scholarly journals Multiobjective Two-Stage Stochastic Programming Problems with Interval Discrete Random Variables

2012 ◽  
Vol 2012 ◽  
pp. 1-21 ◽  
Author(s):  
S. K. Barik ◽  
M. P. Biswal ◽  
D. Chakravarty

Most of the real-life decision-making problems have more than one conflicting and incommensurable objective functions. In this paper, we present a multiobjective two-stage stochastic linear programming problem considering some parameters of the linear constraints as interval type discrete random variables with known probability distribution. Randomness of the discrete intervals are considered for the model parameters. Further, the concepts of best optimum and worst optimum solution are analyzed in two-stage stochastic programming. To solve the stated problem, first we remove the randomness of the problem and formulate an equivalent deterministic linear programming model with multiobjective interval coefficients. Then the deterministic multiobjective model is solved using weighting method, where we apply the solution procedure of interval linear programming technique. We obtain the upper and lower bound of the objective function as the best and the worst value, respectively. It highlights the possible risk involved in the decision-making tool. A numerical example is presented to demonstrate the proposed solution procedure.

OPSEARCH ◽  
2012 ◽  
Vol 49 (3) ◽  
pp. 280-298 ◽  
Author(s):  
Suresh Kumar Barik ◽  
Mahendra Prasad Biswal ◽  
Debashish Chakravarty

2012 ◽  
Vol 2012 ◽  
pp. 1-13 ◽  
Author(s):  
S. K. Barik ◽  
M. P. Biswal

We present a solution procedure for a quadratic programming problem with some probabilistic constraints where the model parameters are either triangular fuzzy number or trapezoidal fuzzy number. Randomness and fuzziness are present in some real-life situations, so it makes perfect sense to address decision making problem by using some specified random variables and fuzzy numbers. In the present paper, randomness is characterized by Weibull random variables and fuzziness is characterized by triangular and trapezoidal fuzzy number. A defuzzification method has been introduced for finding the crisp values of the fuzzy numbers using the proportional probability density function associated with the membership functions of these fuzzy numbers. An equivalent deterministic crisp model has been established in order to solve the proposed model. Finally, a numerical example is presented to illustrate the solution procedure.


Author(s):  
Zhiren Long ◽  
Xianxiu Wen ◽  
Mei Lan ◽  
Yongjian Yang

AbstractThe nursing rescheduling problem is a challenging decision-making task in hospitals. However, this decision-making needs to be made in a stochastic setting to meet uncertain demand with insufficient historical data or inaccurate forecasting methods. In this study, a stochastic programming model and a distributionally robust model are developed for the nurse rescheduling problem with multiple rescheduling methods under uncertain demands. We show that these models can be reformulated into an integer program. To illustrate the applicability and validity of the proposed model, a study case is conducted on three joint hospitals in Chengdu, Chongzhou, and Guanghan, Sichuan Province. The results show that the stochastic programming model and the distributionally robust model can reduce the cost by 78.71% and 38.92%, respectively. We also evaluate the benefit of the distributionally robust model against the stochastic model and perform sensitivity analysis on important model parameters to derive some meaningful managerial insights.


2017 ◽  
Vol 9 (3) ◽  
pp. 59
Author(s):  
Carina Simionato de Barros ◽  
Gabriela Geraldi Mendonça ◽  
Augusto Hauber Gameiro

Farm schools offer a learning environment for the education of students in Agricultural Technical Programs and offer this program adopting boarding systems (“farm-boarding schools” or “FBS”). The big challenge in FBS is balancing education and production, that is, provide resources for practical classes and at the same time provide food for farm residents from a pre-defined budget by the sponsoring institution. The aim of this paper is to present a linear programming model to plan and optimize FBS production and supply. The model was applied in two FBS in Brazil. The model developed could show the complexity of the FBS system, which features a variety of productions and the interactions among them. The modeling process presented positive results from a technical and managerial point of view, including people management. The formulated model showed an optimized scenario which extended the managers’ analysis horizon and allowed safer decision making. The system’s complexity hampers dialogue between the farm-boarding school team and managers. From the modeling process and the standardization of data and generated results, there was a greater safety margin to present investment proposals and analyzes, accelerating the decision-making process, which was a positive addition to the system.


