scholarly journals Formulation of Two-Stage Stochastic Programming with Fixed Recourse

2019 ◽  
Vol 1 (1) ◽  
pp. 18-21
Author(s):  
Hashnayne Ahmed
Keyword(s):  
Stochastic Programming ◽  
Objective Function ◽  
Past Experience ◽  
Optimization Techniques ◽  
Random Parameters ◽  
Two Stage ◽  
Second Stage ◽  
Multi Stage ◽  
Time Period ◽  
Primary Model

Stochastic Programming is an asset for the next world researchers due to its uncertainty calculations, which has been skipped in deterministic world experiments as it includes complicated calculations. Two-stage stochastic programming concerns two time period decisions based on some random parameters obtained from past experience or some sort of survey. The objective function for formulating two-stage stochastic programming with fixed recourse includes two parts: first-stage forecast and second-stage fixed decisions based on the experiment results. The constraints are similar to the normal optimization techniques rather some adjustments of requirements and technology assets. The fixed recourse decisions are sort of decisions from the deterministic world.  Formulation techniques of two-stage stochastic programming with fixed recourse may be used for further complications arises in stochastic programming like complete recourse problems, multi-stage problems, etc. And that’s why Two-stage stochastic programming with fixed recourse is called the primary model for stochastic programming.

scholarly journals Formulation of Two-Stage Stochastic Programming with Fixed Recourse

2019 ◽  
Author(s):  
Hashnayne Ahmed
Keyword(s):  
Stochastic Programming ◽  
Objective Function ◽  
Past Experience ◽  
Optimization Techniques ◽  
Random Parameters ◽  
Two Stage ◽  
Second Stage ◽  
Multi Stage ◽  
Time Period ◽  
Primary Model

Stochastic Programming is an asset for the next world researchers due to its uncertainty calculations, which has been skipped in deterministic world experiments as it includes complicated calculations. Two-stage stochastic programming concerns two time period decisions based on some random parameters obtained from past experience or some sort of survey. The objective function for formulating two-stage stochastic programming with fixed recourse includes two parts: first-stage forecast and second-stage fixed decisions based on the experiment results. The constraints are similar to the normal optimization techniques rather some adjustments of requirements and technology assets. The fixed recourse decisions are sort of decisions from the deterministic world. Formulation techniques of two-stage stochastic programming with fixed recourse may be used for further complications arises in stochastic programming like complete recourse problems, multi-stage problems, etc. And that’s why Two-stage stochastic programming with fixed recourse is called the primary model for stochastic programming.

Genetic Algorithm to Solve Multi-Period, Multi-Product, Bi-Echelon Supply Chain Network Design Problem

2009 ◽  
Vol 2 (4) ◽  
pp. 24-42 ◽  
Author(s):  
R. Dhanalakshmi ◽  
P. Parthiban ◽  
K. Ganesh ◽  
T. Arunkumar
Keyword(s):  
Supply Chain ◽  
Supply Chains ◽  
Raw Materials ◽  
Optimization Techniques ◽  
Network Design Problem ◽  
Production Plant ◽  
Two Stage ◽  
Cost Structures ◽  
Multi Stage ◽  
Product Costs

In many multi-stage manufacturing supply chains, transportation related costs are a significant portion of final product costs. It is often crucial for successful decision making approaches in multi-stage manufacturing supply chains to explicitly account for non-linear transportation costs. In this article, we have explored this problem by considering a Two-Stage Production-Transportation (TSPT). A two-stage supply chain that faces a deterministic stream of external demands for a single product is considered. A finite supply of raw materials, and finite production at stage one has been assumed. Items are manufactured at stage one and transported to stage two, where the storage capacity of the warehouses is limited. Packaging is completed at stage two (that is, value is added to each item, but no new items are created), and the finished goods inventories are stored which is used to meet the final demand of customers. During each period, the optimized production levels in stage one, as well as transportation levels between stage one and stage two and routing structure from the production plant to warehouses and then to customers, must be determined. The authors consider “different cost structures,” for both manufacturing and transportation. This TSPT model with capacity constraint at both stages is optimized using Genetic Algorithms (GA) and the results obtained are compared with the results of other optimization techniques of complete enumeration, LINDO, and CPLEX.

