Production Planning In Industry Research Articles

Aggregate production planning and energy supply management in steel industry with an onsite energy generation system: A multi-objective robust optimization model

This paper introduces an optimization model developed for the steel industry, the most energy-intensive industrial sector in many countries. New methodologies for both aggregate production planning and energy supply management are developed to maximize profit while minimizing energy consumption and greenhouse gas (GHG) emissions. Moreover, uncertainties are considered, and robustly optimized strategies are identified for a grid-connected steel production company with an onsite energy generation system. The performance of the developed model is evaluated through extensive computational experiments. The results show how energy tariffs affect integrated production planning. Also, the selected production planning time depends on several factors such as electricity, labor, and maintenance costs, and pending orders that need to be fulfilled. The onsite power plant produces electricity when the electricity production cost is lower than the price of electricity provided by the connected grid. Furthermore, the company’s environmental policies influence the choice of energy generation technology. However, implementing an onsite energy generation is a viable policy that benefits both profitability and pollutant reduction. This study helps decision-making in production planning and energy supply management in the iron and steel industry (which can be applied to other industries too), ensuring environmental improvement at the lowest costs.

On-Board Multi-Class Geospatial Object Detection Based on Convolutional Neural Network for High Resolution Remote Sensing Images

Multi-class geospatial object detection in high-resolution remote sensing images has significant potential in various domains such as industrial production, military warning, disaster monitoring, and urban planning. However, the traditional process of remote sensing object detection involves several time-consuming steps, including image acquisition, image download, ground processing, and object detection. These steps may not be suitable for tasks with shorter timeliness requirements, such as military warning and disaster monitoring. Additionally, the transmission of massive data from satellites to the ground is limited by bandwidth, resulting in time delays and redundant information, such as cloud coverage images. To address these challenges and achieve efficient utilization of information, this paper proposes a comprehensive on-board multi-class geospatial object detection scheme. The proposed scheme consists of several steps. Firstly, the satellite imagery is sliced, and the PID-Net (Proportional-Integral-Derivative Network) method is employed to detect and filter out cloud-covered tiles. Subsequently, our Manhattan Intersection over Union (MIOU) loss-based YOLO (You Only Look Once) v7-Tiny method is used to detect remote-sensing objects in the remaining tiles. Finally, the detection results are mapped back to the original image, and the truncated NMS (Non-Maximum Suppression) method is utilized to filter out repeated and noisy boxes. To validate the reliability of the scheme, this paper creates a new dataset called DOTA-CD (Dataset for Object Detection in Aerial Images-Cloud Detection). Experiments were conducted on both ground and on-board equipment using the AIR-CD dataset, DOTA dataset, and DOTA-CD dataset. The results demonstrate the effectiveness of our method.

A machine learning framework for improving refinery production planning

AbstractWe propose a framework that relies on machine learning techniques and statistical modeling to enhance industrial production planning. Supervised learning is employed to improve the production planning model, whereas unsupervised learning is used to achieve economic synchronization between the process control and production planning layers. Finally, an upgraded production planning decision‐making structure is formulated where model uncertainty, the effect of process control/disturbances, and time correlation are considered. The proposed framework is implemented on an industry‐relevant refinery model demonstrating that the performance of the framework is substantially better than established industrial production planning techniques.

Redesigning industrial composite production planning and control for energy awareness

The European Union's requirement for eliminating greenhouse gas emissions from all value chains by 2050 affects most of the manufacturing activities all over the continent. The industrial production of composite parts, favored for new designs throughout the aerospace industry, does not constitute an exemption. For securing global competitiveness, CFRP manufacturers need to adapt to increasing fluctuations in energy supply and prices, induced by a growing share of renewable energy sources. Besides employing more efficient production technologies, decoupling of labor and energy intensive tasks will play a key part. Thereby, energy intensive tasks can be performed while general energy supply is high and demand is low, leading to comparatively lower prices. The perspective of energy supply and price becomes a crucial part in the design of modern production planning and control systems, not only for smokestack businesses, but for small and medium load energy consumers as well. In this contribution, a mathematical representation for modeling industrial scale composite manufacturing is proposed. Beside manufacturing's fundamental production planning and control constraints, CRFP manufacturing's very own peculiarities are outlined and considered as well. It is shown how the virtual mathematical representation of a CFRP manufacturing environment can assist in its energy aware redesign and optimization.

