About this Journal Submit a Manuscript Table of Contents
Abstract and Applied Analysis

Volume 2014 (2014), Article ID 396420, 9 pages

Research Article

Harvesting Renewable Resources of Population with Size Structure and Diffusion

Institute of Operational Research and Cybernetics, Hangzhou Dianzi University, Hangzhou 310018, China

Received 10 April 2014; Accepted 24 May 2014; Published 18 June 2014

Academic Editor: Abdul Latif

Copyright © 2014 Qiang-Jun Xie et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The aim of this work is to explore the optimal exploitation way for a biological resources model incorporating individual’s size difference and spatial effects. The existence of a unique nonnegative solution to the state system is shown by means of Banach’s fixed point theorem, and the continuous dependence of the population density with the harvesting effort is given. The optimal harvesting strategy is established via normal cone and adjoint system technique. Some conditions are found to assure that there is only one optimal policy.

1. Introduction and Problem Setting

Since the classical work by Skellam [1], dispersal or diffusion of biological individuals has been recognized as one of the most significant features, which affect the dynamics and evolution of populations. Researchers made lots of efforts to understand the role of dispersal in the distribution of populations and the structure of communities; see, for example, [2, 3]. On the other hand, size-structured models are still an active field due to their ecological importance and mathematical challenge (see, e.g., [4, 5]).

This paper is concerned with an optimal harvesting problem of a species model, which incorporates the dispersal and body size of organisms. There are a number of results in some particular situations, such as [68] for age- and space-structured models and [918] for size-structured models without consideration of diffusion. In [9], Botsford constructed a size-specific population model based on the continuity equation and suggested that the inclusion of individual growth rates could reveal optimal harvesting policies. Some linear optimal harvesting population models structured by size were introduced in [10, 11]. The optimality conditions of Pontryagin’s type were obtained in [12] for an optimal control problem for a size-structured system. Hritonenko et al. [13] analyzed nonlinear optimal control of integral-differential equations that described the optimal management of a forest; Gasca-Leyva et al. [14] analyzed the optimal harvesting time for husbanded biological assets consisting of individuals of different sizes. In [15], Davydov and Platov established size-structured population dynamics in the case where the growth rate, mortality, and exploitation intensity depend only on the size. The global stability of a nontrivial stationary state and some necessary optimality conditions were obtained. Kato in [16] sought the optimal harvesting rate in a profit maximization problem for a nonlinear size-structured model of two-species population. Other nonlinear size-specific models can be found in [17, 18]. He and Liu [17] took fertility as the control variable and established the necessary optimality conditions of first order in the form of an Euler-Lagrange system. The existence of a unique optimal controller was established by means of Ekeland’s variational principle. Similar techniques were introduced in [18], and the optimality conditions describing the optimal strategy were also obtained via tangent-normal cone technique.

There are, however, only few models that combine size structure with spatial diffusion. In Section 1.2 of [5], Webb introduced a kind of population models with size structure and spatial position. The theory of semigroup of operators was employed to study the existence and asymptotical behaviors; some numerical simulations were made as an aid to understand the theoretical results. Moreover, a tumor growth model was examined. Faugeras and Maury established an advection model of fish with length and plane position distribution in [19], for which the well-posedness was rigorously treated and approximation procedure presented. Furthermore, the model was applied to skipjack tuna population in the Indian Ocean. Hadeler (in [20]) emphasized the value of introducing diffusion to structured population models and discussed how to supply appropriate boundary conditions for the models.

The objective of this paper is to investigate an optimal harvesting problem for a size- and space-structured population model and to analyze the structure of the optimal strategies. We are motivated to optimize the economic profits functional of resources exploitation as follows: subject to , where , is the finite horizon of control, and is a bounded open domain with a boundary smooth enough. Constants and stand for, respectively, the minimal and maximal size of individuals. denotes the economic value of an individual of size at time at location . is a costs factor for implementing the control policy . is the population density corresponding to , which is governed by the following system: where , is the outward unit normal, and the system (2) is endowed with the homogeneous Neumann boundary condition, which means no exchange of population across . Control variable is the harvesting effort; functions and give the minimum and maximum harvesting efforts, respectively. The vital parameters and are the death and birth rates, is the diffusive coefficient, and is the initial size and spatial distribution of our target population. is the growth modulus of size; that is, .

