Operation Of Production Systems Research Articles

Relevantni parametri u pripremi proizvodnih procesa

In an effort to meet the rising needs of customers, the buyers of industrial products, in the conditions of the growing diversity of products on the market, operation of business production systems sometimes fails, due to imprompt supstitution with new products. If we observe the production system as a set of elements that define everything that is going on with the product itself, from raw materials as input to a finished product, as an output, then it can be represented as a set of subsystems: subsystem preparation, technological subsystem, transport subsystem, storage subsystem, information control subsystem, the subsystem maintenance and supply subsystem. Technological systems, as the most important subsystem of the production system is designed to perform operations in the workplace. Along with the existence of the described subsystem, there are: business, manufacturing, technology and machining processes. The production process as a part of the production system is a set of interrelated activities, or work processes as means of transformation of raw materials or semi-finished into finished products. Optimal selection of process parameters is essential for the economic, efficient and effective use of the process. In terms of the more successful adaptation to the specified, the authors identified relevant parameters in the preparation of the production process.

EXTRACTING PROCESS AND MAPPING MANAGEMENT FOR HETEROGENNOUS SYSTEMS

Abstract A lot of papers describe three common methods of data selection from primary systems. This paper defines how to select the correct method or combinations of methods for minimizing the impact of production system and common operation. Before using any method, it is necessary to know the primary system and its databases structures for the optimal use of the actual data structure setup and the best design for ETL process. Databases structures are usually categorized into groups, which characterize their quality. The classification helps to find the ideal method for each group and thus design a solution of ETL process with the minimal impact on the data warehouse and production system.

Comparison of the environmental performance of light mechanization and animal traction using a modular LCA approach

Abstract Animal traction has supported humans in most field operations since the origin of agriculture. With the introduction of mechanization, humans gained access to much more work power at similar management costs and were able to significantly increase the productivity and time efficiency of field operations. This achievement completely changed food production systems for all populations able to access such technology. Nowadays, animal traction is mainly used in the developing countries, in specific contexts such as mountainous areas due to the difficulties in using tractors, and within farm tourism in the developed countries. Although the consumption of non-renewable resources is clearly higher in crop production systems that use mechanized traction, tractor traction may involve low consumption of fuel relative to that needed for feed production for equivalent draught animals. Mechanical traction can also facilitate precision agriculture, which uses less fuel, while animals, as living systems, consume resources even when they are not working. This study compared the environmental performance of animal traction with that of machine traction in two scenarios: (I) A forest harvesting system, using as the functional unit the logging operations needed to obtain 50 t market-ready wood and (II) a seedbed preparation system, using as the functional unit the management of 1000 m2 of prepared seedbed. Use of animal traction for these two systems was evaluated on the La Masca farm in Roccaverano, Asti, Northern Italy, while use of machine traction was evaluated using field data on two-wheel tractors performing the operations in similar production systems, converted to the specific functional unit. Owing to the differing properties of mechanical and living systems, it was difficult to establish a reliable standard LCA model of the forestry and food production system. In particular, it proved necessary to include the whole life cycle impacts from tractors and animals. Therefore, we applied a modular LCA approach in which all mechanical implements and animals were accounted as independent modules, a complete life cycle impact assessment phase was performed and results were related to the contribution of the module in the main workflow of the scenario. The final results showed better environmental performance of animal traction both per unit weight of market-ready wood and per unit surface area of prepared seedbed.

Prediction of quality parameters of biomass pellets from proximate and ultimate analysis

The real-time prediction of crucial biomass pellet quality parameters such as higher heating value (HHV) and mechanical durability (MD) will allow for more efficient operation of energy production systems. Multiple linear regression (MLR) models were developed to predict HHV and MD from proximate and ultimate analysis of biomass pellets. A diverse range of biomasses from energy crops including pine, Miscanthus, reed canary grass, tall fescue and short rotation coppice willow were used to produce the pellets. HHV and MD of the pellets were predicted with coefficients of determination of 0.99 and 0.94, respectively, and standard errors of the estimate of 0.08MJkg−1 (Range: 16.39–18.92MJkg−1) and 0.49% (Range: 92.6–97.5%), respectively. This study demonstrates that MLR can be used to predict additional information of HHV and MD of biomass pellets from proximate and ultimate analysis. Important quality indices for diverse biomass pellets are also reported.

