Production Planning Under Supply and Demand Uncertainty: A Stochastic Programming Approach

2010 ◽  
pp. 297-315 ◽  
Author(s):  
Julia L. Higle ◽  
Karl G. Kempf
Keyword(s):  
Stochastic Programming ◽  
Production Planning ◽  
Demand Uncertainty ◽  
Supply And Demand ◽  
Programming Approach

Solving Solid Transportation Problems With Multi-Choice Cost and Stochastic Supply and Demand

2018 ◽  
pp. 137-170 ◽  
Author(s):  
Sankar Kumar Roy ◽  
Deshabrata Roy Mahapatra
Keyword(s):  
Stochastic Programming ◽  
Objective Function ◽  
Transportation Problem ◽  
Supply And Demand ◽  
Optimal Solution ◽  
Programming Approach ◽  
Solid Transportation Problem ◽  
New Approach ◽  
Non Linear Programming ◽  
The Cost

In this chapter, the authors propose a new approach to analyze the Solid Transportation Problem (STP). This new approach considers the multi-choice programming into the cost coefficients of objective function and stochastic programming, which is incorporated in three constraints, namely sources, destinations, and capacities constraints, followed by Cauchy's distribution for solid transportation problem. The multi-choice programming and stochastic programming are combined into a solid transportation problem, and this new problem is called Multi-Choice Stochastic Solid Transportation Problem (MCSSTP). The solution concepts behind the MCSSTP are based on a new transformation technique that will select an appropriate choice from a set of multi-choice, which optimize the objective function. The stochastic constraints of STP converts into deterministic constraints by stochastic programming approach. Finally, the authors construct a non-linear programming problem for MCSSTP, and by solving it, they derive an optimal solution of the specified problem. A realistic example on STP is considered to illustrate the methodology.

Stochastic Programming Approach for Resource Selection Under Demand Uncertainty

2019 ◽  
pp. 107-126 ◽  
Author(s):  
Tanveer Hossain Bhuiyan ◽  
Mahantesh Halappanavar ◽  
Ryan D. Friese ◽  
Hugh Medal ◽  
Luis de la Torre ◽  
...  
Keyword(s):  
Stochastic Programming ◽  
Resource Selection ◽  
Demand Uncertainty ◽  
Programming Approach

Solving Solid Transportation Problem with Multi-Choice Cost and Stochastic Supply and Demand

2014 ◽  
Vol 5 (3) ◽  
pp. 1-26 ◽  
Author(s):  
Sankar Kumar Roy ◽  
Deshabrata Roy Mahapatra
Keyword(s):  
Stochastic Programming ◽  
Objective Function ◽  
Transportation Problem ◽  
Supply And Demand ◽  
Optimal Solution ◽  
Programming Approach ◽  
Solid Transportation Problem ◽  
New Approach ◽  
Non Linear Programming ◽  
The Cost

This paper proposes a new approach to analyze the solid transportation problem (STP). This new approach considers the multi-choice programming into the cost coefficients of objective function and stochastic programming which is incorporated in three constraints namely sources, destinations and capacities constraints followed by Cauchy's distribution for solid transportation problem. The multi-choice programming and stochastic programming are combined into solid transportation problem and this new problem is called multi-choice stochastic solid transportation problem (MCSSTP). The solution concepts behind the MCSSTP are based on a new transformation technique which will select an appropriate choice from a set of multi-choice which optimizes the objective function. The stochastic constraints of STP convert into deterministic constraints by stochastic programming approach. Finally, the authors have constructed a non-linear programming problem for MCSSTP and have derived an optimal solution of the specified problem. A realistic example on STP is considered to illustrate the methodology.

scholarly journals Scenario Based Two-Stage Stochastic Programming Approach for the Midterm Production Planning of Oil Refinery

MATEMATIKA ◽  
2018 ◽  
Vol 34 (3) ◽  
pp. 45-55 ◽  
Author(s):  
Norshela Mohd Noh ◽  
Arifah Bahar ◽  
Zaitul Marlizawati Zainuddin
Keyword(s):  
Linear Programming ◽  
Stochastic Programming ◽  
Production Planning ◽  
Oil Price ◽  
Profit Margin ◽  
Oil Refinery ◽  
Scenario Tree ◽  
Programming Approach ◽  
Two Stage ◽  
The Past

Recently, oil refining industry is facing with lower profit margin due to uncertainty. This causes oil refinery to include stochastic optimization in making a decision to maximize the profit. In the past, deterministic linear programming approach is widely used in oil refinery optimization problems. However, due to volatility and unpredictability of oil prices in the past ten years, deterministic model might not be able to predict the reality of the situation as it does not take into account the uncertainties thus, leads to non-optimal solution. Therefore, this study will develop two-stage stochastic linear programming for the midterm production planning of oil refinery to handle oil price volatility. Geometric Brownian motion (GBM) is used to describe uncertainties in crude oil price, petroleum product prices, and demand for petroleum products. This model generates the future realization of the price and demands with scenario tree based on the statistical specification of GBM using method of moment as input to the stochastic programming. The model developed in this paper was tested for Malaysia oil refinery data. The result of stochastic approach indicates that the model gives better prediction of profit margin.

A stochastic programming approach for sawmill production planning

2013 ◽  
Vol 5 (1) ◽  
pp. 1 ◽  
Author(s):  
Masoumeh Kazemi Zanjani ◽  
Daoud Ait Kadi ◽  
Mustapha Nourelfath
Keyword(s):  
Stochastic Programming ◽  
Production Planning ◽  
Programming Approach
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