Average Flow Time Research Articles

Enhancing internal supply chain management in manufacturing through a simulation-based digital twin platform

Digital Twin (DT) technology is profoundly changing the manufacturing landscape and supply chain management with its ability to create real-time digital replicas of physical processes, allowing for enhanced monitoring and optimized decision-making. However, the analysis of scientific literature reveals that further efforts are needed to spread the use of Industry 4.0 technologies, in the specific context of Internal Supply Chains (ISCs). The main aim of this study is to design, develop test and validate a multi-plant Simulation-Based DT production planning platform for ISCs management. A modular architecture is adopted, and the focus is on a Simulation-Based Digital Twin module, which uses an object-oriented structure and enables what-if analyses, involving several scheduling rules and ISC configurations. The proposed solution ensures flexibility and scalability, two crucial features in a constantly evolving market environment. The platform is tested and validated through a case study, involving a corporate group in the Oil & Gas manufacturing sector, which needs to improve the ISC performance, under a Make-To-Order production strategy. The comparison with a baseline scenario, where the platform is not adopted, shows that the proposed approach can significantly reduce the average flow time, the average tardiness, the number of late orders. This study has important practical implications because enables proactive and smart decision-making, aimed at resource optimization and continuous improvement, through predictive analytics and scenario analysis.

Trade-offs between optimal and robust scheduling in one-stage production

Given stochastic disturbances, such as variation in processing times, robust scheduling is recommended over optimal scheduling for production. Different from optimal scheduling that seeks an optimal solution to a key performance indicator (KPI), which relates to the average of a KPI, robust scheduling is to minimize the largest deviation from the optimum for the worst-case scenarios, which relates to the variance of a KPI. However, minimizing the variance does not necessarily optimize the average of a KPI. As one of the fundamental KPIs in production scheduling, total completion time (TCT) drives many other KPIs, such as average flow time, waiting time, due dates, and length of stay. Stochastic processing times and NP-hardness to minimize the variance of TCT, i.e., min(VTCT), are two challenges in production scheduling. To investigate the trade-offs between optimal and robust scheduling, we apply the differentiation method to analyze the first and second moments of TCT. In our approach, we use three statistical measures for processing times, which are the lower bound, the expected value, and the upper bound. We also use three terms for sequencing, which are x(1) the processing time of the initial job, x(i) the processing time of a job in the current position i of a sequence, and x′(i) the difference of processing times between two adjacent jobs. Applying the three measures for processing times to each of the three independent terms for sequencing, we generate 27=3·3·3 sequences to analyze the dynamics of VTCT. Through numerical analysis in our case studies, we show that our sequencing scheme can generate optimal solutions to min(TCT), and solid variation ranges of VTCT. Consequently, we can not only balance the trade-offs between min(TCT) and min(VTCT), but also analyze the trade-offs between optimal scheduling focusing on the first-moment of a KPI and robust scheduling focusing on the second moment. Moreover, our analysis approach using the differentiation method is unique for production scheduling, which enables us to develop analytical methods and heuristics for balancing trade-offs between optimal and robust scheduling.

MODEL PENJADWALAN BACKWARD HYBRID FLOW SHOP 2 STAGE UNTUK MEMINIMASI MEAN FLOWTIME

The scheduling problem is a common challenge when it comes to executing tasks, making the resolution of this issue crucial for decision-making in production activities. The problem addressed in this research is the company's desire to conduct production without waiting times between stages to maintain the quality of pharmaceutical raw materials. Additionally, the production results must meet predefined due dates. The production system formed is a Hybrid Flow Shop system, which is an extension of the Flow Shop concept where one or all stages have identical parallel machines. The objective of this study is to minimize the time between job start and job completion, defined in scheduling problems as the minimization of mean Flow time. The research collected data from 5 products in the company to model according to the research objectives. Process times for each stage and due dates for each product have been predetermined. After modeling and processing using the LINGO application, the optimal result obtained is backward scheduling with an average flow time of 12.6. This demonstrates that the model in this research effectively addresses the Hybrid Flow Shop problem using backward scheduling to minimize mean flow time

Use of singular spectral analysis to reconstruct average monthly flow time series with complex behavior

