A level set approach for the solution of a state-constrained optimal control problem

Summary. State constrained optimal control problems for linear elliptic partial differential equations are considered. The corresponding first order optimality conditions in primal-dual form are analyzed and linked to a free boundary problem resulting in a novel algorithmic approach with the boundary (interface) between the active and inactive sets as optimization variable. The new algorithm is based on the level set methodology. The speed function involved in the level set equation for propagating the interface is computed by utilizing techniques from shape optimization. Encouraging numerical results attained by the new algorithm are reported on.Mathematics Subject Classification (1991): 35R35, 49K20, 49Q10, 65K10Revised version received March 19, 2003     

Stochastic optimal multi-modes switching with a viscosity solution approach

We consider the problem of optimal multi-modes switching in finite horizon, when the state of the system, including the switching cost functions are arbitrary (_gij(t,x)≥0$g_{ij}(t,x)\ge 0$). We show existence of the optimal strategy, via a verification theorem. Finally, when the state of the system is a Markov process, we show that the vector of value functions of the optimal problem is the unique viscosity solution to the system of m$m$ variational partial differential inequalities with inter-connected obstacles.     

Bilinear modeling solution approach for fixed charge network flow problems

We present a continuous, bilinear formulation for the fixed charge network flow problem. This formulation is used to derive an exact algorithm for the fixed charge network flow problem converging in a finite number of steps. Some preliminary computational experiments are reported to show the performance of the algorithm.     

An adaptive parameter approach for an approximate solution of optimal control problems

In this paper, we consider a class of nonlinear optimal control problems (Bolza-problems) with constraints of the control vector, initial and boundary conditions of the state vectors. The time interval is fixed. Our approach to parametrize both the state functions and the control functions is described by general piecewise polynomials with unknown coefficients (parameters), where a fixed partition of the time interval is used. Here each of these functions in a suitable way individually will be approximated by such polynomials. The optimal control problem thus is reduced to a mathematical programming problem for these parameters. The existence of an optimal solution is assumed. Convergence properties of this method are not considered in this paper.     

A direct approach to the finite element solution of elliptic optimal control problems

A general framework is developed for the finite element solution of optimal control problems governed by elliptic nonlinear partial differential equations. Typical applications are steady‐state problems in nonlinear continuum mechanics, where a certain property of the solution (a function of displacements, temperatures, etc.) is to be minimized by applying control loads. In contrast to existing formulations, which are based on the “adjoint state,” the present formulation is a direct one, which does not use adjoint variables. The formulation is presented first in a general nonlinear setting, then specialized to a case leading to a sequence of quadratic programming problems, and then specialized further to the unconstrained case. Linear governing partial differential equations are also considered as a special case in each of these categories. © 1999 John Wiley & Sons, Inc. Numer Methods Partial Differential Eq 15:371–388, 1999     

An improved solution methodology for the arsenal exchange model (AEM)

We develop an iterative approach for solving a linear programming problem with prioritized goals. We tailor our approach to preemptive goal programming problems and take advantage of the fact that at optimality, most constraints are not binding. To overcome the problems posed by redundant constraints, our procedure ensures redundant constraints are not present in the problems we solve. We apply our approach to the arsenal exchange model (AEM). AEM allocates weapons to targets using linear programs (LPs) formulated by the model. Our methodology solves a subproblem using a specific subset of the constraints generated by AEM. Violated constraints are added to the original subproblem and redundant constraints are not included in any of the subproblems. Our methodology was used to solve five test cases. In four of the five test cases, our methodology produced an optimal integer solution. In all five test cases, solution quality was maintained or improved.     

The monotone case approach for the solution of certain multidimensional optimal stopping problems

This paper studies explicitly solvable multidimensional optimal stopping problems of sum- and product-type in discrete and continuous time using the monotone case approach. It gives a review on monotone case stopping using the Doob decomposition, resp. Doob–Meyer decomposition in continuous time, also in its multiplicative versions. The approach via these decompositions leads to explicit solutions for a variety of examples, including multidimensional versions of the house-selling and burglar’s problem, the Poisson disorder problem, and an optimal investment problem.     

