Dynamic Production Model Research Articles

Optimal Dynamic Production Planning for Supply Network with Random External and Internal Demands

This paper focuses on joint production/inventory optimization in single and multiple horizons, respectively, within a complicated supply network (CSN) consisting of firm nodes with coupled demands and firm nodes with coupled demands. We first formulate the single-epoch joint optimal output model by allowing the production of extra quantity for stock underage, considering the fixed costs incurred by having inventory over demand and shortfalls. Then, the multi-temporal dynamic joint production model is further investigated to deal with stochastic demand fluctuations among CSN nodes by constructing a dynamic input–output model. The K-convexity defined in Rn space is proved to obtain the optimal control strategy. According to physical flow links, all demands associated to the nodes of CSN are categorized into the inter-node demand inside CSN (intermediate demand) and external demand outside CSN (final demand). We exploit the meliorated input–output matrix to describe demand relations, building dynamic input–output models where demand fluctuates randomly in single-cycle CSN and finite multi-cycle CSN. The novel monocyclic and multicyclic dynamic models have been developed to minimize system-wide operational costs. Unlike existent literature, we consider fixed costs incurred by overdemand and underdemand inventory into system operational cost functions and then demonstrate the convexity of objective functions. The cost function with two fixed penalty costs due to excess and shortage of inventory is developed in a multicycle model, and the K-convexity defined in Rn is proved to find out the optimal strategy for joint dynamic production of CSNs in the case of multi-products and multicycles.

Uncertain random optimal control model for deteriorating inventory with the finite horizon

This paper examines a deteriorating inventory model with combined pricing and production during a limited selling time in an uncertain random setting. Demand is influenced by market size and price, which are portrayed in terms of random variables. The uncertainty factors affecting the inventory are represented by multiple Liu processes. Inventories deteriorate by a certain percentage and back-orders for inventories are permitted. This study aims to determine the optimal level of production rate and pricing that maximizes the producer’s total expected profit through the application of uncertain random optimal control theory, which is based on uncertain theory and probability theory. Numerical examples are used to compare joint pricing dynamic production model with dynamic production model under an optimal static price, highlighting the benefits of implementing joint pricing dynamic production model. Furthermore, it is evident that an increase in deterioration rate results in decreased on-hand inventory, yet higher production rate and price. It should also be noted that different levels of probability expectation of demand can have an impact on production inventory problem.

Mathematical modeling of fermentation from glucose, xylose, and food waste of clostridia sp. strain BOH3 for the production of ABE solvents and hydrogen

In this study we develop a robust, structured dynamic model of ABE and hydrogen production using Clostridium BOH3 strain. The model represents simultaneous fermentation of glucose and xylose, and concurrent saccharification and fermentation when grown on a variety of complex feedstocks, using only a single adjustable parameter. The model simulations were developed and validated with experimental data of BOH3 fermentations from the literature. Sugar consumption and ABE production dynamics are successfully predicted at different glucose to xylose ratios, and from hydrolyzed lignocellulose, representative of horticultural waste. Butanol and hydrogen production are in close agreement with the experimental data in simulations with rice starch and food waste as substrates. The sensitivity analysis highlights the important roles of xylose transport and xylose kinase on the xylose consumption and butanol formation, in accordance with experimental findings on how these enzymes enable the efficient xylose utilization by BOH3.

Dynamic Model on Palm Oil Production Capacity Using Variable CPO Stock for Biofuel Production

The role of biofuel for running diesel power plants is therefore indispensable for compensating various renewable plants with fluctuating output, maintaining stability to the overall power system. For expanding biofuel production, keeping the rate of investment in CPO mills is critical, as it is often driven by CPO business profit. The seasonal nature of palm-oil plantation harvest directly influences CPO production behaviour. This research investigates system dynamics model simulation for CPO production system, indicated by the feedback loop from market demand, hence influencing the daily (or monthly) CPO production to provide revenue stream to the business. Subsequently, it influences the rate of investment for increasing production capacity, forming the dynamic hypothesis for the system. The model was developed using endogenous variables, while trying to reduce the number of external inputs required to run the simulation. By substituting the price variable with the level of CPO stock at the national level, less uncertainties would affect the system such as price instability situation. Feedback signal from the level of CPO stock was utilized to control the simulated amount of CPO production, as well as the rate of re-investment for expanding the country’s production capacity. Model simulation results was able to reproduce the system behaviour for capacity increase, to meet CPO market demand. The model structure could be further replicated for efficiently developing the dynamic model for basic commodity production, where the rate of commodity production would not influence the overall market demand (decoupling of production rate from global market)