Time series analysis is a useful tool for many practical water resource management applications, such as planning and anticipating conflict around water use. These analyses are also necessary for the use, design and operation of hydrotechnical works and for the protection of aquatic ecosystems. Basically, his review focuses on classic models such as Box and Jenkins. However, restrictive assumptions about data and residuals make its use difficult. As an alternative, non-parametric approaches are interesting due to their application versatility. One of the techniques that has been widely used in the analysis of flow time series is Singular Spectral Analysis (SSA). However, its application is concentrated as a pre-processor or in hybrid approaches and, because of this, many studies focus only on the model to be improved, leaving gaps in the process understanding that underlie the SSA. Within this context, and because of this, the objective of this study is to present the SSA technique in detail, through an application to a time series of monthly average flows with complex behavior (Jucu River, located in Southeast Brazil), in order to overcome gaps associated to a rigorous understanding of the procedures that make up the SSA. The Sequential SSA technique was used to decompose signal and noise components. The reconstructed series preserved the dynamics of the observed series, suggesting a strong influence of the signal component (trend and seasonality) on its behavior. It’s expected that the technique can be used with greater fluidity by specialists and contribute to the management of water resources.
 Keywords: decomposition, reconstruction, singular spectrum analysis.

Optimizing smart manufacturing systems using digital twin

Presented paper investigates the application of digital twins for the optimisation of intelligent manufacturing systems and focuses on the comparison between simulation modelling results and real-world production conditions. A digital twin was created in the Simio software environment using a data-driven simulation model derived from a real-world production system. Running the digital twin in real time, which was displayed graphically, facilitated the analysis of key parameters, including the number of finished products, average flow time, workstation utilization and product quality. The discrepancies were attributed to the use of random distributions of input data in the dynamic digital twin, as opposed to the long-term measurements and averages in the real-world system. Despite the limitations in the case study, the results underline the financial justification and predictive capabilities of digital twins for optimising production systems. Real-time operation enables continuous evaluation and tracking of parameters and offers high benefits for intelligent production systems. The study emphasises the importance of accurate selection of input data and warns that even small deviations can lead to inaccurate results. Finally, the paper high-lights the role of digital twins in optimising production systems and argues for careful consideration of input data. It highlights the importance of analysing real-world production systems and creating efficient simulation models as a basis for digital twin solutions. The results encourage extending the research to different types of production, from job shop to mass production, in order to obtain a comprehensive optimisation perspective.

Transaction Processing Policies in a Flexible Shuttle-based Storage and Re-trieval System by Real-time Data Tracking under Agent-based Modelling

This study investigates priority assignment rules (PARs) for transaction processing in automated warehouses featuring a shuttle-based storage and retrieval system (SBSRS). By incorporating real-time data tracking through agent-based modeling, the research explores the unique aspect of the SBSRS design, which involves flexible travel of robotic order picker shuttles be-tween tiers. The paper proposes PARs under agent-based modeling to enhance multi-objective performance metrics, including average flow time (AFT), maximum flow time (MFT), outlier transaction AFT, and standard deviations of flow times (SD) within the system. Experimental evaluations are conducted with various warehouse designs, comparing the results against commonly used static scheduling rules. The findings demonstrate that real-time tracking policies significantly improve system performance. Specifically, prioritizing the processing of outliers based on transaction waiting time enhances MFT, SD, and other performance metrics, while minimizing adverse effects on AFT. Certain rules exhibit notable improvements in MFT and SD, while others achieve the lowest AFT values among all experiments. This paper contributes to the existing literature by presenting a multi-objective performance improvement procedure and highlighting the advantages of real-time data track-ing-based scheduling policies in automated warehousing systems.

Gaussian process-based storage location assignments with risk assessments for progressive zone picking systems

E-commerce warehouses are under constant pressure to adapt their order picking systems and reassign product storage locations to meet fluctuating customer demands. Most existing approaches optimize storage location reassignments based on customer orders and operational configurations to maintain high order picking performance. This paper presents a Gaussian process surrogate model (GPSM) approach to predict the performance metrics for storage location reassignments. The GPSM estimates the expected flow time of orders from the historical data on previous storage location assignments and aids in identifying the new assignments that yield the minimum estimated average flow times. Management can also take advantage of the GPSM’s uncertainty quantification capability to assess the probability of improvement for a given storage reassignment and its implementation. The proposed model and assignment policy are validated using discrete-event simulations and industrial data. Experimental results demonstrate that the GPSM can improve expected flow time by 7.51% and reduce unnecessary reassignment operations by 43.25%.

