Quantity Model Research Articles

Surrogate-based model parameter optimization in simulations of the West African monsoon

Abstract. The West African monsoon (WAM) system is a critical climatic phenomenon with significant socio-economic impacts on millions of people. Despite many advancements in numerical weather and climate models, accurately representing the WAM remains a challenge due to its intricate dynamics and inherent uncertainties. Building upon our previous work utilizing the ICON (icosahedral nonhydrostatic) numerical model to construct statistical surrogate models for quantities of interest (QoIs) characterizing the WAM, this paper focuses on the optimization of the three uncertain model parameters of entrainment rate, fall speed of ice, and soil moisture evaporation fraction through innovative multi-objective optimization (MOO) techniques. The problem is approached in two distinct ways: (1) optimization of 15 designated QoIs, such as the latitude and magnitude of the African rain belt or African easterly jet, using existing surrogate models and (2) optimization of twelve 2D meteorological output fields, such as precipitation, cloud cover, and pressure, using new surrogate models that employ principal component analysis. The objectives subject to minimization in the MOO process are defined as the difference between the surrogate model and reference data for each QoI or output field. As reference data, Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM IMERG) mission are used for precipitation, and the ERA5 reanalysis data from the European Centre for Medium-Range Weather Forecasts are used for all other quantities. The multi-objective optimization problems are tackled through two strategies: (1) assignment of weights with uncertainties to the objectives based on expert opinion and (2) variation in these weights in order to assess their influence on the optimal values of the uncertain model parameters. Results show that the ICON model is already generally well-tuned for the WAM system. However, a lower entrainment parameter would lead to a more accurate simulation of accumulated precipitation, averaged 2 m dew point temperature, and mean sea level pressure over the considered domain (15° W to 15° E, 0 to 25° N). An improvement in 2D output fields instead of QoIs is barely possible with the considered parameters, which confirms meaningful default values of the model parameters for the region. Nevertheless, optimal model parameters strongly depend on the assigned weights for the objectives. To further enhance the accuracy of climate simulations and potentially improve weather predictions, it is crucial to prioritize the refinement of the overall physical models, including the reduction in inherent structural errors, rather than solely adjusting the uncertain parameters in existing model parametrizations. Nevertheless, our methodology demonstrates the potential of integrating statistical and expert-driven approaches to assess and improve the simulation accuracy of the WAM. The findings underscore the importance of considering uncertainties in MOO and the need for a holistic understanding of the WAM's dynamics to enhance prediction skills.

Integrated optimization model of economic manufacturing quantity and hybrid condition-based maintenance for continuous-production systems

Integrated optimization model of economic manufacturing quantity and hybrid condition-based maintenance for continuous-production systems

EOQ Model versus (Q,r) Model: Case Study of a Company's Inventory in Londrina, Paraná, Brazil

This article presents mathematical and statistical methods applicable to inventory management. Inventory analysis using ABC curves is used to identify priority items, the most expensive items, and those with the highest turnover (demand). Based on this information, it is possible to determine, through inventory control models, the optimal order quantity and frequency that minimize the total storage costs of these items. Using the Economic Order Quantity (EOQ) model and the (Q, r) model, inventory control models, the minimization of a company's inventory costs in Londrina, Paraná, Brazil was simulated. The comparison of the results provided by the models was discussed. Specifically, it was observed that for some inventory items, it would theoretically be possible to achieve savings margins of over 50% on inventory expenses for this company.

Artificial intelligence in lung cancer: current applications, future perspectives, and challenges

Artificial intelligence (AI) has significantly impacted various fields, including oncology. This comprehensive review examines the current applications and future prospects of AI in lung cancer research and treatment. We critically analyze the latest AI technologies and their applications across multiple domains, including genomics, transcriptomics, proteomics, metabolomics, immunomics, microbiomics, radiomics, and pathomics in lung cancer research. The review elucidates AI’s transformative role in enhancing early detection, personalizing treatment strategies, and accelerating therapeutic innovations. We explore AI’s impact on precision medicine in lung cancer, encompassing early diagnosis, treatment planning, monitoring, and drug discovery. The potential of AI in analyzing complex datasets, including genetic profiles, imaging data, and clinical records, is discussed, highlighting its capacity to provide more accurate diagnoses and tailored treatment plans. Additionally, we examine AI’s potential in predicting patient responses to immunotherapy and forecasting survival rates, particularly in non-small cell lung cancer (NSCLC). The review addresses technical challenges facing AI implementation in lung cancer care, including data quality and quantity issues, model interpretability, and ethical considerations, while discussing potential solutions and emphasizing the importance of rigorous validation. By providing a comprehensive analysis for researchers and clinicians, this review underscores AI’s indispensable role in combating lung cancer and its potential to usher in a new era of medical breakthroughs, ultimately aiming to improve patient outcomes and quality of life.

