Idle Time Research Articles

Optimization of Pickup Vehicle Scheduling for Steel Logistics Park with Mixed Storage

Pickup vehicle scheduling in steel logistics parks is an important problem for determining the outbound efficiency of steel products. In a steel logistics park, each yard contains different types of steel products, which provides flexible yard selection for each pickup operation. In this case, the yard allocation and the loading sequence for each vehicle must be considered simultaneously in pickup vehicle scheduling, which greatly increases the scheduling complexity. To overcome this challenge, in this paper, we propose a pickup vehicle scheduling problem with mixed steel storage (PVSP-MSS) to optimize the makespan of pickup vehicles and the makespan of steel logistics parks simultaneously. The optimization problem is formulated as a multi-objective mixed-integer linear programming model, and an enhanced algorithm based on SPEA2 (ESPEA) is proposed to solve the problem with a high efficiency. In the ESPEA, a cooperative initialization strategy is firstly proposed to initialize the vehicle pickup sequence for each yard. Then, an insertion decoding method is designed to improve the scheduling efficiency, utilizing the idle time of a yard. Furthermore, local search technology based on critical paths is proposed for the ESPEA to improve the solution quality. Experiments are executed based on data collected from a real steel logistics park. The results confirm that the ESPEA can significantly reduce both the makespan of each pickup vehicle and the makespan of the steel logistics park.

Bill It Right: Evaluating Public Charging Station Usage Behavior under the Presence of Different Pricing Policies

This study investigates for the first time how public charging infrastructure usage differs under the presence of diverse pricing models. About 3 million charging events from different European countries were classified according to five different pricing models (cost-free, flat-rate, time-based, energy-based, and mixed) and evaluated using various performance indicators such as connection duration; transferred energy volumes; average power; achievable revenue; and the share of charging and idle time for AC, DC, and HPC charging infrastructure. The study results show that the performance indicators differed for the classified pricing models. In addition to the quantitative comparison of the performance indicators, a Kruskal–Wallis one-way analysis of variance and a pairwise comparison using the Mann–Whitney-U test were used to show that the data distributions of the defined pricing models were statistically significantly different. The results are discussed from various perspectives on the efficient design of public charging infrastructure. The results show that time-based pricing models can improve the availability of public charging infrastructure, as the connection duration per charging event can be roughly halved compared to other pricing models. Flat-rate pricing models and AC charging infrastructure can support the temporal shift of charging events, such as shifting demand peaks, as charging events usually have several hours of idle time per charging process. By quantifying various performance indicators for different charging technologies and pricing models, the study is relevant for stakeholders involved in the development and operation of public charging infrastructure.

When outpatient appointment meets online consultation: A joint scheduling optimization framework

While the provision of Online Consultation Services (OCS) has brought convenience to patients, the original offline outpatient workflow may struggle to maintain efficiency. Hospital managers’ concerns lie on how to reconcile the operations of offline service with the provision of OCS and what are the latent impacts of OCS on the operational performances of healthcare system. The outpatient appointment scheduling is a crucial part of the workflow decision that affects both quality of offline services and the fragmented idle time that doctors can reply to their Follow-up patients (FUP) during working hours. To smooth the fluctuations in workload of OCS, we introduce a novel decision variable—the recommended maximum number of FUP that a doctor should serve during overtime hours (FUP-MN)—and propose an inter-day service shifting strategy that any requests for OCS exceeding the FUP-MN will be deferred to be served in the fragmented idle time of the next session. We formulate a stochastic programming model to search for the optimal combination of the appointment schedule and FUP-MN. Propositions are conducted to demonstrate the model is valid not only in coordinating the dual demand for online and offline services, but also in restricting the potential overflow of OCS requests. We provide a general solution approach and also develop a heuristic algorithm to quickly obtain optimal workflows at different OCS demand levels. The numerical experimental results indicate that an appropriate introduction of OCS can give the healthcare system a strong ability to resist the adverse effects of uncertainty.

Evaluating the Effect of Deposition Strategy on Mechanical Characteristics of 316L Parts Produced by Laser Powder Directed Energy Deposition Process

The production of large components, the possibility to repair damaged parts and the ability to produce components of multi-graded or functional-graded materials are the main drivers of the research and the application of the Laser Powder Directed Energy Deposition (LP-DED) process. However, the knowledge of the process and of the relationships among the process parameters and the characteristics of the produced part is currently not completely understood. The aim of this paper is to fill the current gap in the scientific literature related to the effect of the deposition strategy on the part quality. Therefore, the influence of two of the most important parameters, namely the deposition path and the idle time, on the residual stresses and porosity is evaluated on 316L samples produced by the LP-DED process. The obtained results show that both parameters have a high influence on the porosity level and the residual stresses. In particular, it can be seen that a high value of idle time worsens both porosity and residual stresses. Then, it is also observed that the raster deposition path leads to a lower value of stresses.

