Calculating risk neutral probabilities and optimal portfolio policies in a dynamic investment model with downside risk control

This paper presents a method for solving multiperiod investment models with downside risk control characterized by the portfolio’s worst outcome. The stochastic programming problem is decomposed into two subproblems: a nonlinear optimization model identifying the optimal terminal wealth distribution and a stochastic linear programming model replicating the identified optimal portfolio wealth. The replicating portfolio coincides with the optimal solution to the investor’s problem if the market is frictionless. The multiperiod stochastic linear programming model tests for the absence of arbitrage opportunities and its dual feasible solutions generate all risk neutral probability measures. When there are constraints such as liquidity or position requirements, the method yields approximate portfolio policies by minimizing the initial cost of the replication portfolio. A numerical example illustrates the difference between the replicating result and the optimal unconstrained portfolio.     

Forward differential dynamic programming

The dynamic programming formulation of the forward principle of optimality in the solution of optimal control problems results in a partial differential equation with initial boundary condition whose solution is independent of terminal cost and terminal constraints. Based on this property, two computational algorithms are described. The first-order algorithm with minimum computer storage requirements uses only integration of a system of differential equations with specified initial conditions and numerical minimization in finite-dimensional space. The second-order algorithm is based on the differential dynamic programming approach. Either of the two algorithms may be used for problems with nondifferentiable terminal cost or terminal constraints, and the solution of problems with complicated terminal conditions (e.g., with free terminal time) is greatly simplified.     

Resource-optimal control of linear systems

A numerical method for minimizing the resource consumption for linear dynamical systems is proposed. It is based on forming a finite-time control that steers the linear system from an arbitrary initial state to the desired terminal state in a given fixed time; this control gives an approximate solution of the problem. It is shown that the structure of the finite-time control makes it possible to determine the structure of the resource-optimal control. A method for determining an initial approximation is described, and an iterative algorithm for calculating the optimal control is proposed. A system of linear algebraic equations relating the deviations of the initial conditions in the adjoint system to the deviations of the phase coordinates from the prescribed terminal state at the terminal point in time is obtained. A computational algorithm is described. The radius of local convergence is found and the quadratic rate of convergence is established. It is proved that the computational procedure and the sequence of controls converge to the resource-optimal control.     

基于博弈论的铁路集装箱中心站装卸作业研究

考虑铁路集装箱中心站不同装卸任务之间的竞争性因素,采用博弈论的方法,研究中心站在客户竞争驱动下的装卸资源调度及作业顺序问题。建立博弈模型并设计遗传算法,通过求解该问题的典型算例,将得到的均衡解与静态最优解进行对比,分析考虑竞争因素的作业方案与最优作业方案的差异。结果表明该方法在对最优方案影响较小的情况下,充分考虑了各装卸任务间的竞争公平性,为中心站确定更加合理的装卸作业顺序和资源调度提供科学的依据。     

On the efficiency of optimal grid synthesis in optimal control problems with fixed terminal time

In the paper, we consider nonlinear optimal control problems with the Bolza functional and with fixed terminal time. We suggest a construction of optimal grid synthesis. For each initial state of the control system, we obtain an estimate for the difference between the optimal result and the value of the functional on the trajectory generated by the suggested grid positional control. The considered feedback control constructions and the estimates of their efficiency are based on a backward dynamic programming procedure. We also use necessary and sufficient optimality conditions in terms of characteristics of the Bellman equation and the sub-differential of the minimax viscosity solution of this equation in the Cauchy problem specified for the fixed terminal time. The results are illustrated by the numerical solution of a nonlinear optimal control problem.     