Lot streaming in workforce scheduling problem for seru production system under Shojinka philosophy

Drawing upon efficiency, flexibility, and responsiveness, this study attempts to explore the impact of lot streaming and worker assignment strategies in the workforce scheduling problem for the seru production system (SPS), which has emerged as an alternative to traditional assembly lines. The addressed problem involves decisions related to employee timetabling, lot splitting, and sublot scheduling. While scheduling problems in SPS have been extensively researched in recent years, the workforce scheduling problem has not received much attention in the literature. Moreover, to our best knowledge, it has not been investigated with the sublot division methodologies within the context of SPS so far. To bridge this gap, a generic novel optimization model is developed with the objective of minimizing average flow time (AFT) by integrating lot streaming and the Shojinka philosophy through modules within the model. Additionally, the structural properties of the problem are examined, and lower and upper bound formulations are developed based on these properties. Given the NP-hard nature of the problem, customized approaches based on the genetic algorithm (GA) are proposed for solving large-sized problems, considering the Shojinka and division methodologies. The computational results clarify that achieving Shojinka with variable sublots division methodology significantly reduces the AFT objective. Furthermore, the findings confirm that adapting variable sublots in operational scenarios results in a drastic improvement in system performance, regardless of the implemented worker assignment strategy.

Multi-objective task scheduling method for cyber–physical–social systems in fog computing

Cyber–physical–social systems refer to new systems that involve a large number of Internet of Things (IoT) devices connected through an interconnected network. These systems gather data for various services such as transportation, energy, healthcare, etc. This data is then transmitted to the cloud layer for storage, processing, and access. To handle the significant workload offloaded from resource-constrained IoT devices without latency or bandwidth issues, a technology called fog computing has emerged as an intermediary layer between the cloud and IoT devices. The quality of service (QoS) provided by fog computing relies on efficient task scheduling to optimize energy consumption and maximum execution time. This task scheduling problem is known to be NP-hard and cannot be solved with high precision in a reasonable time using traditional techniques or existing metaheuristic-based task schedulers. Therefore, this study proposes a new multi-objective task scheduling approach based on a modified marine predators algorithm (MMPA) to simultaneously minimize energy consumption and make-span under the Pareto optimality theory. This variant is named the multi-objective MMPA (M2MPA). M2MPA enhances the performance of the MPA by incorporating the polynomial crossover operator to improve its exploration operator and the adaptive CF parameter to enhance its exploitation operator. The effectiveness of M2MPA is evaluated using eighteen tasks with different scales (small, large, and medium) and heterogeneous workloads assigned to two hundred fog devices with varying processing speeds. Finally, M2MPA is compared with six well-established techniques using various performance indicators, including carbon dioxide emission rate, flowtime, make-span, and energy consumption. The experimental findings demonstrate the substantial superiority of M2MPA over all competing algorithms across different performance indicators and the Wilcoxon rank-sum test. Quantitatively, M2MPA achieves an average make-span of 10.095, average energy consumption of 3.77E+06, average flowtime of 8.39E+03, and average carbon dioxide emission rate of 8.88E+06.

Surgery Scheduling and Perioperative Care: Smoothing and Visualizing Elective Surgery and Recovery Patient Flow

This paper addresses the practical problem of scheduling operating room (OR) elective surgeries to minimize the likelihood of surgical delays caused by the unavailability of capacity for patient recovery in a central post-anesthesia care unit (PACU). We segregate patients according to their patterns of flow through a multi-stage perioperative system and use characteristics of surgery type and surgeon booking times to predict time intervals for patient procedures and subsequent recoveries. Working with a hospital in which 50+ procedures are performed in 15+ ORs most weekdays, we develop a constraint programming (CP) model that takes the hospital’s elective surgery pre-schedule as input and produces a recommended alternate schedule designed to minimize the expected peak number of patients in the PACU over the course of the day. Our model was developed from the hospital’s data and evaluated through its application to daily schedules during a testing period. Schedules generated by our model indicated the potential to reduce the peak PACU load substantially, 20-30% during most days in our study period, or alternatively reduce average patient flow time by up to 15% given the same PACU peak load. We also developed tools for schedule visualization that can be used to aid management both before and after surgery day; plan PACU resources; propose critical schedule changes; identify the timing, location, and root causes of delay; and to discern the differences in surgical specialty case mixes and their potential impacts on the system. This work is especially timely given high surgical wait times in Ontario which even got worse due to the COVID-19 pandemic.

