Optimization Model For Production Research Articles

Cost‐effective use of abandoned agricultural land for biofuel production

AbstractBiofuel can be used to abate greenhouse gas emissions in the transport sector, by replacing fossil fuel. To avoid the problem of competition with food production, the use of abandoned agricultural land (AAL) for production of the feedstock for biofuel has been proposed. AAL has generally low productivity but has also low opportunity costs, and production of perennial bioenergy crops on it can lead to carbon sequestration. A spatially explicit optimization model of biofuel production and transport fuel consumption, applied to Sweden, was used for an analysis of how AAL can alter costs for greenhouse gas emissions abatement. Results show that, compared to the case without AAL, AAL could decrease the costs of reducing greenhouse gas emissions by 29%, for emissions reductions equivalent to 50% of current emissions from gasoline in Sweden. The carbon sequestration from establishing perennial bioenergy crops on AAL is the main driver of the positive results. High carbon sequestration on AAL implies larger emissions reduction for a given volume of biofuel, and the results show that the total biofuel production can be both smaller and larger with AAL. The use of arable land for biofuel production is generally smaller with AAL, but larger at some of the highest analyzed target levels. The low AAL feedstock costs contribute to lower costs of the total biofuel production, which pushes for more total biofuel production and less fuel use reduction and therefore counteracts the reduced use of arable land.

Scheduling redundancy optimization for precast production to enhance disruption management in prefabricated construction

Redundancy is a highly effective approach for minimizing losses in precast production when faced with inevitable disruptions. However, existing studies have primarily focused on enhancing production redundancy in order to mitigate losses caused by disruptions, without considering the added production costs resulting from decreased efficiency. Besides, the cost-effectiveness of redundancy design may be influenced by the disruption types and their associated risk levels, which has not been thoroughly investigated. In this paper, a redundancy optimization model for precast production is proposed to achieve the trade-off between the redundancy benefits and the additional costs involved. The concept of production scheduling redundancy is firstly defined, and the benefits derived from its implementation are quantified. Additionally, different scenarios with high, moderate, and low occurrence levels of disruptions are designed to optimize the production redundancy in different situations. On this basis, the effectiveness and cost-benefits of redundancy are compared subject to various types and levels of disruptions. Finally, a case study is conducted to illustrate the model. The findings show that the machine breakdown is the most critical disruption in the precast production, and implementing redundancy measures prove to be more cost-effective in situations characterized by higher levels of risk. Besides, the optimal degree of redundancy is related to both the levels and the types of disruptions. The methodology of this paper can provide insights for disruption management in precast production and give full play to the benefits of prefabricated construction.

Optimal Scheduling of Off-Site Industrial Production in the Context of Distributed Photovoltaics

A reasonable allocation of production schedules and savings in overall electricity costs are crucial for large manufacturing conglomerates. In this study, we develop an optimization model of off-site industrial production scheduling to address the problems of high electricity costs due to the irrational allocation of production schedules on the demand side of China’s power supply, and the difficulty in promoting industrial and commercial distributed photovoltaic (PV) projects in China. The model makes full use of the conditions of different PV resources and variations in electricity prices in different places to optimize the scheduling of industrial production in various locations. The model is embedded with two sub-models, i.e., an electricity price prediction model and a distributed photovoltaic power cost model to complete the model parameters, in which the electricity price prediction model utilizes a Long Short-Term Memory (LSTM) neural network. Then, the particle swarm optimization algorithm is used to solve the optimization model. Finally, the production data of two off-site pharmaceutical factories belonging to the same large group of enterprises are substituted into the model for example analysis, and it is concluded that the optimization model can significantly reduce the electricity consumption costs of the enterprises by about 7.9%. This verifies the effectiveness of the optimization model established in this paper in reducing the cost of electricity consumption on the demand side.

Multi-objective optimization for sustainable and economical polycarbonate production with reaction kinetics inference for real-world industrial process

Polycarbonate has a wide range of applications in electronic devices and medicine because of its remarkable properties such as transparency and mechanical strength. However, the polycarbonate production process using diphenyl carbonate leads to the inevitable generation of nitrogen oxide (NOx) from byproduct incineration. Therefore, it is essential to optimize the process conditions while simultaneously considering the production costs and NOx emissions. Herein, we propose a multi-objective optimization model for sustainable and economical polycarbonate production using a high-fidelity reaction model for real-world industrial processes. Additionally, a data-driven model was developed using industrial data to identify the operating conditions that minimize NOx emissions and maximize profitability. Four of the ten reaction parameters were selected through variance-based global sensitivity analysis, and the posterior distribution of each parameter was determined through Bayesian inference. In addition, NOx emissions were predicted using the data-driven model developed from industrial data. A process model with coefficients of determination above 0.7 and a data-driven model with R-squared value above 0.99 for pilot-scale experimental and industrial data were generated using our approach. The Pareto optimality, identified based on the developed models, suggested operating conditions that optimize both economic and environmental benefits. Consequently, NOx emissions could be reduced by 27.2%–27.4% and the unit costs of production could be reduced by 0.8%–1.6% compared with the base case.

