Improving prediction computation for time series analysis is still a challenge. Finding a method that combines the benefits of different methodologies is still an open problem. Besides the very efficient prediction co...
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Improving prediction computation for time series analysis is still a challenge. Finding a method that combines the benefits of different methodologies is still an open problem. Besides the very efficient prediction combination techniques proposed, there is still a lack of procedures that jointly consider error measure combinations and model constraints. In this work, we propose anew forecast combination procedure based on multi-criteria methods that allows the assignment of weights to different error measures in the objective function and the incorporation of constraints. Areal case from the pharmaceutical industry for the sale of a probiotic product is presented to illustrate the performance of the proposal. This method is capable of considering different error measures and non distance based errors, is enriched by the consideration of constraints that consider desirable properties of the solution and is robust with respect to different time series characteristics such as trends, seasonality, etc. Results shows similar accuracy to the best known forecasting methods to date.
In recent years, intuitionistic fuzzy theory has gained significant attention for its ability to handle uncertainty through both membership and non-membership degrees. This paper presents a novel modification to gamma...
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In recent years, intuitionistic fuzzy theory has gained significant attention for its ability to handle uncertainty through both membership and non-membership degrees. This paper presents a novel modification to gamma-operator technique by introducing distinct gamma-operators for membership and non-membership functions to tackle intuitionistic fuzzy environment. The proposed technique is rigorously validated through the proof of relevant theorems that demonstrate its capability to derive efficient solutions for multi-objective nonlinear programming problems, where all parameters are represented as triangular intuitionistic fuzzy numbers. To elucidate the proposed technique, an illustrative example is presented. Furthermore, a comparative study with existing techniques is conducted, which highlights the superior performance of the proposed method. Finally, an application in the agriculture sector demonstrates the practical relevance and effectiveness of the proposed method in real-world scenarios.
Recently, due to global climate change and population growth, environmental protection has become more interested. Water is the main critical issue because it is the most significant environmental resource. Therefore,...
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Recently, due to global climate change and population growth, environmental protection has become more interested. Water is the main critical issue because it is the most significant environmental resource. Therefore, this study introduces a novel approach to examine, modeling, and addressing the monitoring of water quality (WQ) critical scenario related to unexpected extreme variations of crucial indicators (UEVCI). Therefore, this research integrates ensemble machine learning (EML) techniques with Non-linear programming (NLP) and Simulated annealing algorithm (SAA) to develop an optimal weighted ensemble models. New development models were nonlinear-programmed ensemble machine learning (NLEML) and simulated annealing ensemble machine learning (SAEML). Besides, we developed least-squared boosted regression tree (LsBRT), artificial neural network (ANN), and multiple linear regression (MLR) models individually to compare the performance of new ensemble models. The South Platte River Basin in Colorado, USA was the study region. The initial dataset was extracted through the United States Geologic Survey (USGS) from 2023 to 2024. Preprocessing approaches such as cleaning missing data (CMD), cleaning outlier data (COD), and k-fold cross validation (KFCV) with k = 5 were used to prepare the dataset. The final dataset was utilized to examine variations of essential parameters that affect water health and quality, including the power of hydrogen (pH) and dissolved oxygen (DO). The results showed that the NLEML provided the most accurate results in estimating fluctuation of pH parameter with an R2 coefficient of 0.85. Also, the NLEML estimated the variance of the DO parameter with an R2 equal to of 0.79, resulting in an outperforming simulation.
This research introduces a novel approach to optimize the distribution of relief items in post-disaster scenarios. Unlike traditional approaches, the model integrates dynamic demand and supply considerations, accounti...
