An Optimal Chance Constraint Multivariate Stratified Sampling Design Using Auxiliary Information

When we are dealing with multivariate problem then we need an allocation which is optimal for all the characteristics in some sense because the individual optimum allocations usually differ widely unless the characteristics are highly correlated. So an allocation called “Compromise allocation” is to be worked out suggested by Cochran. When auxiliary information is also available, it is customary to use it to increase the precision of the estimates. Moreover, for practical implementation of an allocation, we need integer values of the sample sizes. In the present paper the problem is to determine the integer optimum compromise allocation when the population means of various characteristics are of interest and auxiliary information is available for the separate and combined ratio and regression estimates. This paper considers the optimum compromise allocation in multivariate stratified sampling with non-linear objective function and probabilistic non-linear cost constraint. The probabilistic non-linear cost constraint is converted into equivalent deterministic one by using Chance Constrained programming. The formulated multi-objective nonlinear programming problem is solved by Fuzzy Goal programming approach and Chebyshev approximation. Numerical illustration is also given to show the practical utility of the approaches.     

Complexity of linear programming

The complexity of linear programming is discussed in the “integer” and “real number” models of computation. Even though the integer model is widely used in theoretical computer science, the real number model is more useful for estimating an algorithm's running time in actual computation.Although the ellipsoid algorithm is a polynomial-time algorithm in the integer model, we prove that it has unbounded complexity in the real number model. We conjecture that there exists no polynomial-time algorithm for the linear inequalities problem in the real number model. We also conjecture that linear inequalities are strictly harder than linear equalities in all “reasonable” models of computation.     

On the distribution of integer random variables related by a certain linear inequality: I

We consider the mathematical problem of the allocation of indistinguishable particles to integer energy levels under the condition that the number of particles can be arbitrary and the total energy of the system is bounded above. Systems of integer as well as fractional dimension are considered. The occupation numbers can either be arbitrary nonnegative integers (the case of “Bose particles”) or lie in a finite set {0, 1, …, R} (the case of so-called parastatistics; for example, R = 1 corresponds to the Fermi-Dirac statistics). Assuming that all allocations satisfying the given constraints are equiprobable, we study the phenomenon whereby, for large energies, most of the allocations tend to concentrate near the limit distribution corresponding to the given parastatistics.     

Bidimensional allocation of seats via zero-one matrices with given line sums

In some proportional electoral systems with more than one constituency the number of seats allotted to each constituency is pre-specified, as well as, the number of seats that each party has to receive at a national level. “Bidimensional allocation” of seats to parties within constituencies consists of converting the vote matrix V into an integer matrix of seats “as proportional as possible” to V, satisfying constituency and party totals and an additional “zero-vote zero-seat” condition. In the current Italian electoral law this Bidimensional Allocation Problem (or Biproportional Apportionment Problem—BAP) is ruled by an erroneous procedure that may produce an infeasible allocation, actually one that is not able to satisfy all the above conditions simultaneously. In this paper we focus on the feasibility aspect of BAP and, basing on the theory of (0,1)-matrices with given line sums, we formulate it for the first time as a “Matrix Feasibility Problem”. Starting from some previous results provided by Gale and Ryser in the 60’s, we consider the additional constraint that some cells of the output matrix must be equal to zero and extend the results by Gale and Ryser to this case. For specific configurations of zeros in the vote matrix we show that a modified version of the Ryser procedure works well, and we also state necessary and sufficient conditions for the existence of a feasible solution. Since our analysis concerns only special cases, its application to the electoral problem is still limited. In spite of this, in the paper we provide new results in the area of combinatorial matrix theory for (0,1)-matrices with fixed zeros which have also a practical application in some problems related to graphs.     