Multiserver Retrial Queue with Two-Way Communication and Synchronous Working Vacation

This work investigates a two-way communication retrial queue with synchronous working vacation and a constant retrial policy. During the idle time, a server makes an outgoing call after a random length. The service time of the incoming call and outgoing call obeys exponential distribution with different rates. If the incoming call finds all servers to be unavailable, it may or may not enter orbit. All servers immediately go on vacation simultaneously as soon as they find an empty system after the service finishes. During vacation, the servers can provide a service to those incoming calls, but this is at a lower-speed rate. The stationary probability distribution and the ergodic condition are obtained utilizing the matrix geometric technique. Some system characteristics are developed. Using MATLAB software, the variation in average orbit length, idle ratio, and the average number of servers in different server states is plotted for different values of the incoming/outgoing call rate and retrial rate. We further propose a multi-objective optimization model from which the optimal rate of outgoing calls and optimal vacation rate are explicitly obtained.

Influence of selected contaminations and melt filtration on the properties of ABS and ABS recyclate

AbstractPlastic recyclates can be contaminated with a variety of impurities due to their previous life, which cannot always be perfectly separated in recycling pretreatment steps. Hence melt filtration is an important step in reprocessing plastic recyclates to achieve material of sufficient purity, but it also leads to more stress on the material due to higher pressures and longer processing times. In our study, we investigated the influence of typical particulate impurities as may occur in recyclate on the mechanical and rheological properties of ABS. Even low amounts of contaminations lowered the key properties. Melt filtration of post‐consumer ABS recyclate with different mesh size melt screens showed little improvement of mechanical properties, but longer idle times and loss of material. Damage to the material caused by finer filtration could be shown by thermal measurements.Highlights Determination of properties of ABS contaminated with 0.5–2.0 wt.% of selected impurities (wood and rubber particles). Melt filtration of post‐consumer ABS recyclate with different melt screens. Investigation of process and properties of filtered ABS recyclate.

Optimal maintenance policy considering imperfect switching for a multi‐state warm standby system

AbstractA novel maintenance policy for a two‐component warm standby system with multiple standby states is presented in this paper. Two standby states, that is, cold and warm standby, for components in the system are considered. Components can realize the transition from the cold standby to the warm standby state by periodic switching, intended to shorten recovery time and save system operating costs. Preventive maintenance (PM) and preventive switching (PS) of components are considered. In the PS strategy, the standby component can switch before the operating component fails. In addition, additional standby failure modes based on idle time are studied. Derive the long‐term average cost of the system through the semi‐regenerative process. A numerical example eventually verifies the feasibility of this paper's proposed maintenance and switching strategy.

An integrated Bi-objective green vehicle routing and partial disassembly line problem for electronic waste: an industrial case study

ABSTRACT Today, serious environmental problems arise with overconsumption. The most effective way of protecting the environment is recycling. To obtain maximum benefit from the recycling activities depends on the effective and robust design of the disassembly lines. In addition, the distribution plan of products to be recycled is as important as the disassembly process. Therefore, this study includes the integrated optimization of the partial disassembly line balancing problem and the multi-objective green vehicle routing problem for the first time. The integrated problem is formulated as a Multi-Objective Mixed Integer Linear Programming. The model’s objectives consist of minimization of total CO2 emission and cost. The Pareto optimal solution set is found by solving this problem with the Augmented ℇ-Constraint Method. The model respectively determines (i) the total number of opened workstations in the disassembly center, (ii) optimal task assignments to workstations in disassembly centers and non-disassembled tasks, (iii) total workstation times and idle times of each workstation, (iv) optimal distribution routes for all vehicles. This study examines a case study in Turkey. To determine the best Pareto optimal solution for homogeneous and heterogeneous fleets, the integrated multi-criteria decision-making method consisting of EWM (Entropy Weight Method) and COPRAS (Complex Proportional Assessment) is used.

