Total Throughput Time Research Articles

Lean Production for Reducing Wastes in Convex Lens Production Process

This research aimed to investigate the waste generated during the production of traditional convex lens. The study proposed a method to eliminate such waste by utilizing value stream mapping (VSM) as a tool for data collection and waste identification throughout the entire production process. Through the analysis of data using VSM, three specific types of waste were identified. These included losses resulting from ineffective utilization of the oven machine during the polymerization process, inefficiencies in personnel performance, and inappropriate working procedures. To address these waste issues, the task employed the work study method to optimize machine utilization, develop efficient work processes for personnel, and improve inappropriate procedures to enhance overall efficiency. The ECRS method was utilized to improve the process of waste elimination. The research findings demonstrated substantial reductions in waste within the production process. Specifically, the total throughput time decreased from 661.07 minutes to 480.68 minutes, representing a reduction of 27.29%. Furthermore, the total production lead time decreased from 1.20 days to 0.94 days, indicating a reduction of 21.67%. In terms of personnel, the workforce decreased from 42 employees per shift to 29 employees, reflecting a decrease of 30.95%. Finally, the number of oven machinery units required for all three shifts decreased from 3 ovens to 2 ovens, resulting in a reduction of 33.33%.

Type-2-Soft-Set Based Uncertainty Aware Task Offloading Framework for Fog Computing Using Apprenticeship Learning

Abstract Fog computing is one of the emerging forms of cloud computing which aims to satisfy the ever-increasing computation demands of the mobile applications. Effective offloading of tasks leads to increased efficiency of the fog network, but at the same time it suffers from various uncertainty issues with respect to task demands, fog node capabilities, information asymmetry, missing information, low trust, transaction failures, and so on. Several machine learning techniques have been proposed for the task offloading in fog environments, but they lack efficiency. In this paper, a novel uncertainty proof Type-2-Soft-Set (T2SS) enabled apprenticeship learning based task offloading framework is proposed which formulates the optimal task offloading policies. The performance of the proposed T2SS based apprenticeship learning is compared and found to be better than Q-learning and State-Action-Reward-State-Action (SARSA) learning techniques with respect to performance parameters such as total execution time, throughput, learning rate, and response time.

A simulation-based factory layout planning approach using reinforcement learning

The planning process of a factory is of high importance for the future performance of the respective manufacturing system. However, the planning process is characterized by its high complexity, given the large variety of possible layout variants. Recent research has demonstrated that reinforcement learning can be successfully used in the early stage of factory layout planning to generate and optimize a factory layout. However, dynamic aspects in the material flow that influence the manufacturing system's throughput time are usually neglected since only analytic optimization objectives are considered. This paper addresses this research gap by presenting a reinforcement learning-based planning approach that incorporates a discrete event material flow simulation as part of the environment. The primary focus of investigation is the formulation of an appropriate reward function since the reward function has a direct influence on the solution quality. The results of the investigation show that the reinforcement learning approach is able to successfully interact with the simulation and optimize the layout with respect to the total throughput time if the incentives given by the reward function are aligned with the optimization objective. The results are additionally compared to a manual planning approach and the computed optimal solution which demonstrates that the developed approach is a suitable support tool for factory layout planning. Concluding, the approach is applied to an application scenario with 25 functional units to investigate the scalability of the approach.

Multi-skilled worker assignment in seru production system for the trade-off between production efficiency and workload fairness

PurposeThe paper formulates a bi-objective mixed-integer nonlinear programming model, aimed at minimizing the total labor hours and the workload unfairness for the multi-skilled worker assignment problem in Seru production system (SPS).Design/methodology/approachThree approaches, namely epsilon-constraint method, non-dominated sorting genetic algorithm 2 (NSGA-II) and improved strength Pareto evolutionary algorithm (SPEA2), are designed for solving the problem.FindingsNumerous experiments are performed to assess the applicability of the proposed model and evaluate the performance of algorithms. The merged Pareto-fronts obtained from both NSGA-II and SPEA2 were proposed as final solutions to provide useful information for decision-makers.Practical implicationsSPS has the flexibility to respond to the changing demand for small amount production of multiple varieties products. Assigning cross-trained workers to obtain flexibility has emerged as a major concern for the implementation of SPS. Most enterprises focus solely on measures of production efficiency, such as minimizing the total throughput time. Solutions based on optimizing efficiency measures alone can be unacceptable by workers who have high proficiency levels when they are achieved at the expense of the workers taking more workload. Therefore, study the tradeoff between production efficiency and fairness in the multi-skilled worker assignment problem is very important for SPS.Originality/valueThe study investigates a new mixed-integer programming model to optimize worker-to-seru assignment, batch-to-seru assignment and task-to-worker assignment in SPS. In order to solve the proposed problem, three problem-specific solution approaches are proposed.