Plant-wide optimal scheduling of multi-grade PET production with time window constraints: A hybrid discrete/continuous-time optimization formulation

Plant-wide scheduling optimization of multi-grade PET production is of great significance for increasing the economic benefits and market competitiveness of PET plants. However, dealing with changeover operations between the production of products with different grades is challenging due to multiple timing limitations in the actual process. This study proposes a novel plant-wide scheduling optimization model for multi-grade PET production. The model focuses on three major categories of products, namely PET fibers, PET chips, and bottle-grade PET. Considering the impacts of nights and weekends on the production, a hybrid discrete/continuous-time representation method is developed to handle changeover operations with time window constraints. Afterward, a multi-objective optimization framework for multi-grade PET production is formulated to balance economic benefits and production stability. Case studies on a real-world PET plant are presented to verify the effectiveness and applicability of the proposed method.

Research on Decision Optimization and the Risk Measurement of the Power Generation Side Based on Quantile Data-Driven IGDT

In an environment marked by dual carbon goals and substantial fluctuations in coal market prices, coal power generation enterprises face an urgent imperative to make scientifically informed decisions regarding production management amidst significant market uncertainties. To tackle this challenge, this paper proposes a methodology for optimizing electricity generation side market decisions and assessing risks using quantile data-driven information-gap decision theory (QDD-IGDT). Initially, a dual-layer decision optimization model for electricity production is formulated, taking into account coal procurement and blending processes. This model optimizes the selection of spot coal and long-term contract coal prices and simplifies the dual-layer structure into an equivalent single-layer model using the McCormick envelope and Karush–Kuhn–Tucker (KKT) conditions. Subsequently, a quantile dataset is generated utilizing a short-term coal price interval prediction model based on the quantile regression neural network (QRNN). Interval constraints on expected costs are introduced to develop an uncertainty decision risk measurement model grounded in QDD-IGDT, quantifying decision risks arising from coal market uncertainties to bolster decision robustness. Lastly, case simulations are executed by using real production data from a power generation enterprise, and the dual-layer decision optimization model is solved by employing the McCormick–KKT–Gurobi approach. Additionally, decision risks associated with coal market uncertainties are assessed through a one-dimensional search under interval constraints on expected cost volatility. The findings demonstrate the effectiveness of the proposed research methodology in cost optimization within the context of coal market uncertainties, underscoring its validity and economic efficiency.

Evaluation of tieback developments for marginal oil fields with timing flexibility

The average size of new oil discoveries on the Norwegian Continental Shelf (NCS) is steadily decreasing. As standalone developments are often not economically viable for marginal fields, tiebacks to existing production facilities are considered in many cases. At the same time, many production facilities in mature production areas have spare capacity due to depleted reservoirs. In this paper we evaluate tieback development concepts for a marginal field having the choice between two hosts with different characteristics. We develop a model that allows to (1) evaluate the tieback development concepts; (2) determine the optimal choice of host facility for the field operator; and (3) optimize the timing of development taking into account oil, gas price and CAPEX uncertainty using a real options approach. In order to be able to reflect how the capacity constraints of a host affects the production potential of the field over time, we incorporate a production optimization model as part of the methodology. We apply the model to a real case on the NCS. We identify characteristics that drive the optimal choice of hosts. Specifically, we show how lifetime extension, reduction of CAPEX and additional spare capacity individually and in combination affect the competitiveness of one host over another therewith, providing valuable insight for tariff negotiations and portfolio planning. Apart from that we find that timing flexibility is of high importance in case of high downside risk. This makes our approach particularly relevant for marginal oil field development which are often characterized by prominent uncertainties.