An exact method for scheduling a yard crane

This paper studies an operational problem arising at a container terminal, consisting of scheduling a yard crane to carry out a set of container storage and retrieval requests in a single container block. The objective is to minimize the total travel time of the crane to carry out all requests. The block has multiple input and output (I/O) points located at both the seaside and the landside. The crane must move retrieval containers from the block to the I/O points, and must move storage containers from the I/O points to the block. The problem is modeled as a continuous time integer programming model and the complexity is proven. We use intrinsic properties of the problem to propose a two-phase solution method to optimally solve the problem. In the first phase, we develop a merging algorithm which tries to patch subtours of an optimal solution of an assignment problem relaxation of the problem and obtain a complete crane tour without adding extra travel time to the optimal objective value of the relaxed problem. The algorithm requires common I/O points to patch subtours. This is efficient and often results in obtaining an optimal solution of the problem. If an optimal solution has not been obtained, the solution of the first phase is embedded in the second phase where a branch-and-bound algorithm is used to find an optimal solution. The numerical results show that the proposed method can quickly obtain an optimal solution of the problem. Compared to the random and Nearest Neighbor heuristics, the total travel time is on average reduced by more than 30% and 14%, respectively. We also validate the solution method at a terminal.     

Two new algorithms for solving optimization problems with one linear objective function and finitely many constraints of fuzzy relation inequalities

This paper studies the optimization model of a linear objective function subject to a system of fuzzy relation inequalities (FRI) with the max-Einstein composition operator. If its feasible domain is non-empty, then we show that its feasible solution set is completely determined by a maximum solution and a finite number of minimal solutions. Also, an efficient algorithm is proposed to solve the model based on the structure of FRI path, the concept of partial solution, and the branch-and-bound approach. The algorithm finds an optimal solution of the model without explicitly generating all the minimal solutions. Some sufficient conditions are given that under them, some of the optimal components of the model are directly determined. Some procedures are presented to reduce the search domain of an optimal solution of the original problem based on the conditions. Then the reduced domain is decomposed (if possible) into several sub-domains with smaller dimensions that finding the components of the optimal solution in each sub-domain is very easy. In order to obtain an optimal solution of the original problem, we propose another more efficient algorithm which combines the first algorithm, these procedures, and the decomposition method. Furthermore, sufficient conditions are suggested that under them, the problem has a unique optimal solution. Also, a comparison between the recently proposed algorithm and the known ones will be made.     

Optimal control with connected initial and terminal conditions

An optimal control problem with linear dynamics is considered on a fixed time interval. The ends of the interval correspond to terminal spaces, and a finite-dimensional optimization problem is formulated on the Cartesian product of these spaces. Two components of the solution of this problem define the initial and terminal conditions for the controlled dynamics. The dynamics in the optimal control problem is treated as an equality constraint. The controls are assumed to be bounded in the norm of L₂. A saddle-point method is proposed to solve the problem. The method is based on finding saddle points of the Lagrangian. The weak convergence of the method in controls and its strong convergence in state trajectories, dual trajectories, and terminal variables are proved.     

Optimal input system identification for homogeneous and nonhomogeneous boundary conditions

A recent paper by Mehra has considered the design of optimal inputs for linear system identification. The method proposed involves the solution of homogeneous linear differential equations with homogeneous boundary conditions. In this paper, a method of solution is considered for similar-type problems with nonhomogeneous boundary conditions. The methods of solution are compared for the homogeneous and nonhomogeneous cases, and it is shown that, for a simple numerical example, the optimal input for the nonhomogeneous case is almost identical to the homogeneous optimal input when the former has a small initial condition, terminal time near the critical length, and energy input the same as for the homogeneous case. Thus tentatively, solving the nonhomogeneous problem appears to offer an attractive alternative to solving Mehra's homogeneous problem.     

基于隐式离散极大值原理的聚合物驱最优注入策略

为了获得聚合物驱油的最大利润,建立了确定最佳聚合物注入浓度的最优控制模型.利用全隐式差分格式将连续模型离散化得到离散系统的状态方程.通过隐含离散系统的极大值原理获得了该最优控制问题的必要条件.给出了基于梯度的数值求解方法,在求解状态方程的过程中直接构造了伴随问题的系数矩阵.通过一个三维聚合物驱模型的计算实例表明了所提出方法的可行性和有效性.     

