Multiple Production Lines Research Articles

Surgical Coiled Tubing Stimulations: Step Changes in Effectiveness, Efficiency, and Characterization of Waterflooding Interventions

Summary An innovative coiled tubing (CT) real-time flow measurement tool was introduced in Ecuador to reformulate the stimulation workflow in water injectors, which comprised evaluation and treatment. This new technology enabled an integrated, single-run workflow instead—initial injectivity measurements, diagnostics, treatment, post-stimulation injectivity measurements, and final diagnostics. This novel, rigless approach reduced equipment footprint, operational time, and cost, and it improved production as compared with the conventional approach, despite accrued capital discipline constraints. Conventionally, operators rely on workover rigs and multiple product lines to diagnose, stimulate, and evaluate injector wells. Several challenges and inefficiencies were addressed by deploying the CT real-time flow measurement tool. Each intervention was designed to be completed with a single CT run and without the need for a workover rig, thus saving cost and time. Tailored diversion methods substituted the need for drillpipe to set mechanical packers. Prestimulation injection logging test (ILT) results obtained with that innovative tool, coupled with real-time control of depth and high-pressure jetting during execution, enabled effective placement of the stimulation treatment. Ultimately, post-treatment ILTs confirmed treatment effectiveness and final wellbore downhole conditions. Introduction of the CT real-time downhole flow measurement tool allowed operational objectives to be met in a single run, without additional interventions, with or without a workover rig on site. When workover rigs were present, this improved workflow saved an average of 15% operational time. In cases without a workover rig, 105 hours of rig time were saved (without considering rig mobilization time). Four case studies are presented. The first two cases demonstrate how acquisition of ILTs throughout the intervention enabled optimization of fluid placement and introduction of diverter methods. The third case covers a scenario where there was an initially low injectivity and highlights the challenges and lessons associated with recovering injectivity. The fourth case presents challenges unique to flowmeter measurements in heavy-oil environments. In each case, effectiveness of the optimized treatment was measured by two metrics: improvements in net injectivity and uniformity of injection profile, both of which drive the effectiveness of secondary recovery in connected producer wells. On average, wells intervened with this approach featured an improvement in injectivity of 301% (compared with 226% conventionally) and in their injection profile homogeneity by 13%. As a result, the productivity in connected wells improved by as much as 74% and an average of 39% (compared with 14% conventionally). This innovative workflow is a step change over conventional approaches to rejuvenate waterflooding. It combines the capabilities of delivering treatments via CT and the power of real-time downhole flow measurements to break the paradigm of multiline, multirun operations to remediate and stimulate injector wells. This yields logistically leaner operations, which are less costly, and it enables breakthroughs in secondary recovery through data-enriched interventions in times of budget pressure, not only in Ecuador, but also across the globe.

Textile Production Line Monitoring System Using Wavelet-Regression Neural Network

This paper presents design and experiments for a production line monitoring system. The system is designed based on an existing production line which mapping to the smart grid standards. The Discrete wavelet transform (DWT) and regression neural network (RNN) are applied to the operation modes data analysis. DWT used to preprocess the signals to remove noise from the raw signals. The output of DWT energy distribution has given as an input to the GRNN model. The neural network GRNN architecture involves multi-layer structures. Mean Absolute Percentage Error (MAPE) loss has used in the GRNN model, which is used to forecast the time-series data. Current research results can only apply to the single production line but in future, it will used for multiple production lines.

Investigation of an international outbreak of multidrug-resistant monophasic Salmonella Typhimurium associated with chocolate products, EU/EEA and United Kingdom, February to April 2022.

An extensive multi-country outbreak of multidrug-resistant monophasic Salmonella Typhimurium infection in 10 countries with 150 reported cases, predominantly affecting young children, has been linked to chocolate products produced by a large multinational company. Extensive withdrawals and recalls of multiple product lines have been undertaken. With Easter approaching, widespread product distribution and the vulnerability of the affected population, early and effective real-time sharing of microbiological and epidemiological information has been of critical importance in effectively managing this serious food-borne incident.

Training Data Selection by Categorical Variables for Better Rare Event Prediction in Multiple Products Production Line

Manufacturers are struggling to use data from multiple products production lines to predict rare events. Improving the quality of training data is a common way to improve the performance of algorithms. However, there is little research about how to select training data quantitatively. In this study, a training data selection method is proposed to improve the performance of deep learning models. The proposed method can represent different time length multivariate time series spilt by categorical variables and measure the (dis)similarities by the distance matrix and clustering method. The contributions are: (1) The proposed method can find the changes to the training data caused by categorical variables in a multivariate time series dataset; (2) according to the proposed method, the multivariate time series data from the production line can be clustered into many small training datasets; and (3) same structure but different parameters prediction models are built instead of one model which is different from the traditional way. In practice, the proposed method is applied in a real multiple products production line dataset and the result shows it can not only significantly improve the performance of the reconstruction model but it can also quantitively measure the (dis)similarities of the production behaviors.