A Hybrid Biobjective Markov Chain Based Optimization Model for Sustainable Aggregate Production Planning

This research addresses the sustainable aggregate production planning problem by considering the outsourcing option and workforce skill levels as well as taking a Markov process approach for the inventory level. For this purpose, a hybrid biobjective mixed-integer nonlinear programming model featuring a continuous-time Markov chain to accommodate the inventory decision process is developed. The proposed Markov chain approach efficiently describes system dynamics modeling of the production system through a stochastic process. The objective functions are to minimize total cost and total environmental pollution at the same time. To validate the applicability of the methodology and to evaluate the model complexity, three numerical examples are generated based on one of the previous studies in the literature. It is demonstrated that the suggested methodology is able to come up with the final feasible solution based on optimal inventory decisions in less than 65 s. Finally, a number of sensitivity analyses are presented to study the behavior of the objectives under real-world instability and discuss the practical implications and managerial insights. As one of the main findings, it is revealed that the objective functions have no sensitivity to some change intervals of the parameters, which can be analyzed more earnestly by the management in case of the resource allocation process.

Land use planning to minimize the export of phosphorus: An optimization model for dairy production at a catchment area scale

The intensification of activities in the Santa Lucia River basin in Uruguay has resulted in an increase in the concentration of nutrients in surface waters with the consequent acceleration of the eutrophication. Dairy production, of great relevance in the area, needs to increase production to ensure the economic viability of the activity. In this context, it is necessary to find solutions that address trade-offs between productivity and polluting potential. A dairy sub-basin of the Santa Lucia River was selected as a case study to develop an optimization model with the aim of minimizing phosphorus exportation over a four-year planning horizon while meeting restrictions on the crop sequence allowed, dry matter productivity and number of crops in each sub-basin producer. An integer linear programming formulation was proposed to model the problem. It was possible to obtain a model that managed to reduce phosphorus runoff losses by planning land uses and complying the restrictions. Exists a wide field of improvement of the model, which is a useful tool for analysis of scenarios and trade-off between productivity and environmental care.

Post COVID-19 International Economy Recovery: Forecasting Bilateral Trade Between U.S. and China

This paper aims to use an empirical analysis to study and forecast the bilateral trade between the United States of America and the Peoples Republic of China in the post-pandemic era. This study was conducted from three perspectives: aggregate demand (import), supply (export), and trading potentials. In sections of import and export analysis, the paper focuses on analyzing the aggregate behavior by households with Schmitt-Grohe, Uribe, Woodfords risk-averse demand model in an open economy and the firm production optimization model by Giovanni, Kalemli-zcan, Silva, and Yildirim. In the analysis of the bilateral trade potentials, a gravity model of trade with one least square regression was applied and subsidized with the test method from Liu. From the study, it was found that the bilateral import and export between the U.S. and China are facing challenges from stringent policies, fragmentation of global value chains, and the residual effects of the pandemic on consumption. Thereby, the trading potential between the U.S. and China is restrained to an intermediate level, which is not beneficial for the robust growth of bilateral trade in the future.

Flowshop scheduling optimization for multi-shift precast production with on-time delivery

With the increasing popularity of prefabricated construction around the world, precast plants face increasingly complex production scenarios. Because it is important for precast components to be delivered on time and assembled according to the planned schedule, it is a common phenomenon to increase production shifts and break the routine production schedule to meet time-pressured orders. But there is still a lack of research on flowshop multi-shift scheduling methods in precast production. To address this issue, a flowshop scheduling optimization model of multi-shift precast production with on-time delivery is established, which is the application in civil engineering. A solution framework and its corresponding optimization processes are designed to avoid a combinatorial explosion of solutions and save solution time, which is contribution in artificial intelligence. The validity of the model is proven using a practical case. The results show that, compared to the conventional scheduling method, the proposed method significantly reduces costs under on-time delivery and stably obtains optimized feasible scheduling schemes.

Multi-objective optimization model for sustainable production planning in textile MSMEs

Textile MSMEs are characterized by their high influence on the economy of the countries, both for their contribution to the gross domestic product as well as for the generation of employment, in recent years the complexity of their operations, instability and lack of balance between economic, environmental and social factors, axes of sustainable development, stand out. It is necessary to implement approaches such as sustainable manufacturing and production planning, which seeks the creation of products with minimal environmental impact, safe for workers, and economically robust. In this context, this study aims to develop a multi-objective optimization model that enhances sustainable production planning in textile MSMEs. The methodology is based on two phases, the first one focused on the acquisition of information and the second one dedicated to the mathematical formulation of the model, where three objective functions focused on economic, environmental and social factors are proposed. The model is validated with real data from a textile MSME in Ecuador and different production alternatives are generated by proposing the implementation and use of photovoltaic energy as well as a greater use of personal protective equipment. One of the relevant conclusions of the study is the contribution to the textile industry with a sustainable decision support tool, where different scenarios for production planning and their respective economic, environmental and social consequences are shown.

