Idle Time Research Articles

Optimizing Ketchup Manufacturing Process: A Tecnomatix Simulation for Reduced Waiting Time and Improve Operational Efficiency

This research investigates the optimization of a ketchup production factory layout using Tecnomatix Plant Simulation software to enhance resource utilization and production efficiency. Both push and pull system were used to analyzed the entire ketchup production process, from receiving raw tomatoes to the final product storage, incorporating various processing stations with different processing times. In a push environment, driven by predetermined schedules, the simulation results showed an increasing trend in throughput over 4, 7, 11, and 14 days, with significant efficiency gains over time. In a pull environment, driven by actual customer demand, throughput simulations of 50, 100, 200, and 500 units demonstrated increasing productivity with higher workloads. The findings highlight the importance of optimized workstation layouts to reduce idle time and improve workflow, emphasizing that while push environments maintain consistent productivity, pull environments offer greater efficiency through flexibility and responsiveness to demand.

An Intelligent Manufacturing Management System for Enhancing Production in Small-Scale Industries

Industry 4.0 integrates the intelligent networking of machines and processes through advanced information and communication technologies (ICTs). Despite advancements, small mechanical manufacturing enterprises face significant challenges transitioning to ICT-supported Industry 4.0 models due to a lack of technical expertise and infrastructure. These enterprises commonly encounter variable production volumes, differing priorities in customer orders, and diverse production capacities across low-, medium-, and high-level outputs. Frequent issues with machine health, glitches, and major breakdowns further complicate optimizing production scheduling. This paper presents a novel production management approach that harnesses bio-inspired methods alongside Internet of Things (IoT) technology to address these challenges. This comprehensive approach integrates the real-time monitoring and intelligent production order distribution, leveraging advanced LoRa wireless communication technology. The system ensures efficient and concurrent data acquisition from multiple sensors, facilitating accurate and prompt capture, transmission, and storage of machine status data. The experimental results demonstrate significant improvements in data collection time and system responsiveness, enabling the timely detection and resolution of machine failures. Additionally, an enhanced genetic algorithm dynamically allocates tasks based on machine status, effectively reducing production completion time and machine idle time. Case studies in a screw manufacturing facility validate the practical applicability and effectiveness of the proposed system. The seamless integration of the scheduling algorithm with the real-time monitoring subsystem ensures a coordinated and efficient production process, ultimately enhancing productivity and resource utilization. The proposed system’s robustness and efficiency highlight its potential to revolutionize production management in small-scale manufacturing settings.

A living lab for Structural Health Monitoring at the Nibelungen Bridge Worms for Transfer Learning of Structural Dynamics

The Nibelungen Bridge in Worms, Germany has been selected as a national demonstration structure for advanced non-destructive testing (NDT) and structural health monitoring concepts to extend the lifetime of civil structures and to optimize O&M actions. Parts of the research that involves this bridge as a demonstrator belong to the focus area program SPP100+. In this program, the bridges SHM System has been extended and combined with an additional setup of vibration sensors. The used digital smart sensor with pre-processing functions, the arrangement of the sensors at the structure and additional edge computing capability allows the investigation of transfer learning and other methods directly into the real structure. The living lab with seven triaxial accelerometers can be reconfigured in real-time and adjusted to the needs of AI models for classification. The comparison with the existing conventional SHM sensors has been made possible by hardware synchronization to the existing SHM System and collocating sensors at similar positions, so that a hardware exchange can be an investigated use-case for the transfer learning. During idle times, the system collects vibration data like a conventional SHM system. Keywords: Structural Health Monitoring (SHM), Data acquisition, Sensor technology.