A conceptual framework for smart production planning and control in Industry 4.0

This article aims to introduce the challenge (i.e., integration of new collaborative models and tools) posed by the automation and collaboration of industrial processes in Industry 4.0 (I4.0) smart factories. Small- and medium-sized enterprises (SMEs) are particularly confronted with new technological and organisational changes, but a conceptual framework for production planning and control (PPC) systems in the I4.0 context is lacking. The main contributions of this article are to: (i) identify the functions making up traditional PPC and smart production planning and control in I4.0 (SPPC 4.0); (ii) analyse the impact of I4.0 technologies on PPC systems; (iii) propose a conceptual framework that provides the systematic structuring of how a PPC system operates in the I4.0 context, dubbed SPPC 4.0. Thus SPPC 4.0 is proposed by adopting the axes of the RAMI 4.0 reference architecture model, which compiles and contains the main concepts of PPC systems and I4.0. It also provides the technical description, organisation and understanding of each aspect, which can provide a guide for academic research and industrial practitioners to transform PPC systems towards I4.0 implementations. Finally, theoretical implications and research gaps are provided.

Title: production planning and control in industry 4.0 environment: a morphological analysis of literature and research agenda

Title: production planning and control in industry 4.0 environment: a morphological analysis of literature and research agenda

Minimization of multiproduct fabrication cost featuring rework, commonality, external provider, and postponement

This study presents a multiproduct fabrication cost-minimization model featuring external providers, commonality, rework, and postponement in the supply chain environment. Customers’ requirements simultaneously emphasize quality, variety, and fast response time in current markets. To satisfy customer needs, most manufacturers in various industries (e.g., clothing, household goods, automotive, etc.) plan their multiproduct fabrication by incorporating a postponement strategy, rework process, and an outsourcing option. Motivated by the viewpoints above, this study offers a decision support system to address customers’ external expectations while optimizing internal operating expenses and machine utilization. We propose a single-machine, two-stage delayed differentiation system under a rotation cycle policy. All needed common parts are made in stage one, and stage two fabricates different end products. An external provider is hired to supply partially needed common parts to shorten uptime. The defective items are inevitably produced in both stages. They are categorized and reworked to maintain the desired product quality. Finally, we derive an optimal cost-minimization rotation cycle for our model and use a numerical example to investigate the collective and individual influences of reworking, postponement, and outsourcing to external providers on the multiproduct fabrication problem. In summary, this study can offer an optimization solution for production planning in various modern industries.

An Innovative Fuzzy Computing for Industrial Production Planning

In general, this research work has achieved its objectives in formulating a new form of membership function and in investigating its applications in industrial production planning. This new form is a modified version of the S-curve membership function that is logistic within non linear membership functions.

Growth kinetics and biomass characteristics of Lactobacillus plantarum L14 isolated from sourdough: Effect of fermentation time on dough machinability

Screening of the kinetic behavior of microorganisms is essential for industrial production planning, determining the changes depending on environmental factors and designing technologically the optimum reactor. In this study, the growth kinetics of Lactobacillus plantarum L14 naturally existing in sourdough was investigated in order to determine its biotechnological performance and the role of the effect on the fermentation process during dough-making. Growth kinetics of L. plantarum L14 were examined at 37 °C for 28 h fermentation using MRS medium. The maximum specific rate of L. plantarum L14 in the exponential phase was calculated to be 0.551 h−1 and the productivity was 1.08 g/Lh. When the stationary phase was reached, maximum increase of 2.9 log cfu/mL was observed and average biomass productivity was determined as 9.19 g/L (2.14 g/L on dry matter basis). Cell sizes of L. plantarum L14 were examined using a particle size analyzer; D4,3 and D90 results were measured as 4.38 and 9.01 μm, respectively. Wheat doughs prepared by adding L. plantarum L14 biomass were fermented for 1, 15, 20 and 25 h and the acidity, cell number and machinability properties of the doughs were investigated. With increasing acidity during fermentation, more cohesive and sticky dough formation was detected.

