Total Delay Cost Research Articles

Improved NSGA2 algorithm for recovery of interrupted departure flights

To solve the problem of airline flight disruption caused by emergencies, the disrupted departing flights are restored, a dual-objective optimization model is constructed to minimize the total delay cost of airlines and minimize the total delay time of passengers. An adaptive non-dominated sorting genetic algorithm based on dominance intensity is designed, and three improved operations are proposed: a fast dominance sorting method, a new congestion distance and an adaptive elite retention strategy. The proposed algorithm is verified by the operation data of an airline at Fuzhou Airport. The experimental results show that compared with the traditional plan-first-serve method, the solution obtained by the proposed algorithm is greatly optimized. Compared with the constraint method, the solution time is generally lower than the constraint method and the solution result is close to the optimal result obtained by the constraint method. Compared with multi-objective optimization algorithms such as NSGA2 and MOEAD, the proposed algorithm shows better performance and can solve the problem effectively and efficiently, providing a basis for airlines to achieve optimized solutions.

ANALYSIS OF THE COSTS OF PREVENTING WORK ACCIDENTS IN THE IMPLEMENTATION OF THE OCCUPATIONAL HEALTH AND SAFETY MANAGEMENT SYSTEM IN THE PALOPO CITY ARTS AND SPORTS BUILDING CONSTRUCTION PROJECT

Introduction: The construction industry is characterized by many small companies working with limited resources and substandard safety management systems. The aim of this study: To analyze the costs of preventing work accidents in implementing the work health and safety management system in the arts and sports building construction project in Palopo City. This research uses the method: mixed qualitative and quantitative research with descriptive analysis research design. The selected samples in this research were project contractors, K3 experts and several workers who almost had work accidents. Results: The costs incurred by the company do not meet the requirements of the PUPR ministerial regulations in Circular Letter Number 11/SE/M/2019. Based on the analysis of tangible benefits, the total cost of treatment and the total cost of project delays is IDR 317,859,200. And based on the BCR analysis the results obtained are B/C ≥ 1 for the benefits realized.

A route network resource allocation method based on multi-objective optimization and improved genetic algorithm

In order to solve the problem of imbalance between traffic demand and airspace capacity of high-altitude air route network, reduce unnecessary delay costs, and improve air route operation efficiency, the resource allocation problem of multi-objective air route network for CTOP program is studied. Taking the affected flights in the congested area of air routes as the research object, taking into account the constraints of actual flight operation, FCA time slot resource availability limit, FCA capacity limit, etc., aiming at minimizing the total delay time of each flight and maximizing the fairness of airlines, a multi-objective optimization model for air route network resource allocation is established, and an improved NSGA-II algorithm is designed to solve the model. Based on the actual operation data of air routes in East China, the Pareto optimal solution set is obtained and compared with the traditional RBS algorithm, the average delay time is reduced by 5.49% and the average fair loss degree is reduced by 66.76%. The results show that the proposed multi-objective optimization model and the improved NSGA-II algorithm have better performance, which can take into account the fairness of each airline on the basis of reducing the total delay cost, realize the allocation of optimal flight trajectories and time slot resources, and provide a reference scheme for air traffic control resource scheduling.

An Improved Gravitational Search Algorithm for Task Offloading in a Mobile Edge Computing Network with Task Priority

Mobile edge computing (MEC) distributes computing and storage resources to the edge of the network closer to the user and significantly reduces user task completion latency and system energy consumption. This paper investigates the problem of computation offloading in a three-tier mobile edge computing network composed of multiple users, multiple edge servers, and a cloud server. In this network, each user’s task can be divided into multiple subtasks with serial and parallel priority relationships existing among these subtasks. An optimization model is established with the objective of minimizing the total user delay and processor cost under constraints such as the available resources of users and servers and the interrelationships among the subtasks. An improved gravitational search algorithm (IGSA) is proposed to solve this optimization model. In contrast with the other gravitational search algorithm, the convergence factor is introduced in the calculation of the resultant force and the crossover operation in a genetic algorithm is performed when generating the new particles during each iteration. The simulation results show that the proposed IGSA greatly improves the system performance compared with the existing algorithms.

