This study investigates the problem of determining the minimum distance between two smooth 3D curves. We propose its solution on the basis of the newly formulated idea of Charged Balls Method. The main idea behind thi...
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ISBN:
(纸本)9783031734199;9783031734205
This study investigates the problem of determining the minimum distance between two smooth 3D curves. We propose its solution on the basis of the newly formulated idea of Charged Balls Method. The main idea behind this method is the physical model which tends to the solution of the original problem. We can derive equations of motion for the system and apply the difference scheme for the solution of the obtained ordinary differential equations. This is the way we get an iterative algorithm for the initial problem. We employ Lyapunov theory of stability to prove the convergence of the proposed method. More specifically, the Barbashin-Krasovskii theorem on asymptotic stability is employed. The convergence rate of the algorithm is analyzed and the corresponding results are presented. We provide a number of numerical examples to demonstrate its work. These examples acknowledge the effectiveness of the method and stresses that the choice of the parameters one may significantly affect the convergence rate.
mathematical programming has been proposed in the literature as an alternative technique to simulating a special class of Discrete Event Systems. There are several benefits to using mathematical programs for simulatio...
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mathematical programming has been proposed in the literature as an alternative technique to simulating a special class of Discrete Event Systems. There are several benefits to using mathematical programs for simulation, such as the possibility of performing sensitivity analysis and the ease of better integrating the simulation and optimisation. However, applications are limited by the usually long computational times. This paper proposes a time-based decomposition algorithm that splits the mathematical programming model into a number of submodels that can be solved sequentially to make the mathematical programming approach viable for long running simulations. The number of required submodels is the solution of an optimisation problem that minimises the expected time for solving all of the submodels. In this way, the solution time becomes a linear function of the number of simulated entities. (c) 2013 Elsevier B.V. All rights reserved.
This paper presents a quantitative approach to hedging financial risks associated with changes in international oil prices for companies that import crude oil. The authors utilize the Geometric Brownian Motion model t...
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This paper presents a quantitative approach to hedging financial risks associated with changes in international oil prices for companies that import crude oil. The authors utilize the Geometric Brownian Motion model to capture the dynamic behavior of prices over time. To determine the optimal use of Call-options, the authors formulate a linear problem that minimizes the Conditional Value-at-Risk of the distribution of losses relative to the expected budget. The solution to this problem is obtained through a combination of Linear programming optimization and Monte Carlo simulation. It enables the identification of the best Call-option offer that minimizes the risk of financial losses while staying within budget constraints. The validity of the proposed methodology is demonstrated through detailed examples that showcase its capabilities.
mathematical programming can be used for the optimal design of shell-and-tube heat exchangers (STHEs). This paper proposes a mixed integer non-linear programming (MINLP) model for the design of STHEs, following rigoro...
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mathematical programming can be used for the optimal design of shell-and-tube heat exchangers (STHEs). This paper proposes a mixed integer non-linear programming (MINLP) model for the design of STHEs, following rigorously the standards of the Tubular Exchanger Manufacturers Association (TEMA). Bell-Delaware Method is used for the shell-side calculations. This approach produces a large and non-convex model that cannot be solved to global optimality with the current state of the art solvers. Notwithstanding, it is proposed to perform a sequential optimization approach of partial objective targets through the division of the problem into sets of related equations that are easier to solve. For each one of these problems a heuristic objective function is selected based on the physical behavior of the problem. The global optimal solution of the original problem cannot be ensured even in the case in which each of the sub-problems is solved to global optimality, but at least a very good solution is always guaranteed. Three cases extracted from the literature were studied. The results showed that in all cases the values obtained using the proposed MINLP model containing multiple objective functions improved the values presented in the literature. (C) 2013 Elsevier Ltd. All rights reserved.
In the past two decades, various process integration methods have been proposed for the optimum synthesis of resource conservation networks, for the recovery of energy and material resources such as water, gas, and so...
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In the past two decades, various process integration methods have been proposed for the optimum synthesis of resource conservation networks, for the recovery of energy and material resources such as water, gas, and solvent. In this paper, a mathematical programming framework is proposed for simultaneous optimization of mass exchanger networks (MENs) and direct reuse/recycle networks (DRNs). Both MEN and DRN synthesis are now relatively established fields with numerous methods developed. Note however that there is a lack of work that considers both areas simultaneously. The newly proposed simultaneous optimization method in this work identifies opportunity for a DRN that allows the material waste to be recycled within the MEN, which is the main novelty of this work. The approach is demonstrated with a vinyl acetate monomer production problem. The latter consists of several mass exchange operations, in which an MEN is synthesized. Opportunity to develop an DRN is also identified, which allows its waste to be recycled without regeneration. Another novelty of the work is that, the supply and target compositions in the MEN problem are expressed in terms of mass ratios as the compositions are relatively large. Results show that the integrated network has a total annualized cost that is reduced by 24.9% as compared to the base case configuration, where both networks were solved independently. This shows the importance of considering entire process systems during process synthesis, as opposed to subsystems independently.
PurposeDue to mitigate against natural disasters like earthquake and to distribute relief items, designing humanitarian relief chain networks is an attentional issue. Agile and efficient distribution of relief items a...
