scholarly journals Modelo de otimização de recursos financeiros para o gerenciamento de riscos empresariais

2017 ◽  
Vol 1 (1) ◽  
pp. 98 ◽  
Author(s):  
Wanderlei Lima Paulo ◽  
Francisco Carlos Fernandes ◽  
Marcia Zanievicz Silva
Keyword(s):  
Linear Programming ◽  
Decision Process ◽  
Control Policy ◽  
Financial Resources ◽  
Business Risk ◽  
Risk Manager ◽  
Operational Costs ◽  
Proposed Model ◽  
Control Target ◽  
Reduced Costs

<p>This article aims to propose a model to determine the best allocation of financial resources for business risk management, permitting the risk manager to define a control policy with reduced costs that reaches a desired control target. The problem of study is presented as an issue of optimization of costs, formulated as a model of whole linear programming, which basic restrictions are associated to the demanded levels of control. The proposed model is applied to a problem of resource allocation for the control of operational costs. The results show that the model is an adequate instrument to better allocate financial resources, which its use proportionates better conditions for the decision process of business risks.</p>

Development of a risk-based tool for groundwater well rehabilitation and replacement decisions

2019 ◽  
Vol 68 (6) ◽  
pp. 411-419 ◽  
Author(s):  
Gamze Güngör-Demirci ◽  
Juneseok Lee ◽  
Jonathan Keck ◽  
Stephen J. Harrison ◽  
Geoffrey Bates
Keyword(s):  
Water Utilities ◽  
Risk Exposure ◽  
Decision Making Process ◽  
Business Risk ◽  
California Water ◽  
Operational Costs ◽  
Replacement Decisions ◽  
Well Rehabilitation ◽  
Hierarchy Process ◽  
Financial Regulatory

Abstract Groundwater wells are critical drinking water infrastructure elements that widely support basic system supply needs while also providing supply reliability, better water quality (in some cases), and comparatively lower operational costs. Well rehabilitation and replacement are thus an area where water utilities could benefit from rational decision support frameworks and quantitative tools that enable them to better navigate the complex trade-off relationship(s) that exist among a variety of environmental quality, public health, financial, regulatory, organizational, and technological dimensions. Consistent with these considerations, a business risk-based prioritization tool was developed for this study that augments/extends California Water Service (Cal Water)'s well rehabilitation and the replacement decision-making process. For this derivation, a business risk exposure methodology is combined with an analytical hierarchy process (AHP), with the AHP being utilized to determine the weights of the factors involved in the likelihood of failure and the consequence of failure calculation. It is expected that the new tool will assist in optimizing inspection and action plans and identify the wells requiring attention and/or additional work for water utilities.

scholarly journals A Semi-Empirical Model for Predicting Frost Properties

Processes ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 412
Author(s):  
Shao-Ming Li ◽  
Kai-Shing Yang ◽  
Chi-Chuan Wang
Keyword(s):  
Thermal Conductivity ◽  
Linear Programming ◽  
Numerical Model ◽  
Empirical Model ◽  
Quantitative Method ◽  
Predictive Ability ◽  
Dimensionless Temperature ◽  
Crystal Shape ◽  
Proposed Model ◽  
Semi Empirical

In this study, a quantitative method for classifying the frost geometry is first proposed to substantiate a numerical model in predicting frost properties like density, thickness, and thermal conductivity. This method can recognize the crystal shape via linear programming of the existing map for frost morphology. By using this method, the frost conditions can be taken into account in a model to obtain the corresponding frost properties like thermal conductivity, frost thickness, and density for specific frost crystal. It is found that the developed model can predict the frost properties more accurately than the existing correlations. Specifically, the proposed model can identify the corresponding frost shape by a dimensionless temperature and the surface temperature. Moreover, by adopting the frost identification into the numerical model, the frost thickness can also be predicted satisfactorily. The proposed calculation method not only shows better predictive ability with thermal conductivities, but also gives good predictions for density and is especially accurate when the frost density is lower than 125 kg/m3. Yet, the predictive ability for frost density is improved by 24% when compared to the most accurate correlation available.

How can mixed integer linear programming assist dairy manufacturers by integrating supply decisions and production planning?