Genetic Algorithm to Solve Multi-Period, Multi-Product, Bi-Echelon Supply Chain Network Design Problem

2011 ◽  
pp. 275-289
Author(s):  
R. Dhanalakshmi ◽  
P. Parthiban ◽  
K. Ganesh ◽  
T. Arunkumar
Keyword(s):  
Supply Chain ◽  
Supply Chains ◽  
Raw Materials ◽  
Optimization Techniques ◽  
Network Design Problem ◽  
Production Plant ◽  
Two Stage ◽  
Cost Structures ◽  
Multi Stage ◽  
Product Costs

In many multi-stage manufacturing supply chains, transportation related costs are a significant portion of final product costs. It is often crucial for successful decision making approaches in multi-stage manufacturing supply chains to explicitly account for non-linear transportation costs. In this article, we have explored this problem by considering a Two-Stage Production-Transportation (TSPT). A two-stage supply chain that faces a deterministic stream of external demands for a single product is considered. A finite supply of raw materials, and finite production at stage one has been assumed. Items are manufactured at stage one and transported to stage two, where the storage capacity of the warehouses is limited. Packaging is completed at stage two (that is, value is added to each item, but no new items are created), and the finished goods inventories are stored which is used to meet the final demand of customers. During each period, the optimized production levels in stage one, as well as transportation levels between stage one and stage two and routing structure from the production plant to warehouses and then to customers, must be determined. The authors consider “different cost structures,” for both manufacturing and transportation. This TSPT model with capacity constraint at both stages is optimized using Genetic Algorithms (GA) and the results obtained are compared with the results of other optimization techniques of complete enumeration, LINDO, and CPLEX.

Scientific Approach to Planning and Implementation of Blind Refracturing in Horizontal Wells with MSF Completion in Low-Permeability Reservoirs

2021 ◽  
Author(s):  
Almaz Makhmutovich Sadykov ◽  
Sergey Anatolyevich Erastov ◽  
Maxim Sergeevich Antonov ◽  
Denis Vagizovich Kashapov ◽  
Tagir Ramilevich Salakhov ◽  
...  
Keyword(s):  
Hydraulic Fracturing ◽  
Surface Pressure ◽  
Horizontal Wells ◽  
Well Logging ◽  
Low Permeability ◽  
Geomechanical Model ◽  
Two Stage ◽  
Second Stage ◽  
Multi Stage ◽  
Low Permeability Reservoirs

Abstract One of the fundamental methods of developing low-permeability reservoirs is the use of multi-stage hydraulic fracturing in horizontal wells. Decreasing wells productivity requires geological and technical measures, where one of the methods is "blind" refracturing. Often, only one "blind" hydraulic fracturing is carried out for all ports of multistage hydraulic fracturing, the possibility of carrying out two or more stages of "blind" hydraulic fracturing is considered in this article. The purpose of the article is to increase the productivity of horizontal wells with multi-stage hydraulic fracturing by the "blind" refracturing method. A one-stage and two-stage approach was implemented when planning and performing "blind" hydraulic fracturing with analysis of treatment pressures, indicating a possibility for reorientation of the fracture during the second stage in a horizontal wellbore. Based on the experience of the "blind" hydraulic fracturing performed at the Kondinskoye field, "NK "Kondaneft" JSC carried out pilot work on "blind" refracturing at four horizontal wells of the Zapadno -Erginskoye field. A geomechanical model was used, built based on well logging and core studies carried out at "RN-BashNIPIneft" LLC. The total mass of the planned proppant per well was 280-290 tons, while this tonnage was pumped in one or more stages. A one-stage "blind" refracturing approach was successfully performed in one well, two-stage hydraulic fracturing was implemented in three wells, where in one of the wells, after two stages to open ports, initial hydraulic fracturing was also carried out to the last, previously non-activated port. In the case of two-stage hydraulic fracturing, the first stage purpose was to saturate the reservoir-fracture system with the injection of a "sand plug" with a high concentration of proppant at the end of the job to isolate the initial injectivity interval, determined based on the interpretation of well logging data and analysis of the wellhead treatment pressure. The second stage purpose was the initiation and possible reorientation of the fracture in a new interval, confirmed by an increase in surface pressure during hydraulic fracturing and instantaneous shut-in pressure. This article summarizes the results and lessons learned from the pilot works carried out using the geomechanical model and well productivity assessment before and after "blind" fracturing. The analysis of surface pressure based on production data indicating fracture reorientation is presented. The recommendations and accumulated experience presented in this work should increase the effectiveness of repeated "blind" refracturing in horizontal wells with multi-stage hydraulic fracturing.

