Idle Time Research Articles

Spatial layout and optimization of e-commerce logistics management based on combinatorial optimization algorithm

Abstract Warehousing space layout is crucial for e-commerce logistics. A refined and scientific approach to warehouse design enhances space utilization and operational efficiency. This minimizes unused space and idle time, lowers inventory costs, and strengthens the competitive edge of e-commerce logistics. In this study, we conceptualize the logistics space layout issue as a crate problem, harnessing the rapid optimization capabilities of genetic algorithms (GA) and simulated annealing (SA). We propose a hybrid algorithm where SA forms the core, using GA to generate initial solutions and new iterations. Design parameters for the combined operation process are systematically developed. This algorithm is evaluated using both datasets and real arithmetic cases, demonstrating superior performance in large-scale combinatorial optimization problems. It achieves a search accuracy of 0.5% to 3% higher than GA alone, converging more reliably to the global optimum, thus reducing search time and operational scope. This research offers vital theoretical insights for optimizing space layouts in e-commerce logistics management.

A Hybrid Discrete Grey Wolf Optimizer with Local Search for Multi-UAV Patrolling

This paper addresses the multi- UAV patrolling problem, a NP-hard optimization problem that is focused on minimizing idleness, which is defined as the time between consecutive visits to specific locations. We propose the Discrete Grey Wolf Optimizer (D-GWO), which is specifically developed to handle the discrete aspects of UAV patrolling routes. This new algorithm is enhanced with a 2-opt local search strategy, which integrates the global search capabilities of D-GWO with the precision of local optimization to effectively refine solutions. Comparative experimental results show that our algorithm outperforms established methods such as ant colony optimization and simulated annealing in terms of reducing global worst idleness and overall exploration time. Our findings suggest that the D-GWO algorithm is particularly effective for complex multi-UAV patrolling tasks, significantly enhancing efficiency in security and disaster response missions.

A systematic analysis of design choices in short-term taxi demand prediction models

Accurate prediction of short-term taxi demand allows taxi services to strategically position idle vehicles in areas with insufficient supply, thus reducing idle times for taxis and waiting times for passengers. Towards this end, state-of-the-art approaches use Machine Learning (ML) models based on historic demand data, such as Long Short-Term Memory (LSTM), Fully Connected Feed Forward Neural Network (FCNN), Convolutional Neural Network (CNN), Graph Convolutional Network (GCN), or a combination of these. The majority of approaches use a grid representation of the environment to discretize the pickup locations of trips and time bins to order the temporal dimension of the large multi-target regression problem. While these models show good accuracy, many \ extit{design choices}, such as the size of the grid cells or the number of previous time steps inserted into a model, are made without justification or without proper tuning. This paper systematically investigates these design decisions. Our hypothesis is that considering them jointly rather than in isolation can improve accuracy. We investigate the influence of (1) the selected grid cell size, (2) the number of input maps, (3) the usage of time shift to enlarge the training data, and (4) the potential of transfer learning on the accuracy of short-term taxi demand prediction. We selected different network types and evaluated them on two large real-world datasets. The evaluation shows that our model outperforms state-of-the-art short-term taxi demand prediction models.

Queuning Modelling on The Performance of Automated Teller Machine (ATM) In Uba Lokoja, Kogi State.

This paper work investigates the performance of United Bank for Africa (UBA) Automated Teller Machine (ATMs) using M/M/S queue model. The data collected for this work was for a period of five working days (Monday-Friday), 10 hours was considered from (8.00am-5.00pm). The result showed that the average time a customer spent waiting in the system is 1.13 hours which is equivalent to 6.41 minutes and the utilization of factor is 0.28, It was observed that the machines have busy time 0f 2.80 hours while the idle time of the machines is 7.20 hours out of the 10 hours considered in each day and the probability of no. customer in the queue is 0.56, though there are four ATM machines in the location but 2 were considered in this paper work. The result indicates that the service delivery by the machine is efficient and fast, and addition of ATM is not required in the location. However, we recommend that adequate maintenance of the machine should be routinely carried out to ensure efficient and good service delivery.