An Auto-MILP Model for Flexible Job Shop Scheduling Problem

An auto mixed integer linear programming (Auto-MILP) model is proposed to tackle the flexible job shop scheduling problem. The Auto-MILP model allows the precedence between operations of a job to be given by an arbitrary directed acyclic graph rather than a linear order. The goal is the minimization of the makespan. By incorporating the operation assignment to resources, the Auto-MILP model also allows multiple production lines in the workshop, which means more than one machine could complete the same operation, and the machine assignment is determined by the algorithm. We utilize time separation to define the machine capacity, which makes it adaptive to various kinds of job scheduling scenarios. The effectiveness and efficiency of the Auto-MILP model are evaluated using instances from simulated scheduling case and real data from an industrial job shop.

Dynamic Data Scheduling of a Flexible Industrial Job Shop Based on Digital Twin Technology

Aiming at the problems of premature convergence of existing workshop dynamic data scheduling methods and the decline in product output, a flexible industrial job shop dynamic data scheduling method based on digital twin technology is proposed. First, digital twin technology is proposed, which provides a design and theoretical basis for the simulation tour of a flexible industrial job shop, building the all‐factor digital information fusion model of a flexible industrial workshop to comprehensively control the all‐factor digital information of the workshops. A CGA algorithm is proposed by introducing the cloud model. The algorithm is used to solve the model, and the chaotic particle swarm optimization algorithm is used to maintain the particle diversity to complete the dynamic data scheduling of a flexible industrial job shop. The experimental results show that the designed method can complete the coordinated scheduling among multiple production lines in the least amount of time.

Production Planning Problem of a Two-Level Supply Chain with Production-Time-Dependent Products

This study considers a production planning problem with a two-level supply chain consisting of multiple suppliers and a manufacturing plant. Each supplier that consists of multiple production lines can produce several types of semi-finished products, and the manufacturing plant produces the finished products using the semi-finished products from the suppliers to meet dynamic demands. In the suppliers, different types of semi-finished products can be produced in the same batch, and products in the same batch can only be started simultaneously (at the same time) even if they complete at different times. The purpose of this study is to determine the selection of suppliers and their production lines for the production of semi-finished products for each period of a given planning horizon, and the objective is to minimize total costs associated with the supply chain during the whole planning horizon. To solve this problem, we suggest a mixed integer programming model and a heuristic algorithm. To verify performance of the algorithm, a series of tests are conducted on a number of instances, and the results are presented.

Home Palliative Care Savings.

Objective:The aim of this study was to evaluate an adult home palliative care (HPC) program for multiple insurance product lines using multiple vendors to determine if the annual costs of health care decreased for those enrolled in HPC.Study Design:Of the 506 members who were referred to and qualified for palliative care in 2019, a retroactive review was done comparing annual health care costs between the 396 members in the enrolled group and the 110 members in the group receiving usual care.Methods:The total health care costs for the calendar year 2019 were compared between the group enrolled in HPC and those who received usual care. Cost savings were further evaluated based on whether the member was enrolled in the palliative care program for 1–5 versus 6–12 months.Results:Overall medical costs for these 396 enrollees for the calendar year 2019 showed a gross savings of $24,643 per member (16.7% decrease in cost). For members enrolled for 1–5 months, annual gross savings were $23,314 per member (15.8% decrease from the comparison group), and for members enrolled for 6–12 months, annual gross savings were $26,409 per member (17.9% decrease). The savings were most prominent for the commercial insurance product with a 51% decrease in annual costs.Conclusions:Adult home-based palliative care delivered by multiple vendors (consisting of multiple insurance product lines) to a population is effective in decreasing total medical costs by 16.7% during a calendar year compared with a control group. The gross savings for those enrolled for 6–12 months (17.9%) were greater than the gross savings for those enrolled for 1–5 months (15.8%). The savings were most prominent for the commercial insurance product, while an increase in cost was seen for the Medicaid product.