A market mechanism for multiple air traffic resources

We introduce a model that extends the concept of air traffic flow management slot to the concept of time window, allowing to effectively deal with a network of interacting regulations. The model aims at minimising the total cost of delay of a time window allocation to flights and is based on an integer programming problem. It consists in a market-based mechanism between flights and a central authority to trade time windows, which fulfils the properties of individual rationality (every participating airline has a non-negative profit from the mechanism) and weak budget-balance (the mechanism requires no external subsidisation). Equity is assumed to be respected because the First Planned First Served allocation is an endowment guaranteed to all flights and allocated for free. The proposed market mechanism can be implemented in a distributed manner preventing the disclosure of confidential information by airlines, and is based on the Lagrangian relaxation of the integer optimisation problem, solved through the subgradient algorithm. We present some computational experiments conducted to test the model on some real instances of air traffic data.

Reassigning Departure Slots with Preferences of the Airline and Passengers

The recovery of a departure flight is an important part of airline disruption management. The commonly used optimization objectives for assessing airport slot-scheduling efficiency are delay cost and delay time. However, the current literature does not consider the preferences of airlines and passengers in the recovery process simultaneously. The objective of this paper is to focus on the departure slot reassignment problem and develop a bi-objective optimization model. We introduce a metric for the price of fairness and formulate the airport slot scheduling problem as a bi-objective optimization model that considers the trade-off between the total airline delay cost and total passenger delay time. To quantify the trade-off between total airline delay costs and total passenger delay time, we apply the metric of the price of fairness in the bi-objective model, treating the total airline delay cost as an efficiency metric and the total passenger delay time as a fairness metric, calculating the price of fairness for each Pareto solution. An adaptive non-dominated sorting genetic algorithm-II based on dominant strengths (ANSGA2-DS) is developed to solve this problem. More precisely, three improved operations are presented: the improved fast dominant strength sorting method, new crowding distance improvement, and an adaptive elitist retention strategy. Three scenarios derived from a Chinese airline’s operation data are applied to the proposed bi-objective model and algorithm. The experimental findings demonstrate that the proposed model and method can effectively and efficiently address the problem. This may provide a basis for airline operation controllers to achieve a generally acceptable solution.

A two-stage approach to aircraft recovery under uncertainty

The paper presents a two-stage approach for minimizing the impact of daily disruptions on an airline’s published flight schedule. The problem is characterized by uncertainty in the duration of the disruption and the point in time when its length becomes known. Both a single-commodity network model and multi-commodity network model with side constraints are developed to first determine the flights that are most likely to be affected, and then to adjust their schedules to achieve system-wide optimality. The overall objective is to minimize the weighted sum of total passenger delay costs, cancellation costs, curfew violation costs, and variation from the original schedule. The two types of uncertainty are addressed by examining a range of scenarios that reflect the most likely outcomes. The results provide guidance and a measure of robustness for the flight director as the disruption unfolds. A rolling horizon approach that closely mimics current procedures used by several airlines is also presented to provide a benchmark for comparisons with the two-stage solutions.

TriNymAuth: Triple Pseudonym Authentication Scheme for VANETs Based on Cuckoo Filter and Paillier Homomorphic Encryption.

In VANETs, owing to the openness of wireless communication, it is necessary to change pseudonyms frequently to realize the unlinkability of vehicle identity. Moreover, identity authentication is needed, which is usually completed by digital certificates or a trusted third party. The storage and the communication overhead are high. This paper proposes a triple pseudonym authentication scheme for VANETs based on the Cuckoo Filter and Paillier homomorphic encryption (called TriNymAuth). TriNymAuth applies Paillier homomorphic encryption, a Cuckoo Filter combining filter-level and bucket-level, and a triple pseudonym (homomorphic pseudonym, local pseudonym, and virtual pseudonym) authentication to the vehicle identity authentication scheme. It reduces the dependence on a trusted third party and ensures the privacy and security of vehicle identity while improving authentication efficiency. Experimental results show that the insert overhead of the Cuckoo Filter is about 10 μs, and the query overhead reaches the ns level. Furthermore, TriNymAuth has significant cost advantages, with an OBU enrollment cost of only 0.884 ms. When the data rate in VANETs dr≤ 180 kbps, TriNymAuth has the smallest total transmission delay cost and is suitable for shopping malls and other places with dense traffic.