An Integrated Approach to Precedence-Constrained Multi-Agent Task Assignment and Path Finding for Mobile Robots in Smart Manufacturing

Mobile robots play an important role in smart factories, though efficient task assignment and path planning for these robots still present challenges. In this paper, we propose an integrated task- and path-planning approach with precedence constrains in smart factories to solve the problem of reassigning tasks or replanning paths when they are handled separately. Compared to our previous work, we further improve the Regret-based Search Strategy (RSS) for updating the task insertions, which can increase the operational efficiency of machining centers and reduce the time consumption. Moreover, we conduct rigorous experiments in a simulated smart factory with different scales of robots and tasks. For small-scale problems, we conduct a comprehensive performance analysis of our proposed methods and NBS-ISPS, the state-of-the-art method in this field. For large-scale problems, we examine the feasibility of our proposed approach. The results show that our approach takes little computation time, and it can help reduce the idle time of machining centers and make full use of these manufacturing resources to improve the overall operational efficiency of smart factories.

Water-in-Oil Emulsion Temporal and Spatial Evolution at Static Conditions and Its Impact on Pipeline Restart

Summary Understanding emulsion evolution at static conditions is crucial for production operations, such as pipeline operations during the shut-in and restart process and separator optimal design. This study experimentally investigated the temporal and spatial evolution of water-in-oil emulsion properties under static conditions. Numerical simulations were conducted to study their impacts on pipeline restart operations. The experiments were conducted in graduated glass cylinders, with mineral oil and tap water as the testing fluids and Span® 80 as the surfactant. Different water cuts, mixing speeds, and surfactant concentrations were investigated. Along with idle time at static conditions, the mixture demonstrated two layers, namely an upper oil layer and a lower emulsion layer, except for the lowest surfactant concentration that gave a third additional free-water layer at the bottom. Experimental results showed a dramatic increase in viscosity in the emulsion layer with time and depth, which was closely related to the increase in the water volumetric fraction. The increase rate slowed down and plateaued out with time. The increase rate is also related to water cut, mixing speed, and surfactant concentration. Experimental results also show that the relationships between the viscosity and water cut for separated emulsion follow the master curve of viscosity and water cut for homogeneous emulsion. This suggests that one can estimate the viscosity using the master curve given the water volumetric fraction. The numerical simulation was conducted for pipelines with a valley configuration and with the fluid properties obtained from the experimental measurements. It demonstrates that a higher pressure is required to restart the flow to the original flow rate. It also shows that the flow rate may not be able to resume its original value given the same pressure boundaries due to the accumulation of dense emulsion layers in the horizontal and upward inclined sections. For example, for a 16-m pipe, the flow cannot be restarted given the same inlet pressure (100 Pa). It can only resume 4.6% of the original flow rate when the pressure is elevated to 300 Pa.

Energy Storage Capacity Configuration Planning Considering Dual Scenarios of Peak Shaving and Emergency Frequency Regulation

New energy storage methods based on electrochemistry can not only participate in peak shaving of the power grid but also provide inertia and emergency power support. It is necessary to analyze the planning problem of energy storage from multiple application scenarios, such as peak shaving and emergency frequency regulation. This article proposes an energy storage capacity configuration planning method that considers both peak shaving and emergency frequency regulation scenarios. A frequency response model based on emergency frequency regulation combined with low-frequency load shedding is established, taking into account the frequency safety constraints of the system and the principle of idle time reuse, to establish a bi-level programming model. In the upper-level model, the optimization objective is to minimize the annual operating cost of the system during the planning period, combined with the constraints of power grid operation to plan the energy storage capacity. The lower-level model embeds frequency safety constraints with the optimization objective of minimizing the cost of fault losses. To solve the bi-level optimization problem, the Karush–Kuhn–Tucher (KKT) condition and Big-M method were used to transform the bi-level model into a single-layer linear model. Finally, an improved IEEE RTS-24 system was used for numerical verification. The results show that the method proposed in this article can reasonably plan the capacity of energy storage, improve frequency safety during system operation, and reduce the operating cost of the power grid.

Enhancement of Cryptocurrency Mining through Artificial Intelligence for Workstation Systems

For workstation computers, it is highly recommended to use AI-powered bitcoin mining to maximise processing time and resource allocation. Conventional PC mining systems have a number of difficulties, such overheating, system availability algorithms, and controlling power consumption in relation to bitcoin production. The study has introduced an AI board called LSAI48266x in order to encounter the traditional PC mining systems. It uses intelligent algorithms to solve the problems existence with traditional PC based crypto mining. Even in the absence of human workers at the workstation PCs, LSAI48266x detects the best times to mine cryptocurrency by incorporating a variety of variables. The goal of the suggested solution is to minimise total electricity use while optimising CPU power utilisation. Using AI to increase computer resource efficiency during idle times is the major goal of the idea, which also aims to reduce excessive power consumption, preserve system dependability, and increase income.