Patient Throughput Times for Supplemental Breast Cancer Screening Exams

Background: To optimize screening abbreviated breast MRI (ABMR) operations, patient throughput times of ABMR were compared to breast ultrasound (US) and full protocol breast MRI (FPMR).Methods: Patient throughput times (mean ± standard error) and its subcomponents were analyzed for 95 ABMRs, 90 breast US exams, and 50 FPMRs. Total patient throughput was measured from registration time to the time of the last acquired image. Actual exam time was time difference between the first and last acquired images and pre-examination time was the calculated difference between throughput and actual exam times. Results: ABMR total patient throughput time was shorter than FPMR (55.7 ± 1.7 vs. 63.1 ± 2.0 min; difference, 7.4 min, 13%; p<0.001), but longer than breast US (39.1 ± 1.3 min; difference, 16.6 min, 30%; p<0.001). ABMR had shorter actual scan times than FPMR (13.4 ± 0.14 vs. 18.6 ± 0.25 min; p<0.001), but longer than US (9.6 ± 0.46 minutes; p<0.001). There was no difference in the pre-examination times between ABMR and FPMR (42.3 ± 1.7 vs. 44.6 ± 1.9 min; p = 0.357); pre-examination times were longer for both MR exam types compared to US (29.5 ± 1.3 minutes; p<0.001). Conclusion: ABMR patient throughput times are faster than FPMR, but these gains are limited as they have no impact on pre-examination activities which comprise the lengthiest components of the patient flow process. US patient flow currently remains faster than ABMR; however, comparable ABMR times could be achieved by further omitting certain sequences and optimizing pre-examination processes.

Modeling, Assessment and Design of an Emergency Department of a Public Hospital through Discrete-Event Simulation

Emergency departments in hospitals are having many difficulties in achieving the performance levels required by health regulators and society. The waiting times as well as the total throughput time are examples of performance indicators that emergency departments need to improve in order to provide a better service to the community. To achieve improvement of performance, the present paper shows a methodology to assist the design process of an emergency department using simulation techniques. In this study, the emergency department of a hospital located in the northern region of Portugal was considered to test the proposed simulation technique. The emergency department initial state was assessed, in terms of patient flow, as well as the human resources needed at every stage of the service. In order to understand in depth the process that a patient goes through during an emergency episode, a comprehensive study was performed on the hospital database. This allowed the analytical description of an emergency episode, which was further used as an input to the simulation model. After developing the simulation model with the information obtained by the hospital’s database, a validation stage was performed. Finally, in order to achieve an optimized design for the emergency department several variant scenarios were considered and evaluated. This methodology proved to be very useful in determining an optimized operation for complex, and non-linear systems.

Impact of professional interpreters on outcomes for hospitalized children from migrant and refugee families with limited English proficiency: a systematic review.

The objective of the review was to identify, critically appraise and synthesize evidence on the impact of professional interpreters on outcomes for hospitalized children from migrant and refugee families with limited English proficiency. A key strategy to facilitate effective communication between limited-English-proficient migrant and refugee families and healthcare providers is the use of professional interpreters. Despite awareness of the importance of professional interpreters, interpreters are underutilized. This results in suboptimal care, including poor access to health care, increased risk of serious medical events, increased admission to hospital and inadequate health literacy. Limited-English-proficient migrant, refugee or asylum-seeker families with a hospitalized child aged 0 to 18 years, who had used a professional interpreter, were considered for this review. Comparators included standard care or no interpreter, and reported results of comparisons of the duration, frequency or mode of interpreter delivery. Outcomes were length of stay in hospital, unplanned readmission rates to hospital, non-attendance at hospital clinic appointments and ambulatory care, child and/or parent satisfaction with care in hospital, adherence to treatment, medication errors, and other adverse events relating to patient safety. The review considered all studies conducted using a quantitative approach. A three-step search strategy was used. Databases were searched in December 2018 for published and unpublished articles. Studies published in English were considered for inclusion. The recommended JBI approach to critical appraisal, study selection, data extraction and data synthesis was used. Six articles reporting results from three randomized controlled trials and one observational study were included. Studies were undertaken in the United States involving Spanish-speaking participants. Three studies were in emergency departments. There was a total of 1813 families, of whom 1753 had limited English proficiency. Migrant and refugee families with limited English proficiency reported greater satisfaction with aspects of care when a professional interpreter service was used compared with using ad hoc interpreters. Using professional in-person interpreters resulted in a shorter total emergency department throughput time compared to using professional interpreters via telephone. There was no difference in concordance of child's discharge diagnoses between parents assigned professional interpreters and those assigned bilingual physicians. Video interpretation provided better understanding of diagnoses than phone interpretation. There is evidence that use of ad hoc interpreters or no interpreter is inferior to use of professional interpreters of any mode. Although video and in-person interpreters are more favorable for some outcomes, mode of delivery may not be as important as the fact that a professional interpreter is being used. The mode of professional interpreter delivery should be based on accessibility, availability, language requirements and patient preference. PROSPERO CRD42017058161.