2008 ◽  
pp. 26-49 ◽  
Author(s):  
Yong Shi ◽  
Yi Peng ◽  
Gang Kou ◽  
Zhengxin Chen

This chapter provides an overview of a series of multiple criteria optimization-based data mining methods, which utilize multiple criteria programming (MCP) to solve data mining problems, and outlines some research challenges and opportunities for the data mining community. To achieve these goals, this chapter first introduces the basic notions and mathematical formulations for multiple criteria optimization-based classification models, including the multiple criteria linear programming model, multiple criteria quadratic programming model, and multiple criteria fuzzy linear programming model. Then it presents the real-life applications of these models in credit card scoring management, HIV-1 associated dementia (HAD) neuronal dam-age and dropout, and network intrusion detection. Finally, the chapter discusses research challenges and opportunities.


Author(s):  
Yong Shi ◽  
Yi Peng ◽  
Gang Kou ◽  
Zhengxin Chen

This chapter provides an overview of a series of multiple criteria optimization-based data mining methods, which utilize multiple criteria programming (MCP) to solve data mining problems, and outlines some research challenges and opportunities for the data mining community. To achieve these goals, this chapter first introduces the basic notions and mathematical formulations for multiple criteria optimization- based classification models, including the multiple criteria linear programming model, multiple criteria quadratic programming model, and multiple criteria fuzzy linear programming model. Then it presents the real-life applications of these models in credit card scoring management, HIV-1 associated dementia (HAD) neuronal damage and dropout, and network intrusion detection. Finally, the chapter discusses research challenges and opportunities.


Author(s):  
Xianrui Liao ◽  
Chong Meng ◽  
Zhixing Ren ◽  
Wenjin Zhao

The optimization of ecological water supplement scheme in Momoge National Nature Reserve (MNNR), using an interval-parameter two-stage stochastic programming model (IPTSP), still experiences problems with fuzzy uncertainties and the wide scope of the obtained optimization schemes. These two limitations pose a high risk of system failure causing high decision risk for decision-makers and render it difficult to further undertake optimization schemes respectively. Therefore, an interval-parameter fuzzy two-stage stochastic programming (IPFTSP) model derived from an IPTSP model was constructed to address the random variable, the interval uncertainties and the fuzzy uncertainties in the water management system in the present study, to reduce decision risk and narrow down the scope of the optimization schemes. The constructed IPFTSP model was subsequently applied to the optimization of the ecological water supplement scheme of MNNR under different scenarios, to maximize the recovered habitat area and the carrying capacity for rare migratory water birds. As per the results of the IPFTSP model, the recovered habitat areas for rare migratory birds under low, medium and high flood flow scenarios were (14.06, 17.88) × 103, (14.92, 18.96) × 103 and (15.83, 19.43) × 103 ha, respectively, and the target value was (14.60, 18.47) × 103 ha with a fuzzy membership of (0.01, 0.83). Fuzzy membership reflects the possibility level that the model solutions satisfy the target value and the corresponding decision risk. We further observed that the habitat area recovered by the optimization schemes of the IPFTSP model was significantly increased compared to the recommended scheme, and the increases observed were (5.22%, 33.78%), (11.62%, 41.88%) and (18.44%, 45.39%). In addition, the interval widths of the recovered habitat areas in the IPFTSP model were reduced by 17.15%, 17.98% and 23.86%, in comparison to those from the IPTSP model. It was revealed that the IPFTSP model, besides generating the optimal decision schemes under different scenarios for decision-makers to select and providing decision space to adjust the decision schemes, also shortened the decision range, thereby reducing the decision risk and the difficulty of undertaking decision schemes. In addition, the fuzzy membership obtained from the IPFTSP model, reflecting the relationship among the possibility level, the target value, and the decision risk, assists the decision-makers in planning the ecological water supplement scheme with a preference for target value and decision risk.


2016 ◽  
Vol 5 (3) ◽  
pp. 1-13 ◽  
Author(s):  
Suresh Kumar Barik ◽  
M. P. Biswal

A new solution procedure of possibilistic linear programming problem is developed involving the right hand side parameters of the constraints as normal random variables with known means and variances and the objective function coefficients are considered as triangular possibility distribution. In order to solve the proposed problem, convert the problem into a crisp equivalent deterministic multi-objective mathematical programming problem and then solved by using fuzzy programming method. A numerical example is presented to illustrate the solution procedure and developed methodology.


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