Data-Driven Distributionally Robust Optimization for Railway Timetabling Problem

Unpredictable disturbances that occur when the train is running often make the actual timetable deviate from the planned timetable and deteriorate service quality for passengers. To provide a robust planned timetable that is reliable in operation, this paper presents a two-stage distributionally robust railway timetabling (DRRT) model, where the decisions in the first and second stages are the planned and actual timetables, respectively. To deal with the uncertain travel time, the proposed DRRT model assumes the true distribution belongs to a Wasserstein ambiguity set centered at the empirical distribution constructed from finite samples and aims to minimize the sum of the total idle time, travel time, and worst-case expected delay over the ambiguity set. To solve this model, we design a decomposition algorithm with provable convergence, which iteratively solves a master problem and a set of subproblems. To enhance the algorithm efficiency, a blend of two popular decomposition methods is proposed to integrate their advantages, and the solution of the subproblems is accelerated by using an uncapacitated minimum-cost flow interpretation of the second-stage optimization problem. Experiments on real-world instances demonstrate the effectiveness of these enhancements. The results also show that our model prominently outperforms the sample average approximation model in terms of the out-of-sample performance, especially when only a small sample set is available. Moreover, at the expense of equal traffic efficiency, our model is more robust than those models that do not utilize distribution information. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper was motivated by the problem of using finite historical data to provide a robust planned railway timetable, aiming at minimizing the traffic efficiency losses and expected delays under the worst-case distribution. Existing studies on dealing with the uncertain travel time usually assumed that the distribution is exactly known, or the distribution is completely unknown but the travel time belongs to an uncertainty set. However, in practice, the exact distribution is hard to know but can only be coarsely estimated from data. This paper proposes a data-driven two-stage model by assuming that the true distribution belongs to a 1-Wasserstein ball centered at the empirical distribution with a bounded support set, which is a middle ground between the above two distribution assumptions. To solve this problem efficiently, we propose a customized algorithm with provable convergence and then enhance it by exploiting problem-specific properties. This distributionally robust model can be easily shifted to other timetabling problems, and the algorithm enhancement techniques can also be applied to efficiently solve other two-stage recourse models with similar properties.

A VNS-based approach for solving the Manhattan metric straddle carrier routing problem with buffer areas

This paper presents a metaheuristic approach for solving an optimization problem that arises at container terminals where straddle carriers (SCs) transport containers between the stacking areas and the seaside. In such container terminals, operational efficiency depends mainly on SC routing. SCs routes should consider the order in which containers are unloaded and loaded at the quay cranes (QCs), taking into account the limited capacity of the buffer area of each QC where containers are temporarily stored after being handled by a QC or an SC. Besides the precedence relations (i.e., container sequences) and buffer capacities, the solution framework considers safety constraints. Efficient routing of SCs directly contributes to minimizing the idle time of QCs, thereby improving their overall productivity and minimizing the turnaround time of vessels, which is the objective of the problem. Specifically, we present two different variants of the Variable Neighborhood Search (VNS) algorithm. Each variant is initialized in both a greedy and a random manner. These algorithms address the problem by incorporating four LS operators commonly utilized in vehicle routing problems. We perform a comparative analysis of the results of these four approaches against each other and against solutions generated by an exact solver. Our numerical experiments show that the proposed algorithms perform better than the used solver, especially for bigger instances. A comparison with the results from the literature is also given and shows that the proposed VNS-based approach provides competitive results.

Deep reinforcement learning task scheduling method based on server real-time performance.

Server load levels affect the performance of cloud task execution, which is rooted in the impact of server performance on cloud task execution. Traditional cloud task scheduling methods usually only consider server load without fully considering the server's real-time load-performance mapping relationship, resulting in the inability to evaluate the server's real-time processing capability accurately. This deficiency directly affects the efficiency, performance, and user experience of cloud task scheduling. Firstly, we construct a performance platform model to monitor server real-time load and performance status information in response to the above problems. In addition, we propose a new deep reinforcement learning task scheduling method based on server real-time performance (SRP-DRL). This method introduces a real-time performance-aware strategy and adds status information about the real-time impact of task load on server performance on top of considering server load. It enhances the perception capability of the deep reinforcement learning (DRL) model in cloud scheduling environments and improves the server's load-balancing ability under latency constraints. Experimental results indicate that the SRP-DRL method has better overall performance regarding task average response time, success rate, and server average load variance compared to Random, Round-Robin, Earliest Idle Time First (EITF), and Best Fit (BEST-FIT) task scheduling methods. In particular, the SRP-DRL is highly effective in reducing server average load variance when numerous tasks arrive within a unit of time, ultimately optimizing the performance of the cloud system.