U.S. light tight oil supply flexibility - A multivariate dynamic model for production and rig activity

I examine U.S. light tight oil (LTO) production theoretically and empirically. The theoretical model combines endogenous rig activity and stylized reservoir pressure mechanics with the Hotelling model for exhaustible resources. The empirical section presents a vector error correction model for U.S. LTO production. A one percent increase in the West Texas Intermediate (WTI) oil price results in up to a 0.7 percent increase in LTO supply and a 1.5 percent increase in rig activity. A positive shock to LTO supply has a negative but insignificant effect on the WTI oil price. The results indicate a structural break in March 2020, with LTO being less responsive to the WTI after the outbreak of Covid-19.

Parallel dynamic NSGA-II with multi-population search for rescheduling of Seru production considering schedule changes under different dynamic events

Seru production is usually accompanied by various dynamic events in the real world, such as worker tool breakdowns, new batch arrivals, batch cancellations, and batch size changes. Therefore, dynamic Seru production is proposed. To cope with the dynamic seru production, we adopt an event-driven predictive-reactive scheduling method, i.e., when a dynamic event occurs, rescheduling is triggered to produce a new schedule with good efficiency. However, the new schedule may deviate significantly from the previous schedule. Therefore, it is critical to minimize schedule changes while maximizing efficiency (e.g. makespan) simultaneously. First, we formulate a multi-objective dynamic seru production model that applies to various dynamic events to minimize both schedule changes and makespan. Then, we develop dynamic NSGA-II to produce a new schedule. To effectively respond to dynamic events, a population re-initialization strategy is proposed to fast-track non-dominated solutions by making use of the characteristics of dynamic events, and a communication mechanism is designed to accelerate the convergence speed. However, these two mechanisms may lead to reducing its diversity. To maintain population diversity, a multi-population search strategy based on hyper-heuristic is adopted. The hyper-heuristic algorithm is incorporated to evolve an additional seru rescheduling population, which employs NSGA-ΙΙ as the high-level strategy to manipulate several low-level heuristics to generate efficient local search heuristic sequences. However, this approach also results in a longer computation time. Therefore, a parallel mechanism is designed to save computational time. Finally, we have conducted extensive experiments to demonstrate that our algorithm outperforms existing algorithms and provides several management insights.

Development of a multi-scale mathematical model for green hydrogen production via biogas steam reforming process

In this work, a dynamic 2D heterogeneous model for catalytic hydrogen production by biogas steam reforming process was developed, considering both the macroscale and the microscale of the system. The partial differential equations (which describe mass and energy balances) of the mathematical model, have been converted into total differential equations, by spatial discretization and implemented in Matlab/Simulink for process simulation. Based on simulation results, the profiles for mass and concentration of the reactants and products were obtained. The developed model has been validated with available literature data. For the conversion of methane, it predicted 72.43% and for the hydrogen percentage on the dry basis, it gave the value of 72.72%. In this study, a special emphasis was placed on highlighting the influence of a high content of carbon dioxide in biogas on the size of the reactor. The developed model is used to study the effects of step and ramp flow inputs disturbances for the methane conversion and hydrogen percentage on the dry basis and to observe how the reactor design parameters change when the CO2 concentration in the feed flow increases. Furthermore, the developed model for biogas reforming was integrated in a green hydrogen plant with CO2 capture feature for overall performance evaluation (e.g., energy efficiency, CO2 capture rate etc.). The decarbonized green hydrogen plant has promising performance indicators (high overall energy efficiency of 54.5% and a high CO2 capture rate 70%).

Modeling and control of nuclear–renewable integrated energy systems: Dynamic system model for green electricity and hydrogen production

The need for decarbonization and diversification of energy resources has led to the development of integrated energy systems (IESs), where multiple resources supply more than one energy sector. One such IES with small modular nuclear reactors and renewables (wind and solar) as generating resources, catering to the demand of the electric grid while producing hydrogen for industries, is modeled in this paper. The physics-based component models are represented using the Modelica language and interconnected to form the IES. The control and coordination of the overall system are ensured by designing a suitable control architecture composed of individual subsystem-level controls and supervisory control. The dynamic performance and the load-following capability of the IES are evaluated, while satisfying the safe operational limits of the components. Different configurations and modes of IES operation are considered, where the adaptability of the control system in the presence of varying demands and renewable generations is validated. The simulation results indicate that hydrogen as a flexible load facilitates the supply of varying grid demand. Additionally, the renewables are also accommodated into the IES owing to the flexibility of the balance of plant associated with the nuclear reactors.