A weak solution approach to a reaction–diffusion system modeling pattern formation on seashells

We investigate a reaction–diffusion system proposed by H. Meinhardt as a model for pattern formation on seashells. We give a new proof for the existence of a local weak solution for general initial conditions and parameters upon using an iterative approach. Furthermore, the solution is shown to exist globally for suitable initial data. The behavior of the solution in time and space is illustrated through numerical simulations. Copyright © 2009 John Wiley & Sons, Ltd.     

An effective modeling and solution approach for the generalized independent set problem

The generalized independent set (GIS) problem was first introduced by Hochbaum and Pathria (Forest Sci 43(4), 544–554, 1997) and independently explored in greater detail by Hochbaum (Manage Sci 50(6), 709–123, 2004). This problem, with applications in forest management and a variety of related areas, is a generalization of the classical maximum independent set problem. In this paper we highlight a natural, nonlinear formulation for the problem that is an attractive alternative to the linear model found in the literature. The effectiveness of this alternative formulation is demonstrated by computational experience on test problems of varying size and density, disclosing a dramatic reduction in the time to obtain optimal and near optimal solutions and an ability to solve much larger problems.     

A 2-approximation for minmax regret problems via a mid-point scenario optimal solution

In this note, a 2-approximation method for minmax regret optimization problems is developed which extends the work of Kasperski and Zielinski [A. Kasperski, P. Zielinski, An approximation algorithm for interval data minmax regret combinatorial optimization problems, Information Processing Letters 97 (2006) 177-180] from finite to compact constraint sets.     

Absolute optimal solution for a compact and convex game

This paper investigates the existence of absolute optimal solutions for a partition P in continuous and quasiconcave games. We show that the P-consistency property introduced in the paper, together with the quasiconcavity and continuity of payoffs, permits the existence of P-absolute optimal solutions in games with compact and convex strategy spaces. The P-consistency property is a general condition that cannot be dispensed with for the existence of P-absolute optimal solutions. We also characterize the existence of P-absolute optimal solutions by providing necessary and sufficient conditions. Moreover, we suggest an algorithm for efficiently computing P-absolute optimal solutions.     

A decomposition approach for a very large scale optimal diversity management problem

This paper focuses on the solution of the optimal diversity management problem formulated as a p-Median problem. The problem is solved for very large scale real instances arising in the car industry and defined on a graph with several tens of thousands of nodes and with several millions of arcs. The particularity is that the graph can consist of several non connected components. This property is used to decompose the problem into a series of p-Median subproblems of a smaller dimension. We use a greedy heuristic and a Lagrangian heuristic for each subproblem. The solution of the whole problem is obtained by solving a suitable assignment problem using a Branch-and-Bound algorithm.Received: June 2004 / Revised version: December 2004MSC classification: 49M29, 90C06, 90C27, 90C60All correspondence to: Antonio SforzaIgor Vasilev: Support for this author was provided by NATO grant CBP.NR.RIG.911258.     

A new approach for the optimal distribution of assemblies in a nuclear reactor

Summary. The aim of this paper is to propose a new approach for optimizing the position of fuel assemblies in a nuclear reactor core. This is a control problem for the neutronic diffusion equation where the control acts on the coefficients of the equation. The goal is to minimize the power peak (i.e. the neutron flux must be as spatially uniform as possible) and maximize the reactivity (i.e. the efficiency of the reactor measured by the inverse of the first eigenvalue). Although this is truly a discrete optimization problem, our strategy is to embed it in a continuous one which is solved by the homogenization method. Then, the homogenized continuous solution is numerically projected on a discrete admissible distribution of assemblies. Received January 13, 2000 / Published online February 5, 2001     

A general approach to optimal control of a regression experiment

Let $\text{θ}\text{θ}\text{?}$ = ($\text{θ}\text{θ}\text{?}$₁, $\text{θ}\text{θ}\text{?}$₂, …, $\text{θ}\text{θ}\text{?}$_n)′ be the least-squares estimator of θ = (θ₁, θ₂, …, θ_n)′ by the realization of the process y(t) = Σ_{k = 1}ⁿθ_kf_k(t) + ξ(t) on the interval T = [a, b] with f = (f₁, f₂, …, f_n)′ belonging to a certain set X. The process satisfies E(ξ(t))≡0 and has known continuous covariance r(s, t) = E(ξ(s)ξ(t)) on T × T. In this paper, A-, D-, and D_s-optimality are used as criteria for choosing f in X. A-, D-, and D_s-optimal models can be constructed explicitly by means of r.     