2021 ◽  
Vol 11 (2) ◽  
pp. 178-193
Author(s):  
Juliana Emidio ◽  
Rafael Lima ◽  
Camila Leal ◽  
Grasiele Madrona
Keyword(s):  
Linear Programming ◽  
Supply Chain ◽  
Integer Linear Programming ◽  
Mixed Integer Linear Programming ◽  
Dairy Products ◽  
Raw Milk ◽  
Mixed Integer ◽  
Content Type ◽  
Proposed Model ◽  
By Products

PurposeThe dairy industry needs to make important decisions regarding its supply chain. In a context with many available suppliers, deciding which of them will be part of the supply chain and deciding when to buy raw milk is key to the supply chain performance. This study aims to propose a mathematical model to support milk supply decisions. In addition to determining which producers should be chosen as suppliers, the model decides on a milk pickup schedule over a planning horizon. The model addresses production decisions, inventory, setup and the use of by-products generated in the raw milk processing.Design/methodology/approachThe model was formulated using mixed integer linear programming, tested with randomly generated instances of various sizes and solved using the Gurobi Solver. Instances were generated using parameters obtained from a company that manufactures dairy products to test the model in a more realistic scenario.FindingsThe results show that the proposed model can be solved with real-world sized instances in short computational times and yielding high quality results. Hence, companies can adopt this model to reduce transportation, production and inventory costs by supporting decision making throughout their supply chains.Originality/valueThe novelty of the proposed model stems from the ability to integrate milk pickup and production planning of dairy products, thus being more comprehensive than the models currently available in the literature. Additionally, the model also considers by-products, which can be used as inputs for other products.

scholarly journals A multi-objective approach under uncertainty for designing a green meat supply chain network

2021 ◽  
Author(s):  
Fatemeh Mohebalizadehgashti
Keyword(s):  
Linear Programming ◽  
Supply Chain ◽  
Integer Linear Programming ◽  
Mixed Integer Linear Programming ◽  
Mixed Integer ◽  
Environmental Concerns ◽  
Supply Chain Network ◽  
Food Supply Chain ◽  
Multi Objective ◽  
Proposed Model

Traditional logistics management has not focused on environmental concerns when designing and optimizing food supply chain networks. However, the protection of the environment is one of the main factors that should be considered based on environmental protection regulations of countries. In this thesis, environmental concerns with a mathematical model are investigated to design and configure a multi-period, multi-product, multi-echelon green meat supply chain network. A multi-objective mixed-integer linear programming formulation is developed to optimize three objectives simultaneously: minimization of the total cost, minimization of the total CO2 emissions released from transportation, and maximization of the total capacity utilization. To demonstrate the efficiency of the proposed optimization model, a green meat supply chain network for Southern Ontario, Canada is designed. A solution approach based on augmented εε-constraint method is developed for solving the proposed model. As a result, a set of Pareto-optimal solutions is obtained. Finally, the impacts of uncertainty on the proposed model are investigated using several decision trees. Optimization of a food supply chain, particularly a meat supply chain, based on multiple objectives under uncertainty using decision trees is a new approach in the literature. Keywords: Meat supply chain; Decision tree; Multi-objective programming; Mixed-integer linear programming; Augmented εε-constraint.

Supply Chain Design of Manufacturing Processes with Blending Technologies

2017 ◽  
Vol 261 ◽  
pp. 509-515 ◽  
Author(s):  
Ágota Bányainé Tóth ◽  
Béla Illés ◽  
Fabian Schenk
Keyword(s):  
Mathematical Model ◽  
Linear Programming ◽  
Supply Chain ◽  
Literature Review ◽  
Linear Programming Problem ◽  
Integrated Approach ◽  
Integrated Model ◽  
Supply Chain Design ◽  
Manufacturing Processes ◽  
Proposed Model