Model Optimasi Produksi Kue Menggunakan Metode Simpleks Pada Toko Kue Rosalina Cabang Jatake

The continuity and development of the cake production business needs to be maintained with steps to be able to allocate raw materials and increase profits. Therefore, an effort is needed using a method in determining the right combination of production factors from the products made and the combination of products produced. This problem can be overcome by creating an optimization model using the simplex method. The simplex method is a method that mathematically starts from a basic feasible solution to another basic feasible solution and is done repeatedly (iterative) so that finally an optimal basic solution is obtained. The optimum value is obtained by analyzing whether the resources have been fully used (scarce) or excessively (Abundant) used with the optimal simplex table approach. Based on the results of iteration 3, it is obtained that line Z no longer has a negative value so that the solution obtained is optimal, meaning that Rosalina Bakery cake shop if you want to get maximum profit, it produces brownie cakes (x1) as much as 3.3333; Bolu Pandan (x2) as much as 8.6667. The maximum profit will be achieved at Rp 93,999.9 rounded up to Rp 94.000,- per day. Based on the results of the analysis, it shows that the cake production optimization model can help maximize profits from limited resources..Keywords: model, simplex, optimization, production, profit, maximum

Application of Enterprise Architecture and Artificial Neural Networks to Optimize the Production Process

Production optimization is a complex process because it must take into account various resources of the company and its environment. In this process, it is necessary to consider the enterprise as a whole, taking into account the interaction between its key elements, both in the technological and business layer. For this reason, the article proposes the use of enterprise architecture, which facilitates the interaction of these layers in the production optimization process. As a result, a proprietary meta-model of enterprise architecture was presented, which, based on good practices and the assumptions of enterprise architecture, facilitates the construction of detailed optimization models in the area of planning, scheduling, resource allocation, and routing. The production optimization model formulated as a mathematical programming problem is also presented. The model was built taking into account the meta-model. Due to the computational complexity of the optimization model, a method using an artificial neural network (ANN) was proposed to estimate the potential result based on the structure of the model and a given data instance before the start of optimization. The practical application of the presented approach has been shown based on the example of optimization of the production of an exemplary production cell where the cost of storage and the number of unfulfilled orders and maintenance are optimized.

Integrated scheduling optimization of production and transportation for precast component with delivery time window

PurposeProduction and transportation of precast components, as two continuous service stages of a precast plant, play an important role in meeting customer needs and controlling costs. However, there is still a lack of production and transportation scheduling methods that comprehensively consider delivery timeliness and transportation economy. This article aims to study the integrated scheduling optimization problem of in-plant flowshop production and off-plant transportation under the consideration of practical constraints of customer order delivery time window, and seek an optimal scheduling method that balances delivery timeliness and transportation economy.Design/methodology/approachIn this study, an integrated scheduling optimization model of flowshop production and transportation for precast components with delivery time windows is established, which describes the relationship between production and transportation and handles transportation constraints under the premise of balancing delivery timeliness and transportation economy. Then a genetic algorithm is designed to solve this model. It realizes the integrated scheduling of production and transportation through double-layer chromosome coding. A program is designed to realize the solution process. Finally, the validity of the model is proved by the calculation of actual enterprise data.FindingsThe optimized scheduling scheme can not only meet the on-time delivery, but also improve the truck loading rate and reduce the total cost, composed of early cost in plant, delivery penalty cost and transportation cost. In the model validation, the optimal scheduling scheme uses one less truck than the traditional EDD scheme (saving 20% of the transportation cost), and the total cost can be saved by 17.22%.Originality/valueThis study clarifies the relationship between the production and transportation of precast components and establishes the integrated scheduling optimization model and its solution algorithm. Different from previous studies, the proposed optimization model can balance the timeliness and economy of production and transportation for precast components.