Offline Planning and Online Learning Under Recovering Rewards

Motivated by emerging applications, such as live-streaming e-commerce, promotions, and recommendations, we introduce and solve a general class of nonstationary multi-armed bandit problems that have the following two features: (i) the decision maker can pull and collect rewards from up to [Formula: see text] out of N different arms in each time period and (ii) the expected reward of an arm immediately drops after it is pulled and then nonparametrically recovers as the arm’s idle time increases. With the objective of maximizing the expected cumulative reward over T time periods, we design a class of purely periodic policies that jointly set a period to pull each arm. For the proposed policies, we prove performance guarantees for both the offline and the online problems. For the offline problem when all model parameters are known, the proposed periodic policy obtains a long-run approximation ratio that is at the order of [Formula: see text], which is asymptotically optimal when K grows to infinity. For the online problem when the model parameters are unknown and need to be dynamically learned, we integrate the offline periodic policy with the upper confidence bound procedure to construct on online policy. The proposed online policy is proved to approximately have [Formula: see text] regret against the offline benchmark. Our framework and policy design may shed light on broader offline planning and online learning applications with nonstationary and recovering rewards. This paper was accepted by J. George Shanthikumar, data science. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2021.04202 .

No idle flow shop scheduling models for optimization of machine rental costs with processing and separated setup times

Scheduling is one of the many skills required for advancement in today’s modern industry. The flow-shop scheduling problem is a well-known combinatorial optimization challenge. Scheduling issues for flow shops are NP-hard and challenging. The present research investigates a two-stage flow shop scheduling problem with decoupled processing and setup times, where a correlation exists between probabilities, job processing times, and setup times. This study proposes a novel heuristic algorithm that optimally sequences jobs to minimize the makespan and eliminates machine idle time, thereby reducing machine rental costs. The proposed algorithm’s efficacy is demonstrated through several computational examples implemented in MATLAB 2021a. The results are compared with the existing approaches such as those by Johnson, Palmer, NEH, and Nailwal to highlight the proposed algorithm’s superior performance.

The Lived Experiences of Individuals and Coping Strategies in the Context of Internet Gaming Disorder: A Qualitative Study Within Higher Education Setting in Uganda.

Internet Gaming Disorder (IGD), recognized as a mental disorder in both the Diagnostic and Statistical Manual (DSM-5) and International Classification of Diseases (ICD-11), poses significant threats to physical, social, and mental well-being. This study aims to delve into the experiences of individuals grappling with IGD. The study employed an interpretive phenomenology, conducting interviews with 10 graduate students at Makerere University. Participants were purposefully sampled until data saturation was achieved during interviews, which took place between May and July 2023. An interview guide facilitated data collection (Supplementary File 1), and thematic analysis was manually applied for data interpretation, utilizing intuition and imaginative approaches. The findings revealed that the majority of participants started gaming during childhood, starting with offline games. Exposure to gadgets and games, idle time, and stress emerged as key triggers for IGD. Participants reported experiencing sleep deficits, deteriorating interpersonal relationships, declining job performance, unhealthy eating habits, academic challenges, and wastage of money and time. The study also identified strategies employed by participants to mitigate their gaming behaviors, such as refraining from purchasing data, seeking support from friends, and uninstalling the game app, although relapses were common. The study highlights a global pattern of early initiation into gaming, emphasizing the need for early intervention and preventive measures. Factors such as easy accessibility and affordability of gaming platforms, idleness, and stress play significant roles in motivating internet gaming, contributing to a higher prevalence among the studied population. The research underscores the adverse effects of IGD on students, affecting academic performance, interpersonal relationships, and job performance. Notably, participants demonstrate agency in addressing IGD through practical coping strategies, including controlling data access, seeking social support, and uninstalling games. These coping mechanisms provide valuable insights into the complex nature of addressing IGD and form a basis for developing targeted interventions and support systems within the higher education setting in Uganda.

