Linear Programming Software Research Articles

Optimized design of a 4-bits absolute-value detector based on linear programming

Nowadays, neural signal acquisition systems are constantly developing, and spike classification algorithms have been widely studied and concerned. This paper designed a practical spike detection circuit, named as the absolute value detector. Project is committed to adopting only simple gate circuits, by using Morgans theorem to optimize the circuit structure, so that we can ensure that the detector has the following several advantages, simple and beautiful, easy to understand, powerful performance. In addition, this paper also considered the optimization of performance including latency and energy consumption. The increase of Vdd will increase the energy consumption and reduce the delay, while the increase of size will reduce the energy consumption and increase the delay. Using MATLAB software for linear programming, Under the condition of a 1.5-fold increase in the delay, and then adjusting Vdd and size, energy consumption was down by 78 percent.

Trapping Sets Search Using the Method of Mixed Integer Linear Programming with a Priori List of Variable Nodes

Purpose of research is to develop a new high-speed method for searching trappin sets in graph codes, ensuring the completeness of the search.Methods. There are two approaches to finding trappin sets. The first, based on the Monte Carlo method with a biased probability estimation using Importance Sampling, involves the use of a decoder. The advantage of this approach is its high performance. The disadvantages are the dependence on decoder parameters and channel characteristics and the finite probability of missing trappin sets. The second approach is based on the use of linear programming methods. The advantage of this approach is the completeness of the resulting list of trappin sets, due to its independence from the decoder parameters and channel characteristics. The disadvantage of this approach is its high computational complexity. In the article, within the framework of the second approach, a new method for searching trappin sets with less computational complexity is proposed. The method involves solving a mixed integer linear programming problem using an a priori list of code vertices participating in the shortest cycles in the code graph. Results. Using the proposed method, a search for trappin sets was performed for several low-density codes. For this purpose, the mathematical linear programming package IBM CPLEX version 12.8 was used, which was run on 32 threads of a 16-core AMD Ryzen 3950X processor with 32GB of RAM (DDR4). In the Margulis code (2640, 1320), using the proposed method, the trappin set TS(6,6) was found in a time of 0.53 s. The speedup provided by the method proposed in the paper compared to the Velazquez-Subramani method is 8252.415 times. Thanks to the high speed and completeness of the search, trappin sets were found for the first time TS(62,16) and TS(52,14) in the Margulis code (4896, 2474 ).Conclusion. The paper proposes a new method for searching trapping sets by solving a mixed integer linear programming problem with an a priori list of code. The method is fast and provides completeness of the search.

Optimasi Sistem Panel Surya dan Baterai Untuk Meminimasi Biaya Pemakaian Energi Listrik Rumah Tangga

The use of photovoltaic-battery energy storage systems to meet the electrical energy needs of house while reducing the cost of using electricity from the grid is increasing. Optimization of this system can be performed by optimizing the energy consumption of solar panels and batteries. This paper presents the optimization of solar panels and battery systems to minimize the cost of using electricity from the grid. Circuit models and mathematical formulations of grid-connected solar panel and battery systems were created for optimization purposes. The optimization problem is solved by using mixed-integer linear programming and Pyomo software based on the Python programming language. Data on household electricity consumption and output power from two solar panels with a capacity of 100 Wp and 200 Wp respectively were obtained from the results of simultaneous measurements for one month. The test results show that the optimization of the solar panel and battery system can reduce the cost of electricity consumption at home with optimal energy consumption from solar panels and batteries.

