Fuzzy programming (FP), which aims to find optimal solutions under a set of linear or nonlinear objectives and constraints involving fuzzy parameters or variables, is an important optimization technique in both theory and application. Some indicators such as mean and variance are employed to characterize and defuzzify the models. In this section, the entropy and semi-entropies are used in FP models as alternative measures of characterizing the uncertainty with regard to return and cost-oriented problems.
5.1. Mean-Entropy and Mean-Semi-Entropy Optimization Models in Return-Oriented Problems
For a decision maker with a given input, achieving more returns at lower risks is the main purpose while making decisions among multiple alternatives. Generally, the decision maker has an expectation of the return, but little knowledge of predicting the risk due to information vagueness and imprecision. Using fuzzy parameters to express the returns of the alternatives and entropy to denote the risks, the general paradigm of a mean-entropy optimization model aiming to minimize the risk under certain expected constraints can be described as follows,
where
is the overall return with
as decision variables and
as fuzzy parameters,
are the constraint functions, and
is a predetermined constant denoting the lower limit of the expected overall return.
Considering that entropy measures bilateral risks, minimizing the entropy will certainly sacrifice high returns. For a decision maker who focuses on the downside risk only and is willing to get high returns, a mean-semi-entropy model with the objective to minimize the lower semi-entropy is more appropriate. The model satisfying certain expected return constraints is shown below,
where
is the lower semi-entropy of the overall return.
In particular, if
f and
are linear functions of independent and regular fuzzy parameters
, i.e.,
then Model (
36) can be transformed into the following form based on Theorems 2 and 6,
where
and
are the entropies and expectations of
respectively. Since the entropy of a constant is 0,
is eliminated in the objective function.
Likewise, the equivalent model of Model (
37) by Theorem 9 can be depicted as:
where
herein is an integral expression of
. When the CDs
and the expected values
of
are known, it can be expressed as Equation (
31).
5.2. Mean-Entropy and Mean-Semi-Entropy Optimization Models in Cost-Oriented Problems
For cost-oriented problems under uncertain environments, a decision maker usually wants to keep the costs within reasonable boundaries with the risks controllable rather than minimize the costs for unpredictable risks. In such a situation, using fuzzy parameters to express the costs of the choices and entropy to measure the risks, then the general paradigm of a mean-entropy optimization model with the objective of entropy minimization under certain expected constraints is:
where
is the overall cost,
are constraint functions, and
is a predetermined constant denoting the upper limits of the expected cost.
Compared with the lower semi-entropy measuring the downside risk that the return is less than the expected value, the upper semi-entropy weighing the upside risk that the cost exceeds the expected value is more applicable in this case. Thus, the mean-semi-entropy optimization model of minimizing the upper semi-entropy can be established to handle cost-oriented problems as follows,
where
is the upper semi-entropy of the overall cost.
Similarly, when
and
are linear with respect to fuzzy parameters
, Models (
40) and (
41) can be transformed into:
and:
where
and
are the entropies and expectations of
, respectively, and
herein is an integral expression of
like Equation (
32) when
and
are known.
In brief, when the objective and constraints are linear functions of the fuzzy parameters, the generalized FP models above can be transformed into their equivalent forms like Model (
38), (
39), (
42), or (
43). Since the CDs
of the fuzzy parameters tend to be known in practice, the expectations
and entropies
(semi-entropies
,
) in these models can be derived by the proposed calculation formulas in advance, and then, the models turn into crisp linear or nonlinear programming models not including uncertain operators and can be solved by classical mathematical optimization methods or algorithms readily with the help of software.
5.3. Numerical Examples
In this section, a portfolio selection problem and a project selection problem are illustrated to show the solution framework of the two types of entropy optimization models, respectively. The results are solved by MATLAB 2015a.
Example 15. This example is illustrated to compare the performance and differences of the mean-entropy and mean-semi-entropy optimization models for return-oriented problems, where the fuzzy parameters are triangular fuzzy numbers.
Suppose that an investor would like to build a portfolio from four stocks in which Stock 1 is a junk equity with large downside risks, Stock 2 is a plain and stable equity, Stock 3 is a blue chip, and Stock 4 is a growth equity. Denote by
the fuzzy return of stock
i, and the parameters of the four stocks are presented in
Table 1, in which the expected values and entropies are calculated by Equations (
13) and (
16).
Denote by
a the lower limit of the expected overall return. In this example, the investor expects to minimize the uncertainty of obtaining a return greater than
a. Thus, the entropy optimization models in light of Models (
36) and (
37) are:
respectively, where
is the proportion allocated to stock
i.
