A new approximation algorithm for the multilevel facility location problem

In this paper we propose a new integer programming formulation for the multilevel facility location problem and a novel 3-approximation algorithm based on LP-rounding. The linear program that we use has a polynomial number of variables and constraints, thus being more efficient than the one commonly used in the approximation algorithms for these types of problems.     

Benders Decomposition for a Location-Design Problem in Green Wireless Local Area Networks

We consider a problem arising in the design of green (or energy-saving) wireless local area networks (GWLANs). Decisions on both location and capacity dimensioning must be taken simultaneously. We model the problem as an integer program with nonlinear constraints and derive valid inequalities. We handle the nonlinearity of the formulation by developing a Benders decomposition algorithm. We propose various ways to improve the Benders master problem and the feasibility cuts.     

An Improved IP Formulation for the Uncapacitated Facility Location Problem: Capitalizing on Objective Function Structure

In this paper we examine the Uncapacitated Facility Location Problem (UFLP) with a special structure of the objective function coefficients. For each customer the set of potential locations can be partitioned into subsets such that the objective function coefficients in each are identical. This structure exists in many applications, including the Maximum Cover Location Problem. For the problems possessing this structure, we develop a new integer programming formulation that has all the desirable properties of the standard formulation, but with substantially smaller dimensionality, leading to significant improvement in computational times. While our formulation applies to any instance of the UFLP, the reduction in dimensionality depends on the degree to which this special structure is present. This work was supported by grants from NSERC.     

Contour line construction for a new rectangular facility in an existing layout with rectangular departments

In a recent paper, Savas et al. [S. Savas, R. Batta, R. Nagi, Finite-size facility placement in the presence of barriers to rectilinear travel, Operations Research 50 (6) (2002) 1018–1031] consider the optimal placement of a finite-sized facility in the presence of arbitrarily shaped barriers under rectilinear travel. Their model applies to a layout context, since barriers can be thought to be existing departments and the finite-sized facility can be viewed as the new department to be placed. In a layout situation, the existing and new departments are typically rectangular in shape. This is a special case of the Savas et al. paper. However the resultant optimal placement may be infeasible due to practical constraints like aisle locations, electrical connections, etc. Hence there is a need for the development of contour lines, i.e. lines of equal objective function value. With these contour lines constructed, one can place the new facility in the best manner. This paper deals with the problem of constructing contour lines in this context. This contribution can also be viewed as the finite-size extension of the contour line result of Francis [R.L. Francis, Note on the optimum location of new machines in existing plant layouts, Journal of Industrial Engineering 14 (2) (1963) 57–59].     

Equality Constraints in Multiobjective Robust Design Optimization: Decision Making Problem

Robust design optimization (RDO) problems can generally be formulated by incorporating uncertainty into the corresponding deterministic problems. In this context, a careful formulation of deterministic equality constraints into the robust domain is necessary to avoid infeasible designs under uncertain conditions. The challenge of formulating equality constraints is compounded in multiobjective RDO problems. Modeling the tradeoffs between the mean of the performance and the variation of the performance for each design objective in a multiobjective RDO problem is itself a complex task. A judicious formulation of equality constraints adds to this complexity because additional tradeoffs are introduced between constraint satisfaction under uncertainty and multiobjective performance. Equality constraints under uncertainty in multiobjective problems can therefore pose a complicated decision making problem. In this paper, we provide a new problem formulation that can be used as an effective multiobjective decision making tool, with emphasis on equality constraints. We present two numerical examples to illustrate our theoretical developments.     

The discrete facility location problem with balanced allocation of customers

We consider a discrete facility location problem where the difference between the maximum and minimum number of customers allocated to every plant has to be balanced. Two different Integer Programming formulations are built, and several families of valid inequalities for these formulations are developed. Preprocessing techniques which allow to reduce the size of the largest formulation, based on the upper bound obtained by means of an ad hoc heuristic solution, are also incorporated. Since the number of available valid inequalities for this formulation is exponential, a branch-and-cut algorithm is designed where the most violated inequalities are separated at every node of the branching tree. Both formulations, with and without the improvements, are tested in a computational framework in order to discriminate the most promising solution methods. Difficult instances with up to 50 potential plants and 100 customers, and largest easy instances, can be solved in one CPU hour.     