THE CRIMINAL JUSTICE SYSTEM'S RESPONSE TO GAMBLING CRIME

Gambling in all its forms is contrary to religious, moral, and societal values, thus posing a threat to the nation and its interests. Gambling is defined as the act of placing a wager on the outcome of a game or event, with the expectation of improving the probability of a favorable result. This outcome is dependent on a number of factors, including chance, which distinguishes it from other forms of betting. Gambling is considered a deliberate criminal activity because those who engage in it are fully aware of the nature of their actions and the consequences they cause. The formulation of the problem is to identify the elements of the crime of gambling. This research is of the normative variety. The results of this study indicate that the elements of the crime of gambling are as follows: a) Game/race. The actions taken are usually in the form of a game or race. Therefore, it is conducted for the sake of leisure or to fill idle time, with the intention of providing entertainment for the mind. This is a reactive act. However, it is not necessary for the perpetrators to be directly involved in the game. They may simply be spectators or individuals who engage in betting on the course of a game or race.b) Fortune: The outcome of the game or race is more dependent on speculative elements, such as chance or luck, than on the skill or strategy of the players.c) In this game or competition, bets are placed by players or bookmakers. These can be in the form of money or other property, including wives. Due to the existence of bets, there are parties who benefit and are disadvantaged. This element is the most important in determining whether an action can be called gambling.

Idle Time and Its Repercussions on Youth in South Africa: A Recipe of Unemployment Crisis

Purpose: This paper aims to examine the repercussions of idle time on youthful population through critically analyzing the status quo of youth unemployment in South Africa. Youth unemployment has become a major source of concern in South Africa and around the world. The unemployment challenge has thus left many young people in limbo, a state of misery, idleness, and social death. It subsequently exposed many young South African to socially immoral activities such as, inter alia, drug and alcohol use, deceit and theft. Idle time, the period people wastefully spend doing nothing productive, vehemently led many young people to perish, and ostensibly resulted into the economic stagnation in South Africa. This paper fastened its philosophical argument on the biblical aphorism that “an idle mind is the devil’s workshop”. Design/Methodology/Approach: The paper adopted a qualitative desktop-based research methodology as well as document analysis as data analysis tool. Findings: The results established that the persistent challenge of youth unemployment has pushed South Africa’s economic development into a downward spiral toward underdevelopment and vulnerability. Therefore, the country lacks a holistic approach to address idleness and unemployment, to unlock the potential of its youthful population, stimulate economic growth and development. Implications/Originality/Value: It is therefore concluded that youth unemployment has become a serious dilemma in South Africa, that it subjected majority of young people to idleness and encourages them to engage in immoral activities such as crime, making them more susceptible to the onset of drug and alcohol use. The paper recommended that the government should adopt effective mechanisms to engage unemployed youth into the economic mainstream, to enhance development and reduce idle time tragedies.

Retrospective Analysis of Associated Costs and Sources of Variability in OR Utilization Across Weekdays.

Few studies have examined variability in OR utilization across weekdays. We conducted a retrospective analysis to determine OR utilization differences by day of the week and the source and financial effects of any variability. We extracted 55 months of data from a surgical data repository to calculate OR utilization, late starts, idle times, and delays for each weekday. Declines in OR utilization occurred as the week progressed and were attributed to compounding changes in late start, delay, and idle time. The average weekly cost for each OR associated with unused staffed minutes below a target OR utilization of 85% was $19,383, and the comparable lost weekly revenue was $60,256. Perioperative leaders should identify sources of OR utilization variability when developing strategies that enhance outcomes for patients, minimize costs, and maximize revenue.

Markov-reward based estimation of the idle-time in vehicular networks to improve multimetric routing protocols

Analyzing vehicular ad hoc networks (VANETs) poses a considerable challenge due to their constantly changing network topology and scarce network resources. Furthermore, defining suitable routing metrics for adaptive algorithms is a particularly hard task since these adaptive decisions should be taken according to the current conditions of the VANET. The literature contains different approaches aimed at optimizing the usage of wireless network resources. In a previous study, we introduced an analytical model based on a straightforward Markov reward chain (MRC) to capture transient measurements of the idle time of the link formed between two VANET nodes, which we denote as Tidle. This current study focuses on modeling and analyzing the influence of Tidle on adaptive decision mechanisms. Leveraging our MRC models, we have derived a concise equation to compute Tidle. This equation provides a quick evaluation of Tidle, facilitating quick adaptive routing decisions that align with the current VANET conditions. We have integrated our Tidle evaluation into multihop routing protocols. We specifically compare performance results of the 3MRP protocol with an enhanced version, I3MRP, which incorporates our Tidle metric. Simulation results demonstrate that integrating Tidle as a decision metric in the routing protocol enhances the performance of VANETs in terms of packet losses, packet delay, and throughput. The findings consistently indicate that I3MRP outperforms 3MRP by up to 50% in various scenarios across high, medium, and low vehicular densities.