Hereafter, let . We need the following definition.

Definition 1. The set of curves is called the family of characteristic curves.

Throughout this paper, we make the following hypotheses: ) , , and for ;( ) , , a.e. in , where and , a.e. ;( ) a.e. in , , and let ;( ) a.e. in , , and let ;( ) a.e. in from (1), , and let .

Denote by the directional derivative operator of ; that is, It is obvious that for smooth enough.

Define Then, we introduce the definition of weak solutions as follows.

Definition 2. By solution of system (2), we mean a function , which belongs to for almost any characteristic curve and satisfies

The remainder of this paper is organized as follows. The next section deals with the well-posedness of the state system (2) for given parameters. Section 3 derives the necessary optimality conditions and describes an optimal feedback law of control, while Section 4 establishes the existence and uniqueness of optimal strategies. The paper ends with a remarks section.

2. Well-Posedness of the State System

Lemma 3. If and hypotheses ( ), ( ), and ( ) hold, then the system (2) has a unique nonnegative solution.

Proof. Without loss of generality, we may assume that . Let be the density of population at the minimum size , and denote by the solution of the following system:

In what follows, we prove the existence and uniqueness of following the spirits in [6].

Firstly, we replace in the right-hand side of the first equation in (6) with arbitrarily fixed: Then, we regard the above problem as a collection of linear parabolic systems on the characteristic curves (let ( ) be an initial point of ): Here, and is the solution to (7), which is a linear heat equation. By standard theory of PDE (see, e.g., [21]), we assure that the system (7) has a unique solution . Clearly, for almost any characteristic curve .

Secondly, by Banach’s fixed point theorem, we infer that there exists a unique bounded solution to the system (6). The proof is trivial and is omitted here.

We now define an operator as Consider a norm in space given by Clearly, it is equivalent to the usual norm. The constant will be determined later.

Let , and . It can be readily verified that is the solution of the following system:

Multiplying the first equation in (11) by and integrating over , we get that where by we mean ; the style will be used hereafter.

By ( ), we obtain that In addition, we are able to write down that So we have Then, we can derive the following inequalities:

It is now obvious that, for any , is a contraction on . Banach’s fixed point theorem allows us to conclude that there exists a unique such that . Let Since and is closed in , then . Consequently, is the desired solution of system (2).

Using the approximating procedure of Banach’s fixed point, we obtain the following monotonicity result.

Lemma 4. Under the hypotheses of Lemma 3, let be the solutions of the system (2) corresponding to . If ,   ,   , then

Then, we prove the main result of this section.

Theorem 5. Under the hypotheses ( )–( ), system (2) has a unique solution in . Furthermore, the solution is nonnegative and bounded: where and is the solution of system (2) with , , and .

Proof. For any (the set of all positive integers), we define It is apparent that satisfies the assumption of in Lemma 3, and the sequence is increasing. Denote by the unique nonnegative solution of system (2) corresponding to . For , we have , and so by Lemma 4. Beppo Levi’s theorem implies that So   a.e.  in   . It is not difficult to prove that is the unique solution of system (2) (see, e.g., Theorem   in [6]), but we omit the details.

Then, we examine the boundedness of solutions. Since is the unique nonnegative solution of (2) corresponding to , , and , where and are constants given in ( ) and ( ), Lemma 4 implies that Letting leads to which completes the proof.

3. Optimal Feedback Policy

In order to establish our main theorem, we need the following auxiliary results.

Lemma 6. Let ,   be the solutions of system (2) corresponding to the controls , respectively. Then, one has where is a constant independent of ,   .

Proof. Let . Then, is the solution of Multiplying the first equation of (25) by , integrating on , we obtain Proceeding in a similar way to the proof of Lemma 3, we arrive at where is as in Theorem 5 and . Bellman’s lemma implies that holds for any . Consequently, Then, letting gives the conclusion of Lemma 6.

We now define the following dual problem associated with the system (2): Denote by the solution to (30) corresponding to . Introducing the transformation , we then treat the system (30) in the same manner as that in Theorem 5 and Lemma 6 and obtain the properties of as follows.

