Product Tracking and Flow Control

Modern enterprises have to react quickly to dynamic changes in the market, due to changing customer requirements and expectations. One of the key area of production management, that must continuously evolve by searching for new methods and tools for increasing the efficiency of manufacturing systems is the area of production flow planning and control. These aspects are closely connected with the ability to implement the concept of Virtual Enterprises (VE) and Virtual Manufacturing Network (VMN) in which integrated infrastructure of flexible resources are created. In the proposed approach, the players role perform the objects associated with the objective functions, allowing to solve the multiobjective production flow planning problems based on the game theory, which is based on the theory of the strategic situation. For defined production system and production order models ways of solving the problem of production route planning in VMN on computational examples for different variants of production flow is presented. Possible decision strategy to use together with an analysis of calculation results is shown.

In the paper the idea of the system of production flow and quality control and its data model is presented. There are distinguished the functional moduls of preparing technological processes’ variants, of production scheduling, and of quality control. The solution is dedicated to the enterprises characterized by a make-to-order production approach, which have to react flexibly to changing production conditions and the backlog of orders.

We study the dynamic routing problem for a flexible manufacturing system consisting of two unreliable machines and a finite buffer. One product‐type is produced which requires two operations in sequence. The demand rate is assumed to be constant. Each machine is capable of performing both operations. The objective is to trace the demand while keeping the work‐in‐process low and the cycle‐time short. An optimal control formulation is established for the dynamic routing problem. A production flow control algorithm is developed based on a combination of mathematical modeling and heuristics. The control policy is simulated and a comparison with the numerical optimal solution shows that it performs well for the instances under consideration.

In this paper, we propose a procedure for production flow control in reentrant manufacturing systems. The system under study consists ofN machines and producesM product types simultaneously. Each part goes through the system following a predefined process and may visit a machine many times. All machines are subject to random failures and need random repair times. The scheduling objectives are to keep the production close to demand and to keep the WIP inventory level and cycle times at low values. The model is motivated by semiconductor fabrication production. A three-level hierarchical controller is constructed to regulate the production. At the top level of this hierarchy, we perform capacity planning by selecting the desirable buffer sizes and the target production level for each operation. A production flow rate controller is at the middle level which recalculates the production rates whenever a machine fails or is starved or blocked. The loading times for individual parts are determined at the bottom level of the hierarchy. Comparison with alternative control is made through simulation and it shows that the control policy performs well.

With the development of Mobile Internet, it provides a good technological environment for the development of agricultural e-commerce. The fresh agricultural products e-commerce is to design, planning and control of the information flow, the trade flow, the fund flow and the material flow of the fresh agricultural products with a view to strengthening competitiveness. Because of the previous view, the paper design the functions of the e-commerce platform suitable for fresh agricultural products. According to the function design, we design the app prototype of the fresh agricultural products e-commerce platform, which can more accurately express the functions of the e-commerce platform.

AimsThe aim of this study was to report the metrological qualities of techniques currently used to quantify skeletal muscle volume and 3D shape in healthy and pathological muscles.MethodsA systematic review was conducted (Prospero CRD42018082708). PubMed, Web of Science, Cochrane and Scopus databases were searched using relevant keywords and inclusion/exclusion criteria. The quality of the articles was evaluated using a customized scale.ResultsThirty articles were included, 6 of which included pathological muscles. Most evaluated lower limb muscles. Partially or completely automatic and manual techniques were assessed in 10 and 24 articles, respectively. Manual slice-by-slice segmentation reliability was good-to-excellent (n = 8 articles) and validity against dissection was moderate to good(n = 1). Manual slice-by-slice segmentation was used as a gold-standard method in the other articles. Reduction of the number of manually segmented slices (n = 6) provided good to excellent validity if a sufficient number of appropriate slices was chosen. Segmentation on one slice (n = 11) increased volume errors. The Deformation of a Parametric Specific Object (DPSO) method (n = 5) decreased the number of manually-segmented slices required for any chosen level of error. Other automatic techniques combined with different statistical shape or atlas/images-based methods (n = 4) had good validity. Some particularities were highlighted for specific muscles. Except for manual slice by slice segmentation, reliability has rarely been reported.ConclusionsThe results of this systematic review help the choice of appropriate segmentation techniques, according to the purpose of the measurement. In healthy populations, techniques that greatly simplified the process of manual segmentation yielded greater errors in volume and shape estimations. Reduction of the number of manually segmented slices was possible with appropriately chosen segmented slices or with DPSO. Other automatic techniques showed promise, but data were insufficient for their validation. More data on the metrological quality of techniques used in the cases of muscle pathology are required.