Operational strategy optimization of an existing ground source heat pump (GSHP) system using an XGBoost surrogate model

Ground source heat pumps (GSHPs) are experiencing a surge in popularity due to their efficient use of geothermal energy for building heating and cooling. Optimizing GSHP systems is crucial for maximizing heat extraction efficiency. However, existing research primarily focuses on design-stage optimization, neglecting the potential of improving operational strategies in already deployed systems. This study proposes a novel method for optimizing the operational strategy of existing GSHPs with machine-learning-based surrogate models. Our approach considers both demanded heat extraction and the variability of GSHP operation, including running and idle times. First, a numerical model is established with a closed-loop procedure replicating real-world GSHP operation, capturing dynamic inlet temperature changes. The Latin hypercube sampling is subsequently employed to generate sufficient data for XGBoost model development, where a designed disassembly method is proposed to shed light on the problem of a fixed number of input parameters. The operational strategy of the GSHP system is then implemented to offer solutions for both regular and irregular modes. The effectiveness of the method is validated using 40 days of real-case monitoring data. The results demonstrate that the surrogate model achieves high accuracy, with an absolute temperature difference error below 0.1 K and a heat extraction absolute percentage error (APE) less than 2.0 %. Additionally, the optimized operational strategy reduces total operation time by 2–9 % solely through scheduling adjustments, showcasing its significant practical potential. This study presents an efficient method for optimizing the operational strategies of existing GSHPs while highlighting the value of process-accelerated optimization using numerical simulation-based surrogate models.

Assessment and analysis of the effects of implementing building information modelling as a lean management tool in construction management

PurposeThe purpose of this research paper is to investigate and evaluate the impacts of utilising Building Information Modelling (BIM) as a lean management tool in Bangladesh's construction management field. The paper explores how adopting BIM as a lean management tool can improve and expedite a number of processes in building projects, which will ultimately increase project success, cost-effectiveness and efficiency.Design/methodology/approachA comprehensive survey was conducted to investigate how BIM deployment as a lean management tool affected project outcomes. This research involved a structured survey amongst construction professionals and a case study on a real project in Bangladesh to assess the effects of using BIM as a lean management tool. The data collected from 112 respondents were analysed statistically and qualitatively to identify the effect of BIM as a lean management tool. Additionally, several software tools, including Revit, Navisworks, Design Review and BIM 360, were used to compare conventional and BIM-based methods.FindingsThe research findings demonstrate that utilising BIM practices improves the quality and safety of construction in Bangladesh. The “Improving the quality of construction” (RII = 0.732) achieved the highest rank in the questionnaire survey. In addition, the case study represents that the construction industry can benefit from BIM-based project management. The BIM implementation can shorten the design process by over 50% and save up to 1.5 weeks by minimising idling time. By applying BIM, it is possible to avoid a price rise of roughly 2.5% and a delay of about 11.9% of the original contract period.Practical implicationsThe significance of the results goes beyond the direct advantages of the project's achievements. The successful integration of BIM as a lean management tool in Bangladesh's construction sector suggests transformative potential for the industry. The enhanced coordination and decreased errors point to a future where construction projects can achieve higher levels of precision and reliability. The improved efficiency observed implies a more sustainable and cost-effective future for construction projects in the region.Originality/valueThe research provides a unique perspective on the impact of BIM implementation on project outcomes. It includes a comprehensive survey on BIM adoption as a lean management tool, gathering real-world experiences from construction professionals in Bangladesh. The case study explores the practical implications and advantages of implementing BIM in construction projects. By comparing conventional methods with BIM-based approaches and utilising BIM software, the study contributes value to the construction sector.