The Convergence of Factors That Affect the Dairy Product Market: A Comparative Analysis of European Union Countries

The European Union is a large producer of milk and dairy products. It is also a significant market for the consumption of dairy products, which is characterised by effective demand, and it significantly influences the markets of other food products. The main milk producers in the EU are Germany, France, Poland, the Netherlands, Italy and Spain. Intensive migration has contributed to additional demand for food products, in particular, dairy products. This provided the basis for the construction and calculation of a dynamic model of dairy production, the export of dairy products, and the coexistence with the existing population as the main consumers in the EU–27 countries and Great Britain between 2004 and 2020.
 An additional research value is that based on the presentation of the analysis and modeling, the relationship between the growth in the demand for dairy products and the growth in the population is established. The reason for the large population in the countries of Central and Eastern Europe is intensive migration flows, which increase the demand for dairy products. The increase in volumes reduces the cost price, which contributes to the export of dairy products, and also contributes to the increase in the volume of production of dairy products. The resulting models made it possible to analyse the influence of population growth on the increase in production volume. At the same time, the dependence and influence of the export of dairy products on the increase in the volume of production of dairy products in the EU–27 and Great Britain countries is investigated.
 The results of the mathematical modelling indicate unique opportunities to develop the dairy sector of individual EU–27 countries, i.e., the Czech Republic, the Netherlands, and Poland. These countries have a rational ratio of the influence of the population growth factor on the increase in the volume of dairy production and the optimal influence of the export of dairy products on the increase in the volume of dairy production.

Dynamic Modeling and Plantwide Control of a Production Process for Biodiesel and Glycerol

Biodiesel production comprises several interconnected process steps with complex dynamic behavior. Dynamic plant modeling and simulation enhance process understanding and enable model-based control. We present a modular and rigorous dynamic model for biodiesel and glycerol production by alkali-catalyzed oil transesterification. We share the implemented model in Modelica open-source. Moreover, we implement two plantwide control (PWC) structures, where we assume the availability of information-rich measurements in the first and only conventional measurements in the second. To study the process dynamic behavior, we investigate several disturbance scenarios, demonstrating the importance of the plantwide perspective. The PWC structure based on an information-rich configuration shows satisfactory control performance, whereas the other fails to always satisfy product quality requirements, underlining the importance of developing dynamic models for advanced control and estimation techniques. Moreover, the model can be used as a digital twin for industrial plants as well as for model-based control and estimation applications.

AgriLOVE: Agriculture, land-use and technical change in an evolutionary, agent-based model

This paper presents a dynamic agent-based model of land use and agricultural production under environmental boundaries, finite available resources and endogenous technical change. In particular, we model a spatially explicit smallholder farming system populated by boundedly-rational agents competing and innovating to fulfill an exogenous demand for food, while coping with a changing environment shaped by their production choices. Given the strong technological and environmental uncertainty, agents learn and adaptively employ heuristics which guide their decisions on engaging in innovation and imitation activities, hiring workers, acquiring new farms, deforesting virgin areas and abandoning unproductive lands. Such activities in turn impact farm productivity, food production, food prices and land use. We firstly show that the model can replicate key stylized facts of the agricultural sector. We then extensively explore its properties across several scenarios featuring different institutional and behavioral settings. Finally, we simulate the model across different applications considering deforestation and land abandonment; human-induced soil degradation; and climate impacts. AgriLOVE offers a flexible simulation environment to study the endogenous emergence of different agricultural production regimes from the interaction of spatially dispersed farms subject to resource constraints, spatial influence and climate change.

The roles of innovation and internationalisation in explaining dynamic business performance

This study examines the effects of innovation (patent-related proxies), internationalisation (proxies measuring overseas business), and the interaction terms of the two variables on dynamic business performance (technical efficiency). A dynamic production model has been developed and assessed using a directional distance function (DDF) model under the Luenberger benefit function to measure technical efficiency of 467 firm-year observations of Taiwanese listed semiconductor companies during the sample period 2009-2015. The DDF efficiency analysis shows that technical efficiency fluctuated but remained relatively stable, with an overall mean value of 0.828. Regression results indicate that innovation and internationalisation contribute positively to dynamic business performance; although, the interaction between the two variables is not related to dynamic business performance. While innovation and internationalisation are important factors driving the dynamic business performance of the semiconductor companies in Taiwan, the vital economic assets of the industry have to be continuously protected.