Taking the common continuous chemical reaction process in industry as the research object, this paper analyzes the function of instruments and equipment, system process flow and sequence control requirements of each part of chemical reaction process. Combined with the object control circuit and operation experience, taking safety, stability and energy saving as the production goal, the start-up and stop sequence control system of chemical reaction process is designed, and the flow chart is given. Based on SIMATIC PCS7 control platform and SMPT-1000 as an object, the experiment was carried out. The experimental results show that the operation of the system is stable and the performance indexes are controlled within the requirements of safe and efficient operation of the system, i.e. the flow of final product, product concentration, reactor temperature, reactor liquid level, flash tank pressure, flash tank liquid level, mixing tank liquid level and preheater temperature are all kept within the safe production range, which can meet the requirements of sequence control of chemical reaction process.

Applications that have already been developed on using infrared (IR) imaging are proposed for a better understanding of breast cancer analysis. The first part of this chapter presents the use of interval data to classify breast abnormalities. Authors have been adapting machine learning techniques to work with interval variables that can handle the intrinsic variation of data. The second part evaluates segmentation techniques applied to breast IR images. Many authors use automatic image segmentation techniques that must consider the natural anatomical variation between people. Manual segmentation techniques can be used to minimize the problem of anatomical variations. The main purpose of such techniques is to seek to avoid the errors due to the natural asymmetry of the human body. A process that uses ellipsoidal elements to represent each breast has been chosen. The manual technique is more precise and can correct possible failures presented in the automatic method. Validation of each segmentation type was also included by using Jaccard, DICE, False Positive, and False Negative methods.

Slugging is a dynamic flow behavior observed in many multiphase production systems. It is due to the rapid change in volumetric phase flowrates within a process. Various slug mitigation strategies have been implemented in production facilities over the world to ensure stable production and maximum field recovery. This paper aims to present the project overview, workflow and use cases for the implementation of an active slug control technology based on real-field project experiences. An active slug control algorithm can be used for slug mitigation on upstream production systems. The purpose is to stabilize production flow from the flowline inlet to the first stage separator so that stable and safe production is ensured within the system design constraints. This in turn has the potential to increase production flow and further extend field life of aging production facilities. This paper presents examples of feasibility assessments and field implementations, covering both conventional oil and gas-condensate production. An active slug control technology can be applied to both deep-water and shallow-water facilities. Installation of this slug control system requires only an update to the existing flow controller to include an additional algorithm for slug suppression at the riser top choke valve. This simple addition eliminates the requirement of active operators’ intervention in the event of slugging, as well as reducing the frequency of process trips. In comparison with other slug control methods, this technology is cost-effective since no additional process equipment is required to be installed and maintained. As a result, this significantly reduces the project execution time, avoids production disruptions, and minimizes potential HSE issues. The technology can be implemented within a year from the outset of a feasibility study. However, the technology is not appropriate for all situations due to differences in slugging nature and other subsurface issues and therefore its applicability needs to be evaluated on a case-by-case basis. With increasing number of aging offshore assets and competitiveness in the energy market, it is expected that active slug control technology will become a crucial tool to reduce production downtime and deferment. This paper also presents a workflow which aims to provide a general guideline for the feasibility assessments and implementation of this slug control technology on producing assets. The objectives of slug mitigation may vary from asset to asset due to differences in production conditions, slugging characteristics and facilities design. Therefore, strong client engagement is necessary to ensure the most optimal workflow for the technology to be implemented.

