thing, we need a reliable application tool and utilizing web-based artificial intelligence technology for the selection of suppliers in the manufacturing industry. The selection of new suppliers is a complex problem because it involves multiple criteria, therefore it is necessary to make a decision support system that is able to complete supplier selection properly. Although the selection of suppliers in the manufacturing industry is not new, in the era of Industry 4.0, a decision support system that is used to choose an absolute supplier based online that must be accessible via the web or mobile application. Therefore the writer's idea in this paper is very relevant to be implemented in the manufacturing industry in all fields in the industrial era 4.0.

Multi-Staged, Multi-Objective Optimization for Operation Management in Line-less Mobile Assembly Systems (LMAS)

Manufacturing companies are often forced to higher levels of production system’s transformation abilities by competitive circumstances in industrial assembly. The Line-less Mobile Assembly System (LMAS) principle, taking advantage of mobilization of productive resources, maximizing responsiveness to market trends, was previously introduced. To date, determining resource assignment, locations and scheduling problems as raised by this principle, are treated separately. Therefore, an integrated mixed-integer program (MIP) is proposed. For system state description as optimization input, an operation management framework is necessary, contrary to the classic automation pyramid. Model-validation is performed based on industry-data for both, mapping accuracy and identifying economic advantages.

The new conceptual model for print management: total production management

This paper presents the development of a conceptual framework for operations management in the printing industry. The implications of using the proposed model (total production management) in a printing press has been analysed thoroughly. A novel conceptual model called 'total production management' has been introduced which is a combination of the best practices: total productive maintenance (TPM) and total quality management (TQM) in conjunction with the human factors (HF) and contextual variables (CV). The study identifies the best practices adopted in the print industry and adopts an integrative approach with socio-technical issues as the addendum. System Dynamics modelling and simulation has been used to study the model performance. Even though the proposed model is in the context of print industry, it can be applied to any manufacturing industry with minor modifications.

Probabilistic operation management of automated distribution networks in the presence of electric vehicles and renewable energy sources

Distribution automation is well recognized as an effective solution to enhance the reliability and efficiency of these grids in a timely manner. This paper introduces an effective probabilistic operation framework for the automated distribution networks (ADNs) incorporating the plug-in electric vehicles (PEVs) charging/discharging schemes in the presence of different renewable energy sources (RESs). To this end, this paper pursues four different strategic approaches. Firstly, an effective fuzzy based probabilistic method is proposed to model the forecast error in the wind and solar units well as the load demand through the cloud theory. Secondly, an appropriate framework is devised to model the PEVs random behaviour considering their essential parameters such as the charging/discharging rate and arrival/departure time to/from the parking lots (PLs), the discharging level at driving mode on the road and the effects of battery degradation. As the third goal, an appropriate objective function which can consider automation indices including the social welfare and reliability is considered. Since the operation problem is a nonlinear continuous non-numerical problem, it requires an applicable and effective optimization algorithm which is regarded as the fourth goal of this paper. In this regard, a new θ-modified bat algorithm is introduced to find the optimal solution of the problem. The proposed model is simulated and examined on the IEEE 69-bus standard test system wherein results reveal the effectiveness and applicability of the proposed operation management framework.