Effective Crude Blending for Improving Naphtha Productivity Using Linear Programming Modelling

Petroleum refining is a complex industry that involves a wide spectrum of activities starting from crude purchase, transportation to refineries, refining operations, product transportation and delivering the product to end users. Its nature encounters complex economies that are heavily linked, that is, the selection of the right crude is not linked only to transportation costs, else related to considerations of refinery configuration, capabilities and constraints in converting the crude into products, as well as the product volumes and prices fluctuation modes. Solution packages based on Linear Programming (LP) technique have evolved through years among various mathematical optimization techniques, throughout the process routing from crude evaluation, selection, scheduling and product logistics planning. The objective of this study is to develop a mathematical programming model for solving a blending problem in a major refinery in Alexandria, Egypt with the objective of maximising Naphtha productivity. Refinery planning and optimization is basically addressed through special purpose linear programming software packages that remain a black box for the users and that are very cosdy for the organizations. The model developed in this work was proved to be highly effective at the level of solving the blending problem. This research has shown that the developed linear programming model for the blending problem has yielded better overall Naphtha productivity for the case of the oil refinery studied, as compared to results obtained by the commercial software.

Multi-level distributed demand response study for a multi-park integrated energy system

Current studies of integrated demand response (IDR) across multiple campuses often use centralized, unified scheduling with individual campuses as the object of analysis, which ignores the independent autonomy of users within the park. To this end, this paper proposes a multi-level distributed demand response model for a multi-park integrated energy system, which is solved using a combination of the particle swarm optimization algorithm and mixed integer linear programming software. First, based on the existing two-part tariff, an energy management system is introduced to construct a park integrated demand response market based on a noncooperative game. Second, load aggregators are used as a link to form an optimized alliance of multiple parks, and the overall economy of the coalition is optimized through a cooperative game of multiple parks. The model allows for coordination and complementarity based on zonal autonomy, maximizing economic efficiency and exploring the demand response capability of users. Finally, an arithmetic simulation is carried out with a scenario of a day-ahead electricity peaking auxiliary service market, and the results verify the feasibility and economics of the designed model in a scenario where multiple parks participate in integrated demand response.

Edukasi Mengoptimalkan Sumber Daya Untuk Pembuatan Noken Menggunakan Program Linier

Mr. Meki Murpa is a craftsman as well as a Noken seller with his address at Jalan F. Kalasuat, Sorong City, West Papua. Noken made from Balam bark. This business experienced ups and downs in its journey, this was due to the lack of manpower, Balam wood raw materials and also the Covid-19 pandemic storm. One of the factors that makes it increasingly difficult to find skilled workers in the manufacture of Noken and Balam wood raw materials because Sorong City is starting to experience a shift towards modernization, so there is a lot of development that makes it difficult to get Balam wood. For this reason, the Community Service (PkM) team at Victory Sorong University offers an educational program, namely providing training on optimizing resources for making noken using the Linear Program (PL) and POM-QM software for windows version 5.3. This PkM activity has been successfully implemented, this can be clearly seen by the high enthusiasm of the participants, the increased level of understanding of partners, and a positive impact on all stakeholders involved. The success of this PkM activity is a motivation for the team to continue to do the same thing and even improve the performance of the PkM team.

LINEAR PROGRAMMING MODEL OF PRODUCTION, INVENTORY AND DISTRIBUTION PROBLEM BASED ON RANDOM SAMPLING

A linear programming model using the random sampling technique was used for the optimization of the products in the production line of Nigerian Bottling Company (NBC), Lagos, which has many production facilities and multi-products systems. Products from the company are distributed to a number of depots across the country of which the demand for each product is known. The problem of interest involves determining what product/s should be made, how much of each product should be produced and where production should take place. The objectives of the company are to minimize the total cost as well as maximize the total sales revenue based on the set of decisions including demands, capacity restriction and budget constraints. Linear Programming software solver (LiPs) was used to solve large linear programming problems based on the mathematical model developed. The model improved the profit of the company under study by 5%.