Lemma 7. The dual system (30) has a unique solution and where is a positive constant.

Furthermore, let ,   be the solutions of the system (30) corresponding to , respectively. Then, there exists a positive constant , which is independent of ,   , such that

By a standard reasoning, we may derive the following result.

Lemma 8. Suppose that is a solution for the optimal control problem (1), and is the corresponding solution of system (2). Then, for any , such that for small enough, the following holds: where satisfies

We now establish the structure of optimal controllers in a feedback form.

Theorem 9. Let be an optimal policy for problem (1)-(2) and and the corresponding solutions of systems (2) and (30). Then, in which the mapping is defined as

Proof. Let be the tangent cone to at (see [22]). For any , we know that for small enough. Since is optimal, it follows that which implies that Passing to limit in (38) and using Lemma 8, we have where is the solution of the system (34).

In order to derive the optimality conditions, we will use the structure of the corresponding normal cone. Firstly, we verify the following integral relation: Actually, multiplying the first equation of (30) by , integrating on , and using (34), we obtain that So we have Similarly, it follows from the system (34) that

The relations (42) and (43) imply that (40) is true. Then, from (38) and (40), it follows that Therefore, according to the properties of normal cone (see [22]), the expression in the square brackets of (44) satisfies , the normal cone to at . Consequently, the conclusion follows.

4. Existence of a Unique Optimal Policy

In this section, we prove the existence and uniqueness of optimal policies. The main result reads as follows.

Theorem 10. Let the hypotheses ( )–( ) be satisfied. If where is as in ( ) and constants , are given in Theorem 5 and Lemmas 6 and 7, then the optimal harvesting problem (1)-(2) has a unique solution.

Proof. Firstly, we define a function of as where is of the form (1) and and are arbitrarily fixed. We will show that is strictly decreasing, and then is strictly concave in .

Denote by , the solutions of (2) corresponding to and , respectively. It follows from (1) that where is the solution of (34) corresponding to . By the same argument as that in Theorem 9, we have where is the solution of (30) corresponding to . Therefore, Next, choosing and , we get that Combining Theorem 5 and Lemmas 6 and 7 with Cauchy-Schwarz inequality, it follows from ( ) that Consequently, If , then we have Hence, is strictly decreasing, which implies the strict concavity of in .

Define the functional as follows: It is clear that is concave in . By Lemma 6, we claim that is upper semicontinuous. Since is convex, closed, and bounded and is strictly concave, attains its unique maximum in , which is the solution to problem (1)-(2). The proof is completed.

5. Concluding Remarks

As a main research result in the present paper, Theorem 9 describes the law of optimal harvesting, which is given by a feedback manner in terms of the corresponding state and costate variables. The results in this paper may serve as a base to many realistic applications. In those situations, one must estimate the parameters in the harvesting problem, such as price function , growth function , and vital rates ,   , and then apply some proper numerical procedure or algorithm to obtain an approximating solution with errors small enough. We should be satisfied with such treatment since the coupled state-costate system cannot be expected to get analytical solutions.


All authors have seen the paper and agreed to submit it to this journal.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.


The authors thank the reviewers for their helpful comments and suggestions, which improved the paper very much. This research was supported by the National Natural Science Foundation of China (nos. 11271104, 41101508, and 61174074) and the Scientific Project of Educational Commission in Zhejiang Province of China (no. Y201327022).


