Application Of Linear Programming Research Articles

Developing Schedule With Linear Programming (Case Study: STTF II Project Komplek Sukamukti Banjaran)

Shift to The Front II Komplek Sukamukti Banjaran Project is one of the projects implemented by one of the companies engaged in telecommunications. In its implementation, each project including Shift to The Front II Komplek Sukamukti Banjaran has a time limit specified in the contract. Project scheduling is an important role in predicting both the cost and time in a project. Every project should be able to complete the project before or just in the time specified in the contract. Delay in a project can be anticipated by accelerating the duration of completion by using the crashing method with the application of linear programming. Linear programming will help iteration in the calculation of crashing because if linear programming not used, iteration will be repeated. The objective function in this scheduling is to minimize the cost. This study aims to find a trade-off between the costs and the minimum time expected to complete this project. The acceleration of the duration of this study was carried out using the addition of 4 hours of overtime work, 3 hours of overtime work, 2 hours of overtime work, and 1 hour of overtime work. The normal time for this project is 35 days with a service fee of Rp. 52,335,690. From the results of the crashing analysis, the alternative chosen is to add 1 hour of overtime to 34 days with a total service cost of Rp. 52,375,492. This acceleration will affect the entire project because there are 33 different locations worked on Shift to The Front II and if all these locations can be accelerated then the duration of completion of the entire project will be effective

Grey linear programming: a survey on solving approaches and applications

PurposeThe purpose of this paper is to survey and express the advantages and disadvantages of the existing approaches for solving grey linear programming in decision-making problems.Design/methodology/approachAfter presenting the concepts of grey systems and grey numbers, this paper surveys existing approaches for solving grey linear programming problems and applications. Also, methods and approaches for solving grey linear programming are classified, and its advantages and disadvantages are expressed.FindingsThe progress of grey programming has been expressed from past to present. The main methods for solving the grey linear programming problem can be categorized as Best-Worst model, Confidence degree, Whitening parameters, Prediction model, Positioned solution, Genetic algorithm, Covered solution, Multi-objective, Simplex and dual theory methods. This survey investigates the developments of various solving grey programming methods and its applications.Originality/valueDifferent methods for solving grey linear programming problems are presented, where each of them has disadvantages and advantages in providing results of grey linear programming problems. This study attempted to review papers published during 35 years (1985–2020) about grey linear programming solving and applications. The review also helps clarify the important advantages, disadvantages and distinctions between different approaches and algorithms such as weakness of solving linear programming with grey numbers in constraints, inappropriate results with the lower bound is greater than upper bound, out of feasible region solutions and so on.

The Implementation of Linear Program in Determining Product Combinations to Achieve Optimal Profits in Nganjuk “Syukur” Tile Home Industry

The objective achieved through this research is to study the application of linear programming in determining the combination of products that can produce optimal profits in the Nganjuk "thanksgiving" tile home industry. To achieve these objectives, in this study using this research included in descriptive research, the main data collection methods are interviews, field observations and documentation. Analysis of the data used is linear programming analysis with the simplex method. While to determine the agreement of use, a time series analysis can be used which can be used as a basis for forecasting for the future (linear trend with the method of small squares). From the results of the analysis to determine the optimal product combination in linear programming, there is a function as an objective and a function as a limitation. And in this study it was confirmed as follows: Max Z = 216,250 X1 + 156,000 X2 + 1,363,000 X3. As a combination of tile (X1) = 341 per day; Pilang Karang Tile (X2) = 448 per day and Wuwung Tile (X3) = 203 per day, the benefit is Rp. = 418,937,000. For that, the company must pay attention to the combination of products by calculating the right, if the company produces more than one type of product. Likewise with the limiting factors.
 Keywords: Linear Program, Product Combination, Optimal Profit, Home Industry

Application of stochastic linear programming in managerial accounting

Purpose This paper aims to focus on applications of stochastic linear programming (SLP) to managerial accounting issues by providing a theoretical foundation and practical examples. SLP models may have more implications – and broader ones – in industry practice than deterministic linear programming (DLP) models do. Design/methodology/approach This paper introduces both DLP and SLP methods. In addition, continuous and discrete SLP models are explained. Applications are demonstrated using practical examples and simulations. Findings This research work extends the current knowledge of SLP, especially concerning managerial accounting issues. Through numerical examples, SLP demonstrates its great ability of hedging against all scenarios. Originality/value This study serves as an addition to building a cumulative tradition of research on SLP in managerial accounting. Only a few SLP studies in managerial accounting have focused on the development of such an instrument. Thus, the measurement scales in this research can be used as the starting point for further refining the instrument of optimization in managerial accounting.