Blending technologies play an important role in manufacturing. The design and operation of manufacturing processes using blending technologies represent a special range of manufacturing related logistics because the integrated approach of technological and logistic parameters is very significant. This research proposes an integrated model of supply of manufacturing processes using blending technologies. After a careful literature review, this paper introduces a mathematical model to formulate the problem of supply chain design for blending technologies. The integrated model includes the optimal purchasing strategy depending on the characteristics of components to be mixed in the desired proportion and the costs of supply. The integrated model will be described as a linear programming problem. Numerical results with different datasets demonstrate how the proposed model takes technological and logistic aspects into consideration.

scholarly journals Priority Scheduling in the Planning of Multiple-Structure Construction Projects

2017 ◽  
Vol 63 (4) ◽  
pp. 21-33 ◽  
Author(s):  
E. Radziszewska-Zielina ◽  
B. Sroka
Keyword(s):  
Linear Programming ◽  
Computer Program ◽  
Construction Projects ◽  
Programming Model ◽  
Construction Site ◽  
Priority Scheduling ◽  
Ranking List ◽  
Multiple Structure ◽  
Proposed Model ◽  
Interactive Scheduling

AbstractThe paper presents a method of priority scheduling that is useful during the planning of multiple-structure construction projects. This approach is an extension of the concept of interactive scheduling. In priority scheduling, it is the planner that can determine how important each of the technological and organisational constraints are to them. A planner’s preferences can be defined through developing a ranking list that defines which constraints are the most important, and those whose completion can come second. The planner will be able to model the constraints that appear at a construction site more flexibly. The article presents a general linear programming model of the planning of multiple-structure construction projects, as well as various values of each of the parameters that allow us to obtain different planning effects. The proposed model has been implemented in a computer program and its effectiveness has been presented on a calculation example.

scholarly journals A Framework for the Joint Placement of Edge Service Infrastructure and User Plane Functions for 5G

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3975 ◽  
Author(s):  
Irian Leyva-Pupo ◽  
Alejandro Santoyo-González ◽  
Cristina Cervelló-Pastor
Keyword(s):  
Linear Programming ◽  
Optimal Solution ◽  
Cost Savings ◽  
Computation Time ◽  
Optimal Number ◽  
Use Cases ◽  
Next Generation Networks ◽  
Placement Problem ◽  
Communication Latency ◽  
Operational Costs

Achieving less than 1 ms end-to-end communication latency, required for certain 5G services and use cases, is imposing severe technical challenges for the deployment of next-generation networks. To achieve such an ambitious goal, the service infrastructure and User Plane Function (UPF) placement at the network edge, is mandatory. However, this solution implies a substantial increase in deployment and operational costs. To cost-effectively solve this joint placement problem, this paper introduces a framework to jointly address the placement of edge nodes (ENs) and UPFs. Our framework proposal relies on Integer Linear Programming (ILP) and heuristic solutions. The main objective is to determine the ENs and UPFs’ optimal number and locations to minimize overall costs while satisfying the service requirements. To this aim, several parameters and factors are considered, such as capacity, latency, costs and site restrictions. The proposed solutions are evaluated based on different metrics and the obtained results showcase over 20 % cost savings for the service infrastructure deployment. Moreover, the gap between the UPF placement heuristic and the optimal solution is equal to only one UPF in the worst cases, and a computation time reduction of over 35 % is achieved in all the use cases studied.

scholarly journals Analisis Penentuan Komposisi Optimum Kargo Dan Pax Pesawat Boeing 787 - 8 Dan Airbus 330 – 900 Pada Rute Jakarta – Dubai Menggunakan Optimasi Linear Programming

2021 ◽  
Vol 6 (2) ◽  
Author(s):  
Fernando Fransiskus Rotty ◽  
Freddy Franciscus ◽  
Ericko Chandra
Keyword(s):  
Linear Programming ◽  
Linear Optimization ◽  
Operating Costs ◽  
Transportation Services ◽  
Optimum Point ◽  
Passenger Transportation ◽  
Operational Costs ◽  
Optimization Programming

