Combination Of Linear Programming Research Articles

Indonesian Islamic Banks’ Post-Pandemic Profitability Alignment with Sustainability Goals

Purpose — The Islamic banking sector in Indonesia has recovered from the COVID-19 pandemic. Despite the sector’s resilience to the crisis, Islamic banks have lower levels of profitability than their conventional counterparts. They therefore need to optimise profitability as well as implement sustainability practices due to their high reliance on income from intermediary functions and their core principle of protecting life and preventing harm. This study aims to optimise the post-pandemic profitability of Indonesian Islamic banks while seeking to align profitability with sustainable development goals (SDGs). Design/Methodology/Approach — The model is developed based on the combination of linear programming and financial analysis, with constraints considering banking regulations and practices. The model highlights financing product types, financing fields of business, and types of deposit products that contribute to higher profits and support sustainability practices. Findings — The recommended financing product types, financing fields of business, and deposit product types lead to profitability growth of up to 85 per cent, even better than the pre-pandemic performance of Islamic banking and the overall Indonesian banking industry. This research also finds that Islamic banks’ strategy for profit optimisation is consistent with the increasing support for SDGs. Originality/Value — There is a lack of literature discussing the alignment of Islamic banks’ profit optimisation with the SDGs. This study contributes to the literature by aligning the Islamic banks’ profitability strategy with sustainable financing. Research Limitations/Implications — The study is expected to promote the implementation of sustainability practices as well as increase the profitability of Islamic banks, drawing from evidence in Indonesia.

Efficient parallel scheduling with power control and successive interference cancellation in wireless sensor networks

In wireless sensor networks (WSNs), spectrum efficiency and energy efficiency are two challenging issues to be addressed due to the scarcity of spectrum and its limited energy. In this paper, we study efficient parallel scheduling with power control and successive interference cancellation (SIC) aimed at maximizing spectrum and energy efficiency, and formulate the efficiency maximization scheduling (EMS) problem as a mixed integer nonlinear programming (MINIP) problem. Given the high complexity caused by mixed variables, we first analyze the critical variables affecting efficiency to transform the MINIP problem into a combinatorial linear programming (LP) problem, which is further decomposed into two linear minimization problems. Then, a recursive relationship between the optimization variables is explored, through which we propose a two-stage scheme that solves the minimization problems with lower complexity. Finally, distributed implementation of the scheduling scheme is provided to iteratively explore the maximum feasible concurrent links in the networks. Extensive results demonstrate the superior performance of our proposed scheme in improving spectrum and energy efficiency. Specifically, our scheme achieves 12.16% to 66.15% higher spectrum efficiency and 18.84% to 88.20% higher energy efficiency than the existing schemes.

Panic and propagation in 1873: A network analytic approach

We assess systemic risk in the U.S. banking system before and after the Panic of 1873, using a combination of linear programming and computational optimization to estimate the interbank network. We impose liquidity shocks resembling those of 1873, and find the network captures the distribution of interbank deposits a year later. The network predicts banks likely to panic in the crisis, and those banks see their balance sheets weaken in the year after the crisis more than others. The results shed light on the nature and regional pattern of withdrawals in a classic 19th-century U.S. financial crisis.

Intuitionistic propositional probability logic

AbstractWe give a sound and complete axiomatization of a probabilistic extension of intuitionistic logic. Reasoning with probability operators is also intuitionistic (in contradistinction to other works on this topic), i.e., measure functions used for modeling probability operators are partial functions. Finally, we present a decision procedure for our logic, which is a combination of linear programming and an intuitionistic tableaux method.