Dynamic Approach to Crab Inventory Management in Juata Sea: Balancing Demand and Operational Risks

Harapan Baru is a crab procurement business that serves as a critical link between fishermen and pond farmers. Crabs, as the primary commodity, are subject to significant price fluctuations and quality variations. This study aims to evaluate the economic viability of Harapan Baru's operations by analyzing costs, revenue, and profitability. Employing a critical incident technique, the research found that the First-In-First-Out (FIFO) inventory management method and Economic Order Quantity (EOQ) model significantly enhance operational efficiency. By ensuring timely inventory turnover and optimizing order quantities, these methods mitigate risks associated with demand variability, climate change, and operational challenges. Moreover, the study underscores the essential role of mangrove ecosystems in sustaining crab populations and the economic benefits derived from this natural resource. The integration of technology and SWOT analysis highlights the potential for Harapan Baru to enhance its competitive advantage and contribute to the long-term sustainability of the fisheries industry.

Analisis Penerapan Economic Order Quantity (EOQ) untuk Efisiensi Pengelolaam Bahan Baku

Inventory plays an important role in modern industry, affecting the Company's overall performance. Effective inventory management ensures smooth operations by avoiding material shortages, optimizing storage costs. And maintaining product quality. PT Eteris Prima Wiyasa, an essential oil manufacturer, currently manages its raw material inventory based on market demand, resulting in occasional shortages or overstocks. The Economic Order Quantity (EOQ) model provides a solution by optimizing ordering and storage costs to determine the optimal order quantity, reduce stockouts and lower inventory costs. This study aims to optimize raw material inventory at PT Eteris Prima Wiyasa using EOQ, overcoming problems related to inventory imbalances and supplier minimum order quantities.

Optimal ordering for Product-as-a-Service models with circular economy practices

The Product-as-a-Service (PaaS) model offers the opportunity to implement circular actions such as repairing, reusing, collecting end-of-life products, and recycling. However, adopting circular practices causes more complexities in managing the inventory flow due to repetitive product subscriptions. Accordingly, this paper aims to optimise a PaaS model's order quantity and profit, considering circular economy practices and various quality levels for subscription products. In the proposed model, the subscription firm defines a quality check and repair procedure at the end of each quality period before sending the product to another subscriber. Moreover, the firm recycles end-of-life products and sells the recycled material to the supplier. This study aims to compare a closed-loop PaaS model with the traditional economic order quantity model in terms of operational costs, revenue, and inventory flow. The results show that factors such as the difference between the demand rates of the consecutive periods, the relationship between the recycling capacity and the final collection rate, and the difference between the screening and demand rates have essential roles in alleviating the extra inventory costs related to circular economy actions like reusing, repairing, and recycling.

A Practical Economic Order Quantity Model for Growing Items with Edible Yield and Constraints in Poultry Farming

Growing items like chicken broilers play a critical role in consumption baskets; however, their real characteristics have been neglected in the research focusing on inventory models such as economic order quantity (EOQ). Mainstream research conducted in this field considers an approximation of the edible rate of chicken broilers in cost analysis. On the other hand, this subject was the objective of other research, and the function that represents the relationship between the weight and age of broilers was derived through extensive research. This study proposes an EOQ model that considers a realistic function for extracting the exact value of edible parts of broilers and the correct form of feeding function that is dependent on their age. In other words, this study not only addresses the challenge arising in the estimation of the edible part of broilers but also introduces another function to obtain the correct amount of feeding and nurturing cost of broilers. Solution methodology encompasses these mathematical functions and then provides a straightforward framework to find the optimal solution. An exact heuristic solution is obtained that considers the real factors, and then the approach is applied to case studies, and insights are derived to manage real situations for corporations.