Lightweight SCC Development in a Low-Carbon Cementitious System for Structural Applications.

The utilization of manufactured lightweight aggregates adds another dimension to the cost of the preparation of self-compacting concrete (SCC). The common practice of adding absorption water to the lightweight aggregates before concreting leads to inaccurate calculations of the water-to-cement ratio. Moreover, the absorption of water weakens this interfacial bond between aggregates and the cementitious matrix. A particular type of black volcanic rock with a vesicular texture known as scoria rocks (SR) is utilized. With an adapted sequence of additions, the occurrence of water absorption can be minimized to overcome the issue of calculating the true water content. In this study, the approach of preparing the cementitious paste first with adjusted rheology followed by the addition of fine and coarse SR aggregates enabled us to circumvent the need for adding absorption water to the aggregates. This step has improved the overall strength due to the enhanced bond between the aggregate and the cementitious matrix, rendering a lightweight SCC mix with a target compressive strength of 40 MPa at 28 days, which makes it appropriate for structural applications. Different mixes were prepared and optimized for the best cementitious system that achieved the goal of this study. The optimized quaternary cementitious system included silica fume, class F fly ash, and limestone dust as essential ingredients for low-carbon footprint concrete. The rheological properties and parameters of the optimized mix were tested, evaluated, and compared to a control mix prepared using normal-weight aggregates. The results showed that the optimized quaternary mix satisfied both fresh and hardened properties. Slump flow, T50, J-ring flow, and average V-funnel flow time were in the ranges of 790-800 mm, 3.78-5.67 s, 750-780 mm, and 9.17 s, respectively. Moreover, the equilibrium density was in the range of 1770-1800 kg/m3. After 28 days an average compressive strength of 42.7 MPa, a corresponding flexural load of over 2000 N, and a modulus of rupture of 6.2 MPa were obtained. The conclusion is then drawn that altering the sequence of mixing ingredients becomes a mandatory process with scoria aggregates to obtain high-quality lightweight concrete for structural applications. This process leads to a significant improvement in the precise control of the fresh and hardened properties, which was unachievable with the normal practice used with lightweight concrete.

Forecasting of Average Monthly Flow for Two Stations at Khabour River Using Arima And Ann Models

In this study, autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) models were applied to predict the average monthly flow time series of two stations, named Begova and Chemcermo, for the Khabour River. The analysis of the time series was performed using several criteria and tests. The autocorrelation function (ACF) and partial autocorrelation function (PACF) were applied to check the accuracy of the ARIMA model. The Akaike (AIC) and Bayesian (BIC) equations were performed to determine the optimum model for prediction, which depended on the lowest AIC and BIC values. Applied test results show that the models of order ARIMA (0,0,0)(3,0,0)12 and ARIMA((0,0,5)(5,1,4)12 have higher acceptance compared to the other models for predicting the average monthly flow for Begova and Chemecermo stations, respectively. An ANN model of type multilayer perceptron method (ANN-MLP) was used for predicting the average monthly flow of Begova and Chemecermo stations, where the best models found are MLP (5,3,1) and MLP (9,7,1), respectively. Different statistical tests were applied and showed that the efficiency of the ANN model was better than the ARIMA model, with deterministic coefficients of 0.914 and 0.876 compared to 0.854, and 0.852 for the ARIMA for the monthly time series of Begova and Chemecermo stations, respectively

Forecasting of Average Monthly Flow for Two Stations at Khabour River Using Arima And Ann Models