No idle flow shop scheduling models with separated set-up times and concept of job weightage to optimize rental cost of machines

The current paper investigates a two-stage flow shop scheduling model with no idle restriction, in which the time taken by machines to set-up is separately considered from the processing time. Owing to inherent usefulness as well as relevance in real-world situations, jobs' weight has additionally included. To eliminate machine idle time and cutting machine cost of rental, the reason for the conduct of the study is to provide a heuristic algorithm which, once put into practice, processes jobs in an optimal way, guarantees in smallest conceivable make span. Multiple computational examples generated in MATLAB 2019a serve as testament to the efficacy of the proposed strategy. The outcomes are contrasted with the current methods that Johnson, Palmer and NEH have demonstrated.

Simulation of operations on the production line as a tool for making the production process more efficient

For a company to make a profit and satisfy customers' needs, it must have the correct individual processes - the production process is one of the most important. This paper proposes a solution to eliminate the problem based on identifying the cause of the blocking - idle times in several workplaces of the production line. Another goal was to determine whether increasing productivity and the number of products on the given line is possible. At the beginning of the research, a system analysis of production operations was carried out. A simulation was chosen as a tool for solving the goals. Tecnomatix Plant Simulation creates the simulation model. The model was verified by a simulation experiment that simulated the current state, and the data obtained by the analysis were used. The experiment was performed for three simulated times: 8, 16 and 48 hours. After the verification of the model, experiments were performed on the models. The paper presents the results of three experiments for a simulated time of 48 hours. Based on the experiments, it was found that in the case of shortening the work cycle at critical workplaces No.5 and No.15, it is possible to make the process more efficient - to equalise the workload of individual workplaces and increase production by 25%.

Predictive Analytics for Production Line Downtime: A Comprehensive Study Using Advanced Machine Learning Models

This study delves into predictive analysis, employing advanced machine learning models to predict downtime in production lines. The research follows a detailed methodology, starting by exploring production lines and their conditions. The focal point is Overall Equipment Effectiveness (OEE), which includes availability, performance, and quality, with a special focus on downtime impacts. Through an OEE tracking system, downtime is categorized into planned and unplanned, offering insights into critical components affecting production line availability. The datasets cover three years, including runtime, unplanned downtime due to equipment failures, and idle times, emphasizing instances prone to frequent equipment failures. &#x0D; &#x0D; Examining relationships through a heatmap reveals essential correlations crucial for accurate predictions. We outline the feature selection process and fundamental data preprocessing steps to maintain dataset integrity. Introducing machine learning models such as Xgboost, Prophet, Multi-Layer Perceptron (MLP), and Long Short-Term Memory (LSTM), each model is carefully configured with past window parameters and forecast horizons. To prevent overfitting, the study employs K-fold cross-validation and early stopping mechanisms, ensuring robust model performance. &#x0D; &#x0D; Two datasets, comprising runtime, equipment-induced downtime, and idle time, are utilized for analysis. Data preprocessing involves a heatmap for characteristic-target correlation and basic steps like mean-based null value imputation. Results, presented in terms of Mean Absolute Error (MAE), underscore the superior performance of LSTM. The discussion interprets these findings, confirming the potential of LSTM for downtime prediction, supported by visualizations comparing actual and predicted downtimes. Future directions include comprehensive model comparisons, expanding metrics, and increasing datasets, building on the solid foundations established by this study.