A novel Lower Bound Limit Analysis model with hexahedron elements for the failure analysis of laboratory and thin infill masonry walls in two-way bending

A novel Lower Bound (LB) Limit Analysis (LA) Finite Element (FE) model for the study at failure of laboratory and thin infill masonry walls in two-way bending is presented. In the model, a masonry plate is discretized into infinitely resistant hexahedrons and quadrilateral interfaces where all plastic dissipation occurs. Three internal static variables act on interfaces, namely bending moment, out-of-plane Kirchhoff shear and torque. Equilibrium is imposed on hexahedrons, whereas admissibility is enforced exclusively on interfaces between adjoining elements. The admissibility of the internal actions on masonry interfaces is imposed with a strength domain obtained by means of an already existing LB homogenization technique where joints are reduced to interfaces. The resultant Linear Programming (LP) problem - which allows to estimate collapse loads and distribution of internal actions at collapse - is characterized by a number of variables and constraints relatively limited, which requires little computational burden to be solved through standard interior point LP software. Failure mechanisms are obtained solving the dual LP problem. The procedure is validated against two existing experimental datasets of walls in two-way bending, namely three series of panels with and without perforations tested at collapse at the University of Adelaide Australia and four series of solid and perforated panels tested at the University of Plymouth UK. Comparing the results in terms of collapse loads, active failure mechanisms and distribution of internal actions with both experimental evidence and previously presented numerical models, excellent results are found, an outcome showing the reliability of the procedure proposed.

A Mathematical Model for Nonlinear Optimization Which Attempts Membership Functions to Address the Uncertainties

The problem of optimizing an objective function that exists within the constraints of equality and inequality is addressed by nonlinear programming (NLP). A linear program exists if all of the functions are linear; otherwise, the problem is referred to as a nonlinear program. The development of highly efficient and robust linear programming (LP) algorithms and software, the advent of high-speed computers, and practitioners’ wider understanding and portability of mathematical modeling and analysis have all contributed to LP’s importance in solving problems in a variety of fields. However, due to the nature of the nonlinearity of the objective functions and any of the constraints, several practical situations cannot be completely explained or predicted as a linear program. Efforts to overcome such nonlinear problems quickly and efficiently have made rapid progress in recent decades. The past century has seen rapid progress in the field of nonlinear modeling of real-world problems. Because of the uncertainty that exists in all aspects of nature and human life, these models must be viewed through a system known as a fuzzy system. In this article, a new fuzzy model is proposed to address the vagueness presented in the nonlinear programming problems (NLPPs). The proposed model is described; its mathematical formulation and detailed computational procedure are shown with numerical illustrations by employing trapezoidal fuzzy membership functions (TFMFs). Here, the computational procedure has an important role in acquiring the optimum result by utilizing the necessary and sufficient conditions of the Lagrangian multipliers method in terms of fuzziness. Additionally, the proposed model is based on the previous research in the literature, and the obtained optimal result is justified with TFMFs. A model performance evaluation was completed with different set of inputs, followed by a comparison analysis, results and discussion. Lastly, the performance evaluation states that the efficiency level of the proposed model is of high impact. The code to solve the model is implemented in LINGO, and it comes with a collection of built-in solvers for various problems.

A Review on the Performance of Linear and Mixed Integer Two-Stage Stochastic Programming Software

This paper presents a tutorial on the state-of-the-art software for the solution of two-stage (mixed-integer) linear stochastic programs and provides a list of software designed for this purpose. The methodologies are classified according to the decomposition alternatives and the types of the variables in the problem. We review the fundamentals of Benders decomposition, dual decomposition and progressive hedging, as well as possible improvements and variants. We also present extensive numerical results to underline the properties and performance of each algorithm using software implementations, including DECIS, FORTSP, PySP, and DSP. Finally, we discuss the strengths and weaknesses of each methodology and propose future research directions.

Optimizing the effect of charging electric vehicles on distribution transformer using demand side management

This paper mainly aims to present the demand side management (DSM) of electric vehicles (EVs) by considering distribution transformer capacity at a residential area. The objective functions are formulated to obtain charging schedule for individual owner by i) minimizing the load variance and ii) price indicated charging mechanism. Both the objective functions profit the owner in the following ways: i) fulfilling their needs, ii) minimizing overall charging cost, iii) lessening the peak load, and iv) avoiding the overloading of distribution transformer. The proposed objective functions were worked on a residential area with 8 houses each house with an EV connected to a 20 kVA distribution transformer. The formulations were tested in LINGO platform optimization modeling software for linear, nonlinear, and integer programming. The results obtained were compared which gives good insight of EV load scheduling without actual price prediction.