Integrating simulation and FITradeoff method for scheduling rules selection in job-shop production systems

Scheduling decisions related to due date assignment, order release and priority dispatching affect multiple performance dimensions simultaneously. Previous job-shop studies based on simulation and multiobjective optimization techniques do not explore the goals that a decision-maker (DM) wishes to reach, when assessing compensations between multiple benefits and potential impacts yielded by a combination of rules. With the aim of reflecting the DM's preference structure on scheduling decisions, this paper considers a discrete event simulation approach integrated with the Flexible and Interactive Tradeoff (FITradeoff) compensatory method to identify the best combination of due date assignment, order release and shop dispatching rules. Through discrete event simulation, the performance of different combinations of rules and inherent parameters are evaluated with regard to the attributes of total cost, production quantity, total throughput time, and tardiness. The FITradeoff method is used to compensate the disadvantage in one of the performance attributes with the advantage in other attributes, considering the DM's preferences which are elicited in a procedure that requires less information in order to make comparisions between multiple simulated consequences. A numerical application is set out using realistic data from a manufacturer of ladies' shoes so as to visualize and discuss the steps of the model and its usefulness in practice.

POLCA Control in Two-Stage Production Systems

POLCA (Paired-cell Overlapping Loops of Cards with Authorization) is a decision support system for material flow control under Quick Response Manufacturing. It operates in the context of low-volume, high-mix, and cellular manufacturing. While there is an increasing literature on POLCA performance, current studies usually assume full availability of components (or parts) at assembly stations, neglecting parts manufacturing and feeding. Therefore, this study uses simulation to assess POLCA performance in a two-stage production system, where at the first stage parts are manufactured and at the second, they are assembled into end-products. The study demonstrates that using POLCA to control both production stages, manufacturing and assembly, significantly outperforms the use of POLCA at the assembly stage only, leading to important reductions of the total throughput time of orders and on the percentage of tardy orders. Statistical analysis of our results was conducted using ANOVA.

Investigating the performance improvement by conversion of assembly line configuration to a pure cell system in manufacturing industry

Seru production systems implement reconfiguration of traditional assembly lines to a flexible cell system that aim at reducing the required workforce while at the same time augmenting the productivity manifold. For evaluating the overall performance improvement, cell formatting and assigning workers to serus takes form of a complicated decision problem. In this paper, with the objective of reducing the total cost for training the worker, minimising the processing time and the total throughput time, mathematical insights on the solution space of a multi-objective line-cell conversion model are identified, in turn proving it to be an NP-hard model. By applying the proposed heuristic algorithm on several numerical simulations, a Pareto-optimal solution of this multi-objective model is obtained. With experimental results and comparative studies, the proposed approach proves its effectiveness that may lead to further improvement in seru production systems competitive advantage to cope with fluctuating market demands by enhancing the flexibility as well as the efficiency of the system. [Submitted: 18 June 2016; Revised 29: August 2017; Revised: 25 August 2018; Revised: 5 December 2018; Accepted: 8 January 2019]

Deep learning and WLC: how to set realistic delivery dates in high variety manufacturing systems

The focus is on workload control, a production planning and control technique that reduces and stabilizes the total throughput time. In these conditions, defining realistic delivery dates should become easier, yet the use of basic techniques often proves to be ineffective. Hence, we propose using statistical and/or neural network techniques to estimate, starting from the current workload of the job shop, the expected lead time of entry jobs, and to use this estimation to define reliable delivering dates. To test the approach, we simulated a 6-machines job-shop and we make predictions using a multi-regressive linear model and a multi-layer neural network. In terms of tardy jobs, both approaches performed very well, with the neural network providing the best results.