An approach for an algorithmic solution of discrete optimal control problems and their game-theoretical extension

We consider time discrete systems which are described by a system of difference equations. The related discrete optimal control problems are introduced. Additionally, a gametheoretic extension is derived, which leads to general multicriteria decision problems. The characterization of their optimal behavior is studied. Given starting and final states define the decision process; applying dynamic programming techniques suitable optimal solutions can be gained. We generalize that approach to a special gametheoretic decision procedure on networks. We characterize Nash equilibria and present sufficient conditions for their existence. A constructive algorithm is derived. The sufficient conditions are exploited to get the algorithmic solution. Its complexity analysis is presented and at the end we conclude with an extension to the complementary case of Pareto optima.Dmitrii Lozovanu was Supported by BGP CRDF-MRDA MOM2-3049-CS-03.     

A nonisolated optimal solution for special reverse convex programming problems

In this paper, an efficient algorithm is proposed for globally solving special reverse convex programming problems with more than one reverse convex constraints. The proposed algorithm provides a nonisolated global optimal solution which is also stable under small perturbations of the constraints, and it turns out that such an optimal solution is adequately guaranteed to be feasible and to be close to the actual optimal solution. Convergence of the algorithm is shown and the numerical experiment is given to illustrate the feasibility of the presented algorithm.     

Less-Than-Truckload carrier collaboration problem: modeling framework and solution approach

Less-Than-Truckload (LTL) carriers generally serve geographical regions that are more localized than the inter-city line-hauls served by truckload carriers. That localization can lead to urban freight transportation routes that overlap. If trucks are traveling with less than full loads, there typically exist opportunities for carriers to collaborate over such routes. We introduce a two stage framework for LTL carrier collaboration. Our first stage involves collaboration between multiple carriers at the entrance to the city and can be formulated as a vehicle routing problem with time windows (VRPTW). We employ guided local search for solving this VRPTW. The second stage involves collaboration between carriers at transshipment facilities while executing their routes identified in phase one. For solving the second stage problem, we develop novel local search heuristics, one of which leverages integer programming to efficiently explore the union of neighborhoods defined by new problem-specific move operators. Our computational results indicate that integrating integer programming with local search results in at least an order of magnitude speed up in the second stage problem. We also perform sensitivity analysis to assess the benefits from collaboration. Our results indicate that distance savings of 7–15 % can be achieved by collaborating at the entrance to the city. Carriers involved in intra-city collaboration can further save 3–15 % in total distance traveled, and also reduce their overall route times.     

Emergency ambulance deployment in Barbados: a multi-objective approach

A multi-objective version of the Maximum Availability Location Problem is presented in this paper. The assumption of server independence is relaxed by adopting the approach of the Queuing Probabilistic Location Set Covering Problem for calculating the probability that all servers in a given region are busy. The first objective seeks to maximize the population receiving coverage within a given distance standard and with a given level of reliability. The second objective chooses those locations which minimize the cost of covering the population. This model is used to obtain sets of good locations using data obtained from the Barbados Emergency Ambulance Service. The solutions obtained from the optimization model are then subject to a detailed analysis by simulation. The results reveal the potentially good performance of the system, when locations derived from the optimization model are used.     

A linear programming approach for determining optimal advertising policy

^** Email: wkc{at}maths.hku.hk In this paper, we propose a new advertising model which cancapture the advertising wear out phenomenon. The objective hereis to maximize overall sales. We show how to derive the optimalpulsation advertising strategy. The optimization problem canbe formulated as a linear programming problem. Closed-form optimalsolution can also be obtained under some conditions. We presentnumerical examples to illustrate the proposed model and applythe model to practical sales data.     

On optimal operating conditions for a data processing system: A stochastic approach

In this paper, we consider the problem of determining optimal operating conditions for a data processing system. The system is burned‐in for a fixed burn‐in time before it is put into field operation and, in field operation, it has a work size and follows an age‐replacement policy. Assuming that the underlying lifetime distribution of the system has a bathtub‐shaped failure rate function, the properties of optimal burn‐in time, optimal work size and optimal age‐replacement policy will be derived. It can be seen that this model is a generalization of those considered in the previous works, and it yields a better optimal operating conditions. This paper presents an analytical method for three‐dimensional optimization problem. An algorithm for determining optimal operating conditions is also given. Copyright © 2007 John Wiley & Sons, Ltd.