 Research Article
 Open Access
Hybrid Method for a Class of Stochastic Bicriteria Optimization Problems
 Zhong Wan^{1}Email author,
 AiYun Hao^{1},
 FuZheng Meng^{1} and
 Chaoming Hu^{1}
https://doi.org/10.1155/2010/745162
© Zhong Wan et al. 2010
 Received: 24 June 2010
 Accepted: 20 October 2010
 Published: 7 November 2010
Abstract
We study a class of stochastic bicriteria optimization problems with one quadratic and one linear objective functions and some linear inequality constraints. A hybrid method of chanceconstrained programming (CCP) combined with variance expectation (VE) is proposed to find the optimal solution of the original problem. By introducing the expectation level, the bicriteria problem is converted into a singleobjective problem. By introducing the confidence level and the preference level of decision maker, we obtain a relaxed robust deterministic formulation of the stochastic problem. Then, an interactive algorithm is developed to solve the obtained deterministic model with three parameters, reflecting the preferences of decision maker. Numerical experiments show that the proposed method is superior to the existing methods. The optimal solution obtained by our method has less violation of the constraints and reflects the satisfaction degree of decisionmaker.
Keywords
 Constraint Violation
 Stochastic Matrix
 Satisfaction Degree
 Chance Constraint
 Interactive Algorithm
1. Introduction
In many fields of industrial engineering and management sciences, there often exist some uncertainties, such as the return rate of security and the amounts of demand and supply. Recently, many attentions have been paid to construct optimization models with uncertain parameters for the decision problems in the field of management science and to design some efficient solution methods for these optimization models. For this connection, one can see [1–9] and the references therein.
where is continuously differentiable, is a dimensional stochastic vector, and are given vectors, and are given stochastic matrices. So, Problem (1.1) is a stochastic bicriteria optimization problem. The main difficulties to solve this kind of problems lie in two aspects. The first one is that optimal decisions are required to be prior to the observation of the stochastic parameters. In this situation, one can hardly find any decision that has no constraints violation caused by unexpected random effects. The second one is that no decision can optimize the two objective functions simultaneously.
and developed an algorithm to obtain an approximate solution of the original problem.
Though the expectation method is a convenient way of dealing with stochastic programs [9, 10], it may not ensure that the optimal solution is robust as well as having optimal values of objective functions in general. For this, we are going to propose a hybrid method for the solution of Problem (1.1). The basic idea is as follows.
For the bicriteria problem, we introduce a parameter of expectation level for the second objective, and transform the original problem into a problem with singleobjective function. For the stochastic parameters, we introduce an appropriate combination of the mean and variance of the cost, which is to be minimized subject to some chance constraints. The variance appeared in the cost function can be interpreted as a risk measure, which can make the solution more robust. For the chance constraint, it ensures that the probability for the constraints to be satisfied is greater than or equal to some value. The larger this value is taken, the higher probability the constraints are satisfied. In other words, the chance constraints approach can guarantee that the obtained solution has less degree of constraint violation (see [4, 11]). Based on such a reformulation for the original problem, an interactive algorithm will also be developed to find its solution with some satisfaction degree.
The remainder of this paper is organized as follows. In Section 2, we will deduce the new robust deterministic formulation for the original stochastic model. Then, in Section 3, an interactive algorithm will be developed to solve such a deterministic problem with three parameters, reflecting the preferences of decision maker. Numerical experiments are carried out in Section 4 to show the advantage of the proposed method. Final remarks are given in the last section.
2. Reformulation of Stochastic BiCriteria Model by Hybrid Approach
In this section, we are going to reformulate the original stochastic bicriteria problem into a deterministic problem.
Note that there are various ways to deal with multipleobjective problems. For details, see, for example, [6, 10, 12]. In this paper, Problem (1.1) is converted into a singleobjective model by introducing a parameter, called the expectation level of decision maker.
Notice that the solution of the above problem is a compromising solution of Problem (1.1) by a suitable . Actually, , where is the maximum of the second objective function. When , the solution of (2.1) ensures that the second objective achieve its maximal value.
where and denote, respectively, the expectation and the variance of stochastic matrix, and is introduced to describe the preference of decision maker to the average level and the robustness of objective value, and is called the preference level of decision maker. The variance appeared in the cost function can be interpreted as the risk measure, which can make the obtained solution more robust.
For the first stochastic inequality in (2.1), we introduce the socalled chance constraint method to convert it into a deterministic inequality constraint, which is used to guarantee that the stochastic constraint is satisfied with a probability as higher as possible. For the general stochastic constraints , we obtain their deterministic formulations by expectation method as done in [9].
where is the probability (or confidence) level for the first stochastic constraint to be satisfied.
where .
where is the inverse of the probability density function with standard normal distribution.
From the above deduction, we obtain a new relaxed deterministic formulation (2.6) of the original problem (1.1). Based on this model, an efficient solution method is developed in the next section.
3. Interactive Algorithm
In this section, from Model (2.9), we are going to develop an interactive algorithm to obtain an optimal solution of the original problem (1.1) such that there is less violation of constraints. It is more robust in the sense of less degree of constraint violation taking account of the satisfaction degree of decision maker. The basic idea of this algorithm is to adjust the threelevel parameters of decision maker until a satisfactory solution is obtained.
It is noted that, for given , , and , we solve a subproblem that turns out to be a minimization problem of quartic polynomial with one quadratical constraint and several linear constraints [7]. Then, by comparing the features of the solutions corresponding to a series of subproblems, we decide whether or not the algorithm is to be terminated. The overall algorithm is as follows.
Algorithm 3.1 (Interactive Algorithm for Stochastic Bicriteria Problems).
Step 1.
Choose , , and , where , , . Here, and , and denote, respectively, the minimum and the maximum of and given by the decision maker.
Let , , and be three positive constant scalars, for example, fix , , and . Take , , and . Set , , , .
Step 2.
The optimal solution is denoted by , the corresponding value of the objective function is denoted by . Let , .
Step 3.
If , then go to Step 5. Otherwise, go to Step 4.
Step 4.
Ask the decision maker whether and are satisfactory. If they are, then go to Step 9; Otherwise, ask the decision maker whether needs to be changed. If it does not, then go to Step 2. Otherwise, ask the decision maker to update by , and go to Step 2.
Step 5.
Let , , and . If , then go to Step 7. Otherwise, go to Step 6.
Step 6.
Ask the decision maker whether needs to be changed. If it does not, then go to Step 2. Otherwise, update by , and go to Step 2.
Step 7.
Let , , , and . If , the algorithm stops, and are the desired results. Otherwise, go to Step 8.
Step 8.
Ask the decision maker whether needs to be changed. If it does not, then go to Step 2. Otherwise, update by , and go to Step 2.
Step 9.
and are the desired results. The algorithm terminates.
4. Numerical Experiments
where denotes the normally distributed probability density function with mean and variance .
In Lingo 9.0, we obtain the optimal solution of Model (4.3): , , , and the value of the objective function is 105.682. In the same setting, from Model (1.2) in [9], we obtained an optimal solution , , and , .
Effects of the threelevel parameters on solutions.
( , , ) 