Collaborative Production Planning Based on an Intelligent Unmanned Mining System for Open-Pit Mines in the Industry 4.0 Era

Open-pit mining is a cornerstone of industrial raw material extraction, yet it is fraught with safety concerns due to rough operating conditions. The advent of Industry 4.0 has introduced advanced technologies such as AI, the IoT, and autonomous systems, setting the stage for a paradigm shift towards unmanned mining operations. With this study, we addressed the urgent need for safe and efficient production based on intelligent unmanned mining systems in open-pit mines. A collaborative production planning model was developed for an intelligent unmanned system comprising multiple excavators and mining trucks. The model is formulated to optimize multiple objectives, such as total output, equipment idle time, and transportation cost. A multi-objective optimization approach based on the genetic algorithm was employed to solve the model, ensuring a balance among conflicting objectives and identifying the best possible solutions. The computational experiments revealed that the collaborative production planning method significantly reduces equipment idle time and enhances output. Moreover, with the proposed method, by optimizing the configuration to include 6 unmanned excavators, 50 unmanned mining trucks, and 4 unloading points, a 92% reduction in excavator idle time and a 44% increase in total output were achieved. These results show the model’s potential to transform open-pit mining operations by using intelligent planning.

A multi-objective integrated scheduling of remanufacturing system considering time window constrained outsourcing option

The current strategy of collaborative production is garnering attention in the remanufacturing industry. The collaborative execution of tasks through the sharing of production resources such as facilities and labor can effectively augment the utilization rate of end-of-life products. However, existing studies on scheduling problem in remanufacturing systems does not take into this issue. To fill this gap, this study considers an integrated scheduling problem in remanufacturing systems with parallel disassembly workstations, a flexible-shop-type reprocessing shop and a reassembly workstation, where the components obtained by disassembling can be outsourced within the time window given by the outsourcer. To address this issue, a mathematical model aiming at minimizing the completion time and total cost is established. Then, a knowledge guided artificial bee colony algorithm (KGABC) with a five-layer encoding scheme is proposed to solve it. In the KGABC, a two-step heuristic initialization approach is invented based on the characteristics of the disassembly and reprocessing stage. According to the problem-specific knowledge, a reprocessing advance scheduling approach is proposed to reduce idle time; an outsourcing assessment method is presented for determining the feasibility of outsourcing operation; a reassembly priority management strategy is put forward to determine the reassembly sequence of products. In addition, a knowledge guided search strategy is proposed in the onlooker bee phase to enhance the local search ability of the algorithm. Finally, the KGABC is subjected to comparative testing against the state-of-the-art algorithms on numerical benchmark instances with different scales. Simulation results show that the KGABC exhibits significant advantages in terms of solution accuracy, solution diversity, and convergence properties. Moreover, a real case study is adopted to validate KGABC's supremacy in solving the real-world remanufacturing scheduling problem with relatively lower time and cost.

Experimental validation of a predictive energy management strategy for agricultural fuel cell electric tractors

Efficiency and durability are critical challenges for the large-scale adoption of fuel cell powertrains in agricultural tractors due to the demanding nature of farm-related tasks. This study presents a novel predictive energy management strategy that uses forecasts of the average load of the farming cycle to achieve stationary fuel cell operation, thus limiting degradation caused by load cycling, high-power, and low-power operation. The recurrent characteristics exhibited by agricultural duty cycles allow the calculation of highly accurate average load prediction, which can be sufficient for a predictive energy management strategy to reduce degradation without hindering fuel consumption. As a result, this forecast is utilized in conjunction with a straightforward and real-time-capable energy management strategy, which foresees the operation of the fuel cell system at the level of the average load. The work is structured into two main contributions: model-based design of the energy management strategy, and experimental validation on a fuel cell powertrain testbed. Three typical agricultural duty cycles were studied to evaluate the benefits of the proposed strategy, demonstrating that energy management strategies can mitigate fuel cell degradation without compromising system efficiency. In particular, the predictive energy management strategy offers the potential to mitigate the negative impact of tractor operational idling time at the headland turns through the reduction of fuel consumption, thereby enhancing overall tractor performance. The experimental powertrain setup stands as an innovative contribution within the research landscape concerning fuel cell powertrains for tractors and validates the benefits of the proposed strategy. Furthermore, the conducted testbed analyses assume significance in highlighting the effects of the energy management strategy on the physical fuel cell operating factors and emphasizing the comprehensive benefits across the powertrain system, particularly with regard to the thermal management of the fuel cell system, where the prevention of derating is achievable through the implementation of the proposed energy management approach. Overall, the findings of this study have important implications for the development of sustainable and efficient farming procedures through the adoption of fuel cell technology in agricultural machinery.