Systematization of Globalization Processes in Industrial Concentration

Russia tries to find its own niche in processes of international labour distribution relevant to its intellectual, industrial and scientific potential in order to become a full-fledged participant of global economy, to use advantages of the international exchange, which could provide economic growth and effective reforming of the national economy structure. It implies the necessity to develop the advanced system of international collaboration institutions and shape a set of efficient tools to control processes of Russian economy integration into the global economic system in condition of globalization, as well as to work out a well-grounded state strategy in the field of regional policy. The article investigates planning of concentration processes, managing these processes on scientific basis and choosing the optimum size of enterprises. The mentioned-above issues are very important in the system of industrial production planning. Planning of industrial concentration requires thorough parameter estimation. Some factors are effective in all industries, others - within the frames of certain industries or groups of related ones. The economic effect of concentration is connected with raising technical and economic indicators in product manufacturing as a result of increasing the size of production and enterprises. The author proves that concentration of big production capacities, material and labour resources at one enterprise could provide more effective and economical use of key production components (equipment, raw materials and lab our). At the same time opportunities to raise labour productivity and cut costs were identified.

A general primal bounding framework for large-scale multistage stochastic programs under endogenous uncertainties

Multistage stochastic programming is an approach for modeling and solving optimization problems under uncertainty. Such problems are commonly seen in production planning and scheduling in chemical process industry. This paper considers large-scale multistage stochastic programs under a specific type of uncertainty, i.e., endogenous uncertainty, and develops a general primal-bounding framework based on extending the concepts of expected value solution and value of stochastic solution from multistage stochastic programs under exogenous uncertainties. Under traditional decision-making process with uncertainties, decision makers utilize known information to-date along with the expected results for unrealized information to take action, realize some uncertainty, and repeat this process along the planning horizon. The bounding framework introduced here fits this decision-making process. It yields a tight feasible bound and an implementable solution for multistage stochastic programs under endogenous uncertainties, which we call the absolute expected value solution (AEEV). The framework is tested on three planning problems with up to 16,384 scenarios. It yielded primal bounds within 1% of the true solutions for all tested cases, and generated these implementable solutions up to four orders of magnitude faster than solving the original multistage stochastic programs.

A coupled process configuration, lot-sizing and scheduling model for production planning in the molded pulp industry

This paper proposes an application-oriented model for a coupled process configuration, lot-sizing and scheduling problem in the molded pulp industry. In this case, products are obtained by alternative process configurations, which can produce one or more types of product simultaneously. A process configuration consists of defining a feasible setup state for the main components of the production line, i.e., the molding machine, where molds to shape the products are attached, and a set of conveyors, where products are transported. Production quantities depend on the configurations selected and the time that each one is used over the planning horizon. The number of possible configurations is large and sensitive to the characteristics of the production environment, the size of the molding machine and the number of conveyors. Therefore, determining all possibilities beforehand is a challenging and extensive task. We propose a novel formulation to support production decisions in molded pulp companies, which generates the process configurations implicitly at the same time that lot-sizing and sequencing decisions are made. This optimization problem involves challenging operational and synchronization constraints, and sequencing decisions which include three types of setups. The objective is to minimize the total setup, inventory and backlogging costs. Several sets of instances based on real data and market information were tested. Computational experiments using a commercial Mixed Integer Programming (MIP) solver show the capability of this approach to represent the problem decisions and to provide production schedules for different production environments. Comparisons with real schedules and others formulations show the practical savings and the advantages of this formulation over enumerating modeling approaches. A simplified version of this new formulation is also presented to solve more efficiently particular problem instances. In addition, a heuristic solution strategy is proposed to find feasible production plans for all data sets in shorter computing times.