PRM-based patterns for knowledge formalisation of industrial systems to support maintenance strategies assessment

The production system and its maintenance system must be now developed on “system thinking” paradigm in order to guarantee that Key Performance Indicators (KPI) will be optimized all along the production system (operation) life. In a recursive way, maintenance system engineering has to integrate also KPI considerations with regards to its own enabling systems. Thus this paper develops a system-based methodology wherein a set of KPIs is computed in order to verify if the objectives of the production and maintenance systems are satisfied. In order to help the decision-making process for maintenance managers, a “unified” generic model have been developed. This model integrates (a) the interactions of the maintenance system with its enabling systems, (b) the impact of the maintenance strategies through the computation of some key performance indicators, and (c) different kinds of knowledge regarding the maintenance system and the system of interest, including quantitative and qualitative knowledge. This methodology is based on an executable unified model built with Probabilistic Relational Model (PRM). PRM allows a modular representation and inferences computation of large size models. The methodology added-value is shown on a test-bench.

Scheduling and buffer reduction algorithm for job shop production systems

Buffer management is a complex problem in the operation of production systems. Poor buffer control may cause congestion and may increase work in process and cycle times. This paper proposes a methodology which attempts to reduce the buffer requirements in job shop production systems. The first step of this approach consists of finding a near-optimal schedule. Next a number of right-shift and left-shift movements are performed within the schedule with the objective of reducing the amount of required buffer slots. The initial schedule is calculated with modified simulated annealing algorithm; the second step is essentially changing the start and/or finish operation dates without changing neither the sequence of jobs on each machine nor the value of the objective function. This last step uses machine blockings to further reduce the amount of buffers. The procedure was tested on a number of benchmark problems from the literature. The obtained results show up to 36% in buffer reduction.

Simulating the operation of production systems

Simulating the operation of production systems

Computing RAM indices for reliable operation of production systems

Purpose: The purpose of this paper is to permit the system reliability analysts/managers/engineers/ practitioners to conduct RAM analysis of the system which may helps them to model, analyze and predict the behavior of industrial systems in a more realistic and consistent manner. Design/methodology/approach: Markovian approach is used to model the system behavior. For carrying out study, Root Cause Analysis (RCA) of the subsystems is carried out and transition diagrams for various subsystems are drawn and differential equations associated with them are formulated. After obtaining the steady state solution the corresponding values of reliability and maintainability are estimated at different mission times. Findings: With RAM analysis of the system key performance metrics such as Mean Time between Failure (MTBF), Mean time to Repair Time (MTTR) and System availability values are ascertained. Research limitations/implications: Based on the RAM analysis, possible maintenance strategies can be investigated which might help the plant personnel to improve the system effectiveness. Practical Implications: Without exercising much effort in developing complex system models, the proposed method for analyzing system performance may prove helpful to the reliability analysts/ engineers/practitioners to model analyze and predict the behavior of system more efficiently and resolve the RAM requirements of the system in unison. Originality/value: The simultaneous adoption of both qualitative (RCA) and quantitative (Markov approach) approach to analyze and obtain RAM indices for measuring the system performance helps the maintenance engineers to improve RAM aspects after understanding the failure behavior of component(s) in the system.

