On multiple Markov chains

The aim of this paper is to examine multiple Markov dependence for the discrete as well as for the continuous parameter case. In both cases the Markov property with arbitrary parameter values is investigated and it is shown that it leads to the degeneration of the multiple Markov dependence to the simple one.     

Robustness and information levels in case-based multiple criteria sorting

Case-based preference elicitation methods for multiple criteria sorting problems have the advantage of posing rather small cognitive demands on a decision maker, but they may lead to ambiguous results when preference parameters are not uniquely determined. We use a simulation approach to determine the extent of this problem and to study the impact of additional case information on the quality of results. Our experiments compare two decision analysis tools, case-based distance sorting and the simple additive weighting method, in terms of the effects of additional case information on sorting performance, depending on problem dimension – number of groups, number of criteria, etc. Our results confirm the expected benefit of additional case information on the precision of estimates of the decision maker’s preferences. Problem dimension, however, has some unexpected effects.     

Duality for finite multiple harmonic q-series

We define two finite q-analogs of certain multiple harmonic series with an arbitrary number of free parameters, and prove identities for these q-analogs, expressing them in terms of multiply nested sums involving the Gaussian binomial coefficients. Special cases of these identities—for example, with all parameters equal to 1—have occurred in the literature. The special case with only one parameter reduces to an identity for the divisor generating function, which has received some attention in connection with problems in sorting theory. The general case can be viewed as a duality result, reminiscent of the duality relation for the ordinary multiple zeta function.     

Efficiency in multiple objective optimization problems

We generalize many of the results on efficient points for linear multiple objective optimization problems to the nonlinear case by focusing on an auxiliary problem. The approach, which relies on duality theory, is a straightforward development that even in the linear case yields simpler proofs.This research was partially supported by the National Science Foundation through Grant No. ENG 74-15488.     

A goal programming approach to strategic resource allocation in acute care hospitals

This paper describes a methodology for allocating resources in hospitals. The methodology uses two linear goal-programming models. One model sets case mix and volume for physicians, while holding service costs fixed; the other translates case mix decisions into a commensurate set of practice changes for physicians. The models allow decision makers to set case mix and case costs in such a way that the institution is able to break even, while preserving physician income and minimizing disturbance to practice. The models also permit investigation of trade-offs between case mix and physician practice parameters. Results are presented from a decision-making scenario facing the surgical division of Toronto's Mount Sinai Hospital after the announcement of a 3-year, 18% reduction in funding.     

Searching for a set of correlated patterns

New algorithms for searching simultaneously for a set of patterns in a text are suggested, for the special case where these patterns are correlated and have a common substring. This is then extended to the case where it could be more profitable to look for more than a single overlap, and a problem related to the generalization of this idea is shown to be NP-complete. Experimental results suggest that for this particular application, the suggested algorithm yields significant improvements over previous methods.     

Stochastic single vehicle routing with a predefined customer sequence and multiple depot returns

We study the routing of a single vehicle that delivers multiple products under stochastic demand. Specifically, we investigate two practical variations of this problem: (i) The case in which each product type is stored in its dedicated compartment in the vehicle, and (ii) the case in which all products are stored together in the vehicle’s single compartment. Suitable dynamic programming algorithms are proposed to determine the minimum expected (routing) cost for each case. Furthermore, the optimal routing policy is derived by developing appropriate theorems. The efficiency of the algorithms is studied by solving large problem sets.     

Integer Programming Duality in Multiple Objective Programming

The weighted sums approach for linear and convex multiple criteria optimization is well studied. The weights determine a linear function of the criteria approximating a decision makers overall utility. Any efficient solution may be found in this way. This is not the case for multiple criteria integer programming. However, in this case one may apply the more general e-constraint approach, resulting in particular single-criteria integer programming problems to generate efficient solutions. We show how this approach implies a more general, composite utility function of the criteria yielding a unified treatment of multiple criteria optimization with and without integrality constraints. Moreover, any efficient solution can be found using appropriate composite functions. The functions may be generated by the classical solution methods such as cutting plane and branch and bound algorithms.     

Supplier diversification under binomial yield

We consider supplier diversification in an EOQ type inventory setting with multiple suppliers and binomial yields. We characterize the optimal policy for the model and show that, in this case, it does not pay to diversify, in contrast to previous results in the random yield literature.     

Multiattribute preference models with reference points

In the context of multiple attribute decision making, preference models making use of reference points in an ordinal way have recently been introduced in the literature. This text proposes an axiomatic analysis of such models, with a particular emphasis on the case in which there is only one reference point. Our analysis uses a general conjoint measurement model resting on the study of traces induced on attributes by the preference relation and using conditions guaranteeing that these traces are complete. Models using reference points are shown to be a particular case of this general model. The number of reference points is linked to the number of equivalence classes distinguished by the traces. When there is only one reference point, the induced traces are quite rough, distinguishing at most two distinct equivalence classes. We study the relation between the model using a single reference point and other preference models proposed in the literature, most notably models based on concordance and models based on a discrete Sugeno integral.     

An intractability result for multiple integration

We prove that the problem of multiple integration in the Korobov class is intractable since the number of function evaluations required to achieve a worst case error less than is exponential in the dimension.