 2784.9 










 183.23 




 1057.28 




 823.29 




 1410.02 




 2058.31 




 635.42 




 418.55 




 83.44 



From Table 1, it can be seen that the adjustment of , , and is helpful for the decision maker to choose a favorite solution.
In the end of this section, we are going to investigate the degree of constraint violation for the proposed method. By simulation, in MATLAB 6.5, 48 samples of all stochastic parameters are generated. Thus, we get 48 optimization problems. Next, we are going to investigate the degree of constraint violation for the proposed method in this paper and the expectation method presented in [9].
Comparison between expectation method and hybrid method.
Samples 





1 
 0 
 0 
2 
 1 
 0 
3 
 0 
 0 
4 
 0 
 0 
5 
 0 
 0 
6 
 0 
 0 
7 
 0 
 0 
8 
 0 
 0 
9 
 0 
 0 
10 
 0  0  0 
11 
 0 
 0 
12 
 0 
 0 
13 
 1 
 0 
14 
 0 
 0 
15 
 0 
 0 
16 
 0 
 0 
17 
 0 
 0 
18 
 3 
 0 
19 
 3 
 0 
20 
 1 
 0 
21 
 0 
 0 
22 
 0 
 0 
23 
 0 
 0 
24 
 0 
 0 
25 
 0 
 0 
26 
 1 
 0 
27 
 3 
 0 
28 
 0 
 0 
29 
 0 
 0 
30 
 0 
 0 
31 
 0 
 0 
32 
 0 
 0 
33 
 0 
 0 
34 
 0 
 0 
35 
 0 
 0 
36 
 0 
 0 
37 
 1 
 0 
38 
 0 
 0 
39 
 0 
 0 
40 
 1 
 0 
41 
 0 
 0 
42 
 0 
 0 
43 
 0 
 0 
44 
 0 
 0 
45 
 0 
 0 
46 
 0 
 0 
47 
 3 
 0 
48 
 1  0  0 
From Table 2, it is shown that the optimal solution by the hybrid method has no violation of the constraints for all the 48 samples with 0.95 probability level, while there are 19 times of violating constraints for the expectation method.
5. Final Remarks
In this paper, a class of stochastic bicriteria optimization problems was studied by a new hybrid method where the chanceconstrained programming (CCP) is combined with the varianceexpectation (VE) method. Then an interactive algorithm was developed to find an optimal solution of the original problem, reflecting the satisfaction degree of the decision maker.
Even if some strong assumptions are imposed, it is difficult to obtain explicit expressions of deterministic inequalities constraints that are involved only with the decision variable for the stochastic constraints. Thus, it calls for the investigation of other more efficient approaches.
Declarations
Acknowledgments
The authors would like to express their thanks to the two anonymous referees for their comments on the paper, which have improved its presentation. The work of these authors was supported by the National Natural Science Foundation of China (Grant no. 71071162, and 70921001) and the Project for Excellent Talent of New Century, Ministry of Education, China (Grant no. NCET070864).
Authors’ Affiliations
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