An improved Lagrangian relaxation and dual ascent approach to facility location problems

The literature knows semi-Lagrangian relaxation as a particular way of applying Lagrangian relaxation to certain linear mixed integer programs such that no duality gap results. The resulting Lagrangian subproblem usually can substantially be reduced in size. The method may thus be more efficient in finding an optimal solution to a mixed integer program than a “solver” applied to the initial MIP formulation, provided that “small” optimal multiplier values can be found in a few iterations. Recently, a simplification of the semi-Lagrangian relaxation scheme has been suggested in the literature. This “simplified” approach is actually to apply ordinary Lagrangian relaxation to a reformulated problem and still does not show a duality gap, but the Lagrangian dual reduces to a one-dimensional optimization problem. The expense of this simplification is, however, that the Lagrangian subproblem usually can not be reduced to the same extent as in the case of ordinary semi-Lagrangian relaxation. Hence, an effective method for optimizing the Lagrangian dual function is of utmost importance for obtaining a computational advantage from the simplified Lagrangian dual function. In this paper, we suggest a new dual ascent method for optimizing both the semi-Lagrangian dual function as well as its simplified form for the case of a generic discrete facility location problem and apply the method to the uncapacitated facility location problem. Our computational results show that the method generally only requires a very few iterations for computing optimal multipliers. Moreover, we give an interesting economic interpretation of the semi-Lagrangian multiplier(s).     

Vehicle routing via column generation

This paper explores an approximate method for solving a routing problem in a four-level distribution which has “double-ended” demand. Routes are represented as columns in a linear program and column generation is used to improve the solution by generating new routes. The generation of new routes is based on an LP sub-problem. Its solution is rounded down to integer values to insure its feasibility as a route for inclusion in the restricted master problem. Finally, an illustrative problem is solved.     

Skew chain orders and sets of rectangles

A “skew chain order” is one which can be partitioned into saturated chains all starting at rank 0. A two-part Sperner-type theorem is derived for the direct product of two skew chain orders. This theorem is used to solve an extremal problem about certain sets of integer rectangles.     

An Integer Programming Heuristic for Component Allocation in Printed Circuit Card Assembly Systems

Component allocation is an important element of process planning for printed circuit card assembly systems. The component allocation problem directly impacts the productivity and cost of a circuit card assembly system. Many companies have recognized the importance of component allocation and have started to develop a better decision process. Also, a few commercial software packages have been developed that provide environments to support process planning. However, optimization methods are not yet widely used. We demonstrate that component allocation is amenable to improvement using optimization methods. We present an integer programming heuristic for the component allocation problem and report on several case studies that have been conducted and that demonstrate its effectiveness. The heuristic is based on a mixed integer programming formulation of the component allocation problem that incorporates estimates of downstream process planning decisions.     

Compromise allocation in multivariate stratified sample surveys under two stage randomized response model

A general model for the randomized response (RR) method was introduced by Warner (J. Am. Stat. Assoc. 60:63–69, 1965) when a single-sensitive question is under study. However, since social surveys are often based on questionnaires containing more than one sensitive question, the analysis of multiple RR data is of considerable interest. In multivariate stratified surveys with multiple RR data the choice of optimum sample sizes from various strata may be viewed as a multiobjective nonlinear programming problem. The allocation thus obtained may be called a “compromise allocation” in sampling literature. This paper deals with the two-stage stratified Warner’s RR model applied to multiple sensitive questions. The problems of obtaining compromise allocations are formulated as multi-objective integer non linear programming problems with linear and quadratic cost functions as two separate problems. The solution to the formulated problems are achieved through goal programming technique. Numerical examples are presented to illustrate the computational details.     

Representations of torsion-free arithmetic matroids

We study the representability problem for torsion-free arithmetic matroids. After introducing a “strong gcd property” and a new operation called “reduction”, we describe and implement an algorithm to compute all essential representations, up to equivalence. As a consequence, we obtain an upper bound to the number of equivalence classes of representations. In order to rule out equivalent representations, we describe an efficient way to compute a normal form of integer matrices, up to left-multiplication by invertible matrices and change of sign of the columns (we call it the “signed Hermite normal form”). Finally, as an application of our algorithms, we disprove two conjectures about the poset of layers and the independence poset of a toric arrangement.     

Polyhedral annexation in mixed integer and combinatorial programming

Polyhedral annexation is a new approach for generating all valid inequalities in mixed integer and combinatorial programming. These include the facets of the convex hull of feasible integer solutions. The approach is capable of exploiting the characteristics of the feasible solution space in regions both “adjacent to” and “distant from” the linear programming vertex without resorting to specialized notions of group theory, convex analysis or projective geometry. The approach also provides new ways for exploiting the “branching inequalities” of branch and bound.     