Dynamic network model of production and investment

The paper studies a dynamic network game that models the competitive behavior of firms in a market. It is assumed that firms, under the condition of simultaneous and independent choice of their actions, implement the behavior that determines their production and investment behavior in each period. The production behavior of the firm reflects the ongoing quantities that it should produce and supply to the market. The investment behavior specifies the ongoing amounts of investment that the firm allocates to the modernization of its production technology in order to prevent it from becoming obsolete. Next, the unit cost is assumed to depend on the firm's investment and the investment of its competitors, which are determined by an exogenous network. Two types of Nash equilibria are characterized: open-loop and feedback. Finally, we analyze the impact of the network and related model parameters on firms' behavior, profits, and competitive advantage.

Spatial and temporal evolution patterns of material, energy and carbon emission nexus for power generation infrastructure in China

Climate change and energy transformation has made huge alteration in power system, which led to great uncertainty in relation to material, energy and carbon emissions in power generation infrastructure. We aim to estimate material stock and carbon emissions of power generation infrastructure and explore material-energy-carbon emission nexus in China from 1993 to 2060 using stock-driven dynamic material flow analysis model for materials production and carbon emissions. Results indicate that cement is the largest used material and the southwest economic zone in China has highest cement intensity due to hydropower construction, whilst steel intensity is evenly distributed across the country, aluminum intensity the smallest and more distributed in northwest region, and copper stock is the lowest. Southwest and Yangtze River economic zone contributes most to carbon emissions from national power infrastructure. Material per unit installed capacity from 1993 to 2018 has an overall increasing trend, but carbon emissions per unit installed capacity of power generation are expected to decline. Carbon emissions of new installed capacity is expected to peak around 2028. This study provides a dynamic approach to assess resource consumption and carbon emissions intensity of power generation infrastructure for decision-making on China's carbon peaking and neutrality goals.

Exploring the efficiency of hospital and pharmacy utilizations in Taiwan: An application of dynamic network data envelopment analysis

BackgroundBuilding continuity of care by enabling optimal collaboration between hospitals and community pharmacies is critical. However, existing research on healthcare efficiency has largely focused on hospital services, and few studies have evaluated hospital services alongside pharmaceutical services. ObjectiveTo set up a dynamic network production model to assess the divisional efficiency of hospital and pharmacy utilizations and to investigate the dynamic changes and spatial differences for a more productive utilization of healthcare services. MethodsA dynamic network data envelopment analysis was applied on two interdependent healthcare divisions, hospitals and community pharmacies, in order to assess utilization efficiencies in both divisions simultaneously. Subsequent analyses were performed with a set of decision making units consisting of Taiwan's cities. Data were gathered from the Ministry of Health and Welfare and the Bureau of National Health Insurance, spanning 2014–2019. ResultsThe mean scores for overall healthcare, hospital, and pharmacy among cities were 81.9%, 89.2%, and 76.7% respectively. It appears that pharmacy is not very efficient, possibly because of the low rate of prescription release. A 13.26% reduction in pharmacists in the pharmacy sector is suggested, but the number of community pharmacies remains the same in cities and counties in 2019, based on the results of the slack-based measure. The bootstrapped-truncated regression indicated the different business districts had statistically significant determinants of overall efficiency, including hospital efficiency and pharmacy efficiency. ConclusionsThis study illustrated the inefficient operations of most hospital and pharmacy divisions in Taiwan's southern cities. It is hoped to draw the government's attention to the existing inefficiency in the allocation of healthcare and pharmaceutical resources. For future studies, a dynamic network model can be adapted to introduce the Malmquist productivity index in order to compare divisional productivity changes over time. Other topics for future research include handling undesirable outputs (quality variables) and information on input/output prices.

The role of output reallocation and investment in coordinating environmental markets

We examine the inefficiency of uncoordinated environmental regulation of CO2 emissions from electricity generation for a large regional U.S. wholesale electricity market that spans multiple states. We estimate a dynamic structural model of production and investment to compare the social welfare of two counterfactual regulatory scenarios. In both scenarios, emissions targets set by the regulator are met via endogenously determined CO2 prices in markets aiming to correct the externality. In the first scenario, the CO2 prices are state-specific. In the second scenario, there is a regional CO2 price. According to our social welfare estimates, the inefficiency of uncoordinated regulation is mitigated substantially because of the firms’ participation in an integrated product market in two main ways. First, firms reallocate output from states with high CO2 prices to states with low prices. Second, this reallocation spurs investment in cleaner capacity that is exempt from CO2 regulation. Our finding regarding the mitigation and, potentially, elimination of the inefficiency is robust to alternative models of optimal investment behavior.