An optimal replenishment policy for deteriorating items with stock-dependent demand and relaxed terminal conditions under limited storage space

This paper deals with the problem of determining the optimal replenishment policy for deteriorating items with stock-dependent demand in which the terminal condition of zero-ending inventory is relaxed. In the model, shortages are allowed and partial backlogging/lost sales. That is, the zero/non-zero ending inventory models are considered simultaneously. The items in stock are displayed to the customers in shelves with limited storage capacity. In theoretical analysis, the necessary and sufficient conditions of the existence and uniqueness of the optimal solutions under various cases are shown. We then also provide a simple algorithm to find the optimal solutions for various situations. Further, a couple of numerical examples are presented to demonstrate the developed model and solution procedure, and several management insights are obtained from the numerical examples. Finally, sensitivity analysis of the optimal solution with respect to major parameters is also carried out.     

Single machine stochastic JIT scheduling problem subject to machine breakdowns

In this paper we research the single machine stochastic JIT scheduling problem subject to the machine breakdowns for preemptive-resume and preemptive-repeat.The objective function of the problem is the sum of squared deviations of the job-expected completion times from the due date.For preemptive-resume,we show that the optimal sequence of the SSDE problem is V-shaped with respect to expected processing times.And a dynamic programming algorithm with the pseudopolynomial time complexity is given.We discuss the difference between the SSDE problem and the ESSD problem and show that the optimal solution of the SSDE problem is a good approximate optimal solution of the ESSD problem,and the optimal solution of the SSDE problem is an optimal solution of the ESSD problem under some conditions.For preemptive-repeat,the stochastic JIT scheduling problem has not been solved since the variances of the completion times cannot be computed.We replace the ESSD problem by the SSDE problem.We show that the optimal sequence of the SSDE problem is V-shaped with respect to the expected occupying times.And a dynamic programming algorithm with the pseudopolynomial time complexity is given.A new thought is advanced for the research of the preemptive-repeat stochastic JIT scheduling problem.     

Direct method for solving optimal control problems with kinks

In this paper, we introduce a direct solution concept applicable to a slightly restricted class of optimal control problems with kinks. The method treats the optimal trajectory when it lies on a kink for an interval of time (a kink arc) in a special manner. The nonkink arc must satisfy the classical set of necessary conditions of the maximum principle, excluding from that set terminal equations on the kink terminal (the junction between this nonkink arc and a kink arc). At this junction, certain conditions, which compensate for the missing terminal conditions, have to be satisfied. In this paper, the method is applied to solve a fairly realistic model for an important inventory problem, namely, the problem of scheduling the production of a commodity.     

A mathematical model of market competition

We consider a mathematical model of decision making by a company attempting to win a market share. We assume that the company releases its products to the market under the competitive conditions that another company is making similar products. Both companies can vary the kinds of their products on the market as well as the prices in accordance with consumer preferences. Each company aims to maximize its profit. A mathematical statement of the decision-making problem for the market players is a bilevel mathematical programming problem that reduces to a competitive facility location problem. As regards the latter, we propose a method for finding an upper bound for the optimal value of the objective function and an algorithm for constructing an approximate solution. The algorithm amounts to local ascent search in a neighborhood of a particular form, which starts with an initial approximate solution obtained simultaneously with an upper bound. We give a computational example of the problem under study which demonstrates the output of the algorithm.     

Optimal switching control of a fed-batch fermentation process

Considering the hybrid nature in fed-batch culture of glycerol biconversion to 1,3-propanediol (1,3-PD) by Klebsiella pneumoniae, we propose a state-based switching dynamical system to describe the fermentation process. To maximize the concentration of 1,3-PD at the terminal time, an optimal switching control model subject to our proposed switching system and constraints of continuous state inequality and control function is presented. Because the number of the switchings is not known a priori, we reformulate the above optimal control problem as a two-level optimization problem. An optimization algorithm is developed to seek the optimal solution on the basis of a heuristic approach and control parametrization technique. Numerical results show that, by employing the obtained optimal control strategy, 1,3-PD concentration at the terminal time can be increased considerably.     