A retrospective study about cancellation of elective surgeries in a tertiary care hospital

Abstract Introduction: Operation theaters (OTs) are one among the core functionalities in a health care institution that require a considerable amount of 5 Ms—man, machine, money, material, and management. Any idle time or underutilization in the OT creates a huge loss of these resources and is also inconvenient to patients and those on the waiting list. Thus, a planned and well-conceived operational method has to be followed to reduce the cancelations of scheduled cases and to optimally utilize the OTs so that they become more efficient. This study has been done to understand the basic reasons for the cancelation of elective surgeries at a tertiary care hospital and to effectively tackle them by suggesting suitable solutions to overcome these challenges. Objectives: Primarily, to understand the reasons for the cancelation of surgeries in OTs, and secondarily, to put forward suggestions for optimal usage of OTs. Materials and Methods: This was a retrospective study, with Pareto analysis and root cause analysis (RCA) as the tools. Records were obtained from the Central Scheduling Department of the Hospital between January 2020 and November 2022 (35 months). Results: During the study period, 53,177 surgeries were performed in the main OTs. There were 2289 (4.3%) cancelations. Nearly 84% of canceled surgeries were canceled within 24 h. Cancelations were 63% in males and 37% in females. Seventy-nine percent were of Indian nationality, and the rest were of various nationalities. Conclusion: The reasons for the cancelation of elective surgeries were multifactorial. Controlling these, and thus preventing cancelations, is imperative in the effective functioning of the institution as a whole. We recommend thorough evaluation, early preanesthetic checkup (PAC) and well-planned scheduling without haste as some of the measures to reduce the chances of cancelation.

Analisa Antrian di SPBU Pondok Ungu menggunakan Software Promodel

Queuing is a daily routine experienced by almost everyone. Every activity, such as refueling at gas stations, has a queuing process. The purpose of this research is to reduce the average customer waiting time in the system, reduce the average idle time on each server, and reduce the length of the queue in the system. Because at the gas station there are long queues, it is necessary to improve the queuing system. In addition, Promodel software is used to simulate queuing models at gas stations. This research is qualitative and quantitative in nature. Based on the results of the study it was concluded that. In the initial model entity there were only 40 people while in the new model there were 48 people. In the initial model, the average entity location is 0.33, while the new model is 0.65. But the repair model has an average time spent on the system a little longer, which is 0.60.

The discrete-imitation modeling concept of the “sucker-rod pump-well-reservoir” system and the optimization of the pumping process

A new approach to dynamic modeling based on a discrete-imitation concept has been developed. The fundamentals, including terms and principles of the concept, have been explained. The presented concept has been applied to the integrated modeling of the development of a volatile oil reservoir operated by a sucker-rod pumping well within a pump-well-reservoir system. The discrete-imitation model of the “pump-well-reservoir” system, developed based on the principles of the new concept, takes into account fluid flow between the annulus and the lifting bore. It was employed to investigate the operation of the sucker-rod pumping well. The correlation between pumping rate, pump fillage, and stroke speed has been investigated. It was observed that, as the stroke speed increases, both pump performance and pump fillage increase up to a certain point. Beyond this threshold, the pumping rate stabilizes, and the pump fillage experiences a sharp decline. Notably, the maximum pump fillage does not always align with the peak pumping rate. In such instances, achieving an optimal regime is possible only through intermittent pumping. An expression has been derived to determine the optimal pump idle time in intermittent mode.

Productivity improvement in packaging process throught weight position ranking method in PT.XYZ


 XYZ is a food processing company. One of the snacks made is biscuits. The observation process was carried out at PT. XYZ in the biscuit production packaging process. The problem that arises in the packaging process is lead time. This observation aims to make the path more effective in an effort to increase productivity in the packaging process. Track balance is an effort to minimize imbalance between machine to machine or person to person to get the same time at each station. In this observation, the Ranked Position Weihgt (RPW) method was used to determine the workload allocation for each operator. The data analyzed is lead time which is processed to determine the results of track efficiency, balance delay at each work station. This method prioritizes the longest work element time and will be prioritized first to be placed in another work station that represents the lowest work element. Analysis results using the Ranked Position Weight Method with line efficiency 5.52%, Balance Delay 7.71%, Idle time 4.6%..

Implementation of Productivity Improvement Techniques in Wood Industry

Abstract: In Industries, it is very important to save time and money in manufacturing products, because there is lots of competition in the industrial world. The main objective of this study is to improve productivity of the wood industry by implementing different types of productivity improvement techniques such as 5S, Lean Manufacturing, JIT, TQM, 6 Sigma, Kanban, Jidoka and PDCA, etc. Improvement can be done by above techniques and by observing the working process cycle and study of the machineries, time study, identifying the problems during process, process analysis, study of inventory management process, and make the solution on the based problems. The purpose of this study is to eliminate the wastes and reduce the idle times in industry.