Mixing dynamics and recovery factor during hydrogen storage in depleted gas reservoirs

Large-scale hydrogen storage is strategically important for society and depleted gas reservoirs have been proposed as a potential solution. However, the mixing with remaining hydrocarbon gas during injection/production cycles and the impact of different reservoir parameters on the hydrogen recovery factor (RFH2) have not been fully understood. We investigate mixing dynamics and analyze the effects of initial hydrocarbon recovery factor, reservoir permeability, well perforation length, intelligent completion, and fractures on RFH2. Fine-grid gas reservoir models with compositional simulation were used to simulate cyclic hydrogen storage. Mixing of injected hydrogen with remaining hydrocarbon gas occurred in three distinct phases in each cycle: 1) displacement of remaining hydrocarbon gas through pressure-driven flow during hydrogen injection, 2) density-driven flow of gases during the idle time after hydrogen injection, and 3) pressure-driven flow of gases towards the production intervals during the hydrogen production phase. Higher RFH2 s were obtained when the storage process was initiated at higher hydrocarbon gas recovery factors. Storing hydrogen in reservoirs with lower permeability also led to higher RFH2 s, provided the well pressure limits were not an issue. In conditions of a 5× to 15× increase in permeability, the injected hydrogen moved upward and spread laterally, positioning it farther from the wellbore. This made hydrogen production more difficult and placed the mixing zone and hydrocarbon gas closer to perforations. Shorter perforation lengths at the top of the formation resulted in the best RFH2 s. Additionally, intelligent completions (that closed perforations producing gas with low hydrogen content) improved RFH2 by providing the possibility of purer hydrogen production. The presence of natural fractures significantly reduced hydrogen recovery, especially in the first few cycles. However, the influence decreased over time as highly conductive flow paths provided by fractures can contribute to the production of the accumulated unrecovered hydrogen from previous cycles.

Optimizing Mixed-Model Synchronous Assembly Lines with Bipartite Sequence-Dependent Setup Times in Advanced Manufacturing

In advanced manufacturing, optimizing mixed-model synchronous assembly lines (MMALs) is crucial for enhancing productivity and adhering to sustainability principles, particularly in terms of energy consumption and energy-efficient sequencing. This paper introduces a novel approach by categorizing sequence-dependent setup times into bipartite categories: workpiece-independent and workpiece-dependent. This strategic division streamlines assembly processes, reduces idle times, and decreases energy consumption through more efficient machine usage. A new mathematical model is proposed to minimize the intervals at which workpieces are launched on an MMAL, aiming to reduce operational downtime that typically leads to excessive energy use. Given the Non-deterministic Polynomial-time hard (NP-hard) nature of this problem, a genetic algorithm (GA) is developed to efficiently find solutions, with performance compared against the traditional branch and bound technique (B&B). This method enhances the responsiveness of MMALs to variable production demands and contributes to energy conservation by optimizing the sequence of operations to align with energy-saving objectives. Computational experiments conducted on small and large-sized problems demonstrate that the proposed GA outperforms the conventional B&B method regarding solution quality, diversity level, and computational time, leading to energy reductions and enhanced cost-effectiveness in manufacturing settings.

Digital thread-driven cloud-fog-edge collaborative disturbance mitigation mechanism for adaptive production in digital twin discrete manufacturing workshop

The collaborative capacity among various computing layers within a digital twin workshop directly impacts the handling of disturbances in discrete manufacturing processes. However, the intricate nature of the cross-space (physical and virtual space) and cross-layer (cloud, fog, and edge) interactions within a digital twin system poses challenges to construct a comprehensive data model and interoperable data flow, leading to a fragmented state of computing layers. To tackle these challenges, this study presents a digital thread-driven theory and method for cloud-fog-edge collaboration in digital twin discrete manufacturing workshops. Firstly, the five-dimensions production information that cross spaces and layers is concatenated and integrated by constructing digital thread models. Subsequently, a disturbance response model is conducted based on these digital thread models to detect, track, and evaluate disturbances. Furthermore, corresponding cloud-fog-edge collaboration strategies are devised for different types and severity of disturbances, facilitating the dynamic production tasks adjustments. Finally, a case study to discuss the performance of the proposed method regarding the maturity of the digital thread model, disturbance response capability, and production self-adaptive adjustment capability. The results demonstrate that the proposed method effectively mitigates makespan and machine idle time under gradual and abrupt disturbances, and saves 46.22% of computing processing time.