In this study, autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) models were applied to predict the average monthly flow time series of two stations, named Begova and Chemcermo, for the Khabour River. The analysis of the time series was performed using several criteria and tests. The autocorrelation function (ACF) and partial autocorrelation function (PACF) were applied to check the accuracy of the ARIMA model. The Akaike (AIC) and Bayesian (BIC) equations were performed to determine the optimum model for prediction, which depended on the lowest AIC and BIC values. Applied test results show that the models of order ARIMA (0,0,0)(3,0,0)12 and ARIMA((0,0,5)(5,1,4)12 have higher acceptance compared to the other models for predicting the average monthly flow for Begova and Chemecermo stations, respectively. An ANN model of type multilayer perceptron method (ANN-MLP) was used for predicting the average monthly flow of Begova and Chemecermo stations, where the best models found are MLP (5,3,1) and MLP (9,7,1), respectively. Different statistical tests were applied and showed that the efficiency of the ANN model was better than the ARIMA model, with deterministic coefficients of 0.914 and 0.876 compared to 0.854, and 0.852 for the ARIMA for the monthly time series of Begova and Chemecermo stations, respectively

Simulation Based Analysis of Hierarchical Timed Colored Petri Nets Model of the Restaurant Food Serving Process

The major goal of establishing a restaurant is improved earnings making which can be jeopardized by customers' withdrawals due to lengthy ready lines. The above scenario was studied in this paper using simulation-based analysis of a Hierarchical Timed Coloured Petri Nets (HTCPN) model, which abstracted the serving operation of a renowned restaurant in the Ibadan metropolis. The restaurant was characterized by two or more servers that serially served each Customer from the multiple available servers at the four serving points. One hundred simulation runs were carried out on the HTCPN model using CPN Tools to determine the four (4) parameters,Customers' Average Flow Time (CAFT),Customers' Average Waiting Time (CAWT),Servers' Average Utilization Rate (SAUR) andCustomers' Average Queue Length (CAQL) required in serving 567, 1100, 1103, 770 and 829 customers from Monday to Friday, respectively. The model was validated by carrying out a statistical analysis t-Test between the simulated and the actual customers' flow time andservers' utilization rate at a 5% significant level. The validation results showed no significant differences between the simulated and the actual customers' average flow time andservers' utilization rate at a 5% significance level. Hence, the model can represent the restaurant under consideration.

Online scheduling of parallelizable jobs in the directed acyclic graphs and speed-up curves models

This paper considers scheduling jobs online on m identical machines such that the jobs can be parallelized across the machines. Two models of parallelizability are considered, one is the speed-up curves model, and the other is the directed-acyclic-graph (DAG) model. For both models, the objectives considered are the average, maximum, and ℓk-norms of flow time for k≥1.We establish an Ω(m) lower bound on the competitive ratio of any algorithm for optimizing average flow time in both models without resource augmentation. With resource augmentation, we give a (1+ϵ)-speed O(1ϵ2k+1)-competitive algorithm in the DAG model for the ℓk-norms of flow time. This essentially matches the best-known result in the speed-up curve model for the ℓk-norms of flow time. Finally, we show an O(1)-competitive algorithm for minimizing the maximum flow time in the speed-up curves model.

Dual Institutional Experience with Transcarotid Artery Revascularization.

Transcarotid artery revascularization (TCAR) is a hybrid open and endovascular technique to treat carotid stenosis. The purpose of this study is to present a large cohort of patients who underwent TCAR at 2 high-volume TCAR health systems. This study was a retrospective chart review of all instances of TCAR within the Memorial Hermann Health System and Indiana University Health, from December 2015-January 2022, using the ENROUTE Neuroprotection Device (Silk Road Medical, Sunnyvale, CA). We report patient demographics, intraoperative metrics, 30-day results and long-term results. In all, 750 patients underwent TCAR in the designated time period. Average patient age was 73 years, with 68% being male. Overall, 53.9% of patients had coronary artery disease, 45.4% had diabetes, and 36.9% were symptomatic. Technical success was achieved in 98.8% of patients with conversion to open endarterectomy in 1.1%. Average reverse flow time was 9.1 minutes with length of stay greater than 1 day 38%. Ipsilateral stroke rate within 30 days was 2.3% and long-term cumulative stroke rate was 3.0%. Death within 30 days occurred in 1.2% of patients and in 5.9% over long-term follow up. In all, 1% of patients required reintervention. TCAR is a safe and effective treatment modality for carotid artery stenotic disease. Its outcomes are similar to historical results associated with carotid endarterectomy, long considered the gold standard.