Impact of Adaptive Consistency on Distributed SDN Applications: An Empirical Study

Scalability of the control plane in a software-defined network (SDN) is enabled by means of decentralization of the decision-making logic, i.e., by replication of controller functions to physically or virtually dislocated controller replicas. Replication of a centralized controller state also enables the protection against controller failures by means of primary and backup replicas responsible for managing the underlying SDN data plane devices. In this paper, we investigate the effect of the deployed consistency model on scalability and correctness metrics of the SDN control plane. In particular, we compare the strong and eventual consistency, and make a case for a novel adaptive consistency approach. The existing controller platforms rely on either strong or eventual consistency mechanisms in their state distribution. We show how an adaptive consistency model offers the scalability benefits in terms of the total request-handling throughput and response time, in contrast to the strong consistency model. We also outline how the adaptive consistency approach can provide for correctness semantics that are unachievable with the eventual consistency paradigm in practice. The adaptability of our approach provides a balanced and tunable tradeoff of scalability and correctness for the SDN application implemented on top of the adaptive framework. To validate our assumptions, we evaluate and compare the different approaches in an emulated testbed with an example of a load balancer controller application. The experimental setup comprises up to five extended OpenDaylight controller instances and two network topologies from the area of service provider and data center networks.

Integrated storage-order picking systems: Technology, performance models, and design insights

In many warehouses shuttle-based technologies have replaced the traditional AS/R system based storage technologies. The impact these systems have on downstream order picking performance is largely unknown. To study the interactions between upstream storage and downstream picking systems, we develop a novel analytical model for integrated storage and order picking systems. The resulting semi-open queuing model is solved using the matrix-geometric method. Using the queuing network model, we are able to study the effect of storage system technology on order throughput times, and the effect of the picking station input buffer size on order picking performance. Further, we analyze the effect of a constant work-in-process (CONWIP) control for orders on system performance. Our results indicate that using SBS/R instead of AS/R-based storage systems yields investment cost savings (i.e., fewer aisles in the storage area and fewer picking stations), paired with a lower total throughput time at a given order arrival rate. Numerical studies show how the total throughput time, first, benefits and then becomes stable by increasing the input buffer size at the picking stations. Retrieving item tote at the storage system in advance with respect to the picker availability is also advantageous, especially in the SBS/R system.

Forecasting container throughput using aggregate or terminal-specific data? The case of Tanjung Priok Port, Indonesia

We propose a new approach to forecasting total port container throughput: to generate forecasts based on each of the port’s terminals and aggregate them into the total throughput forecast. We forecast the demand for total container throughput at the Indonesia’s largest seaport Tanjung Priok Port, employing SARIMA, the additive and multiplicative Seasonal Holt-Winters (MSHW) and the Vector Error Correction Model (VECM) on the monthly port and individual terminal container throughput time series between 2003 and 2013. The performance of forecasting models is evaluated based on mean absolute error and root mean squared error. Our results show that the MSHW model produces the most accurate forecasts of total container throughput, whereas SARIMA generates the worst in-sample model fit. The VECM provides the best model fits and forecasts for individual terminals. Our results report that the total container throughput forecasts based on modelling the total throughput time series are consistently better than those obtained by combining those forecasts generated by terminal-specific models. The forecasts of total throughput until the end of 2018 provide an essential insight into the strategic decision-making on the expansion of port’s capacity and construction of new container terminals at Tanjung Priok Port.

Order release in the hybrid MTO–FTO production

In this paper, we analyse and compare the impact of the choice of the release method in a hybrid make-to-order (MTO)–finish-to-order (FTO) production system composed of two stages. The study is motivated by a practical problem from the aluminium profiles manufacturing industry, where for shortening throughput times it is allowed to start production of standard and regular items without confirmed orders, holding a stock of semi-finished products. The first stage manufactures both, items to order and items to a stock of semi-finished products, while the second stage is a customization stage based on customer demand requests and specifications. The paper addresses the questions of where order release control should be exercised, i.e., at only one stage or at both stages, and when should it be exercised. Simulation results suggest that load-based order release can be successfully applied to this hybrid production system when combined with a replenishment control policy for semi-finished products. Load-based order release contributes to reduce the total throughput time and the percentage of tardy orders of both, MTO and FTO, while contributing to a reduction of the semi-finished products inventory levels required to achieve a given fill rate. The study is a contribution toward the alignment of workload control theory to industrial practice.