Optimizing Three-Dimensional Trade-Off Problem of Time–Cost–Quality over Multi-Mode Projects with Generalized Logic

Clients typically tend to aim for reasonable prices, minimum possible makespans, and the best quality for the construction projects that they engage in. Evidently, weighing the available offers and coming up with an optimal decision can pose challenges for the decision makers. In this regard, the generation of a tool that helps decision makers strike a proper balance among the conflicting project objectives (i.e., time, cost, and quality) is imperative. To this end, this study proposes a method which assists in the selection of the best compromise choices among the options available for each of the project activities. In addition to the time and cost, the proposed method is designed to bring the quality aspect into the equation as well. To quantify the quality, a value referring to the weighted importance and performance of each activity is used. The proposed method is based on a modified multi-objective genetic algorithm (GA) that incorporates the domination concept for the selection of the best solutions out of the potential candidates. The GA-based method is capable of handling an unlimited number of precedence relationships for each activity, and above all, it is able to capture and unravel any type of logical relationship. This very feature significantly improves the practical relevance of this research, as the parallelization of activities is a common practice in real-life projects. Planners benefit from the various types of relationships (i.e., Start to Start, Start to Finish, Finish to Start, and Finish to Finish), and the concept of lag time frequently introduces parallelization into the network. Overlapped activities, in turn, help reduce the unwanted idle times and speed up the project significantly. Accordingly, in order to demonstrate the application and effectiveness of the proposed model, it has been used in the solution of four time–cost–quality (TCQ) trade-off problems, three of which are generated within the context of this paper. The practiced instances include a small benchmark TCQT problem with 18 activities taken from the literature in addition to more complex 29- and 63-activity TCQTPs produced herein based on benchmark time–cost trade-off problems. The performance of the presented approach is ultimately examined over a large-scale, real-case construction project with over four hundred activities and generalized logic in an unprecedented attempt to validate a model in the realm of TCQTPs. The successful results of the experiments reveal the effectiveness of the proposed model and corroborate the feasibility of its application by the planners amidst arduous decision-making processes.

Enhancing productivity through work study - A case of electric power pole cross arm fabrication

In the area of globalization any manufacturing industry must be competent in terms of productivity, quality, cost and delivery. A fundamental improvement in production is necessary to succeed in the international markets. Work study is one of the earliest scientific management methods used to determine the best way to perform production tasks in order to reduce idle time and worker fatigue as a result, increase productivity. Kombolcha Steel Products Industry, the member of MIDROC Ethiopia Technology Group, producing a variety of metal and engineering products, such as steel poles, steel structures, structural hot dip galvanization, corrugated sheet metal, etc. The company's main focus was meeting the delivery deadlines, with little attention to implementing standard working method and procedures during the production. The inefficient method of piercing the holes in the u-channels to fabricate the electric pole cross arms is causing a drop in productivity.The primary objective of the current study is to identify the bottlneck in the process of electric power poles cross arms fabrication, to set standard time for the process and reduce the cycle time. Time and method studies were used to identify the flaws in the current fabrication process and layout design. The efficient technique to manufacture the product has been replaced by redesigning and implementing a new layout. This arrangement accelerated the process by reducing the idle time for the three punching machines that undertake the piercing operations. The modified layout raised production from the target daily production of 420 pieces to 720 pieces in 8 h, resulting in a 71 % reduction in the previous work cycle from 65 to 39 s. After a time and method study, a simple change in the layout with no capital investment not only increased the company's productivity and profitability, but it also reduced worker fatigue due to extra material handling operations of raw materials and intermediate products.