The impact of prepreg aging on its processability and the postcure mechanical properties of epoxy-based carbon-fiber reinforced plastics

Due to the intention to increase the product efficiency by weight reduction, the application of carbon fiber reinforced plastics and its processing becomes more and more important for the industrial production planning. The aviation industry mostly uses epoxy-based preimpregnated laminates (prepregs). These prepregs are stored in the refrigerated state and may be processed after defrosting only for a specific shelf-life or so-called out-time. This limitation in processing time reduces the efficiency of the entire production, for example due to the waste of material during production stops. To improve the production effectiveness the shelf-life should be as high as possible, to be able to avoid material waste during production stops. Therefore, the processability and the postcure mechanical properties of two representative prepregs were investigated during this study for different aging stages and correlated with the reactivity of the prepregs.

On production planning and scheduling in food processing industry:Modelling non-triangular setups andproduct decay

Production planning and scheduling in food processing industry (FPI) requires taking specific characteristics into account. First of all, setups are usually sequence-dependent and may include the so-called non-triangular setup conditions. Secondly, planning problems in FPI must take product decay into consideration. We present an MILP model that handles these characteristics. We study its behaviour and complexity and show that optimal production schedules become significantly different when non-triangular setups and product decay are taken into account. Numerical results are provided for medium size instances, including a comparison with a standard MP-based heuristic.

Production planning in the molded pulp packaging industry

Production planning in molded pulp packaging companies involves decisions about setting up appropriately the production process to meet the demands of different products. This production process comprises stages such as blending, molding and drying, and the major challenges for the production planning occur in the molding stage, where the mix of products manufactured simultaneously depends on the combination of molds attached to the molding machine, called molding pattern. Thus, the problem lies in deciding which molding patterns should be used, how long each pattern should be used and the production sequence that they should be scheduled. In this study we propose a novel optimization approach to deal with this problem by considering some possible settings for the molding machine based on specific technological constraints. These preset settings are included into a mixed integer programming model to define the molding patterns and to deal with the production lot sizing and scheduling decisions. Since the molding patterns are defined by the approach, three types of production setup times and costs (some of then are sequence-dependent) must be also calculated and taken into account in the planning decisions. Computational tests with real data from a Brazilian molded pulp plant show that the production plans generated by this approach are suitable in practical settings, as well as define molding patterns different from those commonly used and simultaneously reduce setup times and costs, when compared to the production plans of the company.

Modeling Cycle Times in Production Planning Models for Wafer Fabrication

A key parameter of the linear programming (LP) models widely used for production planning in industry and academia are the lead times, the estimated delay between material becoming available to a resource and its completion at that resource. Lead times are commonly treated as exogenous, workload-independent parameters and assumed to be integer multiples of the planning period. Although formulations with non-integer lead times have been implemented in industry, we are unaware of any studies systematically evaluating their performance. In this paper, we compare the performance of LP models with and without non-integer lead times by simulating the execution of the resulting release plans. We also compare their performance to that of a formulation incorporating workload-dependent lead times using nonlinear clearing functions. We find that the performance of the models with non-integer lead times is substantially better than those with integer lead times, and often superior to that of the much more complex clearing function models. These results suggest that whenever reasonable estimates of the lead times are available, non-integer lead times should be used due to their simplicity of implementation and excellent performance.