Optimisation of Maintenance Operations Involving Three Integrated Departments at a Local Oil Company in Trinidad

A simulation case study was conducted to assess the utility of predictive maintenance and database management for lean maintenance applications within a Petroleum Company. At this company the maintenance of pumps was performed internally where feasible. The study explored a proposed lean strategy on the operations of three departments related to the maintenance of pumping equipment. These departments were, the Pump Shop, Stores Department and Shipping and Receiving. Rockwell Automation’s Arena® simulation software was used to study existing and proposed models of the maintenance system and track the key performance indicators of Flow Time, Waiting Time, and Work-in-Process. Analysis of the performance indicators showed a 76% and a 96% reduction in average Flow Time and Waiting Time, respectively. No difference was determined for Work-in-Process at the 95% confidence interval.

Evaluating the performance of a cellular manufacturing system proposal for the sewing department of a sportswear manufacturing company: A simulation approach

Cellular manufacturing systems (CMS) are a major application of group technology, that brings benefits related to the reduction of wastes, also known as ‘mudas’ in lean manufacturing philosophy, to organisations striving to compete on today’s markets. These benefits include the reduction of machine setup times, material handling and work-in-process inventory, among others. However, many organisations are reluctant to implement such decisions in their production systems, due to the uncertainty and economic and productive impact that these involve. For this reason, a simulation study is performed in this paper to evaluate the performance of a cellular manufacturing system approach for the sewing department of a sportswear manufacturing company. The simulation study was designed, and the results of the proposed model showed improvements of 51,46% in the average flow time, 1102,52% in the average throughput, and 50,65% in the average setup times of machines, compared to the current state of the case study.

Research on Design Optimization of Subway Station Transfer Entrance Based on AnyLogic

With the expansion of the city, the number of subway lines has increased, and the subway stations have become more and more crowded. Whether it is possible to transfer quickly and conveniently has become a concern of subway passengers. Especially in big cities, for the convenience of residents' travel, there are many transfer stations between subway lines, and the transfer layout and connection method between subway stations affect the transfer efficiency of passengers. Jiedaokou Station of Wuhan Metro Line 2 is located in the center of Wuhan and is one of the busiest transfer stations in Wuhan. AnyLogic builds a simulation model of passenger entrances and exits and transfers, and observes whether the design of subway station halls is reasonable from the perspective of regional density, regional density, average passenger flow speed and time distribution of entrances and exits. Then, according to the location of congestion and the degree of congestion, it will be designed, modified, optimized and upgraded. After the analysis, a comparison of various schemes and a new round of simulation analysis are carried out. On the basis of summarizing the analysis results of each strategy, the design optimization strategy of the subway station hall is comprehensively proposed. Finally, the feasibility of the optimization strategy is proved again through simulation experiments. This optimization can enable passengers to walk faster in the station and reduce the occurrence of problems such as stagnation and congestion. This paper aims to provide a certain degree of guidance for the design of subway station halls in the future by using AnyLogic simulation method.

A new approach to solving multiobjective flowshop scheduling problems: A multi-MOORA-based genetic algorithm

Flowshop scheduling problems constitute a type of problem that is frequently discussed in the literature, where a wide variety of methods are developed for their solution. Although the problem used to be set as a single purpose, it became necessary to expect more than one objective to be evaluated together with increasing customer expectation and competition, after which studies started to be carried out under the title of multiobjective flowshop scheduling. With the increase in the number of workbenches and jobs, the difficulty level of the problem increases in a nonlinear way, and the solution becomes more difficult. This study proposes a new hybrid algorithm by combining genetic algorithms, which are metaheuristic methods, and the Multi-MOORA method, which is a multicriterion decision-making method, for the solution of multiobjective flowshop scheduling problems. The study evaluates and tries to optimize the performance criteria of maximum completion time, average flow time, maximum late finishing, average tardiness, and the number of late (tardy) jobs. The proposed algorithm is compared to the standard multiobjective genetic algorithm (MOGA), and the Multi-MOORA-based genetic algorithm (MBGA) shows better results.