Textile Production Line Monitoring System Using Wavelet-Regression Neural Network

This paper presents design and experiments for a production line monitoring system. The system is designed based on an existing production line which mapping to the smart grid standards. The Discrete wavelet transform (DWT) and regression neural network (RNN) are applied to the operation modes data analysis. DWT used to preprocess the signals to remove noise from the raw signals. The output of DWT energy distribution has given as an input to the GRNN model. The neural network GRNN architecture involves multi-layer structures. Mean Absolute Percentage Error (MAPE) loss has used in the GRNN model, which is used to forecast the time-series data. Current research results can only apply to the single production line but in future, it will used for multiple production lines.

Improving Production Performance Through Multi-Plant Cross Learning

Abstract The advancement in Web-/Internet-based technologies and applications in manufacturing sector has increased the tilization of cyber workspace to enable more efficient and effective ways of doing manufacturing from distributed locations. This work introduces a novel continuous improvement framework to enhance the performance of production lines through multi-plant comparison and learning among identical or similar production lines in different locations by leveraging the information stored on factory cloud. In this work, production data from multiple identical production lines are collected and analyzed to learn the “best” feasible action on critical machines, which offers a new way to optimize the management of product lines. Machine learning and system model are used to find the relationships between the performance index and the available data. A real case study based on multiple similar automotive plants is provided to demonstrate the method and the increases of throughput are predicted.

Automatically optimizing dynamic synchronization of individual industrial process variables for statistical modelling

Statistical modelling of industrial production data can lead to improved understanding of the process to benefit process monitoring and control routines. The production data required for such models need however to be synchronized in time, a topic sparsely covered in literature. We propose a strategy for data-driven automated optimization of dynamic synchronization of industrial production data, that optimizes the synchronization per process variable and can be applied for on-line monitoring in real-time. The strategy is tested and validated for two relevant production facilities, each of which has multiple production lines or configurations. For all lines and configurations, models predicting the production quality from process variables improved in accuracy using the presented per-variable optimization strategy. Although the prediction accuracy for two models would still be insufficient for real-time monitoring and control, process operators and engineers may still obtain novel process understanding from applying the presented strategy on these models.

Integrated Capacity Planning and Production Control of an Assembly Manufacturing System

For an assembly manufacturing system, its capacity planning problem and production control problem are usually considered separately. However, production capacity cannot exert its maximum productivity without employing optimal production control; and optimal production control may not satisfy the demand without sufficient production capacity. Therefore, it is important to jointly optimize capacity planning and production control. First, the mathematical formulation of integrated production control and capacity planning problem for an assembly manufacturing system with multiple upstream production lines and one downstream assembly line is proposed in this paper. Second, we develop a method to obtain the marginal probability distribution of the inventory level in the assembly system, which can be employed to calculate the average production cost under an approximate optimal production control policy. Since optimal control policy is subject to production capacity (i.e., machine number), we employ the integer programming method to optimize the machine number (i.e., production capacity), which consequently influences the control policy. Numerical experiments are conducted to verify the correctness of the method of calculating steady-state probability distribution, and the effectiveness of the algorithm of integrated optimization of production control and production capacity.

Optimization models for a lot sizing and scheduling problem on parallel production lines that share scarce resources

The purpose of this paper is to propose mathematical models to represent a lot sizing and scheduling problem on multiple production lines that share scarce resources and to investigate the computational performance of the proposed models. The main feature that differentiates this problem from others in the literature is that the decision on which lines to organize should be taken considering the availability of the necessary resources. The optimization criterion is the minimization of the costs incurred in the production process (inventory, backlogging, organization of production lines, and sequence-dependent setup costs). Nine mixed integer optimization models to represent the problem are given and, also, the results of an extensive computational study carried out using a set of instances from the literature. The computational study indicates that an efficient formulation, able to provide high quality solutions for large sized instances, can be obtained from a classical model by making the binary production variables explicit, using the facility location reformulation as well as the single commodity flow constraints to eliminate subsequences. Moreover, from the results, it is also clear that the consideration of scarce resources makes the problem significantly more difficult than the traditional one.

Hybrid Rescheduling Optimization Model under Disruptions in Precast Production Considering Real-World Environment

Increased emphasis is currently placed on precast production scheduling due to the upward-trending applications of precast technology. However, existing studies primarily focused on static scheduling, and few of them addressed dynamic rescheduling problems subject to disruptions from individual products without considering constraints in the practical environment. In this paper, a hybrid rescheduling optimization model for precast production is proposed to minimize rescheduling costs and ensure on-time delivery under disruptions of machine breakdown. The rescheduling was first optimized based on the genetic algorithm considering the idle time of multiple production lines, time intervals, and operation characteristics in precast production. Subsequently, the feasibility of potential reschedules was further verified via simulating production uncertainties in a real-world environment to achieve a trade-off between a high service level and profit maximization. Finally, a case study with different realistic scenarios was conducted to prove the superiority of the model in comparison with other methods. This methodology will increase the applicability of rescheduling methods in practice and reduce production costs of components in precast construction.