Venture investment under multi-stage incentive model based on fairness concerns

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yadong Shu ◽  
Ying Dai ◽  
Zujun Ma ◽  
Zhijun Hu
Keyword(s):  
Venture Capital ◽  
Two Stage ◽  
Content Type ◽  
Effort Level ◽  
Asymmetrical Information ◽  
Second Stage ◽  
Venture Investment ◽  
Multi Stage ◽  
Fairness Concerns ◽  
The Impact

PurposeThis study explores the impact of EN's (venture entrepreneurs, simplified as EN) jealousy fairness concerns coefficient on two-stage venture capital decision-making in cases of symmetrical and asymmetrical information. It discusses the equilibrium solution of two-stage venture.Design/methodology/approachThe principal-agent model was established based on multiple periods, and differentiated contracts were established at different stages. The validity of the models and the contract was verified by numerical simulation.FindingsThe results suggest that with the increase in the EN fairness concerns coefficient, the effort level of EN decreases continuously and decreases faster in the second stage because this is the last stage. The level of VC's (venture capitalist, simplified as VC) effort declines first and then increases; that is, VC will increase the effort level when the fairness concerns coefficient increases to a certain threshold. To motivate EN to pay more effort, VC will increase the incentive to EN in the first stage. However, it will reduce the level of incentive to EN in the second stage. In the limited stage of venture investment, consider that the fairness concerns of EN do not make the profits of EN and VC achieve Pareto improvement simultaneously.Originality/valueFirst, the authors implanted fairness concerns into multi-stage venture capital and discussed the impact of fairness concerns on the efforts and returns of both parties. Second, among the influencing factors of the project output, the authors consider the bilateral efforts of EN and VC, the working capacity of EN, the initial investment scale, and the external uncertain environment.

Multi-stage sampling

2019 ◽  
pp. 173-199
Author(s):  
David G. Hankin ◽  
Michael S. Mohr ◽  
Ken B. Newman
Keyword(s):  
Natural Resource ◽  
Optimal Allocation ◽  
Sampling Strategy ◽  
Cluster Sampling ◽  
Sampling Variance ◽  
Two Stage ◽  
Unequal Size ◽  
Second Stage ◽  
Resource Setting ◽  
Multi Stage

In multi-stage sampling, there are two or more stages of sampling and the simplest version, which the chapter emphasizes is called two-stage sampling. In two-stage sampling, an initial first-stage sample of n primary units (or clusters) is selected. Then, at the second stage of sampling, m i subunits are selected from the M i subunits in the selected primary units. First- and second-stage units may be selected with equal or unequal probabilities and a wide variety of estimators may be used to estimate totals within selected primary units and to estimate the total of the target variable in the finite population. Illustrative sample spaces are provided for equal sized two-stage cluster sampling with SRS selection at both stages, and for two-stage unequal size cluster sampling, with clusters selected by PPSWOR and units within clusters selected by SRS. Sampling variance is shown to originate from two sources: variation between primary unit totals or means (first-stage variance), and errors of estimation of primary units totals (second-stage variance). Topics of optimal allocation and net relative efficiency are addressed in the two-stage context with equal and unequal size clusters. General expressions for sampling variance are presented for three or more stages of sampling. The multi-stage framework can take powerful advantage of all of the concepts and sampling designs considered in previous chapters and the ecologist or natural resource scientist can apply everything he/she knows about an ecological or natural resource setting to guide development of an intelligent multi-stage sampling strategy.

scholarly journals Pseudo-Valid Cutting Planes for Two-Stage Mixed-Integer Stochastic Programs with Right-Hand-Side Uncertainty

2020 ◽  
Vol 68 (4) ◽  
pp. 1199-1217
Author(s):  
Ward Romeijnders ◽  
Niels van der Laan
Keyword(s):  
Cutting Planes ◽  
Mixed Integer ◽  
Integer Optimization ◽  
Random Parameters ◽  
Two Stage ◽  
Stochastic Programs ◽  
Second Stage ◽  
Right Hand ◽  
Novel Approach ◽  
Decision Variables

Cutting planes need not be valid in stochastic integer optimization. Many practical problems under uncertainty, for example, in energy, logistics, and healthcare, can be modeled as mixed-integer stochastic programs (MISPs). However, such problems are notoriously difficult to solve. In “Pseudo-Valid Cutting Planes for Two-Stage Mixed-Integer Stochastic Programs with Right-Hand-Side Uncertainty,” Romeijnders and van der Laan introduce a novel approach to solve two-stage MISPs. Instead of using exact cuts that are always valid, they propose to use pseudo-valid cutting planes for the second-stage feasible regions that may cut away feasible integer second-stage solutions for some scenarios and may be overly conservative for others. The advantage of using such cutting planes is that the approximating problem remains convex in the first-stage decision variables and thus can be solved efficiently. Moreover, the performance of these cutting planes is good if the variability of the random parameters in the model is large enough.

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