Experimental verification of the frequency spectrum of unwears and wears guidance tube on the technological head vibrations creation in the production system with AWJ technology

The solution and evaluation of the production system (PS) operation with water jet technology was addressed in more works. It is appropriate to complement this knowledge from works focused on investigation of the other factors effect which affecting the emergence and spread of technological head vibrations, affecting reliability, durability, economic efficiency and operation safe of PS with water jet technology. The article presents experimental investigation results of the vibration acceleration frequency spectrums during cutting steel by unwear and wear rectifying tube with water jet technology. The vibrations size was assessed by two basic vibrations parameters - vibrations frequency and vibration acceleration amplitude.

Boosting the competitiveness of agricultural production through an innovation system

The final versions of the European Union’s (EU) support schemes and funding instruments for the 2014-2020 budgeting period have not yet been prepared. What is final, though, is that the ten-year Lisbon Strategy which aimed at strengthening the competitiveness of the EU ended in 2010. In addition to three priorities (smart, sustainable and inclusive growth), the recently launched Europe 2020 Strategy has set five headline targets to be reached, one of them being an increased investment in research and development. This is evidently a difficult challenge owing to the limited economic capacity of individual EU Member States. A considerable share of agricultural production activities are performed by small- and medium-sized enterprises and farmers who face difficulty in reaching the level of concentration need to gain market advantage. Consequently, it is imperative to establish a system that can maintain close connections with producers and improve innovation activities. Without such a system, a significant growth of added value cannot be foreseen in Hungarian agriculture. This paper describes a technology development system that incorporates three elements (measurement of inputs in space and time, market-focused technology development and a self-teaching information system for farmers) and that could be used in rural development, primarily in the area of agricultural production. While developing the system, we relied on experience gained from the operation of previous agricultural production systems and also considered the specific local conditions with the aim of offering a potential solution to meeting the objectives of the Europe 2020 Strategy.

Impact of probability of working day on planning and operation of biomass feedstock production systems

AbstractUnfavorable weather can significantly impact the production and provision of agriculture‐based biomass feedstocks such as Miscanthus and switchgrass. This work quantified the impact of regional weather on the feedstock production systems using the BioFeed modeling framework. Weather effects were incorporated in BioFeed by including the probability of working day (pwd) parameter in the model, which defined the fraction of days in a specific period such as two weeks that were suitable for field operations. Model simulations were conducted for Miscanthus and switchgrass for values of pwd between 20 and 100% and intended biorefinery capacities between 1000 and 5000 Mg d−1; and the impact on total cost and farm machinery requirements was quantified. Results indicated that using production and provision systems designed assuming 100% pwd for lower pwd values increased the cost exponentially by up to 64% for Miscanthus and 85% for switchgrass. It also decreased the supportable biorefinery capacity for the collection region by up to 60%. If the systems were instead optimized for specific values of pwd, the original biorefinery capacity was maintained and the total cost increase was less than 5%. The resulting optimal systems required up to 40% higher investment in farm machinery. For Illinois, production systems designed for regional pwd values required a 34% increase in farm machinery investment for Miscanthus while only a 12% increase for switchgrass. Initiating Miscanthus harvesting in November instead of January reduced the farm machinery investment increase to 17%, which suggests that such an alternative should be rigorously evaluated. © 2012 Society of Chemical Industry and John Wiley & Sons, Ltd

Carbon Emission Assessment to Support Planning and Operation of Low-carbon Production Systems

The issues of climate change and global warming have thrown new challenges for the society. Sustainable development and low-carbon economic are on the global agenda with regard to the environmental problems. The manufacturing processes, as one of the substantial resource consumers and emission sources, have been poorly investigated in terms of carbon emission. To meet the increasing requirement of practical low-carbon thinking in manufacturing, a systematic approach is proposed to assess the carbon emissions in production systems by using a hybrid emission analysis model. Subsequently, procedures for production planning and operation of low-carbon production systems are offered in response to the new changes.

