Chance-Constrained programming problem with Chance-Constrained relative lower bounded variables

1979 ◽  
Vol 30 (2) ◽  
pp. 73-77 ◽  
Author(s):  
S. Kumar
Keyword(s):  
Programming Problem ◽  
Chance Constrained Programming ◽  
Bounded Variables ◽  
Constrained Programming

scholarly journals A Novel Method for Optimal Solution of Fuzzy Chance Constraint Single-Period Inventory Model

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Anuradha Sahoo ◽  
J. K. Dash
Keyword(s):  
Programming Problem ◽  
Fuzzy Random Variable ◽  
Optimal Solution ◽  
Random Variable ◽  
Chance Constraint ◽  
Chance Constrained Programming ◽  
Storage Space ◽  
Weighted Sum Method ◽  
Single Period ◽  
Constrained Programming

A method is proposed for solving single-period inventory fuzzy probabilistic model (SPIFPM) with fuzzy demand and fuzzy storage space under a chance constraint. Our objective is to maximize the total profit for both overstock and understock situations, where the demandD~jfor each productjin the objective function is considered as a fuzzy random variable (FRV) and with the available storage space areaW~, which is also a FRV under normal distribution and exponential distribution. Initially we used the weighted sum method to consider both overstock and understock situations. Then the fuzziness of the model is removed by ranking function method and the randomness of the model is removed by chance constrained programming problem, which is a deterministic nonlinear programming problem (NLPP) model. Finally this NLPP is solved by using LINGO software. To validate and to demonstrate the results of the proposed model, numerical examples are given.

scholarly journals Solution to Chance Constrained Programming Problem in Swap Trailer Transport Organisation based on Improved Simulated Annealing Algorithm

2020 ◽  
Vol 5 (1) ◽  
pp. 47-54 ◽  
Author(s):  
Tao Li ◽  
Wenyin Yang
Keyword(s):  
Simulated Annealing ◽  
Programming Problem ◽  
Vehicle Routing Problem ◽  
Simulated Annealing Algorithm ◽  
Chance Constrained Programming ◽  
Routing Problem ◽  
Theoretical Support ◽  
Annealing Algorithm ◽  
Constrained Programming

AbstractSwap trailer transport organisation problem originates from the traditional vehicle routing problem (VRP). Most of the studies on the problems assume that the travelling times of vehicles are fixed values. In this paper, the uncertainties of driving times are considered and a chance constrained programming problem is proposed. An improved simulated annealing algorithm is used to solve the problem proposed. The model and algorithm described in this paper are studied through a case study, and the influence of uncertainty on the results is analysed. The conclusion of this study provides theoretical support for the practice of trailer pickup transport.

Using Fuzzy Goal Programming Technique to Solve Multiobjective Chance Constrained Programming Problems in a Fuzzy Environment

2012 ◽  
Vol 2 (1) ◽  
pp. 71-80 ◽  
Author(s):  
Animesh Biswas ◽  
Nilkanta Modak
Keyword(s):  
Decision Making ◽  
Programming Problem ◽  
Goal Programming ◽  
Fuzzy Goal Programming ◽  
Chance Constrained Programming ◽  
Membership Functions ◽  
Programming Technique ◽  
Goal Programming Model ◽  
Fuzzy Goal ◽  
Constrained Programming

In this paper a fuzzy goal programming technique is presented to solve multiobjective decision making problems in a probabilistic decision making environment where the right sided parameters associated with the system constraints are exponentially distributed fuzzy random variables. In model formulation of the problem, the fuzzy chance constrained programming problem is converted into a fuzzy programming problem by using general chance constrained methodology. Then by realizing the fuzzy nature of the parameters associated with the system constraints, the problem is decomposed by considering the tolerance ranges of the parameters. The tolerance membership functions of each of the individual objectives are defined in isolation to measure the degree of achievements of the goal levels of the objectives. Then a fuzzy goal programming model is developed to achieve the highest degree of each of the defined membership functions to the extent possible. In the solution process the minsum fuzzy goal programming technique is used to find the most satisfactory decision in the decision making environment. An example is solved to illustrate the proposed methodology and the achieved solution is compared with the solution of another existing technique.

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