Hospital selection in emergency medical service systems: A literature review

Emergency medical service (EMS) systems play a fundamental role in society by providing a vital service in initial emergency care. The purpose of this research is to present the first literature review of hospital selection operational decision within the context of the EMS system. The main findings were the following: The hospital selection problem is integrated with the location, dispatch, routing, and size of the ambulance fleet. The main selection criteria were closeness, hospital care capacities and the shortest queue or greatest number of free beds. The most used performance measures were the shortest transport and waiting time. Solution techniques include discrete event simulation, queuing models, mixed linear integer programming, and CPLEX and Arena software. The application of metaheuristics is scarce; mobile applications and Internet information systems have been implemented for real-time decision making. It is recommended that the design of hospital selection methods be implemented as well as the technological developments, considering the participation of the actors of the EMS system.

Hospital selection in emergency medical service systems: A literature review

Emergency medical service (EMS) systems play a fundamental role in society by providing a vital service in initial emergency care. The purpose of this research is to present the first literature review of hospital selection operational decision within the context of the EMS system. The main findings were the following: The hospital selection problem is integrated with the location, dispatch, routing, and size of the ambulance fleet. The main selection criteria were closeness, hospital care capacities and the shortest queue or greatest number of free beds. The most used performance measures were the shortest transport and waiting time. Solution techniques include discrete event simulation, queuing models, mixed linear integer programming, and CPLEX and Arena software. The application of metaheuristics is scarce; mobile applications and Internet information systems have been implemented for real-time decision making. It is recommended that the design of hospital selection methods be implemented as well as the technological developments, considering the participation of the actors of the EMS system.

Use of the Shor’s r-Algorithm in Linear Robust Optimization Problems

The paper is devoted to the description of a new approach to the construction of algorithms for solving linear programming problems (LP-problems), in which the number of constraints is much greater than the number of variables. It is based on the use of a modification of the r-algorithm to solve the problem of minimizing a nonsmooth function, which is equivalent to LP problem. The advantages of the approach are demonstrated on the linear robust optimization problem and the robust parameters estimation problem using the least moduli method. The developed octave programs are designed to solve LP problems with a very large number of constraints, for which the use of standard software from linear programming is either impossible or impractical, because it requires significant computing resources. The material of the paper is presented in three sections. In the first section for the problem of minimizing a convex function we describe a modification of the r-algorithm with a constant coefficient of space dilation in the direction of the difference of two successive subgradients and an adaptive method for step size adjustment in the direction of the antisubgradient in the transformed space of variables. The software implementation of this modification is presented in the form of Octave function ralgb5a, which allows to find or approximation of the minimum point of a convex function, or approximation of the maximum point of the concave function. The code of the ralgb5a function is given with a brief description of its input and output parameters. In the second section, a method for solving the LP problem is presented using a nonsmooth penalty function in the form of maximum function and the construction of an auxiliary problem of unconstrained minimization of a convex piecewise linear function. The choice of the finite penalty coefficient ensures equivalence between the LP-problem and the auxiliary problem, and the latter is solved using the ralgb5a program. The results of computational experiments in GNU Octave for solving test LP-problems with the number of constraints from two hundred thousand to fifty million and the number of variables from ten to fifty are presented. The third section presents least moduli method that is robust to abnormal observations or "outliers". The method uses the problem of unconstrained minimization of a convex piecewise linear function, and is solved using the ralgb5a program. The results of computational experiments in GNU Octave for solving test problems with a large number of observations (from two hundred thousand to five million) and a small number of unknown parameters (from ten to one hundred) are presented. They demonstrate the superiority of the developed programs over well-known linear programming software such as the GLPK package. Keywords: robust optimization, linear programming problem, nonsmooth penalty function, r-algorithm, least modulus method, GNU Octave.