Performance evaluation and optimization model for closed-loop production lines considering preventive maintenance and rework process

Closed-loop production lines with a constant number of carriers are widely encountered in today's production systems. In this paper, a production performance evaluation and optimization model is proposed for a closed-loop production line taking into account the degradation-level-dependent quality and rework process. Based on a two-machine-one-buffer decomposition method and the system state transition, a production performance evaluation method is presented. Due to the emphasis on quality management, preventive maintenance (PM) is used to ensure the reliability of the machines in the production line, increasing the effective output at minimum cost. A hybrid particle swarm optimization and estimation of distribution algorithm (PSO-EDA) is proposed to efficiently develop the optimal PM strategy. Finally, numerical experiments are performed to verify the effectiveness of the model. With the objective of maximizing the system profit, the optimization of the machines and pallets number is also explored.

Apparel Production Optimization Model with Branch and Bound Method (Case Study: Sawargi Jersey Confectionery, West Java)

The production optimization model can find optimal results and maximum profits from a production activity by considering certain limitations. In this research, a production optimization model was created based on data on apparel production in UMKM (Usaha Mikro, Kecil, dan Menengah) Konfeksi Sawargi Jersey in West Java by applying the Integer Linear Programming model and solving it using the Branch and Bound Method with the help of Software Python. This research was conducted because there are many business actors engaged in the same field, especially in the apparel and sports sectors, considering the problems that are often faced by UMKM owners, such as raw material supplies, production time, production costs, selling prices, production profits, and production limits, minimum and maximum production. Based on this study's results, the Branch and Bound Method application to optimize apparel production obtains more optimal results and maximum profits than the actual production carried out by UMKM Konfeksi Sawargi Jersey.

MOO-GAPS: A multi-objective optimization model for global animal production and sustainability

Agricultural and livestock production involves significant trade-offs between multiple sustainable development goals, including reducing hunger and poverty, and reducing emissions of greenhouse gasses. Here we describe a multi-objective optimization tool for livestock production to evaluate trade-offs among environmental and economic objectives with high spatial granularity and the capability to be aggregated to national or global scales. We illustrate the use of this tool analysing how and where to produce beef to optimize the weighted sum of environmental and economic objectives, in this case minimizing greenhouse gas emissions and minimizing costs of production. We present how the outputs of this model can inform a sustainable transition of the industry and policy decisions, with a focus on inherent trade-offs. By comparing optimal production with current production, we highlight the potential environmental and economic efficiencies that can be made by changing the location or methods of production. As such, this tool provides a platform to identify trade-offs and synergies among multiple diverse sustainability goals, critical in the livestock and agricultural sectors.

Digital twin rehabilitation system based on self-balancing lower limb exoskeleton.

The digital twin concept is the virtual model based on entity design measures, which is used in many enterprises' virtual workshop design models for workshop production scheduling and optimization. However, in the field of medical rehabilitation, the integration of digital twin technology started late compared to traditional industrial manufacturing. Many current digital models are not well suited for information interaction between patients and devices. In order to address the lack of interaction between patients and devices in the field of medical rehabilitation, this paper proposes an automatic gait data control system (AGDCS) for fully actuated lower limb exoskeleton digital twinning. This system improves the integration of digital twinning system with the medical rehabilitation field and analyzes the patient's gait data through simulation experiments. The digital twin system was designed in several steps. Firstly, the upper computer function module was designed and developed according to the rehabilitation treatment needs. After that, the combination of exoskeleton robot and software was carried out, and finally the real rehabilitation treatment environment of patients was simulated through experiments. The proposed system was very reliable in the experimental tests of the host computer and exoskeleton robot. In the upper computer test, the patient specific gait can be generated, and the motion of the exoskeleton robot can be observed in real-time. During the walking test of the exoskeleton robot, the exoskeleton robot completed the specified gait. The result verified the superiority and effectiveness of the digital twin system AGDCS in the field of rehabilitation. The digital twin system proposed in this paper improves the interaction between self-balancing exoskeleton robot and patients, and improves the autonomy and safety of patients in rehabilitation treatment.

OPTIMAL DESIGN OF OXYGEN PRODUCTION SYSTEM BY PATTREN SEARCH METHODS

Optimal design of Oxygen production system along with production rate, pressure in storage tank, compressor power and storage tank volume constraints were formulated in this study. In formulated optimization Oxygen production system the constraints were handled by using the exterior penalty functions. The derivative free methods were used for the optimization of this formulated problem. The methods were basically designed for unconstrained optimization problems. The optimum results of the Oxygen production optimization model were obtained by using MATLAB programming environment which demonstrated the effectiveness and applicability of the model. It was observed that the results of Nelder-Mead method were better than Hooke-Jeeves method. Nelder-Mead method was more efficient with respect to its function value and its number of function evaluations.