A study on the importance of selection rules within unbalanced MTO POLCA-controlled production systems

The present paper is aimed at investigating, through simulation, the behaviour of the POLCA method when a make-to-order (MTO) production system is highly unbalanced, in terms of both routings and times. In particular, the study is addressed to verify: 1) the effective capability of the POLCA method to improve the uncontrolled system; 2) the impact of how orders are processed at each workstation, to show that POLCA performance can be further improved in such circumstances by adopting an appropriate selection rule. Germs and Riezebos (2010) proved that POLCA is very effective in reducing the total throughput time (TTT) with respect to the corresponding unconstrained production systems. Owing to this, it appears evident that POLCA represents a valuable and effective make-to-order (MTO) production control method. However, they only considered balanced systems, hence some observations must be carefully addressed. In fact, often, in real world applications, the systems to be controlled are designed to process units with very different routings, each with significantly different probability to occur.

Mathematical analysis and solutions for multi-objective line-cell conversion problem

The line-cell (or line-seru) conversion is an innovation of assembly system applied widely in the electronics industry. Its essence is tearing out an assembly line and adopting a mini-assembly unit, called seru (or Japanese style assembly cell). In this paper, we develop a multi-objective optimization model to investigate two line-cell conversion performances: the total throughput time (TTPT) and the total labor hours (TLH). We analyze the bi-objective model to find out its mathematical characteristics such as solution space, combinatorial complexity and non-convex properties, and others. Owing to the difficulties of the model, a non-dominated sorting genetic algorithm that can solve large size problems in a reasonable time is developed. To verify the reliability of the algorithm, solutions are compared with those obtained from the enumeration method. We find that the proposed genetic algorithm is useful and can get reliable solutions in most cases.

Optimization of POLCA-controlled production systems with a simulation-driven genetic algorithm

The present work is meant to show the effective capability of optimizing an unbalanced Paired-Cell Overlapping Loops of Cards with Authorization (POLCA)-controlled production system by means of a heuristic algorithm. This objective is suggested by the fact that one of the most significant issues when using card-driven production control systems is represented by the optimized setting of the large number of cards within the control loops. This is particularly true in the case of unbalanced systems, where the number of cards may vary significantly among the different loops. Little law is usually adopted in literature to infer this number from historical data, but the obtained number is usually far from the optimum. Indeed, in real-world applications, the systems to be controlled are designed to process units with very different routings, each with different probability to occur. In all these situations, they result particularly difficult to set correctly. To this aim, in the present work a Genetic Algorithm is used. The objective is that of finding the correct number of cards and to reduce the overall Total Throughput Time and the average Work In Process. The proposed approach may provide a valid support tool to overcome these limitations, making the most of POLCA capabilities in many manufacturing configurations.

Reducing worker(s) by converting assembly line into a pure cell system

The line-cell conversion is established as a new production system towards converting traditional conveyor assembly line to a cell system, in which one (or multiple) worker carries out all of the operations of a job in a cell. Its performance improvement can be enhanced by reducing worker(s) without decreasing productivity. How to conduct this conversion by determining how many cells should be formatted and which workers are assigned in a cell, is a complicated decision problem. This paper presents a multi-objective line-cell conversion model with the two goals of reducing worker(s) and increasing productivity simultaneously, in a production environment that converts traditional conveyor assembly line into a pure cell system. We identify several mathematical insights on solution space of the multi-objective line-cell conversion model and prove that it is an NP-hard problem. Then we provide an improved exact algorithm to obtain Pareto-optimal solutions of the multi-objective model. Several numerical simulation experiments are performed to illustrate that the line-cell conversion can be used to reduce worker(s) and the total throughput time at the same time.

Combining local search into non-dominated sorting for multi-objective line-cell conversion problem

Multi-objective decision-making is often used in line-cell conversion problem. It is the key issue of developing line-cell conversion successfully. In this article, a multi-objective optimisation model is proposed to investigate two line-cell conversion performances: the total throughput time and the total labour hours. It is proved that the model is a non-deterministic polynomial (NP)-hard problem and its Pareto-optimal front is non-convex. Owing to the properties of cell formation and cell loading in line-cell conversions, an approach of combining local search into non-dominated sorting genetic algorithm II is proposed to obtain better non-dominated solutions. Several numerical simulation experiments are performed to show the utility and performance of our approach.