자동차 부품업체의 TDABC 적용사례 연구

[Purpose]If activity－based cost calculation is a cost calculation method that can be applied to blue－chip companies with advanced ERP systems, TDABC is a relatively easy and simple cost calculation method that can be applied even to a small number of SMEs. This study attempted to confirm the usefulness of TDABC by analyzing the difference between the time－active cost calculation (TDABC) method and the existing cost calculation method based on the cost data of S, a case company in the automobile parts manufacturing industry. [Methodology]Unlike the traditional cost calculation of case company S, the process time was calculated by applying the TDABC system by the time equation. Through this, not only the actual operation level was divided into usage time and idle time, but also cost reduction and profit rate improvement measures were prepared by dividing the cost into usage cost and idle cost. [Findings]Summarizing the analysis results of this study, for case companies, the operating margin based on traditional cost calculations was 2.8%, whereas the TDABC system was analyzed as 6.7% of the operating margin for the cost of use, excluding idle costs. In other words, the cost of the TDABC system is a way to increase the operating margin along with the reduction of the idle cost sector. Therefore, it was confirmed that the introduction of the TDABC system for case companies can increase the efficiency of cost management. [implications]Unlike previous studies that calculated the cost incentive rate in the dedicated facility system, this study is significant in that it analyzed the usefulness of TDABC by applying time－driven cost calculation in the multi－species production system because case company S is building a system of general－purpose facilities.

A Fuzzy-Genetic Modelling Approach to Maintenance Scheduling for the Minimization of Fishing Vessel Idle Time in the Maritime Industry

As the marine industry becomes more complex and challenging to manage optimizing the idle time of vessels is critical. Although previous idle time reduction efforts in maintenance were through the evaluation of crisp numerical values in optimization models, none of the studies has considered the effect of uncertainty and imprecision on the system. Consequently, in this paper, a linguistic-based optimization model is developed using the fuzzy-genetic model for the discredited time-maintenance manpower-cost saving problem. The suggested model is moved for the following reasons: (1) it considers linguistic terms to express the optimization of vessels' idle time from a unique perspective of deploying genetic algorithm while considering the minimum and maximum number of times vessels may return for maintenance (2) it has an indirect measure of the quality of maintenance service which is absent in previous models (3) a cost-saving dimension is introduced where the ideas of low, medium and high-cost savings are examined. The results show the feasibility of the approach. The model advanced promises to provide vessel controllers with an idle time assessment framework, which enhances the chances of generating the utmost profit for companies.

Dynamic Tensor Modeling for Missing Data Completion in Electronic Toll Collection Gantry Systems.

The deployment of Electronic Toll Collection (ETC) gantry systems marks a transformative advancement in the journey toward an interconnected and intelligent highway traffic infrastructure. The integration of these systems signifies a leap forward in streamlining toll collection and minimizing environmental impact through decreased idle times. To solve the problems of missing sensor data in an ETC gantry system with large volumes and insufficient traffic detection among ETC gantries, this study constructs a high-order tensor model based on the analysis of the high-dimensional, sparse, large-volume, and heterogeneous characteristics of ETC gantry data. In addition, a missing data completion method for the ETC gantry data is proposed based on an improved dynamic tensor flow model. This study approximates the decomposition of neighboring tensor blocks in the high-order tensor model of the ETC gantry data based on tensor Tucker decomposition and the Laplacian matrix. This method captures the correlations among space, time, and user information in the ETC gantry data. Case studies demonstrate that our method enhances ETC gantry data quality across various rates of missing data while also reducing computational complexity. For instance, at a less than 5% missing data rate, our approach reduced the RMSE for time vehicle distance by 0.0051, for traffic volume by 0.0056, and for interval speed by 0.0049 compared to the MATRIX method. These improvements not only indicate a potential for more precise traffic data analysis but also add value to the application of ETC systems and contribute to theoretical and practical advancements in the field.