Using probabilistic relational models for knowledge representation of production systems: A new approach to assessing maintenance strategies

The production system and its maintenance system must be now developed on “system thinking” paradigm in order to guarantee that key performance indicators (KPI) expected will be optimized all along the production system operation life. In a recursive way, maintenance system engineering has to integrate also KPI considerations with regards to its own enabling systems. Thus this paper develops a system-based methodology wherein a set of KPIs is computed to conclude on the assessing of maintenance strategies. This methodology is based on an executable unified model built with Probabilistic Relational Model. The methodology added-value is shown on a test-bench.

Maintenance cost evaluation of a system to be used in Arctic conditions: a case study

PurposeThe purpose of this paper is to discuss operation and maintenance challenges under Arctic conditions and to propose a methodology to assess systems' reliability, maintainability and maintenance costs under the influence of the Arctic operational environment.Design/methodology/approachA model is suggested for quantifying maintenance costs while taking into account uncertainty due to lack of appropriate data and operational experience using the proportional hazard model and proportional repair model as well as Monte Carlo simulation.FindingsThe results show that the operating environment has a considerable influence on the number of failures, the maintenance and repair times and consequently on maintenance cost. Forecasting the maintenance costs based on technical characteristics (e.g. reliability and maintainability) and considering the operational environment, as well as including uncertainty analysis using Monte Carlo simulation, provide more trustworthy information in the decision‐making process.Practical implicationsThere are few data and little experience available regarding the operation of offshore oil and gas production systems in the Arctic region. Using the available data collected from similar systems, but in a different operational environment, may result in uncertain or incorrect analysis results. Hence, the method that is used for maintenance cost analysis must be able to quantify the effect of the operating environment on the system reliability and maintainability as well as to quantify the uncertainty.Originality/valueThe paper presents a statistical approach that will be useful in predicting maintenance cost considering the lack of appropriate reliability data from equipment operated in Arctic conditions. The approach presented is valuable for the industrial practitioners in the Arctic region, and may also be adapted to other areas where there is lack of data and operational experience.

The Operations-Time Chart: A graphical tool to evaluate the performance of production systems – From batch-and-queue to lean manufacturing

In this paper, we describe the spreadsheet modeling of manufacturing systems by means of the Operations-Time Chart (hereafter, OT-Chart), a graphical tool for an automatic time-phased representation and measurement of the operation of production systems, developed by the authors. In order to improve the design of a production system, it is necessary to know the key performance metrics of the system (productivity, lead-time, inventories, downtimes and wait times) and identify the effects of design parameters on system performance. Calculating some of these magnitudes can be very complicated, especially for production systems involving multiple and confluent processes, with different cycle times and lot sizes. The OT-Chart permits a visual tracking of the aforementioned parameters throughout each process, and like a simulation tool, the program calculates and displays the effects of changing input parameters. A special version of the Chart has been designed for lean manufacturing environments, where visual tools are much appreciated. The OT-Chart provides tracking of different types of waste and supports inventory supermarkets and pull scheduling. The paper includes a case study: a plant is redesigned from a conventional batch-and-queue production system into a lean manufacturing system with the help of the OT-Chart (it is used to test the performance of each layout) allowing managers to evaluate and refine their designs.

Artificial Cognition in Production Systems

Today's manufacturing and assembly systems have to be flexible to adapt quickly to an increasing number and variety of products, and changing market volumes. To manage these dynamics, several production concepts (e.g., flexible, reconfigurable, changeable or autonomous manufacturing and assembly systems) were proposed and partly realized in the past years. This paper presents the general principles of autonomy and the proposed concepts, methods and technologies to realize cognitive planning, cognitive control and cognitive operation of production systems. Starting with an introduction on the historical context of different paradigms of production (e.g., evolution of production and planning systems), different approaches for the design, planning, and operation of production systems are lined out and future trends towards fully autonomous components of an production system as well as autonomous parts and products are discussed. In flexible production systems with manual and automatic assembly tasks, human-robot cooperation is an opportunity for an ergonomic and economic manufacturing system especially for low lot sizes. The state-of-the-art and a cognitive approach in this area are outlined. Furthermore, introducing self-optimizing and self-learning control systems is a crucial factor for cognitive systems. This principles are demonstrated by a quality assurance and process control in laser welding that is used to perform improved quality monitoring. Finally, as the integration of human workers into the workflow of a production system is of the highest priority for an efficient production, worker guidance systems for manual assembly with environmentally and situationally dependent triggered paths on state-based graphs are described in this paper.