A solution procedure for a pattern sequencing problem as part of a one-dimensional cutting stock problem in the steel industry

To cut reinforcing bars for concrete buildings, machines are used which have compartments to store the cut orders until the requirement is met. Number and size of these compartments restrict kind and processing sequence of possible cutting patterns. In this paper we present the so-called “Sequencing algorithm” that tackles the problem of finding a processing sequence for the cutting patterns starting from an integer solution of the cutting stock problem and using an interpretation of relations between orders in patterns as a graph. Computational results are reported.     

Integer lattices of action-angle variables for “spherical pendulum” system

The topology of a “spherical pendulum” mechanical system is studied and an integer lattice generated by action-angle variables is constructed for this system. An algorithm computing numerical marks of Fomenko-Zieschang invariant and monodromy matrices using these lattices is described. This algorithm is applied to the “spherical pendulum” system.     

The solution of the soccer “dream league” game

On January 5, 1996, Maariv, one of the two leading daily newspapers in Israel, announced “The Dream League” game. Every participant in this game was required to “purchase” from a pool of all the soccer players in the Israeli National League, a team which according to his judgment would be chosen as the best team at the end of the season. Purchasing the players was subject to a given budget and to several other constraints. After the soccer season was over, we were requested by Maariv to find the optimal “Dream Team”.The problem of finding the optimal team is shown to be a generalized version of the well-known knapsack problem. It is formulated as an integer program and solved to optimality by the software NAG. Evidently, the optimal Dream Team is much better (in terms of the total cumulative grade) than the actual winning team chosen by the readers of Maariv. A possible heuristic procedure for solving the game in larger settings is also discussed.     

Solving large single allocation p-hub problems with two or three hubs

In this paper we present an efficient approach for solving single allocation p-hub problems with two or three hubs. Two different variants of the problem are considered: the uncapacitated single allocation p-hub median problem and the p-hub allocation problem. We solve these problems using new mixed integer linear programming formulations that require fewer variables than those formerly used in the literature. The problems that we solve here are the largest single allocation problems ever solved. The numerical results presented here will demonstrate the superior performance of our mixed integer linear programs over traditional approaches for large problems. Finally we present the first mixed integer linear program for solving single allocation hub location problems that requires only O(n²) variables and O(n²) constraints that is valid for any number of hubs.     

Solutions of model heat problems in Zygmund spaces

We consider the first boundary value problem and the oblique derivative problem for the heat equation in the model case where the domain is a half-layer and the coefficients of the boundary operator in the oblique derivative problem are constant. Under the corresponding assumptions on the problem data, we show that the solutions belong to anisotropic Zygmund spaces, which “close” the scale of anisotropic Hölder spaces for integer values of the smoothness exponent.     

Approaches to Machine Load Balancing in Flexible Manufacturing Systems

The load balancing problem for a flexible manufacturing system concerns the allocation of operations to machines and of tools to magazines with limited capacity, while seeking to balance the workload on all machines. Previous attempts to tackle this problem have used integer programming and a specialized branch and bound procedure has been developed. A modified integer programming approach is proposed here. The problem has certain features which can be used advantageously for an approximate solution technique. The approximation technique is described and computational results presented. Extensions to the problem of pooling machines are also considered.     

The minimum distance superset problem: formulations and algorithms

The partial digest problem consists in retrieving the positions of a set of points on the real line from their unlabeled pairwise distances. This problem is critical for DNA sequencing, as well as for phase retrieval in X-ray crystallography. When some of the distances are missing, this problem generalizes into a “minimum distance superset problem”, which aims to find a set of points of minimum cardinality such that the multiset of their pairwise distances is a superset of the input. We introduce a quadratic integer programming formulation for the minimum distance superset problem with a pseudo-polynomial number of variables, as well as a polynomial-size integer programming formulation. We investigate three types of solution approaches based on an available integer programming solver: (1) solving a linearization of the pseudo-polynomial-sized formulation, (2) solving the complete polynomial-sized formulation, or (3) performing a binary search over the number of points and solving a simpler feasibility or optimization problem at each step. As illustrated by our computational experiments, the polynomial formulation with binary search leads to the most promising results, allowing to optimally solve most instances with up to 25 distance values and 8 solution points.