     

On the use of multiple sinks to extend the lifetime in connected wireless sensor networks

This paper addresses the maximum network lifetime problem in wireless sensor networks. In this problem, the purpose is to schedule a set of wireless sensors, keeping them connected and guaranteeing that all targets are covered, while network lifetime is maximized. Two variants of this problem are considered; in the first case it is assumed that a single sink (base station) is available, the second case considers the presence of several sinks. To solve the problem, a hybrid Column Generation-GRASP heuristic is proposed. The method is shown to be able to find optimal or near optimal solutions efficiently in both cases.     

Valuation of urban industrial land: An analytic network process approach

In this paper an application of the Analytic Network Process (ANP) to asset valuation is presented. It has two purposes: solving some of the drawbacks found in classical asset valuation methods and broadening the scope of current approaches. The ANP is a method based on Multiple Criteria Decision Analysis (MCDA) that accurately models complex environments. This approach is particularly useful in problems which work with partially available data, qualitative variables and influences among the variables, which are very common situations in the valuation context. As an illustration, the new approach has been applied to a real case study of an industrial park located in Valencia (Spain) using three different models. The results confirm the validity of the methodology and show that the more information is incorporated into the model, the more accurate the solution will be, so the presented methodology stands out as a good alternative to current valuation approaches.     

Exact analytical solution for a thermal boundary layer in a saturated porous medium

The forced convection thermal boundary layer in a porous medium as an analytically tractable special case of a mixed convection problem is considered. It is shown that some general features of the mixed convection solutions reported recently by other authors [B. Brighi, J.-D. Hoernel, On the concave and convex solutions of mixed convection boundary layer approximation in a porous medium, Appl. Math. Lett. (published online, 2005); M. Guedda, Multiple solutions of mixed convection boundary layer approximations in a porous medium, Appl. Math. Lett. (published online, 2005)] can already be recovered from this exactly solvable case.     

External finite-element approximations of eigenfunctions in the case of multiple eigenvalues

The paper deals with the finite-element analysis of second-order elliptic eigenvalue problems when the approximate domains Ω_h are not subdomains of the original domain . The considerations are restricted to piecewise linear approximations. Special attention is devoted to the convergence of approximate eigenfunctions in the case of multiple exact eigenvalues. As yet the approximate solutions have been compared with linear combinations of exact eigenfunctions with coefficients depending on the mesh parameter h. We avoid this disadvantage.     

Integrating stochastic time-dependent travel speed in solution methods for the dynamic dial-a-ride problem

In urban areas, logistic transportation operations often run into problems because travel speeds change, depending on the current traffic situation. If not accounted for, time-dependent and stochastic travel speeds frequently lead to missed time windows and thus poorer service. Especially in the case of passenger transportation, it often leads to excessive passenger ride times as well. Therefore, time-dependent and stochastic influences on travel speeds are relevant for finding feasible and reliable solutions. This study considers the effect of exploiting statistical information available about historical accidents, using stochastic solution approaches for the dynamic dial-a-ride problem (dynamic DARP). The authors propose two pairs of metaheuristic solution approaches, each consisting of a deterministic method (average time-dependent travel speeds for planning) and its corresponding stochastic version (exploiting stochastic information while planning). The results, using test instances with up to 762 requests based on a real-world road network, show that in certain conditions, exploiting stochastic information about travel speeds leads to significant improvements over deterministic approaches.     

On the Expectations of Multiple Stratonovich Integrals

Abstract

In the construction of numerical methods for solving stochastic differential equations it becomes necessary to calculate the expectations of products of multiple stochastic integrals. In the Itô case, explicit formulae for the expectation of a multiple integral with integrand identically equal to 1 and for the product of two such integrals are known. In this paper formulae for the expectation of any multiple Stratonovich integral as well as for the product of a broad class of two Stratonovich integrals have been derived.     

Cyclical scheduling and multi-shift scheduling: Complexity and approximation algorithms

We consider the multiple shift scheduling problem modelled as a covering problem. Such problems are characterized by a constraint matrix that has, in every column, blocks of consecutive 1s, each corresponding to a shift. We focus on three types of shift scheduling problems classified according to the column structure in the constraint matrix: columns of consecutive 1s, columns of cyclical 1s, and columns of k consecutive blocks. In particular, the complexity of the cyclical scheduling problem, where the matrix satisfies the property of cyclical 1s in each column, was noted recently by Hochbaum and Tucker to be open. They further showed that the unit demand case is polynomially solvable. Here we extend this result to the uniform requirements case, and provide a 2-approximation algorithm for the non-uniform case. We also establish that the cyclical scheduling problem’s complexity is equivalent to that of the exact matching problem—a problem the complexity status of which is known to be randomized polynomial (RP). We then investigate the three types of shift scheduling problems and show that, while the consecutive ones version is polynomial and the k-block columns version is NP-hard (for k≥2), for the k-blocks problem we give a simple k-approximation algorithm, which is the first approximation algorithm determined for the problem.     

New convergence results on the global GMRES method for diagonalizable matrices

In the present paper, we give some new convergence results of the global GMRES method for multiple linear systems. In the case where the coefficient matrix A is diagonalizable, we derive new upper bounds for the Frobenius norm of the residual. We also consider the case of normal matrices and we propose new expressions for the norm of the residual.     

Multiple objective minimum cost flow problems: A review

In this paper, theory and algorithms for solving the multiple objective minimum cost flow problem are reviewed. For both the continuous and integer case exact and approximation algorithms are presented. In addition, a section on compromise solutions summarizes corresponding results. The reference list consists of all papers known to the authors which deal with the multiple objective minimum cost flow problem.