Applying the sequence-pair representation to optimal facility layout designs

We present a new formulation for the facility layout problem based on the sequence-pair representation, which is used successfully in VLSI design. By tightening the structure of the problem with this formulation, we have extended the solvable solution space from problems with nine departments to problems with eleven departments.     

Relaxation Through Homogenization for Optimal Design Problems with Gradient Constraints

The problem of the relaxation of optimal design problems for multiphase composite structures in the presence of constraints on the gradient of the state variable is addressed. A relaxed formulation for the problem is given in the presence of either a finite or infinite number of constraints. The relaxed formulation is used to identify minimizing sequences of configurations of phases.     

卫星舱三维布局优化模型及判断不干涉性算法

本以人造卫星仪器舱布局问题为背景。建立了在抛物圆柱体空间中带性能约束的长方体群的布局优化模型。分析模型中不干涉性约束的性质，利用凸集分离定理给出了等价的显式表达式，并构造了判断不干涉性的算法。     

A penalty method for rank minimization problems in symmetric matrices

The problem of minimizing the rank of a symmetric positive semidefinite matrix subject to constraints can be cast equivalently as a semidefinite program with complementarity constraints (SDCMPCC). The formulation requires two positive semidefinite matrices to be complementary. This is a continuous and nonconvex reformulation of the rank minimization problem. We investigate calmness of locally optimal solutions to the SDCMPCC formulation and hence show that any locally optimal solution is a KKT point. We develop a penalty formulation of the problem. We present calmness results for locally optimal solutions to the penalty formulation. We also develop a proximal alternating linearized minimization (PALM) scheme for the penalty formulation, and investigate the incorporation of a momentum term into the algorithm. Computational results are presented.     

Interior point algorithms for linear programming with inequality constraints

Interior methods for linear programming were designed mainly for problems formulated with equality constraints and non-negative variables. The formulation with inequality constraints has shown to be very convenient for practical implementations, and the translation of methods designed for one formulation into the other is not trivial. This paper relates the geometric features of both representations, shows how to transport data and procedures between them and shows how cones and conical projections can be associated with inequality constraints.     

A generalized global optimization formulation of the pooling problem with processing facilities and composite quality constraints

We present a generalized formulation of the pooling problem. Our formulation is different from the standard formulations in explicitly modeling component flows. Modeling the physical components directly, allows easy inclusion of processing facilities that may alter the flow composition. It also allows adding composite quality constraints that cannot be added directly as quality parameters as they do not blend linearly. We provide new test instances motivated by natural gas transport problems at the Norwegian Continental Shelf and give computational results. We show examples of nonlinear composite constraints on quality attributes and give computational results on the effect of adding such constraints to the new set of test instances. The increased flexibility of our formulation comes at the cost of less tight constraints and a performance penalty on existing test cases in literature. The advantage is that it can solve the more general test cases described above. We use GAMS implementations of the various formulations and solve the problems with BARON. We compare the performance of our bilinear formulation in BARON with discretizations solved as a Mixed Integer Linear Program using CPLEX. The discretized versions do not generally perform as well as the continuous model; however, they have better worst-case behavior on the new test cases.     

The capacitated single-allocation hub location problem revisited: A note on a classical formulation

In this paper a well-known formulation for the capacitated single-allocation hub location problem is revisited. An example is presented showing that for some instances this formulation is incomplete. The reasons for the incompleteness are identified leading to the inclusion of an additional set of constraints. Computational experiments are performed showing that the new constraints also help to decrease the computational time required to solve the problem optimally.     