Minimizing Health-Compromising Behaviors via School-Based Programs: An Optimization Approach.

School health programs are united by their desire to promote health and health-related outcomes among youth. They are also united by the fact that their expected effects are contingent on successful program implementation, which is often impeded by a multitude of real-world barriers. Techniques used in management science may help optimize school-based programs by accounting for implementation barriers. In this exploratory study, we present a detailed example of the first known application of linear programming (LP), which is an optimization technique, to Positive Action (PA). PA is a social emotional and character development program that includes a six-unit, teacher-delivered, classroom curriculum. We specify how we used LP to calculate the optimal levels of program implementation needed to minimize substance use, subject to known levels of implementation barriers (e.g., disruptive behavior, teacher education, teacher attitudes towards character development, school resources, and school safety). We found that LP is a technique that can be applied to data from a school health program. Specifically, we were able to develop a model that calculated the number of lessons that should be taught to minimize a specific health-compromising behavior, given expected levels of predetermined implementation barriers. Our findings from this exploratory study support the utility of applying LP during the program planning and implementation processes of school health programs.

Application of linear programming (LP) for optimal use of raw water in Cidanau watershed, Banten

Cidanau watershed area has an important role in fulfilling water needs, especially for industrial, settlements (population), and agricultural needs. However, along with the increasing development of the Cilegon area, the need for clean water also increased. In 1984, the water demand for the Cilegon region for industrial and settlements was only 0.36 m3/second. But, in 1989 it increased to 1102 m3/second and in 2000 it was 2.200 m3/second. One of the ways to optimize the utilization of limited water resources while the community’s need for water is increasing was watershed management using linear programming (LP) method which supports optimal water distribution. Optimization discharge was carried out to determine water requirements. The optimal discharge for domestic needs was 16.50 liters/second to fulfill the water needs of the population of 47,519 people. The optimum discharge for the industrial sector was 27 liters/second with a total industry of 46 (small industries). When it was added with Krakatau Tirta Industri (KTI) discharge, the total industrial discharge became 1,527 liters/second. The optimum agricultural discharge with an area of 4,689.29 ha was 1,219 liters/second.

Application of Linear Programming in Students’ Diet Problem

Application of Linear Programming in Students’ Diet Problem

Computing the maximum value engineering for a building construction costs using linear programming applications.

Value Engineering is an effective technique for reducing costs, increasing productivity and improving quality. Value engineering is a technique directed toward analyzing the functions of an item or process to determine best value or the best relationship between worth and cost functions. Building construction costs comprises 2 major components as materials cost and labor cost. Linear Programming (LP) is a quantitative technique by means of a mathematical modeling technique designs to solve allocation problem (resource allocation) to achieve the maximum profit or lowest cost. Therefore the authors bringing the advantages of both as value engineering and linear programming combined to work together in order to be able to effectively reduced building construction costs or create maximum value engineering that can be best used in the construction projects. Aim of this paper shows the reduction of building construction cost as to achieved the targets for reducing building construction cost or maximum value engineering as much as possible that leads to efficiency of cost reduction by used of principles of value engineering to apply with using linear programming for the reduction of building construction costs based on the material costs and labor costs to serve construction resource reallocation by consideration is made only 3 type construction materials as concrete, rebar and formwork because of 3 type are main component of building construction materials.

An Application of Linear Programming in the Estimation of Technical Efficiency of DMU

The main objective of this research article is to propose linear programming problems for estimating the technical efficiency of DMU. This research article deals with the Shepard’s [1] input distance function and its properties are also evaluated. In addition to these extreme efficiency, efficiency but not extreme, weak efficiency and inefficient of a DMU are specifically examined here. In DEA the nature of returns to scale can be inferred. But we cannot quantify the returns to scale. The computations for the classification of RTS of a DMU are also derived in this discourse. In 2009, Barbara A. Mark et.al [2] in their paper, depicted an innovative method which is non-parametric to estimate technical efficiency. In 2011 S. Nuti et.al [3] inquired into the interrelations among technical efficiency scores, weighted per capita cost and overall performance. Gahe Zing Samuel Yannik et.al [4] used DEA to calculate technical assessment in banking sectors. In 2015 Smita Verma and others chosen a random sample of ten textile mills in India over the time period 2011-2013 and measures its technical efficiency using Data Envelopment Analysis

Application of Linear Programming for Profit Maximization of the Bank and the Investor

The main objective of this paper is to optimize (maximize) the net return of Central Bank of India in the area of interest from loans such as Personal loan, Car loan, Home loan, Agricultural loan, Commercial loan, Education loan and also maximize the net return of the investor by investing some amount in the investment policy of Central Bank of India such as Fixed Deposit, Saving Account, Public Provident fund and other investment Policies. The linear programming technique is applied to maximize profit of the Bank and the investor.