Abstract - The need for airplanes is increasing every day, this is due to the increasing number of passengers and cargo shipments from one area to another. Therefore, airlines need to provide optimal, safe, and efficient cargo and passenger transportation services so that the delivery of goods and passengers can run according to correct procedures. Opportunities in the business of transporting passengers and shipping cargo must be utilized properly by each airline by purchasing planes so that the shipping process from one area to another can quickly arrive at its destination. The purpose of this paper is to find the optimum value of the two aircraft between the Boeing 787-8 and the Airbus 330-900 in terms of the effect on the range, operational costs and to find out which aircraft is more profitable for operating costs on the Jakarta - Dubai route, using linear optimization. programming. Based on the results of the analysis that the optimum point for the calculation of linear programming optimization, the Boeing 787-8 aircraft obtained results (Max payload 41,075 kg, Fuel 6,657 kg, Max Fuel 101,323 kg) where these three results become a limitation for airlines to know the maximum usage of payload and fuel compared Airbus 330-900 aircraft obtained results at the point (Max payload 45,000 kg, Fuel 4,728 kg, Max Fuel 111,272 kg) so that the optimization results are obtained, for Boeing 787-8 aircraft with a max payload of 41,075 kg, max pax 359, max cargo 15,945 kg , compared to Airbus 330-900 with a max payload of 45,000 kg, max pax 460, max cargo 12,800 kg, so it can be concluded that the results of linear programming optimization and the calculation of the operational costs of the Boeing 787-8 aircraft are more optimal with a figure of Rp. 1,541,334,803.96 but in terms of revenue the Airbus 330-900 is bigger than the Boeing 787-8.    

scholarly journals Simulation of a stochastic multi-echelon distribution supply chain under a continuous inventory control policy

2020 ◽  
Vol 9 (2) ◽  
pp. 262
Author(s):  
Hamid Ech-cheikh ◽  
Abdessamad Douraid ◽  
Khalid El Had
Keyword(s):  
Distribution System ◽  
Initial Conditions ◽  
Discrete Event ◽  
Control Policy ◽  
Simulation Framework ◽  
Event Simulation ◽  
Proposed Model ◽  
Inventory Control Policy ◽  
Distribution Process ◽  
Key Variables

Multi Echelon Distribution System (MEDS) is a multifaceted system focusing on integration of all factors involved in the entire distribution process of finished goods to customers. This paper proposes a simulation framework at the operational level of MEDS. The proposed model includes three echelons, based on discrete-event simulation approach, where the performed operations within our system are depending on several key variables that often seem to have strong interrelationships. It is necessary to simulate such complicated system, in order to understand the whole mechanism, to analyze the interactions between various components and eventually to provide information without decomposing the system. The simulation framework is used to evaluate the performance of the considered system at initial conditions and to compare it with different scenarios generated by simulation running. The study concludes with an analysis of system performance and the finding results according to each scenario.   

scholarly journals A Linear Programming Model with Fuzzy Arc for Route Optimization in the Urban Road Network

2019 ◽  
Vol 11 (23) ◽  
pp. 6665
Author(s):  
Escobar-Gómez ◽  
Camas-Anzueto ◽  
Velázquez-Trujillo ◽  
Hernández-de-León ◽  
Grajales-Coutiño ◽  
...  
Keyword(s):  
Linear Programming ◽  
Road Network ◽  
Classical Model ◽  
Route Optimization ◽  
Distribution Area ◽  
Travel Times ◽  
Urban Road Network ◽  
Real Network ◽  
Urban Road ◽  
Proposed Model

In the transport system, it is necessary to optimize routes to ensure that the distance, the amount of fuel used, and travel times are minimized. A classical problem in network optimization is the shortest path problem (SPP), which is used widely in many optimization problems. However, the uncertainty that exists regarding real network problems makes it difficult to determine the exact arc lengths. In this study, we analyzed the problem of route optimization when delivering urban road network products while using fuzzy logic to include factors which are difficult to consider in classical models (e.g., traffic). Our approach consisted of two phases. In the first phase, we calculated a fuzzy coefficient to consider the uncertainty, and in the second phase, we used fuzzy linear programming to compute the optimal route. This approach was applied to a real network problem (a portion of the distribution area of a delivery company in the city of Tuxtla Gutierrez, Chiapas, Mexico) by comparing the travel times between the proposed model and a classical model. The proposed model was shown to predict travel time better than the classical model in this study, reducing the mean absolute percentage error (MAPE) by 25.60%.

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