Integrating material flow analysis into hydrological model for water environment management of large-scale urban-rural mixed catchment

Simultaneous simulation of urban and rural hydrological processes is important for water environment management of mixed land-uses catchments. However, the discharge paths of pollution in the urban drainage system are not described in traditional catchment hydrological models. In this study, an urban-rural water environment (URWE) model is developed through incorporating the material flow analysis (MFA) and the soil and water assessment tool (SWAT) into a general framework. The URWE model is an advancement with respect to traditional hydrological models in terms of simultaneously simulating the urban organized and rural decentralized discharges of pollution. Due to the low data requirement and high computational efficiency of MFA, URWE model is applicable to large-scale catchment with wide urban area. The URWE model is applied to a typical urban-rural mixed catchment, the Dianchi Catchment (China), where the pollution characteristics are analyzed and the pollution control measures are investigated. Results indicate that the URWE model outperforms the conventional SWAT model for both water quantity and quality simulations, with an 8.5 % improvement in average coefficient of determination (R2) and a 67.4 % improvement in average Nash coefficient (NSE). Rural best management practice, rainwater-sewage separation, and storage capacity expansion are identified as the most cost-effective measures for COD, TN, and TP reduction, respectively. Contributions of this study are to improve the accuracy of water environment simulation in urban-rural mixed catchment, as well as to help decision-makers develop synergistic urban-rural water environment management measures.

A sustainable fuzzy economic production quantity model (SFEPQM) with sporadic machine failures, inspection and prepayments under carbon tax and cap policy

Goals for sustainability have caused an upcoming change in the emphasis of Economic Production Quantity models, which was previously limited mainly to financial considerations to include aspects of the environment as well. The primary tools used in many nations to reduce emissions are carbon cap and price legislation. However, companies in the production industry may have difficulties with their planning and oversight procedures as they frequently have to cope with faulty products and machinery failures. Previous research has not addressed the comprehensiveness of green investment in machine breakdowns. Considering this problem, we have developed a sustainable economic production quantity model with sporadic malfunctions of machinery and prepayments under carbon tax and cap regulations. Our aim is to minimise the overall cost of faulty items with the investment in green technologies. An approach that combines with the outstanding effectiveness of green strategies to minimise emissions will culminate in a greener production that generates more profit than the traditional system. This study considers two cases i.e. with and without green technology investment (GTI). It also has many predictions, including the execution of inspections afterwards as well as during manufacturing, faulty goods are scrapped while others are sold at discounted price.

Wrapping input–output multipliers in confidence intervals

Input–output multipliers are typically calculated as point estimates of the Leontief quantity model. From the previous literature, they can also be estimated (and their confidence intervals) directly from establishments’/industries’ inputs and outputs data by running an appropriate econometric regression. However, previous contributions relied on analyses carried out for 1 single year, one country/region and a reduced number of degrees of freedom. This paper tests the robustness of the previous literature by using instead a time series of large-scale inter-country supply, use and input–output database with more than one country and more than 1 year, as well as panel data econometrics.

Optimized Economic Production Quantity Inventory Model with Weibull Decay, Dynamic Holding Cost Under Varied Demand Scenario and Demand Series Analysis

This paper presents a sophisticated modification of the economic production quantity (EPQ) model. It incorporates Weibull-distributed progressive deterioration, dynamic holding costs, variable demand, and strong strategies for effectively handling shortages and backlogs. Targeting products that have a short-term expiration but can still be stored for a long time without significant deterioration, the model aims to increase production quantities while lowering overall variable expenses. The extended EPQ model utilizes Maclaurin series approximations to solve ordinary differential equations, effectively capturing the dynamics of inventory under three different demand patterns. The focus of this handle’s situations when the demand for a product changes in a predictable manner over time and considers the likelihood of defective goods during the manufacturing process. This approach provides useful insights for optimizing inventory management and production planning in the manufacturing of goods that are used up or consumed. The usefulness of the model is confirmed in the study through the use of two illustrative instances. Additionally, sensitivity analysis is utilized to suggest potential areas for additional investigation.