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PurposeDue to mitigate against natural disasters like earthquake and to distribute relief items, designing humanitarian relief chain networks is an attentional issue. Agile and efficient distribution of relief items after occurring a disaster is significant, especially when some of the relief items are perishable. Therefore, the purpose of this paper is to create a resilient and integrated decision-making structure to distribute relief items at demand points, considering two dimensions of sustainability, under ***/methodology/approachThis study developed a mixed-integer nonlinear mathematical model to handle the pre- and post-disaster planning when a disaster occurs. The represented model has two objective functions: minimizing weighted unmet demand and total costs. Therefore, to convert this multi-objective problem into a single objective one, the e-constraint method was *** main results showed that considering some resilience strategies has a significant effect in reducing the weighted amount of unmet demand and saves the total costs. More precisely, considering resilience strategies results in a 60% reduction in total unmet demand and 11% reduction in total pre-positioning costs. On the other hand, reducing the maximum response time with applying resilience strategies is another achievement of the present study. For these reasons, the use of these strategies can reduce people's pain and suffer from natural disasters. In general, the application and effectiveness of sustainability dimensions and resilience strategies in the introduced humanitarian relief chain network were *** implicationsTo verify the applicability of this study, this model is applied on a probable real-life case study in Tehran. Finally, some managerial insights are discussed to help humanitarian organizations, managers and stakeholders to make better decisions to reduce negative effects of natural ***/valueThis paper introduced a tw
We study an optimal matching problem in the context of dual-donor organ exchange, where a portion of two living donors' organs are transplanted to a single patient. This dual-donor transplant technique is becoming...
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We study an optimal matching problem in the context of dual-donor organ exchange, where a portion of two living donors' organs are transplanted to a single patient. This dual-donor transplant technique is becoming more widespread for lung and liver transplants. However, multiple medical compatibility criteria pose a serious challenge for matching a patient with two compatible donors. In the United States and many other countries, laws prohibit commercial (for-profit) deals for human organs, so donor exchanges are run by nonprofit organizations connecting donors with people in need of organs, with the goal of increasing transplant matches. We propose a simple chain mechanism in dual-donor organ exchange to increase the number of patient-dual-donor matches, which would maximize the number of patients receiving transplants. Based on this objective, we propose a general simple chain optimization framework for finding the maximum patient matching, taking into account multiple compatibility criteria (e.g., blood type and weight), and determine the complexity status of the problem. We provide theoretical results on the structures of simple chains, as well as a polynomial time algorithm to obtain the maximum patient matching simple chain with blood type compatibility. Through a numerical study for multiple compatibility criteria, we show that in many scenarios, a simple chain substantially increases the number of patients matched with dual donors for transplants, as opposed to exchange cycles. We also address the problem of maximizing the number of patients matched for dual-donor organ transplants via two-way and three-way exchange cycles, subject to donors' and recipients' medical compatibility criteria, along with a discussion of their computational complexity. Finally, we characterize the general configurations of large n-way exchange cycles and provide theoretical insights for their structural properties. Our findings provide general optimization models for dual-donor o
Surrogate modeling can overcome computational and data-privacy constraints of micro-scale economic models and support their incorporation into large-scale simulations and interactive simulation experiments. We compare...
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Surrogate modeling can overcome computational and data-privacy constraints of micro-scale economic models and support their incorporation into large-scale simulations and interactive simulation experiments. We compare four data-driven methods to reproduce the aggregated crop area response simulated by farm-level modeling in response to price variation. We use the isometric log-ratio transformation to accommodate the compositional nature of the output and sequential sampling with stability analysis for efficient model selection. Extreme gradient boosting outperforms multivariate adaptive regressions splines, random forest regression, and classical multinomial-logistic regression and achieves high goodness-of-fit from moderately sized samples. Explicitly including ratio terms between price input variables considerably improved prediction, even for highly automatic machine learning methods that should in principle be able to detect such input variable interaction automatically. The presented methodology provides a solid basis for the use of surrogate modeling to support the incorporation of micro-scale models into large-scale integrated simulations and interactive simulation experiments with stakeholders.
This article presents a multi-start heuristic approach to a design problem motivated by a real-world application in the Italian transport system. Specifically, it focuses on the problem of designing optimal lots in th...
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This article presents a multi-start heuristic approach to a design problem motivated by a real-world application in the Italian transport system. Specifically, it focuses on the problem of designing optimal lots in the public transport organization. In defining lots (in terms of number, size, and boundaries) both cost and service level have to be considered. Under certain assumptions, we model the problem as a graph partitioning problem and consider the same performance measure indicated by the relevant decree-law enacted by the Italian Ministry of Transport. The multi-start algorithm proposed for individuating high-quality solutions for the problem uses adaptive large neighbourhood search. The results of a computational study based on real data from a region in Southern Italy are reported.
According to the EU's organic regulation, the use of organic seed is generally binding in organic farming. Because of an organic seed shortage, derogations to use nonorganic seed can be obtained. By 2036, the EU p...
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According to the EU's organic regulation, the use of organic seed is generally binding in organic farming. Because of an organic seed shortage, derogations to use nonorganic seed can be obtained. By 2036, the EU plans to phase out these derogations and achieve 100% organic seed use. Previous attempts at achieving this, though, have failed. Ensuring organic seed supply is of particular EU-wide importance to meet EU policy goals, such as the farm-to-fork strategy. To assess the impact of measures to smooth this transition, we developed the VAL-MAS model (VALue chain Multi-Agent System). VAL-MAS is a multiagent model based on a heterogeneous agent population and mathematical programming that can provide insights into the performance of different seed system interventions. We selected organic fresh market carrots in Germany for their importance in the national and European organic sector as an example case. Our model suggests that the end of the derogation system poses a challenge to the seed value chain in terms of seed supply and farm incomes. The most effective mitigation solution is an investment in improved pest control during seed multiplication, accompanied by a stepwise phasing out of derogations for the use of nonorganic seed.
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