A Proposed eFSR Blockchain System for Optimal Planning of Facility Services with Probabilistic Arrivals and Stochastic Service Durations

This research developed a framework for electronic planning and management of facility services utilizing blockchain technology. In this framework, an electronic Facility Service Record (eFSR) in blockchain form was developed to manage and control service orders received by its main service center from university facilities via an electronic system. Mathematical models were formulated to determine the optimal schedule and sequence of facility services under probabilistic service order arrivals and stochastic service durations. Each task of a facility service is treated as a block. The scheduling model then assigned blocks (service tasks) to skilled technicians on each scheduling period at a minimal total cost of delay, undertime, and overtime costs, while the sequencing model determined the start and finish times of each block during the planning period. The optimal information of blocks was then confirmed and shared through an electronic network among all relevant facilities and the service center. The developed framework was implemented in university facilities to plan and manage 47 service orders for a total of 140 tasks over a period of twelve days. The results showed that the proposed eFSR is effective in managing optimal service tasks and efficient in improving the utilization and performance of the service center resources. In conclusion, the proposed eFSR with the optimal facility service planning provides real-time assistance and decentralized technology to facilities managers when planning service tasks over multiple periods. These advantages will result in the effective management of facilities and a considerable savings in maintenance resources.

A bandwidth-aware service migration method in LEO satellite edge computing network

With the development of modern sensing technology, large amounts of data are generated in areas that lack terrestrial networks, such as the field and space. This data is transmitted by satellite networks, where the interesting data is only a small part. By introducing edge computing technology, identifying and transmitting interesting data will improve the efficiency of satellite network and the quality of service for users. A satellite edge computing (SatEC) system is proposed in this paper that provides computing services through LEO satellite network. For satellite characteristics with limited resources and mobility, a bandwidth-aware service migration problem is proposed to minimize the long-term total delay cost of the system while all tasks do not exceed its deadline. Converting long-term optimization problem into a series of one-time MINLP problems, an online lazy migration algorithm is firstly proposed to solve optimal service migration decision scheme with ϵ(1+1β)-competitive ratio. Then a hierarchical iterative algorithm is proposed to decouple communication and computing resources. Finally, a bandwidth-aware virtual CPU scheduling for post-copy migration algorithm is proposed to improve the migration speed. Simulation results show that the proposed scheme effectively deal with the topology change of satellite network and reduce the long-term total delay cost of the SatEC system.

Blockchain Design with Optimal Maintenance Planning

Rapid advancement of data management and sharing technology has urged organizations to develop effective maintenance management systems. This research, therefore, proposes and implements an Electronic Repair Records (ERR) system for the blockchain of maintenance planning and management. In ERR, this research develops two optimization models for scheduling and sequencing failure repairs from different types over multiple periods. Each failure repair is treated as a block, for which the data, current hash, and previous hash are obtained from failure repair parameters, resources availability information, and the optimal values of the start and finish times of the assigned failure repairs. The scheduling model assigns failure repairs to technicians and in maintenance shops on each scheduling period at a minimal total cost of delay, undertime, and overtime costs, while the sequencing model sequences the assigned failure repairs at minimal total overtime costs and minimum total repair start times. Once the blocks are confirmed, the blockchain is then shared through an electronic network among all maintenance departments. The developed ERR system was implemented to manage the repairs of 36 failures from different failures in six maintenance shops over a period of three days. The results showed that this system is found to be effective in managing optimal repairs and efficient in improving the utilization of available resources in maintenance shops. These advantages may result in significant savings in maintenance costs and better utilization of resources. In conclusion, the developed ERR system including the optimization models can provide in real-time assistance and de-centralized technology to planning engineers when managing maintenance activities over multiple periods in a wide range of business applications.

Multiobjective Order Promising for Outsourcing Supply Network of IC Design Houses

To decide which orders to be accepted for an IC design house, besides wafers and capacity, several factors also need to be considered, including product flexibility, minimal cost path, order delay cost, and the fairness of order fulfillment rate between customers. This research focuses on a multi-objective order promising problem for the hybrid MTS-MTO outsourcing supply network of an IC design house. The objectives are to maximize total profit, minimize total delay cost of accepted orders, and minimize the discrepancy of order fulfillment rate between customers. The multi-stage and multi-site outsourcing supply network, technical capacity constraints, product flexibility and multi-chip module are considered. This problem is modeled with a multi-objective mixed integer programming model. A heuristic algorithm, called the fairness-based multi-objective order promising algorithm (FMOPA) with four modes is developed to solve the problem. Moreover, experimental design is conducted to evaluate the efficiency of the proposed model with various factors and levels. Analysis of variance (ANOVA) is used to evaluate the factors in accordance to the objectives. The optimal algorithm mode under thirty-two combinations of different environment factors are also evaluated. This can assist decision makers to use the optimal algorithm mode to generate the best favorable result under different environment factors.