MAGEMin, an Efficient Gibbs Energy Minimizer: Application to Igneous Systems

AbstractPrediction of stable mineral equilibria in the Earth's lithosphere is critical to unravel the tectonomagmatic history of exposed geological sections. While the recent advances in geodynamic modeling allow us to explore the dynamics of magmatic transfer in solid mediums, there is to date no available thermodynamic package that can easily be linked and efficiently be accounted for the computation of phase equilibrium in magmatic systems. Moreover, none of the existing tools fully exploit single point calculation parallelization, which strongly hinders their applicability for direct geodynamic coupling or for thermodynamic database inversions. Here, we present a new Mineral Assemblage Gibbs Energy Minimizer (magemin). The package is written as a parallel C library, provides a direct Julia interface, and is callable from any petrological/geodynamic tool. For a given set of pressure, temperature, and bulk‐rock composition magemin uses a combination of linear programming, extended Partitioning Gibbs Energy and gradient‐based local minimization to compute the stable mineral assemblage. We apply our new minimization package to the igneous thermodynamic data set of Holland et al. (2018), https://doi.org/10.1093/petrology/egy048 and produce several phase diagrams at supra‐solidus conditions. The phase diagrams are then directly benchmarked against thermocalc and exhibit very good agreement. The high scalability of magemin on parallel computing facilities opens new horizons, for example, for modeling reactive magma flow, for thermodynamic data set inversion, and for petrological/geophysical applications.

Dairy cattle nutrition and feed calculator-an android application.

The dairy farmers in the tropical region have limitations regarding "feedstuffs" available. A software program is required to meet the nutrient requirement with the available feeds at the cheapest cost. There are many techniques/software that are implemented to meet dairy animal nutrient requirements. However, there are no specific techniques that are appropriate for formulating rations for dairy cattle in the least cost manner. An android application Dairy Cattle Nutrition and Feed Calculator (DCN & FC) is developed with an aim of least-cost feed balancing system which will help a farmer to choose feeds to meet the nutrient requirement set for ration. The quantity of feed, fed to the cattle to meet the required needs, is determined by nutrient content, dry matter intake (DMI), crude protein (CP) and total digestible nutrients (TDN) of the feed. This program works in three phases. The first phase consists of creating equations for the estimation of DMI, TDN and CP based on the nutrient tables provided by ICAR (2013) for different categories of cattle such as Milch cattle, dry cattle, male calves, female calves and pre-ruminant animals for different physiological stages such as body weight, average daily gain, fat and milk, using regression analysis. The second phase is the development of a linear programming (LP) model to solve different cases of nutrient requirements for least cost. The final phase is the use of the android framework that uses linear programming to suggest sufficient feed that meets the nutrient requirements. The developed application is user friendly and available in different languages. With the combination of linear programming and regression analysis, the ration can be effectively formed using available feeds, and control the cost of the feeds.

Decision-Making Process in the Circular Economy: A Case Study on University Food Waste-to-Energy Actions in Latin America

Economies have begun to shift from linear to circular, adopting, among others, waste-to-energy approaches. Waste management is known to be a paramount challenge, and food waste (FW) in particular, has gained the interest of several actors due to its potential impacts and energy recovery opportunities. However, the selection of alternative valorization scenarios can pose several queries in certain contexts. This paper evaluates four FW valorization scenarios based on anaerobic digestion and composting, in comparison to landfilling, by applying a consistent decision-making framework through a combination of linear programming, Life Cycle Thinking (LCT), and Analytic Hierarchy Process (AHP). The evaluation was built upon a case study of five universities in Costa Rica and portrayed the trade-offs between environmental impacts and cost categories from the scenarios and their side flows. Results indicate that the landfill scenario entails higher Global Warming Potential and Fresh Water Eutrophication impacts than the valorization scenarios; however, other impact categories and costs are affected. Centralized recovery facilities can increase the Global Warming Potential and the Land Use compared to semi-centralized ones. Experts provided insights, regarding the ease of adoption of composting, in contrast to the potential of energy sources substitution and economic savings from anaerobic digestion.

Vanishing of Littlewood–Richardson polynomials is in P

J. DeLoera-T. McAllister and K. D. Mulmuley-H. Narayanan-M. Sohoni independently proved that determining the vanishing of Littlewood-Richardson coefficients has strongly polynomial time computational complexity. Viewing these as Schubert calculus numbers, we prove the generalization to the Littlewood-Richardson polynomials that control equivariant cohomology of Grassmannians. We construct a polytope using the edge-labeled tableau rule of H. Thomas-A. Yong. Our proof then combines a saturation theorem of D. Anderson-E. Richmond-A. Yong, a reading order independence property, and E. Tardos' algorithm for combinatorial linear programming.

Panic and Propagation in 1873: A Computational Network Approach

We assess systematic risk in the U.S. banking system before and after the Panic of 1873 using a combination of linear programming and computational optimization to estimate the interbank network based upon total gross and net positions of national banks a week before the crisis. We impose various liquidity shocks resembling those of 1873, and find the network can capture the distribution of interbank deposits a year later. The network may be used to predict banks likely to panic (i.e., change reserve agent) in the crisis. The identified banks see their balance sheets weaken in the year after the crisis more than other banks. The results shed light on the nature and regional pattern of withdrawals that may have occurred in a classic 19th century U.S. financial crisis.