Production System Optimal Operation Model Based on Large-Scale System Theory

For the disadvantage of traditional production mode, an operation optimization model is put forward on the basis of analyzing workshop production system operation. The Features of the model are real-time, dynamic, multi-objective and multistage. The algorithm called ‘Harmonizing Cluster Results’ for the model is presented in order to solve the difficulties of solution for the workshop production system operation model. Then the model is simplified into some submodels, such as production ability optimization submodel, time optimization submodel, balancing and harmonizing submodel etc. Finally the operation optimization model and its solution algorithm are applied to an assembly line of energy meter in a factory. With the help of ‘Witness’ the application case simulation results showed that the model is accurate and its solution algorithm is reasonable and available.

Analysis of the coffee harvesting process using an electromagnetic shaker

Harvesting is one of the most important operations in coffee production systems. This operation has a high cost and impact on the final quality of the product. To reduce production costs, producers are looking for ways to mechanize this operation. Harvesting machines generally detach fruits from the coffee plant by vibration and/or impact. The objective of this work was to study the effect of the amplitude and frequency of vibration on the efficiency of coffee fruit harvesting. Vibration tests of coffee branches were performed in a laboratory using an electromagnetic shaker. The tests were performed using amplitudes in the range of 3.75 to 7.50 mm and frequencies from 13.33 to 26.67 Hz. Coffee branches from two different varieties were used for evaluation, Catuai Vermelho and Mundo Novo. It was verified that harvesting efficiency is directly related to the acceleration reached by the fruits during the harvesting process. The frequencies of 23.33 and 26.67 Hz and amplitudes of 6.25 and 7.50 mm resulted in the highest harvesting efficiency of ripe coffee cherries for both varieties. However, the harvesting efficiency for the Mundo Novo variety was higher than that of the Catuai Vermelho variety at the studied frequency and amplitude ranges in this work.

Throughput analysis of production systems: recent advances and future topics

Throughput analysis is important for the design, operation and management of production systems. A substantial amount of research has been devoted to developing analytical methods to estimate the throughput of production systems with unreliable machines and finite buffers. In this paper we summarise the recent studies in this area. In addition to the performance evaluation of serial lines, approximation methods for more complex systems, such as assembly/disassembly systems, parallel lines, split and merge, closed-loop systems, etc., are discussed. Moreover, we propose future research topics from the automotive manufacturing systems perspective.

Lean Production System Operation Optimization Model and Method for Algorithmic Problem Solving

In view of the disadvantage of traditional production mode and the requirement of lean production,a standpoint is put forward on the basis of analyzing lean production system operation.The point is that lean production operation optimization is defined as large-scale system multiobjective programming problem with angular structure of the original box.An operation optimization model is constructed by taking workload of manufacturing factors in plant as decision-making variables and the indexes of output,quality,cost and delivery as dynamic constraint conditions.In the model,priority and its factor are introduced into objective function for reflecting important degree of multiple mutually conflicting objectives.So the best operation objective requirement is determined on the basis of dynamic attribute of actual problem.The characteristic of the model is dynamic,multi-objective and multistage.The algorithm called "harmonizing cluster results" for the model is presented in order to solve the difficulties of solution for the lean production system operation model.Then the model is simplified into some submodels,such as production ability optimization submodel,time optimization submodel,balancing and harmonizing submodel.The method of "harmonizing cluster results" is used to solve the above submodels.Finally the lean production operation optimization model and its solution algorithm are applied to an assembly line of energy meter in Zhejiang Province.The results of Witness simulation verify that the model is rational and its solution algorithm is accurate and highly efficient.