The coastal seaspace patrol sector design and allocation problem

In this paper, we model and solve the problem of designing and allocating coastal seaspace sectors for steady-state patrolling operations by the vessels of a maritime protection agency. The problem addressed involves optimizing a multi-criteria objective function that minimizes a weighted combination of proportional measures of the vessels’ distances between home ports and patrol sectors, the sector workload, and the sector span. We initially assure contiguity of each patrol sector in our mixed-integer programming formulation via an exponential number of subtour elimination constraints, and then propose three alternative solution methods, two of which are based on reformulations that suitably replace the original contiguity representation with a polynomial number of constraints, and a third approach that employs an iterative cut generation procedure based on identifying violated subtour elimination constraints. We further enhance these reformulations with symmetry defeating constraints, either in isolation or in combination with a suitable perturbation of the objective function using weighted functions based on such constraints. Computational comparisons are provided for solving the problem using the original formulation versus either of our three alternative solution approaches for a representative instance. Overall, a model formulation based on Steiner tree problem (STP) constructs and enhanced by the reformulation-linearization technique (RLT) yielded the best performance.     

Experiments with primal - dual decomposition and subgradient methods for the uncapacitatied facility location problem

For optimization problems that are structured both with respect to the constraints and with respect to the variables, it is possible to use primal–dual solution approaches, based on decomposition principles. One can construct a primal subproblem, by fixing some variables, and a dual subproblem, by relaxing some constraints and king their Lagrange multipliers, so that both these problems are much easier to solve than the original problem. We study methods based on these subproblems, that do not include the difficult Benders or Dantzig-Wolfe master problems, namely primal–dual subgradient optimization methods, mean value cross decomposition, and several comtbinations of the different techniques. In this paper, these solution approaches are applied to the well-known uncapacitated facility location problem. Computational tests show that some combination methods yield near-optimal solutions quicker than the classical dual ascent method of Erlenkotter     

Optimizing glass coating lines: MIP model and valid inequalities

Glass coating is a specific transformation aiming at improving glass performance. The work presented in this paper deals with the determination of the optimal configuration of the production lines used to perform this operation. We propose a first MIP formulation of the problem and then discuss several types of valid inequalities for improving it. The main idea is to exploit explicit or implicit binary exclusion constraints to derive stronger valid inequalities: the maximal clique constraints. Efficient (polynomial time) separation algorithms exploiting special structure of the problem are described, giving rise to a cutting-plane generation procedure for strengthening the initial formulation. The computational study carried out shows that, with the enhanced formulation, good solutions can be obtained within reasonable computation times using currently available integer programming software.     

Modeling methods and a branch and cut algorithm for pharmaceutical clinical trial planning using stochastic programming

We discuss methods for the solution of a multi-stage stochastic programming formulation for the resource-constrained scheduling of clinical trials in the pharmaceutical research and development pipeline. First, we present a number of theoretical properties to reduce the size and improve the tightness of the formulation, focusing primarily on non-anticipativity constraints. Second, we develop a novel branch and cut algorithm where necessary non-anticipativity constraints that are unlikely to be active are removed from the initial formulation and only added if they are violated within the search tree. We improve the performance of our algorithm by combining different node selection strategies and exploring different approaches to constraint violation checking.     

New tighter polynomial length formulations for the asymmetric traveling salesman problem with and without precedence constraints

We propose a new formulation for the asymmetric traveling salesman problem, with and without precedence relationships, which employs a polynomial number of subtour elimination constraints that imply an exponential subset of certain relaxed Dantzig-Fulkerson-Johnson subtour constraints. Promising computational results are presented, particularly in the presence of precedence constraints.     

Linear Optimal Control Problems and Generalized Linear Programs

Linear optimal control problems with state inequality constraints is an important class of large systems. This paper shows that a generalized programming formulation of these problems does not result in a decomposition over time or a maximum principle as it does for problems without the state constraints. A semi-finite generalized programming formulation, however, can be used to formally produce the maximum principle, i.e. the necessary optimality conditions, for problems under consideration.     

A branch-and-cut procedure for the Udine Course Timetabling problem

A branch-and-cut procedure for the Udine Course Timetabling problem is described. Simple compact integer linear programming formulations of the problem employ only binary variables. In contrast, we give a formulation with fewer variables by using a mix of binary and general integer variables. This formulation has an exponential number of constraints, which are added only upon violation. The number of constraints is exponential. However, this is only with respect to the upper bound on the general integer variables, which is the number of periods per day in the Udine Course Timetabling problem.