To remain competitive, manufacturing enterprises must increase and simultaneously reduce costs. This requires daily and long term examination and analysis of a plant's functions and operations. Using this data, an enterprise can identify production flow bottlenecks and analyze capacity and other factors, which in turn helps identify improvement opportunities. While such information is critical, it is often a challenge to obtain in a complex manufacturing environment. The automotive industry provides some prime examples of complex manufacturing environments. At Saturn Corp., we turned to academic research-work involving model integrated computing (MIC)-as a framework to organize the diverse types of data our information system must deal with. Using MIC and working with the Vanderbilt University researchers, Saturn built the Saturn Site Production Flow system. This system provides real time information-culled from 80000 data points-to Saturn personnel across the plant. Using SSPF provided information helped Saturn improve throughput at its plant by 10 percent. Through models, we can readily explain a complex data structure in a virtual view of a manufacturing plant floor. Diverse services are required for applications such as process monitoring and control, process simulation, statistical analysis packages, and other data manipulation tools. MIC integrates these tools into a common problem solving environment.

This paper proposes a method for real time diagnosis against product quality drifts in an automated manufacturing system. We use Logical Diagnosis model to reduce the search space of suspected equipment in the production flow, which is then formulated as a Bayesian network to compute risk priority for each equipment, using joint and conditional probabilities. The objective is to quickly and accurately localize the possible fault origins and support effective decisions on corrective maintenance. The key advantages offered by this method are (i) reduced unscheduled equipment breakdowns, and (ii) increased and stable production capacities, required for success in highly competitive and automated manufacturing systems. Moreover, this is a generic method and can be deployed on fully or semi automated manufacturing systems.

Priority rules are used in production flow control for determining sequences in which jobs would be completed at work centres.

An optimal production flow control problem of a make-to-stock manufacturing firm with price, cost, and demand uncertainty is studied. The objective of the flow rate control problem is maximizing the average profit that is the difference between the expected revenue and the expected production, inventory holding, and backlog costs. The uncertainties in the system are captured jointly in discrete environment states. In each environment state, the price, cost, and demand take different levels. The transitions between different environment states evolve according to a time-homogenous Markov chain. By using a continuous flow model, the optimal production policy is stated as a state-dependent hedging policy. The performance of the system where the production cost alternates between a high and a low cost level and the demand is either constant or also alternates between a high and a low level is analyzed under the double-hedging policy. According to this policy, the producer produces only when the cost is low and the surplus is between the two hedging levels. However when the backlog is below the lower hedging level, the producer produces with the maximum capacity regardless of the cost. The effects of production cost, production capacity, demand variability, and the dependence of the demand and the cost on the performance of the system are analyzed analytically and numerically. It is shown that controlling the production rate optimally allows producers respond to the fluctuations in price, cost, and demand in an effective way and maximize their profits.

In the domain of reconfigurable production systems, Cyber-Physical Matrix Production Systems (CPMPS) are recognized for their advanced levels of operational flexibility. Given the inherent flexible material flow, these loosely coupled systems are characterized by dynamic interdependencies and rapid changes in order sequencing and allocation. This leads to major challenges in production flow control including the emergence of instable behaviors decreasing robustness and threatening overall performance. Traditional methodologies for assessing and enhancing the reliability and ensuring the robustness of the system do not tackle the dynamic behavior of re-configurable production systems. Due to rigid probabilistic assumptions, efficiency decreases and reasoning in fault propagation is not apparent. For this reason, dependable systems engineering embraces formal descriptions of the systems’ dynamical behaviors and continuous monitoring of system properties. This paper proposes the application of distributed artificial intelligences in the form of holonic multi-agent system (MAS) that integrate the concepts of dependability as part of the system design. Multi-level monitoring of state properties and fault-tolerant control mechanisms are used to minimize deviation between the modelled and observed behavior, therefore ensuring robustness and securing the system’s intended operation. The presented framework demonstrates feasibility by first implementations of dynamic interaction mechanisms for subsidiary decision improving makespan while remaining flexible.

NorTRACKTM product tracking system—development and implementation