A Penalty Groups-Assisted Iterated Greedy Integrating Idle Time Insertion: Solving the Hybrid Flow Shop Group Scheduling with Delivery Time Windows

A Penalty Groups-Assisted Iterated Greedy Integrating Idle Time Insertion: Solving the Hybrid Flow Shop Group Scheduling with Delivery Time Windows

Effect of the duration of vehicles’ idleness at transfer hubs of urban passenger transport on the conditions of synchronization of passenger transfers

Problem. The task of synchronizing the time of passengers' transfers between routes has been defined as one of the important directions for improving the operation of urban passenger transport in the transport and transfer hub. The simultaneous presence of route vehicles within the stops makes it possible to transfer passengers without waiting. Due to the action of external factors, compliance with the terms of coordination of the vehicles’ arrival at the transport hub is significantly complicated, which leads to a decrease in the efficiency of synchronization of passenger transfers and requires the implementation of additional management measures. Goal. The purpose of the work is to determine the effect of the duration of idleness of route vehicles in a transport interchange on the parameters of synchronization of passenger transfers and conflicting traffic within the stops. Methodology. The methodological basis for establishing the effect of the duration of vehicles’ idleness in a transport interchange on the efficiency of synchronization of passenger transfers is the developed structural model of the interaction of incoming route flows. Such a model describes the functional relationship between the traffic parameters of route vehicles, the distance between the initial stopping point of the route and the transfer point, the technical speed on network sections, the traffic intensity on routes, the idle time at intermediate stopping points, the average time of a passenger transfer and the number of conflict situations. Results. In the process of analyzing the object of the study, it was determined that it is possible to improve the conditions of synchronization of passenger transfers by increasing the duration of the stay of route vehicles at the stops. It was determined that the key role in synchronizing the transfer of passengers is played by the determination of the departure time of route vehicles from the initial stopping point, accounting for external impacts and laws of distribution of random values of the technical speed and the idle time at intermediate points. The developed circuit of functional communication allows to present the process of finding vehicles in a transport interchange as a complex system of synchronization of the route flows interaction. Originality. It is proposed to use a rational time of route vehicles idleness at the stop points of the transport and transfer hub, which allows to partially compensate for the decrease in the effectiveness of the synchronization of passenger transfers. When determining the time of vehicles idleness, the frequency of traffic conflicts that occur with adjacent routes at stopping points is also taken into account. The obtained regularities are presented in the form of characteristic functions, which allow to evaluate the effect of the duration of route vehicles stay in the transport interchange hub on the parameters of the efficiency of route flows interaction. Practical value. According to the results of experimental studies, the dependences of changes in the average passenger transfer time and conflicting traffic of route vehicles were established for the transport and transfer hub "Odeska St." (Kharkiv). It was determined that in the conditions of synchronization of the traffic schedule, the idle time of route vehicles within 180 seconds is rational. This enables to reduce the time of inter-route transfer of passengers and ensure a low level of conflict of the route vehicles traffic.

A queueing-based approach for integrated routing and appointment scheduling

This paper aims to address the integrated routing and appointment scheduling (RAS) problem for a single service provider. The RAS problem is an operational challenge faced by operators that provide services requiring home attendance, such as grocery delivery, home healthcare, or maintenance services. While considering the inherently random nature of service and travel times, the goal is to minimize a weighted sum of the operator’s travel times and idle time, and the client’s waiting times. To handle the complex search space of routing and appointment scheduling decisions, we propose a queueing-based approach to effectively deal with the appointment scheduling decisions. We use two well-known approximations from queueing theory: first, we use an approach based on phase-type distributions to accurately approximate the objective function, and second, we use an heavy-traffic approximation to derive an efficient procedure to obtain good appointment schedules. Combining these two approaches results in a fast and sufficiently accurate hybrid approximation, thus essentially reducing RAS to a routing problem. Moreover, we propose the use of a simple yet effective large neighborhood search metaheuristic to explore the space of routing decisions. The effectiveness of our proposed methodology is tested on benchmark instances with up to 40 clients, demonstrating an efficient and accurate methodology for integrated routing and appointment scheduling.