Determining Quantity Model of Built-Up Occupancy Residential in Musi Banyuasin District

ABSTRACTThe determination of the quantity of dwellings in Musi Banyuasin district is a form of activity in seeing the development of the number of dwellings on built-up residential land when the existing data on the quantity of dwellings needed for the base year has not been obtained during secondary data collection. The importance of existing occupancy quantity data in planning activities, of course, is as a basis for determining the level of future occupancy shortages (projections). The purpose of this research is to determine the existing data on the number of dwellings in 2022 as an alternative form of providing the quantity of dwellings on built-up settlement land that will be used as planning data in Musi Banyuasin district. The method used is a quantitative approach with analytical methods, statistical, spatial and technical. Based on the results of the analysis, the quantity of dwelling units in Musi Banyuasin district on 7,528 hectares of built-up settlement land resulted in 205,962 dwelling units spread across all sub-districts with an average dwelling area of 148 square meters per house consisting of 124,441 households. These results are based on an assessment of the standard deviation and correlation level of 3 alternative comparison data, namely 213,645 dwelling units and 243,227 dwelling units, both of which have standard deviation and correlation values below the value of alternative 1.Keywords: Settlement; Residential; Decision; Statistics; TechnicalABSTRAKPenentuan jumlah hunian di Kabupaten Musi Banyuasin merupakan bentuk kegiatan untuk melihat perkembangan jumlah hunian di lahan permukiman terbangun ketika data eksisting mengenai jumlah hunian yang dibutuhkan untuk tahun dasar belum diperoleh selama pengumpulan data sekunder. Pentingnya data kuantitas hunian eksisting dalam kegiatan perencanaan tentu saja sebagai dasar untuk menentukan tingkat kekurangan hunian di masa depan (proyeksi). Tujuan dari penelitian ini adalah untuk menentukan data eksisting mengenai jumlah hunian pada tahun 2022 sebagai bentuk alternatif penyediaan kuantitas hunian di lahan permukiman terbangun yang akan digunakan sebagai data perencanaan di Kabupaten Musi Banyuasin. Metode yang digunakan adalah pendekatan kuantitatif dengan metode analisis, statistik, spasial, dan teknis. Berdasarkan hasil analisis, jumlah unit hunian di Kabupaten Musi Banyuasin pada lahan permukiman terbangun seluas 7.528 hektar menghasilkan 205.962 unit hunian yang tersebar di seluruh kecamatan dengan rata-rata luas hunian sebesar 148 meter persegi per rumah yang terdiri dari 124.441 rumah tangga. Hasil ini didasarkan pada penilaian terhadap nilai standar deviasi dan tingkat korelasi dari 3 data perbandingan alternatif, yaitu 213.645 unit hunian dan 243.227 unit hunian, di mana keduanya memiliki nilai standar deviasi dan korelasi di bawah nilai alternatif 1.Kata kunci: Permukiman; Hunian; Keputusan; Statistik; Teknis.

Fuzzy inventory model for breakable items, including damage costs and inspection errors in a green environment.

Abstract Conventional Economic Order Quantity (EOQ) models operate on the implicit assumption that all received products are flawless. Current EOQ models for items of defectiveness implicitly suggest that suppliers do not conduct a comprehensive assessment, even though they loosen this presumption and are more applicable to scenarios in reality.This paper presents a significant EOQ model. This model takes into account errors in determining whether an item is perfect or broken. Because things are piled high in stock, breaking of products takes place. These goods are stored and sold at once, with damage costs included. Moreover, Governments have prioritized reducing carbon emissions as their main goal. With the aim of creating a greener environment, we have taken carbon emission costs into account in order to minimize overall costs and maximize overall profit. This model is distinct because it closes the research gap by taking into account breakable items under inspection errors with damage cost, and carbon emission cost.The ideal total cost of the EOQ model is produced by the crisp version. It is important to note that this model is constructed in a fuzzy manner by using a triangular fuzzy number to represent the cost of accepting a broken object.Applications of this idea include shipping commodities. A numerical illustration of the ideal solution is given and the current study addresses how changes in a few parameters influence the ideal overall cost.

Single-vendor single-buyer multi-product economic production quantity problem with stochastic constraints: a modified generalized elimination method

This study designs a single-buyer single-vendor multi-product Economic Production Quantity (EPQ) model to optimize the total cost in an inventory system. To do so, a Non-Linear Programming (NLP) model is developed considering a set of stochastic constraints on space or warehouse capacity, backordering cost, procurement, ordering, and obtainable budget. A modified Generalized Elimination Method (GEM) is then offered to treat the complexity of the model wherein the variables are eliminated by adding two equations. The developed method generates much more high-quality solutions compared with the classic GEM, while the number of iterations slightly increases. Three problem instances in different scales are then taken into account to assess the efficiency of the modified GEM in terms of optimality criteria. The results demonstrate that the modified GEM has an excellent performance with respect to optimal solutions, infeasibility, number of iterations, complementarity, and errors of optimality. Finally, the behavior of the objective function is analyzed against order quantity fluctuations to draw out practical implications.