Layout decision system for multiple production lines using work-flow-line and GA

Layout decision system for multiple production lines using work-flow-line and GA

Improving Manufacturing Supply Chain by Integrating SMED and Production Scheduling

Globalization has led to a significant effect on today’s manufacturing sector. Manufacturers need to find new and innovative ways to increase efficiency and reduce waste in the manufacturing supply chain. Lean/six sigma tools can help companies increase production efficiency and stay in competition. Manufacturing in smaller batches can keep the supply chain lean and customizable. This leads to frequent changeovers and downtime. A changeover is usually required when a single machine produces different products based on the requirement. A large-scale industry can either install multiple individual production lines to cater to the demand (usually expensive) or make frequent machinery changes. Single Minute Exchange Die (SMED) is a system designed for reducing the changeover time for machines. It reduces the time taken to complete the activities and eliminates non-essential activities throughout the changeover. Scheduling an operating procedure within SMED in such case is a challenge. Project scheduling model with workforce constraints can be used to create a set of heuristics to provide us with an optimized list of tasks. The paper proposes to design a scheduling heuristic model to allocate tasks to the operators to get the least amount of operator idle time and reduce changeover downtime costs. The paper further illustrates the benefit of the model in a case study and proposes its integration within the existing SMED methodology. This results in a benefit-to-cost ratio of 7.5% for production scheduling compared to that of stages 4 and 5 in SMED, which is 1.2%.

Aspect Multiple Product Lines

Software product line (SPL) approach has known a great attention in the software field due to the advantages it brings to software-development. In some large fields having a single SPL is no longer sufficient to manage variability. Several separated SPLs rise then resulting in what is known by multiple product lines (MPL). However, the adoption of an MPL results in several challenges namely: managing reuse between SPLs, MPL model complexity and distributed-derivation. In this paper, we present a new methodology for MPLs engineering called AMPL. Our approach helps managing reuse across MPLs basing on two key concepts: separation of concerns and partial derivation. AMPL approach is validated in the context of an e-government MPL and its various steps are illustrated along the paper. The results show that our approach helps systemising reuse within MPLs and structuring the MPL model. Moreover, SPLs are integrated early in the development process thus avoiding the distributed-derivation challenges.

DECOMPOSITION BASED HEURISTICS FOR A LOT SIZING AND SCHEDULING PROBLEM ON MULTIPLE HETEROGENEOUS PRODUCTION LINES WITH PERISHABLE PRODUCTS

In this paper, we propose a novel MIP-based heuristic method to deal with a lot sizing and scheduling problem with multiple heterogeneous production lines in a production setting with perishable items. The problem is inspired by the production processes adopted by some Brazilian food industries and it considers that several production lines share the same scarce production resources. Therefore, only a subset of those lines can simultaneously operate in each production period. Moreover, the production environment is characterized by the existence of sequence-dependent setup times and costs, and by the production of perishable items which can be stocked for a short period only. Firstly, we propose a facility location reformulation for a model previously proposed in the literature. Secondly, we propose a heuristic composed of two phases. The first phase has an elaborated approach to building feasible solutions solving initially an aggregated lot sizing problem to decide which production lines to assemble, followed by the resolution of the various single line lot sizing and scheduling problems. The second phase applies improvement heuristics exploring principles of fix-and-optimize and local branching procedures. Computational results carried out using a data set proposed in the literature are presented in order to study the efficiency of the proposed approach. The results demonstrate that our heuristics provide superior results when benchmarked with a heuristic from the literature specifically developed to solve the problem under consideration, and with a commercial MIP solver.

Product life cycle-based digital plant modelling method for process industry

Digital factory is a core technology of intelligent manufacturing, which provides a powerful guarantee for production quality, reliability and stability. For building a digital factory of complex process industry, this paper presents a product life cycle-based data modelling method that is oriented towards the product life cycle and core business in process industry. Firstly, it identifies key manufacturing nodes and management nodes, extracts core characteristics and parameters of production, products, and equipment, and then digitizes physical entities in the plant. A data model including conceptual models and logical models is then built, thereby formalizing the entities in the plant and the relationships among them to achieve unified data integration management. This digital plant model defines equipment data, product data, quality data, product specifications, process specifications, management rules and so on to meet the requirements of both production management and production control. It builds a highly structured data model that horizontally covers multiple production lines and vertically supports multiple levels of production control and management systems, providing core technology for digital plant in complex process industry.

Aspect multiple product lines

Aspect multiple product lines