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This research introduces a novel approach to optimize the distribution of relief items in post-disaster scenarios. Unlike traditional approaches, the model integrates dynamic demand and supply considerations, accounting for fluctuations in population movement within relief camps. The model uniquely incorporates the impact of disaster severity and risk on transportation costs, enabling a more realistic assessment of logistical challenges. By minimizing total costs, including transportation, inventory, and disposal of damaged items, our approach ensures cost-efficient distribution without compromising service delivery levels. A numerical example with a post-disaster relief distribution scenario demonstrates the model's practical application. Sensitivity analysis with multiple parameters confirms the model's robustness and potential to guide efficient and effective humanitarian relief operations. This study contributes to the field of relief chain optimization by offering a comprehensive framework that accounts for several factors influencing disaster logistics. The research highlights the model's potential as a valuable tool for improving the efficiency and effectiveness of humanitarian relief efforts.
Optimization plays a critical role in fields such as economics, engineering, and computational sciences, where finding the optimal values of decision variables is essential for the design of a product, production syst...
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Optimization plays a critical role in fields such as economics, engineering, and computational sciences, where finding the optimal values of decision variables is essential for the design of a product, production system, or service system. However, many optimization problems remain challenging even with advanced solvers. This study integrates machine learning into optimization by employing a regression tree algorithm that is trained on sampled solutions of the problem to improve the efficiency of derivative-free nonlinear programming solvers. The approach is tested on 24 single-objective functions to reduce the domain of decision variables. The results demonstrate better accuracy and consistency in the solver performance. Incorporating a machine learning technique into an optimization method can be extended to solve black-box optimization problems and paves the way for innovative solutions in engineering design and other domains.
The paper presents an analytical approach to the optimization of the topological configuration of a set of functional blocks. By expressing the overlapping of blocks as an inequality constraint in a formulation based ...
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The paper presents an analytical approach to the optimization of the topological configuration of a set of functional blocks. By expressing the overlapping of blocks as an inequality constraint in a formulation based on nonlinear optimization, the blocks can move freely without any overlap during the optimization process. Some special characteristics of this problem, which prohibit the direct application of standard search methods for optimization, have been investigated. On the basis of this analysis, a search algorithm is developed. The proposed method allows fully automated design and exhibits computationally efficient convergence to local optima from random initial designs.
In this paper, we use nonlinear programming to provide an alternative treatment of the economic order quantity problem with planned backorders. Many businesses, such as capital-goods firms that deal with expensive pro...
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In this paper, we use nonlinear programming to provide an alternative treatment of the economic order quantity problem with planned backorders. Many businesses, such as capital-goods firms that deal with expensive products and some service industries that cannot store their services, operate with substantial backlogs. In practical problems, it is usually very difficult to estimate accurately the values of the two types of backorder costs, i.e., the time-dependent unit backorder cost and the unit backorder cost. We redefine the original problem without including these backorder costs and construct a nonlinear programming problem with two service measure constraints which may be easier to specify than the backorder costs. We find that, with this different formulation of our new problem, we obtain results which give implicit estimates of the backorder costs. The alternative formulation provides an easier-to-use model and managerially meaningful results. Next, we show that, for a wide range of parameter values, it usually suffices to consider only one type of backorder cost, or equivalently, only one type of service measure constraint. Finally, we develop expressions which bracket the optimal values of the decision variables in a narrow range and provide a simple method for computing the optimal solution. In the most complicated case, this method requires finding the unique root of a polynomial.
In this paper, we present nonlinear programming methods for capacity planning in a manufacturing system that consists of a set of machines or work stations producing multiple products. We model the facility as an open...
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In this paper, we present nonlinear programming methods for capacity planning in a manufacturing system that consists of a set of machines or work stations producing multiple products. We model the facility as an open network of queues where capacity at each work station in the system may be changed in each of a finite number of time periods. To determine the timing and size of capacity changes, we present two nonlinear programming models and methods for solving the resulting problems. One model involves minimizing total capacity costs such that plant congestion is controlled via upper limits on work-in-process. The other model involves minimizing a weighted sum of product lead times subject to budget constraints on capacity costs. We present solution methods for continuous and discrete capacity options and convex and nonconvex (e.g., economies of scale) capacity cost functions. We use branch and bound and outer approximation techniques to determine globally optimal solutions to the nonconvex problems. Computational testing of the algorithms is reported.
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