Cost-Aware Placement Optimization of Edge Servers for IoT Services in Wireless Metropolitan Area Networks

Edge computing migrates cloud computing capacity to the edge of the network to reduce latency caused by congestion and long propagation distance of the core network. And the Internet of things (IoT) service requests with large data traffic submitted by users need to be processed quickly by corresponding edge servers. The closer the edge computing resources are to the user network access point, the better the user experience can be improved. On the other hand, the closer the edge server is to users, the fewer users will access simultaneously, and the utilization efficiency of nodes will be reduced. The capital investment cost is limited for edge resource providers, so the deployment of edge servers needs to consider the trade-off between user experience and capital investment cost. In our study, for edge server deployment problems, we summarize three critical issues: edge location, user association, and capacity at edge locations through the research and analysis of edge resource allocation in a real edge computing environment. For these issues, this study considers the user distribution density (load density), determines a reasonable deployment location of edge servers, and deploys an appropriate number of edge computing nodes in this location to improve resource utilization and minimize the deployment cost of edge servers. Based on the objective minimization function of construction cost and total access delay cost, we formulate the edge server placement as a mixed-integer nonlinear programming problem (MINP) and then propose an edge server deployment optimization algorithm to seek the optimal solution (named Benders_SD). Extensive simulations and comparisons with the other three existing deployment methods show that our proposed method achieved an intended performance. It not only meets the low latency requirements of users but also reduces the deployment cost.

Recovery of flights disrupted by snowstorm

PurposeTo reduce the time of flight rescheduling, reduce the total delay cost of all flights to a minimum and put forward more references for passengers to take flights, this paper aims to mainly study the recovery of flights affected by snow disaster within the minimum delay time.Design/methodology/approachThe temporal and spatial network flight recovery model is used to optimize all flights of various types of aircraft, and the adjusted flight schedule based on minute delay time is obtained. In addition, for passenger travel flights, the impact of passenger delay cost on the total delay time is minimized as an objective function to calculate the passenger delay cost.FindingsIn this paper, the actual departure time of aircraft is sorted in ascending order. Up to five planes can take off from the runway every 5 min, and the 10-min decision interval is successively delayed. The actual arrival time is sorted by the same method and the sequential delay is calculated to obtain the adjusted flight schedule. As a result, it takes less time to reschedule flights.Originality/valueIn this paper, heuristic algorithm is used to adjust the schedule of delayed flights flexibly, which is convenient for manual modification. This decision method has good robustness and can partially adjust the interrupted flights without affecting other scheduled flights while maintaining the stable operation of the whole plan, greatly improving the efficiency of civil aviation operations and reducing the impact of flight delays.

Optimization Model in Manufacturing Scheduling for the Garment Industry

The garment industry in Vietnam is one of the country's strongest industries in the world. However, the production process still encounters problems regarding scheduling that does not equate to an optimal process. The paper introduces a production scheduling solution that resolves the potential delays and lateness that hinders the production process using integer programming and order allocation with a make-to-order manufacturing viewpoint. A number of constraints were considered in the model and is applied to a real case study of a factory in order to view how the tardiness and lateness would be affected which resulted in optimizing the scheduling time better. Specifically, the constraints considered were order assignments, production time, and tardiness with an objective function which is to minimize the total cost of delay. The results of the study precisely the overall cost of delay of the orders given to the plant and successfully propose a suitable production schedule that utilizes the most of the plant given. The study has shown promising results that would assist plant and production managers in determining an algorithm that they can apply for their production process.

Deep Reinforcement Learning for Computation Offloading and Resource Allocation in Unmanned-Aerial-Vehicle Assisted Edge Computing.

Computation offloading technology extends cloud computing to the edge of the access network close to users, bringing many benefits to terminal devices with limited battery and computational resources. Nevertheless, the existing computation offloading approaches are challenging to apply to specific scenarios, such as the dense distribution of end-users and the sparse distribution of network infrastructure. The technological revolution in the unmanned aerial vehicle (UAV) and chip industry has granted UAVs more computing resources and promoted the emergence of UAV-assisted mobile edge computing (MEC) technology, which could be applied to those scenarios. However, in the MEC system with multiple users and multiple servers, making reasonable offloading decisions and allocating system resources is still a severe challenge. This paper studies the offloading decision and resource allocation problem in the UAV-assisted MEC environment with multiple users and servers. To ensure the quality of service for end-users, we set the weighted total cost of delay, energy consumption, and the size of discarded tasks as our optimization objective. We further formulate the joint optimization problem as a Markov decision process and apply the soft actor–critic (SAC) deep reinforcement learning algorithm to optimize the offloading policy. Numerical simulation results show that the offloading policy optimized by our proposed SAC-based dynamic computing offloading (SACDCO) algorithm effectively reduces the delay, energy consumption, and size of discarded tasks for the UAV-assisted MEC system. Compared with the fixed local-UAV scheme in the specific simulation setting, our proposed approach reduces system delay and energy consumption by approximately 50% and 200%, respectively.

