A STOCHASTIC APPROACH TO HOTEL REVENUE MANAGEMENT CONSIDERING MULTIPLE-DAY STAYS

2006 ◽  
Vol 05 (03) ◽  
pp. 545-556 ◽  
Author(s):  
SHUQIN LIU ◽  
KIN KEUNG LAI ◽  
JICHANG DONG ◽  
SHOU-YANG WANG
Keyword(s):  
Linear Programming ◽  
Stochastic Optimization ◽  
Revenue Management ◽  
Decision Maker ◽  
Optimization Model ◽  
Programming Model ◽  
Stochastic Approach ◽  
Risk Of Falling ◽  
Absolute Deviation ◽  
Linearization Techniques

We present a stochastic optimization model for hotel revenue management with multiple-day stays under an uncertain environment. Since a decision maker may face several scenarios when renting out rooms, we use a semi-absolute deviation model to measure the risk of hotel revenue, and only consider the risk of falling below the expected revenue. The method proposed in this paper can be changed to a linear programming model by applying linearization techniques. Some examples are presented to illustrate the efficiency of this method.

scholarly journals Generalized Transformation Techniques for Multi-Choice Linear Programming Problems

2012 ◽  
Vol 3 (1) ◽  
pp. 45-54 ◽  
Author(s):  
Srikumar ACHARYA ◽  
Mitali Madhumita ACHARYA
Keyword(s):  
Linear Programming ◽  
Programming Problem ◽  
Decision Maker ◽  
Linear Programming Problem ◽  
Programming Model ◽  
Mixed Integer ◽  
Transformation Techniques ◽  
Programming Technique ◽  
Binary Variables ◽  
Non Linear

The multi-choice programming allows the decision maker to consider multiple number of resources for each constraint or goal. Multi-choice linear programming problem can not be solved directly using the traditional linear programming technique. However, to deal with the multi-choice parameters, multiplicative terms of binary variables may be used in the transformed mathematical model. Recently, Biswal and Acharya (2009) have proposed a methodology to transform the multi-choice linear programming problem to an equivalent mathematical programming model, which can accommodate a maximum of eight goals in righthand side of any constraint. In this paper we present two models as generalized transformation of the multi-choice linear programming problem. Using any one of the transformation techniques a decision maker can handle a parameter with nite number of choices. Binary variables are introduced to formulate a non-linear mixed integer programming model. Using a non-linear programming software optimal solution of the proposed model can be obtained. Finally, a numerical example is presented to illustrate the transformation technique and the solution procedure.

Regional Power Planning Optimization Model and Scenario Analysis Considering the Environmental Cost

2013 ◽  
Vol 805-806 ◽  
pp. 1122-1128
Author(s):  
Zong Wu Wang ◽  
Guo He Huang ◽  
Xiao Kun Li
Keyword(s):  
Linear Programming ◽  
Scenario Analysis ◽  
Pollution Control ◽  
Optimization Model ◽  
Programming Model ◽  
Environmental Cost ◽  
Mixed Integer ◽  
Regional Power ◽  
Planning Optimization ◽  
Decision Alternatives

In this study, a regional power planning optimization model (RPPOM) is developed considering the environmental cost and the restriction of resource and environment, based on interval linear programming and mixed integer linear programming. Model is applied to a case study on the power planning in Henan province, and scenario analysis is conducted. Interval solutions associated with scenario of pollution control have been obtained. They can be used for generating decision alternatives and helping decision makers identify desired power policies for power planning to meet the growth in electricity demand considering the constraints of resources and environment with a minimized system cost. Scenario analysis of environmental pollution control at different levels can also be tackled.

scholarly journals Formation of an optimal portfolio of venture projects

2021 ◽  
pp. 128-132
Author(s):  
I.A Korkhina ◽  
V.O Petrenko ◽  
V.L Khomenko ◽  
V.O Kulyk
Keyword(s):  
Mathematical Programming ◽  
Initial Data ◽  
Stochastic Optimization ◽  
Development Strategy ◽  
Optimization Model ◽  
Programming Model ◽  
Optimal Portfolio ◽  
Financial Resources ◽  
Proposed Model ◽  
The Face

Purpose. Development of a method for forming an optimal portfolio of venture projects taking into account risks, uncertainty in initial data and limited financial resources. Methodology. To calculate the accuracy of forecasting prices, which are necessary for calculating the parameters of the stochastic optimization model for the formation of a portfolio of projects, we used the theory of random variables and regression analysis. The problem of choosing the optimal portfolio of venture projects was solved using stochastic mathematical programming. Findings. A model for creating an optimal portfolio of venture projects has been developed. It is a stochastic mathematical programming model that can be used to solve problems of investing in venture projects in the extractive industry. This model takes into account the risks associated with obtaining the expected income from the implementation of each venture project, the uncertainty in the initial data for calculating the income from the projects selected in the portfolio, as well as the limited funds required to finance the project portfolio. Originality. The stochastic optimization model for the formation of an optimal portfolio of projects, taking into account the peculiarities of venture projects, in particular, their high riskiness, has been significantly improved and adapted. Practical value. The proposed model for the formation of an optimal portfolio of venture projects can be used at mining enterprises, whose development strategy involves the implementation of innovative, high-risk projects. The use of this model in strategic planning will allow an enterprise to receive the maximum income from venture projects in the face of a lack of financial resources, as well as instability of the innovation market.