A New Sustainable Warehouse Management Approach for Workforce and Activities Scheduling

Sustainable engineering is very important for logistics systems. Nowadays, sustainable warehouse management is a key factor in market success. Workforce fluctuation and inverting the number of customers’ demands make a lot of problems in distribution warehouses. This study addresses a sustainable approach for the workforce scheduling problem recognized in a real distribution warehouse. The problem arises from the high variability of demand for workers over one workday, which causes workforce surplus in some periods of the workday and shortages in others. Engineering managers of the distribution warehouse already use different full-time and part-time shifts, and schedule workers on different activities, but they still have significant workforce surpluses or shortages in some periods. This study proposes the scheduling of activities’ execution together with workers to face that variability and decrease the cost of the workforce. This idea comes from the fact that some activities in a distribution warehouse can be done in a specific time period after the need for them occurs. In this way, the variability of demand for workers can be decreased, and a lower workforce cost may be ensured. Based on this idea, the entire problem is modeled as integer linear programming. The real example of the problem is solved, and the proposed model is tested on randomly generated instances of the problem in Python by means of the PuLP linear programming package. The results indicate different positive effects in the manner of sustainable warehouse management: lower workforce costs, time savings, better utilization of all types of resources and equipment, increased employee satisfaction, and so on. For even 61% of instances of the introduced problem, the obtained cost of the workforce is lower by more than 20% if activities’ executions are scheduled together with employees.

Optimum Allocation of Water and Land Resource for Maximizing Farm Income of Jabalpur District, Madhya Pradesh

The continuous decrease and irrational use of water resources is the key issue for the proper application of water resources in tribal areas of Jabalpur district. This study attempts to propose a new aspect of optimum allocation of land & water resources in Jabalpur District. The minimum cultivated area that ensures food requirement and land constraint have a direct impact on water resources allocation. To conduct an accurate program for land and water resource allocation for water deficit area a multi- constraint linear programming model (LPM) was developed by implanting land resource as a constraint on water resources allocation which has to be considered by the demand of water resources in the agriculture sector. The result shows that increase in major crops area like rice, wheat, gram, maize and oilseeds crop areas against the reduction in sorghum, lentil, and sugarcane. Existing cropping intensity of the district was 150 %. To achieve the maximum profit per unit of land i.e. cropping intensity more than 200% for district, therefore an extensive measures was made for district to fix out the water demand supply gap for agriculture. In this study a user friendly Linear programming software was used to develop a model for optimum allocation of resources under seasonal and multi-crop condition for Jabalpur district. The net annual profit is increased by 9.1% under optimal allocation conditions. The sensitivity analysis of model parameter shows that the superior price of crop is the most sensitive parameter followed by the crop area. The results obtained from this study will definitely help policy makers to decide how to properly utilize and promote the water and land resources for the available area.

Assessing the Economic Feasibility of Assuring Nutritionally Adequate Diets for Vulnerable Populations in Uttar Pradesh, India: Findings from a "Cost of the Diet" Analysis.