GCS: Collaborative video cache management strategy in multi-access edge computing

With the rapid development of multimedia services in wireless mobile networks, the video data traffic has increased exponentially. Traditional video traffic service based on cloud computing caused a large amount of traffic load and longer access delay, which severely reduces the quality of service (QoS) of users. Multi-Access Edge Computing (MEC) can directly serve user requests so as to greatly reduce the traffic load and shorten access delay. At present, distributed caching is widely used in the caching deployment of base stations (BSs). However, the caching capacity of a single BS is generally particularly limited, which will degrade the performance of wireless mobile network. In this paper, a collaborative caching scheme in the heterogeneous MEC networks is designed, and the edge caching of macro base station (MBS) and small base stations (SBSs) is utilized to bring storage resources closer to users. In addition, an optimization problem of content caching is formulated to minimize the total delay cost of all users requesting content in the MEC networks. In order to solve this problem, cache management strategy (GCS) is proposed, which consists of greedy cache placement strategy and greedy cache update strategy. Finally, numerical simulations demonstrate that the GCS scheme effectively improves the cache hit rate, and significantly reduces the average delay and backhaul traffic load.

Factors influencing peri-operative delays in neurosurgery operating theatres: a prospective study

BackgroundReduction of perioperative delays optimises theatre efficiency, and may provide an opportunity to improve the quality of healthcare delivered. The aim of this quality improvement project was to elucidate the prevalence, causes and impact of perioperative delays in Neurosurgery theatres at our local institution. MethodsA prospective audit analysing elective Neurosurgery cases was conducted from 1st Sept ‘18 to 28th Feb ’19 (6 months).Quantitative and qualitative analysis was performed on data from the Operating Room Scheduling Office System (ORSOS) real-time data capture forms and a custom-made survey filled out by theatre staff. NHS Benchmarking Network national neurosurgery theatre data were utilised as a control group. ResultsA total of 461 (90%) cases started late (average delay of 49 minutes and 21 seconds), 32% (n=164) cases finished early and 31 minutes was the average turnaround time. There was 16.8%(n=124) cancellation rate and 76% theatre utilisation. The most commonly cited causes for late start were lack of patient readiness (62.1%), change in list order (5.3%), anaesthetic delay (5.3%), surgeon delay (4.5%), turnaround delay (2.3%), bed issues (2.3%), emergency (1.5%), fire alarm (0.8%), kit delay (0.8%) and multi-factorial (15.2%). The total cost of late start delays over the study period was £304,266.18. ConclusionsThis study showed late-starts and early-finishes as part of sub-optimal theatre utilisation. Cost-saving could be immense if corporate factors improve such as availability of appropriate beds along with clinical ‘human’ factors. To ameliorate these delays, we are currently implementing strategies to improve compliance of lumbar spinal surgery as day-case guideline. In order to ensure long-term sustainability, this implementation will be re-audited using a Plan-Do-Study-Act cycle in 6 months’ time.

Collaborative delay management towards demand-capacity balancing within User Driven Prioritisation Process

The concept of User Driven Prioritisation Process (UDPP) was introduced to give Airspace Users (AUs) more flexibility under demand-capacity imbalance. This paper presents two UDPP-DCB models, built on the UDPP principle, using the Selective Flight Protection (SFP) approach to minimize the total delay cost. AUs are enabled to adapt their operations in a more cost-efficient way in the presence of capacity constraints in airspace, optimizing their flights to keep the priorities track. Then, ATFM integrates the AUs’ decision to reassign the time slots and the preferred rerouting trajectories. Results suggest that the delay cost for AUs can be largely reduced through applying the UDPP-DCB models proposed in this paper, while allowing rerouting proves effective in reducing the system delay cost.

Research on the Recovery of Irregular Flights under Uncertain Conditions

This paper mainly studies the problem of irregular flights recovery under uncertain conditions. Based on the analysis of the uncertain factors affecting the flight, taking the total delay time and the total cost of flight delay as the objective function, and considering the constraints of flight plan and passenger journey, an uncertain objective programming model is constructed. Finally, taking OVS airport temporarily closed due to bad weather as an example, the results show that better quality optimization scheme can be obtained by integrating passenger recovery with narrow sense flight recovery stage and implementing integrated recovery.