AVOA and ALO Algorithm for Energy-Efficient No-Idle Permutation Flow Shop Scheduling Problem: A Comparison Study

Global energy consumption is a pressing issue and is predicted to continue increasing between 2010 and 2040. Among the various sectors, the industrial sector, particularly manufacturing, is the main driver of this increase. To effectively address this growing problem and support energy conservation efforts, reducing idle time on production-related machines is critical. The No-Idle Permutation Flow Shop Problem (NIPFSP) and, indirectly, the need to reduce energy consumption in manufacturing processes are the driving forces behind this study. The African Vultures Optimization Algorithm (AVOA) and the Ant Lion Optimizer (ALO) are two novel meta-heuristic algorithms designed to achieve this goal. The effectiveness of both AVOA and ALO was rigorously evaluated across three distinct scenarios: small, medium, and large. Statistical analysis, in the form of independent sample t-tests, was employed to compare the performance of these algorithms. We found that, while both algorithms yielded similar results in the small case, AVOA demonstrated a superior capability in optimizing the NIPFSP in the medium and large cases and, consequently, in curbing energy consumption. This implies that AVOA offers a more promising approach to addressing energy consumption concerns in the manufacturing sector, particularly in scenarios involving medium- to large-scale production processes. The implementation of such innovative meta-heuristic algorithms holds the potential to significantly contribute to global energy conservation efforts while enhancing the efficiency of industrial operations.

Comparative studies of Markov chain-based driving cycles for light-duty vehicles

ABSTRACT With the massive ongoing efforts to mitigate climate change and reduce emission factors in the transportation sector, driving cycles (DCs) are becoming an essential tool for the testing and certification of distinct types of vehicles. Nevertheless, the open literature lacks careful comparative studies on the impact of different approaches in the development of Markov chain (MC)-based DCs, as well as DCs, developed specifically for the developing and highly congested urban areas in the Middle East and North Africa. Using a large dataset of 43 light-duty vehicles, driven over different areas in Greater Cairo, Egypt, this study aims to develop, compare, and benchmark 24 candidate MC-based DCs, against two clustering-based DCs, as well as four cycles used widely in the US and Europe. These 24 DCs differ in terms of the clustering algorithm (K-medoids and K-means), clustering parameters (different combinations of vehicle’s speed, acceleration, specific power, and percentage idling time), and definitions of microtrips (start-stop and fixed distance). The results show that the MC method outperforms random chaining of microtrips, with average relative root mean square errors (RRMSEs) of 15.8% and 23.6%, respectively. Clustering 350 m fixed distance-based microtrips using the vehicle’s speed, acceleration, and percentage idling time shows the least RRMSE of 8.207%. Defining microtrips based on fixed distance is also better than starts/stops for most vehicle types. The reference cycles (WLTP, NEDC, UDDS, and FTP-17) showed poor representativeness of the real-world data, with an average RRMSE of 76.8%. The same inferior performance of reference cycles, compared to the newly proposed ones, was also highlighted in the estimation of fuel consumption and emission factors. Hence, the proposed cycles and the reported comparative studies can be valuable tools for the assessment of emissions and fuel consumption in such developing metropolitan areas.

Implementation of lean principles in the yarn manufacturing industry: a system dynamics approach

PurposeThe textile industry contributes 2 and 3% to the global and Indian Gross Domestic Product (GDP), respectively. India supplies a quarter of global cotton yarn. Yet, most yarn manufacturing companies use outdated methods and lack organisational skills and strategies. Improvement in processes in India could significantly help the industry worldwide.Design/methodology/approachThe variables that influence the performance of the system were identified. Their interrelationships and impact were identified from the employees in the chosen case study, a yarn manufacturing industry. A System Dynamics (SD) approach was employed to study the benefits of implementing 5S lean strategies. The impact of each variable on various performance measures such as throughput, Work In Progress, processing time, waiting time, idle time, over-processing and scraps was analysed.FindingsImprovement in outcomes reflected an enhanced adoption of leanness in the industry. The decision-makers can utilise this study to optimise the necessary parameters in the system and attain the desired productivity levels. Better resource management and reduced processing time helped increase the despatch rate by 9.735% and decrease the WIP by 23.01%. Time management helped to reduce the inventory, idle time and waiting time. Over-processing, defects and scraps were minimised, indicating a shift towards lean.Research limitations/implicationsThis study pioneers the use of SD simulation models for optimising yarn manufacturing using lean strategies. Improvement in performance measures by integrating these strategies opens avenues for future research using multiple approaches to address a problem.Practical implicationsImplementing 5S lean principles and simulations enhances productivity, reduces waste and optimises resource management for the yarn manufacturing industry. Decision-makers can employ simulation to witness the outcomes of their changes without investing cost and time and without associated implementation risks.Originality/valueThe use of a simulation model to witness the benefits of incorporating lean strategies in yarn production has not been explored. This approach could help the managers and policymakers understand their existing system's shortcomings and critical areas that require improvement.