Since the overall return
is linear with respect to
, the equivalent formulations according to Models (
38) and (
39) and
Table 1 are:
Obviously, the mean-entropy model on the left is a linear programming, which can be precisely solved by a software package. The mean-semi-entropy model on the right is a nonlinear programming in which the objective can be explicitly expressed by Equation (
31) since
is also a triangular fuzzy number whose parameters are linear functions of
, and therefore, it can be easily solved by the MATLAB toolbox.
With the help of MATLAB 2015a, the optimal portfolios under the mean-entropy and mean-semi-entropy models with different lower limits of the expected overall returns
a can be obtained as shown in
Table 2 and
Figure 3. It can be seen from the left side of
Table 2 that the coefficients of
and
are all 0, i.e., only stocks 2 and 3 are included in the optimal portfolio, and the lower limit of the expected overall return merely influences the proportion allocated to them. The results can well explain that entropy represents the uncertainty of both high and low extreme returns, thereby the mean-entropy optimization model excludes Stock 1, which has the potential to lose 60%, and Stock 4, which can potentially reach a return of 90% simultaneously.
As shown in the right side of
Table 2 and
Figure 3, the optimal portfolios under the semi-entropy optimization model are more distributed, since Stock 4 is included in the portfolios and the proportions allocated to it are more than 20%. Besides, the higher return the investor expects, the higher the proportion allocated to Stock 4 is, indicating that investors who expect higher returns are willing to take higher risks and buy fluctuating stocks. Nevertheless, Stocks 2 and 3 still occupy the majority of the investments, and the coefficients of
are still zero, which is in line with the actual decision-making mode that investors tend to invest large amounts of money in stable assets and reserve a small percentage for risky investments except for junk stocks, which may cause heavy losses, but gain little profits, then realize steady appreciation. The results imply that using semi-entropy as a risk measure can perfectly get rid of junk stocks and does not miss potentially high-return stocks. What is more, owing to the higher expected return of Stock 4, the expected return of the portfolio obtained by the mean-semi-entropy model is higher than that of the mean-entropy model at each lower limit. Note that since entropy measures bilateral uncertainty, yet lower semi-entropy weighs downside risks, the entropies are naturally larger than the semi-entropies, as shown in
Table 2.
Example 16. This example is used to illustrate the entropy and semi-entropy optimization models of cost-oriented problems, where the fuzzy parameters are triangular fuzzy numbers.
Consider a company that plans to develop some new products. There are four products whose R&D costs are supposed to be triangular fuzzy numbers. Let
denote the proportion allocated to product
i for
, respectively,
the fuzzy cost of developing product
i, and
b the upper limit of the expected total cost. The parameters of the four products are represented in
Table 3, in which the expected values and entropies are calculated by Equations (
13) and (
16).
In this example, if the company hopes to pay a total cost less than the upper limit
b and minimize the uncertainty of the expenditure simultaneously, then the entropy optimization models are:
Further, according to Models (
42) and (
43) and
Table 3, the equivalent formulations of the above models are:
Similar to Example 15, the left model is a linear programming, and the right one is a nonlinear programming. By using MATLAB 2015a, the comparative results are obtained and shown in
Table 4 and
Figure 4. As we can see, no matter how much the upper limit is, the mean-entropy model keeps selecting Product 1, which has the potential to reach the lowest cost at three million dollars, and Product 2, which is most stable with the same maximum cost as Product 1. Besides, the higher the total cost that can be tolerated, the higher the proportion allocated to Product 2 is, since the expected total cost is closer to the expected cost of Product 2 and entropy dominates. By contrast, in the schemes obtained by the mean-semi-entropy optimization model, Product 1 accounts for more proportion, and Product 4 is included. As mentioned before, Product 1 is better than Product 2 from the perspective of cost; therefore, it is a better choice to invest more in Product 1. On the other hand, with the increase of the proportion distributed to Product 1, the total expected cost is decreased, and there are more chances to select the fluctuating Product 4. The semi-entropy optimization model can reach a much lower minimum total cost and therefore is recommended.
Together, we can see from the foregoing numerical examples that the results of the mean-entropy and mean-semi-entropy models are quite different, despite that they both rule out the alternatives with undesirable extreme values. Specifically, the mean-entropy model tends to construct concentrative schemes with stable alternatives, since entropy takes both sides of derivation as risks and excludes the alternatives with favorable extreme values mistakenly. Conversely, the mean-semi-entropy model prefers more distributive schemes, as semi-entropies measuring one-side risks retain the favorable extreme ones. As a consequence, the expectations of the mean-semi-entropy models are more desirable, i.e., the schemes derived from the mean-semi-entropy models have higher expected overall returns and lower expected total costs than those of the mean-entropy models.