  1. J. G. Skellam, “Random dispersal in theoretical populations,” Biometrika, vol. 38, no. 1-2, pp. 196–218, 1951. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  2. A. Okubo and S. A. Levin, Diffusion and Ecological Problems: Modern Perspectives, Springer, New York, NY, USA, 2001.
  3. R. Nathan, H. Kokko, A. Lpez-Sepulcre et al., “Migration and Dispersal: Perspectives,” Science, Special Section, vol. 313, no. 5788, pp. 786–795, 2006.
  4. B. Ebenman and L. Persson, Size-Structured Populations: Ecology and Evolution, Springer, Berlin, Germany, 1988.
  5. G. F. Webb, “Population models structured by age, size, and spatial position,” in Structured Population Models in Biology and Epidemiology, P. Magal and S. Ruan, Eds., Lecture Notes in Math, pp. 1–49, Springer, Berlin, Germany, 2008. View at Publisher · View at Google Scholar · View at MathSciNet
  6. S. Aniţa, Analysis and Control of Age-Dependent Population Dynamics, Kluwer, Dordrecht, The Netherlands, 2000. View at Publisher · View at Google Scholar · View at MathSciNet
  7. C. Zhao, M. Wang, and P. Zhao, “Optimal harvesting problems for age-dependent interacting species with diffusion,” Applied Mathematics and Computation, vol. 163, no. 1, pp. 117–129, 2005. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  8. Z. Luo, “Optimal harvesting problem for an age-dependent n-dimensional food chain diffusion model,” Applied Mathematics and Computation, vol. 186, no. 2, pp. 1742–1752, 2007. View at Publisher · View at Google Scholar · View at MathSciNet
  9. L. W. Botsford, “Optimal fishery policy for size-specific, density-dependent population models,” Journal of Mathematical Biology, vol. 12, no. 3, pp. 265–293, 1981. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  10. K. P. Hadeler and J. Müller, “Optimal harvesting and optimal vaccination,” Mathematical Biosciences, vol. 206, no. 2, pp. 249–272, 2007. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  11. N. Kato, “Maximum principle for optimal harvesting in linear size-structured population,” Mathematical Population Studies, vol. 15, no. 2, pp. 123–136, 2008. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  12. O. C. Tarniceriu and V. M. Veliov, “Optimal control of a class of size-structured systems,” in Large-Scale Scientific Computing, I. Lirkov, S. Margenov, and J. Wasniewski, Eds., vol. 4818 of Lecture Notes in Computer Science, pp. 366–373, Springer, Berlin, Germany, 2008. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  13. N. Hritonenko, Y. Yatsenko, R.-U. Goetz, and A. Xabadia, “Maximum principle for a size-structured model of forest and carbon sequestration management,” Applied Mathematics Letters, vol. 21, no. 10, pp. 1090–1094, 2008. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  14. E. Gasca-Leyva, J. M. Hernandez, and V. M. Veliov, “Optimal harvesting time in a sizeheterogeneous population,” Ecological Modelling, vol. 210, no. 1-2, pp. 161–168, 2008.
  15. A. A. Davydov and A. S. Platov, “Optimization of stationary solution of a model of size-structured population exploitation,” Journal of Mathematical Sciences, vol. 176, no. 6, pp. 860–869, 2011. View at Publisher · View at Google Scholar · View at MathSciNet
  16. N. Kato, “Optimal harvesting in a two-species model of size-structured population,” in Optimal Control of Age-Structured Populations in Economy, Demography, and the Environment, Boucekkine, N. Hritoenko, and Y. Yatsenko, Eds., pp. 229–252, Routledge Explorations in Environmental Economics, London, UK, 2011.
  17. Z.-R. He and Y. Liu, “An optimal birth control problem for a dynamical population model with size-structure,” Nonlinear Analysis: Real World Applications, vol. 13, no. 3, pp. 1369–1378, 2012. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  18. Y. Liu, X.-L. Cheng, and Z.-R. He, “On the optimal harvesting of size-structured population dynamics,” Applied Mathematics B: A Journal of Chinese Universities, vol. 28, no. 2, pp. 173–186, 2013. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  19. B. Faugeras and O. Maury, “An advection-diffusion-reaction size-structured fish population dynamics model combined with a statistical parameter estimation procedure: application to the Indian Ocean skipjack tuna fishery,” Mathematical Biosciences and Engineering, vol. 2, no. 4, pp. 719–741, 2005. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  20. K. P. Hadeler, “Structured populations with diffusion in state space,” Mathematical Biosciences and Engineering, vol. 7, no. 1, pp. 37–49, 2010. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  21. V. Barbu, Partial Differential Equations and Boundary Value Problems, Kluwer, Dordrecht, The Netherlands, 1998.
  22. J.-P. Aubin and I. Ekeland, Applied Nonlinear Analysis, John Wiley & Sons, New York, NY, USA, 1984. View at MathSciNet