Chili sauce production planning model considering raw material availability: An application of Mixed Integer Linear Programming Method

PT Bina Usaha Keluarga Sedep Roso is one of agroindustry located in Padang producing chili and soy sauces. Chili and soy sauces main ingredients are sweet potato and black soybean, respectively. Both chili and soy sauces have a broad market in West Sumatera, Indonesia. The problem often faced by companies is that the demand for chili sauce product cannot be fulfilled because of seasonal and perishable factors of the sweet potato. It became scarce and expensive outside the harvest season otherwise the excessive supply and low price in the harvest season. This paper proposes an application of mixed integer linear programming to manage production planning of chili sauce to minimize total operational cost and increase the service level considering raw material availability and production capacity of chili sauce companies. The mixed integer linear programming method is applied because there are some constraints on the problem. The mathematical model is developed to obtain the optimal solution in allocating the available resources with given constraints regarding raw material availability and production capacity. The numerical example is given to illustrate the model and show results. Sensitivity analysis is performed to see the changes in the optimal solution caused by the changes in the input parameters of the model.

Application of linear programming for expatriate assignment planning

An expatriate is a person who lives temporarily or resides outside her/his native country or a foreign who lives in a country. It is usually because of a state or professional duty. The activity of sending employees to branch companies in other countries is called expatriation. Expatriation activities are high cost activities, so the need of sending employees to branch companies in other countries must be carefully calculated. Assignment problems occur in various decision-making situations which include assignment of work for machinery, agents for special tasks, sales personnel in the sales area and others. The distinguishing characteristic of an assignment problem is that one agent is assigned to one task. The research was conducted at PT. Asian Hybrid Seed Technologies Indonesia, where the company will open three new marketing areas and require expatriates to be assigned as project leaders. In this article, a set of assignments to optimize the objectives function, namely minimizing cost and time, was solved by using linear programming model.

Optimal Production Scheduling in the Dairy Industries

This work presents the application of a Mixed-Integer Linear Programming (MILP) model that was previously developed in our group for the lot-sizing and production scheduling problem in a real-life industry. In particular, the case of yoghurt production, a representative food process, in a large-scale dairy facility from Greece is studied in detail. The model takes into account all constraints typically met in production scheduling (inventory limitations, material balances, equipment capacity, etc.) having as major objective the minimization of cost. New information regarding new orders, order modifications and/or cancellations and deviations of actual to planned production is considered by utilizing a rolling horizon algorithm. The suggested solution strategy has been successfully integrated with the industrial partner’s system to facilitate the decision-making process for the production scheduling of the facility.

Achieving Optimal Allocation of Resources: An Application of Linear Programming in Agriculture

The scope of the discipline of Operations Research or Mathematical Programming is not limited to only business management sciences; its application has vastly been extended to many other fields including farming. This paper uses one of the techniques of this mathematical management tool – Linear Programming (LP) - to work out the optimal allocation of resources in a sample of 355 growers, located along sides of two canals of Warsak Dam, running into Peshawar district. Results show that the existing cropping pattern is very much sub-optimal according to the LP suggested solution, wherein 10 out of 12 crops need to be reduced to give space to 2 relatively more enumerative cropping enterprises. The LP results further suggest that for sustainable development a gradual adoption of suggested LP solution would better perform compared to a hastily one-time change process.