A fast numerical method for optimal coordination of overcurrent relays in the presence of transient fault current

In the past, many studies have been conducted on the optimal coordination of overcurrent (OC) relays. In these studies, the short-circuit current flowing through the relays is considered as fixed. In a real power network, however, for various reasons including the transient behaviour of synchronous generators, the short-circuit current that flows through the relays varies with time. Therefore, the relay settings, which are obtained with the assumption of a constant, short-circuit current, may cause miss-coordination under real conditions. In this study, a numerical method based on the dynamic model of the relay is presented using linear programming (LP) to take account of the effect of transient currents on the coordination of OC relays. The method can be used as a fast numerical algorithm in the computing software to obtain accurate settings for OC relays in the presence of various transient fault currents in the network. Furthermore, the combination of LP with a genetic algorithm is used to incorporate the current setting of the relays in the transient coordination algorithm to achieve a better operating time for the relays. The results show the effectiveness of the proposed algorithms as compared with the previously presented method.

Iron ores matching analysis and optimization for iron-making system by taking energy consumption, CO2 emission or cost minimization as the objective

An optimization model for iron-making system covering sinter matching process to blast furnace process is established, in which the energy consumption, CO2 emission and cost minimizations are taken as optimization objectives. Some key constraints are considered according to practical production experience in the modelling. The combination of linear programming (LP) and nonlinear programming (NLP) methods is applied. The optimal sinter matching scheme under given conditions and the optimization results for different objectives are obtained. Effects of sinter grade and basicity on all the optimal objectives and coke ratio in blast furnace process are analyzed, respectively. The results obtained indicate that compared with the initial values, the energy consumption/CO2 emission of iron-making system decreases by 2.03% for objectives of energy consumption/CO2 emission minimizations and 1.89% for the objective of cost minimization, the cost decreases by 17.88% and 18.13%, respectively. All the three criteria decrease with the increasing lump usage, coal powder injection, blast temperature, and decreasing coke ratio for the iron-making system.

Development of die-bonding film by nano-structure control and mathematical optimization

Die-bonding film is widely applied for semiconductor package of electronic equipments. The die-bonding film based on epoxy resin/acrylic polymer system shows the higher flexibility to thermal stress during the heat cycle test compared with epoxy–matrix systems. In addition, properties of the film vary widely depending on the ratio of epoxy resin and acrylic polymer contents. To satisfy the target properties, novel material design method named weak conditioned combinatorial linear programming was developed.

Globally Optimal Superconducting Homogeneous Magnet Design for an Asymmetric 3.0 T Head MRI Scanner

This paper describes a hybrid numerical method with a combination of linear programming (LP) and nonlinear programming to design an asymmetric magnetic resonance imaging (MRI) system for head imaging. The method was successfully employed to a 0.7 T open-style MRI. Recently, we have updated the LP mathematical model in the hybrid optimization strategy by adding maximum axial and radial magnetic field strength limitation to make the magnet safe. The whole magnet consists of eight coaxial coils asymmetry about the z-axis. The six innermost coils are the main coils with positive current direction and the two outermost coils are shielding coils with negative current direction. All coils contribute their efforts to produce a central magnetic field strength of 3.0 T over a 20 cm diameter of spherical volume, and the peak to peak homogeneity is 12 ppm. The temperature bore for the magnet is formed with two different diameters cylindrical bores which makes head and shoulder access to the imaging volume easier. A 5 Gauss footprint area is restricted larger than an elliptical region with the sizes of 3.5 m in radial and 4.0 m in axial direction. The results show that the methodology is very flexible and efficient for asymmetric MRI magnet design.

On Chubanov's Method for Linear Programming

We discuss the method recently proposed by S. Chubanov [Chubanov S (2012a) A strongly polynomial algorithm for linear systems having a binary solution. Math. Programming 134(3):533–570] for the linear feasibility problem. We present new, concise proofs and geometric interpretations of some of his results. From our ideas we derive the first strongly polynomial time algorithm based on relaxation method techniques for special classes of linear feasibility problems. Under certain conditions, these results provide new proofs of classical results obtained by Tardos for combinatorial linear programs. The paper ends with some experimental investigations.