Формирование устного дискурса у детей 4—5 лет: функционально-динамический анализ влияния жанра и регистра на грамматический частотный профиль

Despite a long history of studies in the discourse, we still lack systematic investigations in the discourse acquisition at the preschool and pre-primary school age. Namely, the influence of variables, such as discourse genre, communication register, socio-pragmatic skills, on different linguistic parameters almost has not been investigated yet. Hence, the given study aimed at the corpus-based analysis of different child discourse genres with the main focus on the Part-of-Speech (PoS) Profile. First, each child told three personal narratives, then generated a fictional story according to the picture sequence, and had an informal chat during five individual sessions. Then, five subcorpora, including different genres (story vs. conversation) and different registers (speech addressed to adult vs. peer), were developed to analyze the distribution of parts of speech. Finally, individual and within-group PoS Profiles were built. Statistical analysis revealed some significant differences in the PoS Profile between the genres and communication registers. Namely, the genre had a significant impact on the frequency of nouns, verbs, adverbs, particles, conjunctions, and prepositions, while the communication register impacted significantly on the frequency of nouns, pronouns, adverbs, particles, and conjunctions. In the paper, the dynamic model of speech production is further discussed. Results of the study inspire discussion on the role of the PoS Profile as a manifestation of the concurrent relations between the child’s language competence and cognitive resources.

Dynamic Model for Biomass and Proteins Production by Three Bacillus Thuringiensis ssp Kurstaki Strains

Bacillus thuringiensis is a microorganism used for the production of biopesticides worldwide. In the present paper, different kinetic models were analyzed to study and compare three different strains of Bt ssp kurstaki (LIP, BLB1, and HD1). Bioperformances (vegetative cell, spore, substrate, and protein) and successive culture phases (oxidative growth, limitation and sporulation, and protein release) were depicted with an overarching aim to estimate total protein productivity, yield, and titer. In the end, two models were calibrated using experimental dataset (11 batches culture in 3 L bioreactor with semisynthetic medium), subsequently validated, and statistically compared. Both models satisfactorily followed the dynamics of the experimental data. Finally, a dynamic model was selected following the Akaike information criterion (AIC).

Acid hydrolysis-based sugarcane bagasse biorefining for levulinic acid production: Dynamic mechanistic modeling under varying operating conditions

The study on the lignocellulosic material conversion into bio-based platform chemicals, such as levulinic acid (LA), is one of the most promising routes to promote the development of advanced biorefineries. In this work, a dynamic mechanistic model is developed to simulate the LA production from lignocellulosic material. A wide operating range is used to estimate the parameters of the reaction kinetics. Because multi-parameter estimation problem is complex, a genetic algorithm-based optimization procedure is used to determine the optimum parameters values. Measurements are obtained for various reaction times (0 - 45 min) temperatures (150 – 200 °C) and acid concentration of 7.0 % w/v H2SO4. The calculated reaction rates for the state variables, concentrations of LA, glucose, 5-hydroxymethylfurfural and humins are used to construct the dynamic mechanistic model. The prediction of measured state variables was particularly accurate, as determined by the root mean square error (RMSE) and correlation coefficient (R2). Therefore, a satisfactory agreement between experimental LA yield of 57.2 mol% and computed LA yield of 56.4 mol% was achieved (at 200 °C, 7.0 % w/v H2SO4, 45 min). The proposed methodology drives the systematic development of an industrially reliable dynamic mechanistic model for LA production from sugarcane bagasse as a means to increase the LA yields in the biorefinery.

Food security outcomes in agricultural systems models: Case examples and priority information needs

Analyses of food security with agricultural systems models often focus on indicators of food availability, with limited treatment of the other three dimensions: food access, stability and utilization. We illustrate how three indicators of access (food consumption expenditures, a food insecurity scale and dietary diversity) and their stability can be incorporated into a dynamic household-level model of a maize-based production system in the Kenya highlands and a dynamic regional model of sheep production and marketing in Mexico. Although stylized due to limits on empirical evidence, the analyses suggest that inclusion of multiple access indicators can provide insights because the indicators respond differently to production shocks, demand growth and programs providing production subsidies. We also illustrate how to examine stability of food security outcomes in response to shocks using metrics of hardness (ability to withstand shocks) and elasticity (ability to return to previous conditions). The data required for more widespread empirical implementation of these methods include measurement—preferably at frequent intervals over time—of food access indicators, but also their determinants and linkages to outcomes in agricultural systems models. Analyses of food access and stability will be most valuable for assessments of food security impacts of climate change, when food systems are undergoing transformative change and to identify priority interventions and target audiences.