Revisiting Approximate Linear Programming: Constraint-Violation Learning with Applications to Inventory Control and Energy Storage

Approximate linear programs (ALPs) are well-known models for computing value function approximations (VFAs) of intractable Markov decision processes (MDPs). VFAs from ALPs have desirable theoretical properties, define an operating policy, and provide a lower bound on the optimal policy cost. However, solving ALPs near-optimally remains challenging, for example, when approximating MDPs with nonlinear cost functions and transition dynamics or when rich basis functions are required to obtain a good VFA. We address this tension between theory and solvability by proposing a convex saddle-point reformulation of an ALP that includes as primal and dual variables, respectively, a vector of basis function weights and a constraint violation density function over the state-action space. To solve this reformulation, we develop a proximal stochastic mirror descent (PSMD) method that learns regions of high ALP constraint violation via its dual update. We establish that PSMD returns a near-optimal ALP solution and a lower bound on the optimal policy cost in a finite number of iterations with high probability. We numerically compare PSMD with several benchmarks on inventory control and energy storage applications. We find that the PSMD lower bound is tighter than a perfect information bound. In contrast, the constraint-sampling approach to solve ALPs may not provide a lower bound, and applying row generation to tackle ALPs is not computationally viable. PSMD policies outperform problem-specific heuristics and are comparable or better than the policies obtained using constraint sampling. Overall, our ALP reformulation and solution approach broadens the applicability of approximate linear programming.This paper was accepted by Yinyu Ye, optimization.

Related-tweakey impossible differential attack on reduced-round Deoxys-BC-256

Deoxys-BC is the internal tweakable block cipher of Deoxys, a third-round authenticated encryption candidate at the CAESAR competition. In this study, by adequately studying the tweakey schedule, we seek a six-round related-tweakey impossible distinguisher of Deoxys-BC-256, which is transformed from a 3.5-round single-key impossible distinguisher of AES, by application of the mixed integer linear programming (MILP) method. We present a detailed description of this interesting transformation method and the MILP-modeling process.Based on this distinguisher, we mount a key-recovery attack on 10 (out of 14) rounds of Deoxys-BC-256.Compared to previous results that are valid only when the key size $>204$ and the tweak size $<52$, our method can attack 10-round Deoxys-BC-256 as long as the key size $\\geq174$ and the tweak size $\\leq82$. For the popular setting in which the key size is 192 bits, we can attack one round more than previous studies.Note that this paper only gives a more accurate security evaluation and does not threaten the security of full-round Deoxys-BC-256.

Application of Linear Programming in Optimising Student Outreach for Coaching Institutes Operating in Delhi NCR

Application of Linear Programming in Optimising Student Outreach for Coaching Institutes Operating in Delhi NCR

Application of Linear Programming in Optimizing Labour Scheduling

Employee or labor scheduling is associated with assigning an appropriate number of workers to the jobs during each day of work. It requires determining when staff members will work and when part-time, full-time workers will be needed to work. It is obvious that the number of employees wanted on duty throughout the week may fluctuate depending on health or family issues of the labor and the employer’s requirement for a particular job; Scheduling forces us to systematically identify and analyze about all the tasks that need to be done on a project, the expected time each task might take, the expected requirement of the workforce for the job in terms of size and quality of employee personnel and the expected labor expenses. As the availabilities of the employees may vary and change from week to week; hence the scheduling becomes more essential for the smooth running of a project or shift. This paper focuses on a constructive method for solving Labor Scheduling problem encountered in a construction company, suggesting an estimated labor cost over a week and the requirement of part-time labors in each shift, using linear programming techniques, thus, providing a logical way to organize these tasks and produce a new schedule each week, by the virtue of the changing demand for service while minimizing labor cost and maximizing labor preferences.

Analyzing the Shadow Price of Industrial Water Resources and Water Shortage Scenario - An Application of Water Input-Output Linear Programming -

This study set the water input-output model by linking the amount of water use to inter-industry relations table (IRT), and estimated shadow prices of water resources in agricultural, industrial and service sector using linear programming method. The 2014 water data and IRT data were collected and linked for integration of water usage and input-output table, and 30 major categories of IRT were redefined into 12 sub-sectors. Using the data, the shadow prices and their changes in each sector were estimated according to drought scenarios. The main results derived from the water input-output linear programming are as follows. Shadow prices of agriculture water, industrial water and service industry water were 864 won/㎥, 27,545 won/㎥ and 275,449 won/㎥ respectively. The shadow price changes by water shortage (10% to 90% constraint) ranged from 1,518 to 2,369 won/㎥ for agricultural use, 61,721 to 997,092 won/㎥ for industrial use, and 294,923 to 381,192 won/㎥ for service industry. In particular, it was confirmed that the damage level in the industrial sector increased sharply at the 50% restriction level.

Linear Programming Applications to Managerial Accounting Decision Makings

Linear Programming Applications to Managerial Accounting Decision Makings