弱条件組合せ線形計画法による材料の時系列特性の最適化＊1, ＊2

弱条件組合せ線形計画法による材料の時系列特性の最適化＊1, ＊2

Absolute Value Constraint Based Method for Interval Optimization to SCED Model

This letter proposes an absolute value based method to solve the interval linear optimization problem with application to security constrained economic dispatch (SCED). To avoid expensive computation associated with solving combinatorial linear programming (LP) problems for interval upper bound, firstly a bilinear programming model is formulated using duality theory. The equality constraints are then converted to absolute value constraints. Lastly, the absolute value operator is eliminated through introducing two nonnegative slack variables and complementary slackness condition. The resulting new bilinear programming model can be effectively solved by the branch and bound method with linear relaxation technique to obtain the global optimal solution. Numerical results demonstrate the effectiveness of the proposed method in improving solution and computation time.

Application of weighted goal programming in the optimization of rations for sport horses

The objective of this study was to develop a model to assess economical and balanced feed rations for sport horses. It was based on non- parametric modelling with a combination of linear programming and a multi- objective optimization technique: weighted goal programming. The method incorporates multiple goals into optimization. The technical data presented the feed nutritional value (dry matter, metabolizable energy, metabolizable pro- tein, etc.) and nutritional requirements, which were represented in weighted goal programming as targets. The model was tested for optimization of winter and summer feed rations, where summer feed rations are more expensive but more balanced. Optimization results show that the method of weighted goal programming is more accurate and useful in practice compared with linear pro- gramming, where only one goal (economically optimal feed rationing) is taken into account.

A homogeneous superconducting magnet design using a hybrid optimization algorithm

This paper employs a hybrid optimization algorithm with a combination of linear programming (LP) and nonlinear programming (NLP) to design the highly homogeneous superconducting magnets for magnetic resonance imaging (MRI). The whole work is divided into two stages. The first LP stage provides a global optimal current map with several non-zero current clusters, and the mathematical model for the LP was updated by taking into account the maximum axial and radial magnetic field strength limitations. In the second NLP stage, the non-zero current clusters were discretized into practical solenoids. The superconducting conductor consumption was set as the objective function both in the LP and NLP stages to minimize the construction cost. In addition, the peak–peak homogeneity over the volume of imaging (VOI), the scope of 5 Gauss fringe field, and maximum magnetic field strength within superconducting coils were set as constraints. The detailed design process for a dedicated 3.0 T animal MRI scanner was presented. The homogeneous magnet produces a magnetic field quality of 6.0 ppm peak–peak homogeneity over a 16 cm by 18 cm elliptical VOI, and the 5 Gauss fringe field was limited within a 1.5 m by 2.0 m elliptical region.

A hybrid optimization approach to design of compact self-shielded super conducting magnetic resonance imaging magnet system

A hybrid optimization approach with a combination of linear programming and nonlinear programming algorithm for designing a compact self-shielded magnetic resonance imaging (MRI) superconducting magnet system is presented. The designed coils possess advantages of low construction costs, simple coil structure and the maximum magnetic strength within coils, current margin and electromagnetic stress easy to control. Firstly, in the stage of linear programming optimization, the feasible rectangular region can be divided into two-dimensional meshes, and a current map is calculated for meeting the magnetic field constraints over the surfaces of DSV sphere and 5 gauss stray field ellipse; Secondly, the current map has many nonzero current clusters and each cluster can be discretized into a solenoid. A nonlinear programming algorithm is employed to optimize the positions of all solenoids for minimizing the total coil volume and meeting all constraints including magnetic field which is the same as linear programming stage, and maximum magnetic strength, current margin and the gap between neighborhood inner coils. A 1.5 T compact self-shielded MRI superconducting magnet system is studied, the total coil length is only 1.32 m and the peak-peak homogeneity over 50 cm DSV is 10 ppm. The design approach is flexible and efficient for designing symmetrical and asymmetrical horizontal MRI and also open bi-planar MRI system.

Formulation Design and Risk Mapping by Weak Conditioned Combinatorial Linear Programming

Formulation Design and Risk Mapping by Weak Conditioned Combinatorial Linear Programming