Moshe Sniedovich

Applying the corridor method to a blocks relocation problem

In this paper, we present a corridor method inspired algorithm for a blocks relocation problem in block stacking systems. Typical applications of such problem are found in the stacking of container terminals in a yard, of pallets and boxes in a warehouse, etc. The proposed algorithm applies a recently proposed metaheuristic. In a method-based neighborhood we define a two-dimensional “corridor” around the incumbent blocks configuration by imposing exogenous constraints on the solution space of the problem and apply a dynamic programming algorithm capturing the state of the system after each block movement for exploring the neighborhoods. Computational results on medium- and large-size problem instances allow to draw conclusions about the effectiveness of the proposed scheme.

Wald's maximin model: a treasure in disguise!

Purpose - The purpose of this paper is to illustrate the expressive power of Walds maximin model and the mathematical modeling effort requisite in its application in decision under severe uncertainty. Design/methodology/approach - Decision making under severe uncertainty is art as well as science. This fact is manifested in the insight and ingenuity that the modeller/analyst is required to inject into the mathematical modeling of decision problems subject to severe uncertainty. The paper elucidates this point in a brief discussion on the mathematical modeling of Walds maximin paradigm. Findings - The apparent simplicity of the maximin paradigm implies that modeling it successfully requires a considerable mathematical modeling effort. Practical implications - The paper illustrates the importance of mastering the art of mathematical modeling especially in the application of Walds maximin model. Originality/value - This paper sheds new light on some of the modeling aspects of Walds maximin paradigm.

Black Swans, New Nostradamuses, Voodoo decision theories, and the science of decision making in the face of severe uncertainty

The recent global financial crisis, natural disasters, and ongoing debate on global warming and climate change are a stark reminder of the huge challenges that severe uncertainty presents in decision and policy making. My objective in this paper is to look at some of the issues that need to be taken into account in the modeling and analysis of decision problems that are subject to severe uncertainty, paying special attention to some of the misconceptions that are being promulgated in this area. I also examine two diametrically opposed approaches to uncertainty. One, that emphasizes that the difficulties encountered in the modeling, analysis, and solution of decision problems in the face of severe uncertainty are in fact insurmountable, and another that claims to provide, against all odds, a reliable strategy for a successful handling of situations subject to severe uncertainty.

A bird's view of info-gap decision theory

Purpose - The purpose of this paper is to clarify a number of important facts about info-gap decision theory. Design/methodology/approach - Theorems are put forward to rebut claims made about info-gap decision theory in papers published in this journal and elsewhere. Findings - Info-gaps robustness model is a simple instance of the most famous model in classical decision theory for the treatment of decision problems subject to severe uncertainty, namely Walds maximin model. This simple instance is the equivalent of the well-established model known universally as radius of stability. Info-gaps robustness model has an inherent local orientation. Therefore, it is in principle unable to address the fundamental difficulties presented by the type of severe uncertainty that is postulated by info-gap decision theory. Practical implications - These findings caution against accepting the assertions made in the info-gap literature about: info-gap decision theorys role and place in decision making under severe uncertainty; and its ability to model, analyze, and manage severe uncertainty. Originality/value - This paper exposes the serious difficulties with claims made in papers published in this journal and elsewhere regarding the place and role of info-gap decision theory in decision theory and its ability to handle severe uncertainty.

The simplex method as a global optimizer: A c-programming perspective

In this paper we give a brief account of the important role that the conventional simplex method of linear programming can play in global optimization, focusing on its collaboration with composite concave programming techniques. In particular, we demonstrate how rich and powerful the c-programming format is in cases where its parametric problem is a standard linear programming problem.

Fooled by Local Robustness

One would have expected the considerable public debate created by Nassim Talebs two best selling books on uncertainty, Fooled by Randomness and The Black Swan, to inspire greater caution to the fundamental difficulties posed by severe uncertainty. Yet, methodologies exhibiting an incautious approach to uncertainty have been proposed recently in a range of publications. So, the objective of this short note is to call attention to a prime example of an incautious approach to severe uncertainty that is manifested in the proposition to use the concept radius of stability as a measure of robustness against severe uncertainty. The central proposition of this approach, which is exemplified in info-gap decision theory, is this: use a simple radius of stability model to analyze and manage a severe uncertainty that is characterized by a vast uncertainty space, a poor point estimate, and a likelihood-free quantification of uncertainty. This short discussion serves then as a reminder that the generic radius of stability model is a model of local robustness. It is, therefore, utterly unsuitable for the treatment of severe uncertainty when the latter is characterized by a poor estimate of the parameter of interest, a vast uncertainty space, and a likelihood-free quantification of uncertainty.

Generalized linear programming and sensitivity analysis techniques

In this article we report on numerical experiments conducted to assess the performance of c-programming algorithms for generalized linear programming problems involving the maximization of composite-convex objective functions. The results indicate that the standard parametric sensitivity analysis techniques of the simplex method can play a central role in such algorithms. We also comment on issues concerning the use of commercial LP packages to solve problems of this type.

Algorithmic and Computational Aspects of Composite Concave Programming

Abstract In this paper we discuss the algorithmic and computational aspects of the parametric nonlinear optimization method c-programming. Our objective in looking at the method from this vantage point is twofold. First, to explain more clearly where c-programming sits in optimization theory. Second, to throw more light on the details of the collaboration that it forges with other optimization methods. The first objective is accomplished through an analysis of c-programmings genealogy. The latter is achieved by an examination of the basic structure of c-programming algorithms, and by reporting on extensive numerical experiments conducted with c-programming algorithms in collaboration with linear programming and dynamic programming techniques. These experiments very convincingly show that c-programming has the ability to significantly expand the scope of linear programming, dynamic programming, and possibly other optimization methods.

Fooled by local robustness: an applied ecology perspective

In this short discussion, we point out that it is apparently as easy to be fooled by robustness as it is to be fooled by randomness. Our objective is to bring to the attention of applied ecologists that radius-of-stability robustness models are models of local robustness. As such, these models are utterly unsuitable for the treatment/management of a severe uncertainty characterized by a vast uncertainty space and a likelihood-free quantification of the uncertainty. This observation is particularly pertinent to applications of info-gap decision theory in ecology, conservation biology, and environmental management, where the objective is to identify decisions that are robust against a severe uncertainty of this type.

Solving a class of multiplicative programming problems via c-programming

In this note we show that many classes of global optimization problems can be treated most satisfactorily by classical optimization theory and conventional algorithms. We focus on the class of problems involving the minimization of the product of several convex functions on a convex set which was studied recently by Kunoet al. [3]. It is shown that these problems are typical composite concave programming problems and thus can be handled elegantly by c-programming [4]–[8] and its techniques.