Context sensitive production planning and energy management approach in energy intensive industries

The need for competitiveness on the global market is the main driver for industrial companies to systematically and continuously analyse all possibilities for the optimisation of production process and related costs reduction. Modern industrial processes are increasingly complex with highly dynamic energy use patterns. A systematic approach to industrial energy system analysis requires proper understanding of interactions among the main factors influencing energy performance. Also, systematic approach takes into consideration not only efficiency of individual equipment or machines but also their performances within a system where each piece of equipment delivers specific function.This paper presents a context sensitive production planning and energy management approach for achieving energy savings and emissions reduction in energy intensive industry. Combination of decision support for production planning and simulation of possible future situations with context sensitive monitoring and targeting are vital elements of proposed approach and are necessary for continuous improvement of energy performance. The proposed approach includes four building blocks: (1) ambient intelligent data acquisition and context processing, (2) energy modelling and emissions calculation including the prediction engine, (3) decision support services and the (4) knowledge repository. The proposed approach has been tested in the real industrial environment and initial testing results are indicating the potential of the proposed approach to initiate sustainable changes in the production process. Also, external factors that are beyond control but which do influence daily operations of energy intensive industrial companies have been analysed, for example process operators' behaviour and variations in production outputs.

The Dynamic Nature of the Governance Structure of Large Investments: Measurement and Modeling, Organizational Fit, and Earning Potential

This white paper argues that governance of industrial investments should be connected to their actual and predicted states of operation in relation to the larger system where they operate. Our example system is a container ship that functions as part of an international logistics system. We investigate the measurement, analysis, and management of the ship’s performance. We focus on the actual dynamism of the physical system and how it can be predicted in a way that enables system governance to adapt and fit to changing conditions. This enables continuous discovery of sustainable ways to improve the financial performance of the system. We show that sensing and modeling technologies can increase the adaptability and flexibility of network governance and leadership at various phases of the life cycle.With improved measurement, significant savings can be realized for each individual asset. Increased value for a ship could be up to US $1million annually through decreased fuel consumption, higher utilization, and minimized downtime . Over 90% of world trade is carried by ships using approximately 50,000 vessels. With increased ship performance, the overall system (ports, land transport, production planning in industry) can benefit. The combination of increased data through sensing, connected information that spans various functions, advanced analytics, and well organized human resources can change the earning potential and sustainability impacts associated with shipping. At its core, the basic benefit of this ‘industrial Internet’ is to turn data and information into actionable knowledge. We outline a framework that uses a values-based approach to assess the measurement needs of an investment and determine four different values: social, environmental, financial and functional value. Social value describes the overall benefits provided by the industry, i.e. the industries impact on society. Environmental value reflects the environmental footprint on both the industry and investment level. Our case study specifically focuses on financial value according to the Net Present Value (NPV) curve, and therefore moves toward a lifecycle analysis approach of individual investments. The functional value of an investment reflects its’ core function, and is a variable of the social and financial value. In the case of container ships, the functional value is the amount of transported containers, a variable of the revenues of a container ship, and the main part of the overall impact of the container shipping industry on society. Our case study also presents the multitude of actors and processes that impact the financial value of industrial investments during their operational phase. These actors are essential to consider when in predicting the physical outcome and system requirements to adapt to changing conditions.

Multi-product valid inequalities for the discrete lot-sizing and scheduling problem

We consider a problem arising in the context of industrial production planning, namely the multi-product discrete lot-sizing and scheduling problem with sequence-dependent changeover costs. We aim at developing an exact solution approach based on a Cut & Branch procedure for this combinatorial optimization problem. To achieve this, we propose a new family of multi-product valid inequalities which corresponds to taking into account the conflicts between different products simultaneously requiring production on the resource. We then present both an exact and a heuristic separation algorithm which form the basis of a cutting-plane generation algorithm. We finally discuss computational results which confirm the practical usefulness of the proposed inequalities at strengthening the MILP formulation and at reducing the overall computation time.

Graphical tools for production planning in small medium industries (SMIs) based on pinch analysis

Small medium industries (SMIs) routinely face supply variations in their production cycle. Such firms are typically characterised by limited resources and insufficiency of funds. Thus, SMIs need simple solutions to cope with the production planning issues. Pinch analysis has been proven as a strategy for planning of efficient use of scarce resources. Recently, it has been extended to various production planning problems. In this paper, a simple novel graphical approach is proposed to address two common production planning problems in SMI's, which are warehouse space allocation and production capacity planning. Two industrial case studies are shown in this paper to illustrate the proposed approach.