ABSTRACTBackgroundHealthy diets can help reduce undernutrition, morbidity, and mortality. However, evidence on the accessibility and affordability of recommended diets is limited, particularly in poor-resource settings including India.ObjectivesThis study examined: 1) the minimum cost of different types of household diets; 2) how economic constraints can prevent households from accessing a nutritious diet; and 3) how home production and social protection can improve access to nutritious diets.MethodsWe conducted 24 market and 125 household surveys in Uttar Pradesh, India, to obtain food prices and consumption patterns. Cost of Diet, a linear programming software, was used to assess the minimum cost of different diets, estimate affordability of nutritious diets, and model scenarios of home production and social protection interventions to improve affordability.ResultsThe minimum-cost nutritious diet that met all recommended nutrient requirements [904 US dollars (US$)/y] was over twice as expensive as the diet that only met energy requirements (US$393/y). The nutritious diet was unaffordable for 75% of households given current income levels, consumption patterns, and food prices. Household income and dietary preferences, rather than food availability, were the key barriers to obtain nutritious diets. Home production had potential to reduce the cost of nutritious diets by 35%, subsidized grains by 19%, and supplementary food by 10%. The poorest households could only afford recommended nutritious diets with access to multiple interventions.ConclusionsPractical, habitual, diet-related behavior change communication to middle- and high-income households and additional social protection for poorer households could enable individuals to achieve optimal nutrient intakes.

Alignment of biological networks by integer linear programming: virus-host protein-protein interaction networks

BackgroundThe alignment of protein-protein interaction networks was recently formulated as an integer quadratic programming problem, along with a linearization that can be solved by integer linear programming software tools. However, the resulting integer linear program has a huge number of variables and constraints, rendering it of no practical use.ResultsWe present a compact integer linear programming reformulation of the protein-protein interaction network alignment problem, which can be solved using state-of-the-art mathematical modeling and integer linear programming software tools, along with empirical results showing that small biological networks, such as virus-host protein-protein interaction networks, can be aligned in a reasonable amount of time on a personal computer and the resulting alignments are structurally coherent and biologically meaningful.ConclusionsThe implementation of the integer linear programming reformulation using current mathematical modeling and integer linear programming software tools provided biologically meaningful alignments of virus-host protein-protein interaction networks.

Identification of optimal crop plan using nature inspired metaheuristic algorithms

The present study deals with the identification of optimal crop plan to improve the net benefits from the farming activities for the study area under consideration.Three nature inspired metaheuristic techniques namely Differential Evolution (DE), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) are investigated to identify the most efficient crop plan to maximize the net farm benefits. Different resource constraints considered for the study are maximum available land area, ground water availability and cropped area for different crops. The obtained results are compared with the solutions from LINGO, software for Linear Programming (LP). The results reveal the net benefit per ha derived using DE, PSO, GA and LINGO are 73841.69, 73439.08, 70555.99 and 73841.65 Indian Rupees (INR) respectively for kharif crops and 20184.71, 20172.78, 19860.80 and 20184.70 `Indian Rupees (INR) respectively for rabi crops.

Potential contribution of cereal and milk based fermented foods to dietary nutrient intake of 1-5 years old children in Central province in Zambia.

Zambia is still facing undernutrition and micronutrient deficiencies despite fortification and supplementation programmes stressing the need for additional solutions. Fermented foods have the potential to improve nutrient intake and, therefore, could have an important role in food based recommendations (FBRs) to ensure adequate intake of nutrients for optimal health of populations. Secondary dietary intake data was used in Optifood, a linear programming software to develop FBRs, for children aged 1–3 and 4–5 years in Mkushi district of Zambia. Three scenarios per age group were modeled to determine FBRs based on: (1) FBRs based on local available foods (2) FBR and Mabisi, a fermented milk beverage, and (3) FBR with Munkoyo, a cereal fermented beverage. The scenarios were compared to assess whether addition of Mabisi or Munkoyo achieved a better nutrient intake. FBRs based on only locally available non-fermented foods did not meet ≥70% of recommended nutrient intake (RNI) for calcium, fat, iron and zinc, so-called problem nutrients. The addition of Munkoyo to the FBRs did not reduce the number of problem nutrients, but after adding Mabisi to the FBR’s only iron (67% of RNI) in the 1–3 year age group and only zinc (67% of RNI) in the 4–5 year age group remained problem nutrients. Mabisi, a fermented milk product in combination with the local food pattern is a good additional source of nutrients for these age groups. However, additional nutrition sensitive and cost-effective measures would still be needed to improve nutrient intake, especially that of iron and zinc.