Physiological Indicators of Fluency and Engagement during Sequential and Simultaneous Modes of Human-Robot Collaboration

OCCUPATIONAL APPLICATIONS An understanding of fluency in human-robot teaming from a physiological standpoint is still incomplete. In our experimental study involving 24 participants, we designed a scenario for shared-space human-robot collaboration (HRC) for a material sorting task. When compared to a sequential mode of interaction, the simultaneous mode resulted in significantly higher perceptions of fluency and engagement, primarily by reducing human idle time. These observations were complemented by significant changes in physiological responses, such as ECG entropy and low frequency power. These responses could predict fluency and engagement with accuracies of 90 and 97%, respectively. Notably, the perception of fluency and preferred mode of interaction were influenced by individual preferences. Hence, it is crucial to consider both physiological responses and user preferences when designing HRC systems, to ensure a positive experience with the robot teammate and to foster engagement in long-term teamwork. Furthermore, these signals can be obtained using a single robust, low-cost, and comfortable sensor.

A simulation-based genetic algorithm approach for the simultaneous consideration of reverse logistics network design and disassembly line balancing with sequencing

Reverse logistics (RL) network design and disassembly line balancing (DLB) decisions are generally considered separately. In addition, the effect of disassembly sequencing on DLB is ignored. However, companies can take better-informed decisions by simultaneously considering RL, DLB and disassembly sequencing issues. This integrated approach results in more efficient processes which reduce cost and idle time, while maximizing throughput and customer satisfaction. In this study, we propose a simulation-based genetic algorithm approach for the joint optimization of RL network design and DLB with sequencing decisions. The use of simulation modeling allows for the consideration of stochastic aspects associated with RL and DLB such as transportation and disassembly times. A numerical example was provided to present the applicability of the proposed approach. Moreover, a sensitivity analysis was carried out to study the impact of various parameters. The results indicate the superior performance of the proposed approach with respect to total cost.

Informing directed energy deposition strategies through understanding the evolution of residual stress

Residual stress is a critical concern in directed energy deposition (DED) as it impacts the functional performance of components, in terms of load-bearing capacity and corrosion behavior. In this paper, an analytical model is developed to characterize the temperature distribution, residual stress profiles, and geometric distortions in DED-built components, which enables efficient planning of the DED strategies. The model combines a finite difference method to predict the temperature history, modified Green's functions to quantify thermal stress, and a radial return method to update plastic stress, which features the deposition of materials with heat source movement. The predictions are validated against the temperature, residual stress, and distortion profiles reported in the literature. In the fabrication of multiple-layer thin-wall structures, the model demonstrates a maximum prediction error of 18.2% for the peak temperature at the middle of the deposit-substrate interface and 17.7% for the vertical distortion of the deposit-substrate system. Simulation results from the model provide insights into understanding stress evolution in the deposition and offer guidance in the selection of linear energy density parameters (heat source power and traverse speed) and process planning parameters (idle time and deposition patterns) when designing DED strategies for thin-wall structures. This approach can be implemented in an efficient workflow to optimize the DED technologies for specified residual stress profiles within a component geometry regardless of heat source or material type.

Surplus population in-situ

Abstract The article follows how migrant brick kiln molders are affected and adapt to short and long periods of suspension of work. In brick kilns near Delhi, involuntary idle time is revealed as an important modality of surplus extraction. While idleness is prevalent within many forms of work, idle time in the brick kilns operates at the intersection of other relations, namely, piece-rate wages, debt bondage, and capital's control over social reproduction space and time. It enables capital to flexibly move workers in and out of paid labor while extracting unpaid work and acts as an in-situ mode of rendering workers relative surplus population. Through enacting literal wagelessness and perpetuating wageless life, the article reads idle time as a time regime of capital, breaching and producing instabilities within workers’ life and leisure.