An EOQ Model for Delayed Deteriorating Items: Analysis of Three-Stage Consumption, Dual Storage Facilities, and Variable Partial Backlogging Rates

Study’s Excerpt An Economic Order Quantity (EOQ) model that accounts for items with delayed deterioration by integrating three distinct stages of consumption rates, dual storage facilities, and partially backlogged shortages was proposed. The model can minimize overall variable costs and establish the conditions for the existence and uniqueness of the optimal solution by optimizing cycle length, order quantity, and depletion timing. The model could help in inventory management of perishable goods, particularly in scenarios involving multiple storage options and consumption phases. Full Abstract This paper presents an Economic Order Quantity (EOQ) model for items with delayed deterioration, incorporating three distinct stages of consumption rates, dual storage facilities, and shortages. The model assumes that the average consumption rates before and after the onset of deterioration, as well as during shortages, are constant but differ from one another. The shortages are partially backlogged and vary according to the waiting time until the next replenishment. The model aims to minimize the overall variable cost per unit of time by optimizing the cycle length, order quantity, and the timing at which the inventory in the owned warehouse is depleted. The paper establishes the necessary and sufficient conditions to ensure the existence and uniqueness of the optimal solution. A sensitivity analysis is conducted, and the findings are used to derive managerial insights.

An integrated economic production quantity model with shortages considering energy utilization in production and warehousing

An integrated economic production quantity model with shortages considering energy utilization in production and warehousing

Optimizing substitution of two products model for exponentially increasing demand under inflation and shortages

This study addresses the limited attention given to product substitution in inventory models. Incorporating product substitution is crucial for determining reorder points, and safety stock, enabling businesses to optimize inventory levels, reduce costs, and maintain customer satisfaction. This study introduces an economic order quantity model tailored to an inflationary environment with shortages and one-way substitution between two deteriorating product types. Through comprehensive testing, this study evaluates the model under various substitution scenarios, including partial substitution. Findings highlight the significance of product substitution in inventory management, allowing businesses to optimize inventory levels, manage costs, and ensure customer satisfaction in dynamic environments with inflation and fluctuating product availability. This model provides the firm with the necessary information to determine the optimal ordering quantity of both products to optimize total benefit and enhance supply chain efficiency. The model demonstrates substantial cost advantages, with partial substitution resulting in an average cost reduction of approximately 9% compared to no substitution and about 45% compared to full substitution. Numerical experiments validate the applicability of the proposed model.

Artificial Neural Network Model For Optimization of Forecasting Material Inventory

The increasing competition in the fast-moving consumer goods (FMCG) industry leads to demand fluctuations, negatively impacting the accuracy of demand forecasts and determining optimal lot sizes in material inventory planning. Many companies struggle to adopt appropriate forecasting models, resulting in poor accuracy and higher material costs. This study aims to develop an integrated model for forecasting and material planning using simulation. The artificial neural network (ANN) method is proposed to improve forecasting accuracy, with performance evaluated through mean percentage error (MAPE), mean absolute deviation (MAD), and mean squared error (MSE). The forecast results are then applied to optimize material inventory using the economic order quantity (EOQ) model, considering warehouse capacity constraints. The EOQ model is applied to adjust lot sizes under time-varying demand. The findings highlight the importance of integrating forecasting with inventory planning to provide accurate demand predictions and optimal lot sizing, ultimately minimizing material costs in the FMCG industry. This research contributes to better decision-making in supply chain management by enhancing forecasting accuracy and inventory optimization.

The Continuous-Time Joint Replenishment Problem: ϵ-Optimal Policies via Pairwise Alignment

The main contribution of this paper resides in developing a new algorithmic approach for addressing the continuous-time joint replenishment problem, termed [Formula: see text]-pairwise alignment. The latter mechanism, through which we synchronize multiple economic order quantity models, allows us to devise a purely combinatorial algorithm for efficiently approximating optimal policies within any degree of accuracy. As a result, our work constitutes the first quantitative improvement over power-of-2 policies, which have been state-of-the-art in this context since the mid-1980s. Moreover, in light of recent intractability results, by proposing an efficient polynomial-time approximation scheme for the joint replenishment problem, we resolve the long-standing open question regarding the computational complexity of this classical setting. This paper was accepted by Chung Piaw Teo, optimization. Funding: This work was supported by the Israel Science Foundation [Grant 1407/20].