Integer resource allocations with the objective function separable into pairs of variables

This paper is concerned with the problem of optimal integer allocation having a doubly concave objective function. The problem is studied with application to some model of optimal reliability. To derive the optimality conditions, we introduce the notion of integer local equilibrium, considering three of its variations. We also investigate some additional conditions under which this equilibrium is the optimal solution.An appropriate solution algorithm is presented.     

Spinorial Method of Terminal Control of Spatial Rotations

In this paper, we introduce the notion of three-dimensional generalized rotations. We obtain relations between the parameters of the spinor representation of the group of three-dimensional generalized rotations and the coordinates of the initial and terminal points of rotation. Simple relations between elements of a three-dimensional orthogonal matrix of the basic representation and the Euler angles, on the one hand, and the coordinates of the initial and terminal points of rotation, on the other hand, were derived. The spinor method of solution of the inverse kinematic problem for spatial mechanisms with spherical pairs is developed and the corresponding algorithm is proposed. The obtained results allow one to reduce the three-dimensional problem of spatial motion control to the one-dimensional problem. Simple adaptive algorithms are suggested, by means of which various partial problems on the terminal control are solved under various terminal conditions. New algorithms of control of spatial rotations of manipulating robots are studied.     

Optimal Synthesis of the Asymmetric Sinistral/Dextral Markov–Dubins Problem

We consider a variation of the classical Markov–Dubins problem dealing with curvature-constrained, shortest paths in the plane with prescribed initial and terminal positions and tangents, when the lower and upper bounds of the curvature of the path are not necessarily equal. The motivation for this problem stems from vehicle navigation applications, when a vehicle may be biased in taking turns at a particular direction due to hardware failures or environmental conditions. After formulating the shortest path problem as a minimum-time problem, a family of extremals, which is sufficient for optimality, is characterized, and subsequently the complete analytic solution of the optimal synthesis problem is presented. In addition, the synthesis problem, when the terminal tangent is free, is also considered, leading to the characterization of the set of points that can be reached in the plane by curves satisfying asymmetric curvature constraints.     

Optimal control policies for resource allocation in an activity network

In the paper a class of project-scheduling problems concerning the allocation of continuously divisible resources is considered. It is assumed that performing speeds of activities are continuous functions of the resource amount, and that the initial and terminal states of activities are known. For such mathematical models of project activities the problem of time-optimal resource allocation under instantaneous and integral constrains on a resource, and the problem of cost-optimal resource allocation with fixed project duration are formulated and a general solution concept is proposed. Necessary and sufficient conditions for the existence of a solution in particular cases are derived and properties of optimal schedules are given. The control policies for resource allocation are constructed for the example of the cost-optimal problem.     

Numerical solution of the optimal boundary control of transverse vibrations of a beam

Boundary control is an effective means for suppressing excessive structural vibrations. By introducing a quadratic index of performance in terms of displacement and velocity, as well as the control force, and an adjoint problem, it is possible to determine the optimal control. This optimal control is expressed in terms of the adjoint variable by utilizing a maximum principle. With the optimal control applied, the determination of the corresponding displacement and velocity is reduced to solving a set of partial differential equations involving the state variable, as well as the adjoint variable, subject to boundary, initial, and terminal conditions. The set of equations may not be separable and analytical solutions may only be found in special cases. Furthermore, the computational effort to determine an analytic solution may also be excessive. Herein a numerical algorithm is presented, which easily solves the optimal boundary control problem in the space‐time domain. An example of a continuous system is analyzed. This is the case of the vibrating cantilever beam. Using a finite element recurrence scheme, numerical solutions are obtained, which compare the behavior of the controlled and uncontrolled systems. Also, the analytic solution to the problem is compared with the results obtained using the numerical scheme presented. © 1999 John Wiley & Sons, Inc. Numer Methods Partial Differential Eq 15: 558–568, 1999