scholarly journals PENENTUAN LOKASI TEMPAT EVAKUASI AKHIR PENGUNGSI PADA ANCAMAN BENCANA TSUNAMI KOTA PADANG

2018 ◽  
Vol 7 (2) ◽  
pp. 111
Author(s):  
Rafiqa Fijra
Keyword(s):  
Linear Programming ◽  
Indian Ocean ◽  
Potential Risk ◽  
Optimization Model ◽  
Programming Model ◽  
Capital City ◽  
Linear Programming Model ◽  
Low Area ◽  
The Indian Ocean

Padang is capital city of West Sumatera whose coastal layout and approximately 50% of population live in low area. It opposites to the Indian Ocean which near the Megathrust subduction which can trigger a powerful earthquake and generate tsunami. Because of the potential risk, important to finding the final evacuated region (TEA) location addressed by evacuee of each affected village, and defining the evacuee population and its susceptive population on each TEA. Defining the location TEA is provided by formulating the linear programming model which minimizing the total traveled distance. Then, defining of population and susceptive of evacuee in each TEA also analyzed to support the response disaster occurred. The optimization model is conducted in three level scenarios of tsunami scale, that are big, medium, and small .

scholarly journals Linear Optimization Model for Efficient Use of Irrigation Water

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Wafa Difallah ◽  
Khelifa Benahmed ◽  
Belkacem Draoui ◽  
Fateh Bounaama
Keyword(s):  
Linear Programming ◽  
Water Use ◽  
Knapsack Problem ◽  
Water Consumption ◽  
Irrigation Water ◽  
Optimization Model ◽  
Field Experiments ◽  
Programming Model ◽  
Linear Optimization ◽  
Linear Optimization Model

The implementation of innovative and efficient irrigation techniques is among the greatest challenges facing agriculture. In this regard, a linear programming model is presented in order to optimize water use. The idea behind this model is to assess the effectiveness or ineffectiveness of precipitation to determine the amount of irrigation water required to optimize water use. To achieve this idea, the “knapsack” problem decisional form was used, and the combination of the linear programming and the above-mentioned form proved satisfactory. Field experiments were conducted in Algeria. Based on calculated budgets a model using linear programming was developed. A comparison between the model results and the field findings suggests that the model could reduce water consumption by 28.5%.

scholarly journals Cooperative Optimization Model of BRT Speed and Timing Based on Dual Station at an Intersection

Symmetry ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 1814
Author(s):  
Peng Zhang ◽  
Shuangxi Gao ◽  
Pengfei Wang ◽  
Wenquan Li
Keyword(s):  
Linear Programming ◽  
Optimization Model ◽  
Programming Model ◽  
Green Light ◽  
Current Situation ◽  
Linear Programming Model ◽  
Stopping Times ◽  
Model Based ◽  
Scientific Disciplines ◽  
Priority Method

This paper presents a binary integer linear programming model based on a dual station at an intersection to make BRT (Bus Rapid Transit) go through the intersection without stopping. This paper first gives the setting of the BRT dual station at the intersection and compares the average BRT delay of the single and dual station at the intersection. Through comparison, it is found that dual station can optimize BRT operations, but there is still room for further optimization. Therefore, this paper establishes a 01 linear programming model based on a dual station. The model takes the shortest travel time of BRT as the optimization objective, and takes the selection of the dual station, green light time of the intersection and cycle time as optimization variables. The case analysis and results show that, compared with the current situation, the average intersection delay of signal priority method, simple dual station method and method proposed in this paper are reduced by 3.51 s (8.6%), 17.27 s (42.17%) and 35.99 s (87.89%); compared with the current situation, the average stopping times at intersection of the signal priority method, simple dual station method and the method proposed in this paper are reduced by −3 (−9.4%), 2 (6.2%) and 23 (71.8%), respectively. Under the control of the method proposed in this paper, the average intersection delay is 4.96 s, and the average stopping times at the intersection is 0.18. This shows that the optimization model proposed in this paper can greatly reduce BRT delay and parking times. The setting of dual station makes the two stations symmetrical to each other, which conforms to the name of the journal. This paper belongs to the field of Symmetry and other scientific disciplines and engineering.

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