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In this article we consider the case of a manufacturing firm which has several production centres providing finished goods to several warehouses. Both the manufacturing cost at each production centre as well as the cost of transportation between the production centres and warehouses will vary (typically due to different labour costs or because of varying distances between the production centres and warehouses). The objective is to determine the optimal allocation of goods between the production centres and warehouses which minimises the total production and transportation costs.

In many decision‐making problems under parameter uncertainty, the most popular stochastic approach is not used because of its serious drawbacks. The purpose of this paper is to present another approach, which copes with the uncertainty of parameters. It uses a precise criterion evaluating a decision with respect to uncertain parameters. This precision by the maximum operator is performed on a term based on the criterion and called the relative regret. The approach is applied to the allocation problems in a complex of operations.

The resource allocation problems in a complex of operations of independent and dependent structures to minimize a total execution time of all operations are investigated. Then, the results are extended for the problem of a task allocation in the complex of independent operations. The case is considered when the parameters in the functional models of the operations are uncertain, and their values belong to the intervals of known bounds. The solution algorithms for the uncertain problems are based on known solution algorithms for the corresponding deterministic problems. The solution algorithms for the latter problems are outlined in the paper.

The main contribution of the paper consists in presenting the property that it is possible for the uncertain problems considered to replace the solution of the uncertain allocation problems by solving a number of corresponding deterministic problems.

The useful and interesting property of the solution algorithm for the allocation problems, in general, cannot be applied to the other decision‐making problems under uncertainty. As an example of such a problem, a simple routing‐scheduling problem is presented for which, however, a number of possible parameter scenarios can be substantially limited.

The allocation problems addressed in the paper have a variety of applications in computer systems and in manufacturing systems. Moreover, a lack of crisp values for the parameters in models of individual operations is rather common.

The paper extends previous results for the allocation problems in a complex of operations.

In seeking to extend rational choice theory from “market” to “political” behaviour, economists have encountered a paradox: namely, that the act of voting itself appears to be inconsistent with the assumption of rationality. This is true not only when self‐interest is assumed, but also when altruistic behaviour (at least in its non‐Kantian form) is allowed for. This article surveys the theoretical and empirical literature on the determinants of the decision to participate in voting, and concludes that this decision is responsive to changes in the expected benefits and costs of voting; even though the expected costs of voting must normally outweigh the expected benefits. Interpretations of this behaviour include the possibility that voters act rationally, but are misinformed about the likely effectiveness of their votes; alternatively, the electorate may include more Kantians than economists have generally been willing to admit.

The purpose of this paper is to consider selected optimization problems with parameter uncertainty. A case is studied when uncertain parameters in functions undergoing the optimization belong to intervals of known bounds as well as the absolute regret based approach for coping with such an uncertainty is applied. The paper presents three different cases depending on properties of optimization problems and proposes which method can be used to solve corresponding problems.

The worst‐case absolute regret method is employed to manage interval uncertainty in functions to be optimized. To solve resulting uncertain optimization problems, optimal, approximate as well as heuristic solution algorithms have been elaborated for particular problems presented and described in the paper. The latter one is based on Scatter Search metaheuristics.

Solution algorithms for worst‐case absolute regret versions of the following optimization problems have been determined: resource allocation in a complex of independent operations and two task scheduling problems Q‖∑C_i and P‖C_max.

It is very difficult to generalize the results obtained and to use them for solving other optimization problems which correspond to real‐world applications. Such new cases require separate investigations.

The considered allocations as well as task scheduling problems have plenty of applications, for example in computer and manufacturing systems. Their versions with not precisely known parameters can be met commonly.

The investigations presented correspond to previous works on so‐called minimax regret problems and extend them for new optimization problems.

Competition in the banking sector is more complex than in the past, and survival has become more difficult than before. The purpose of this paper is to propose a grey methodology for evaluating, clustering and ranking the performance of bank branches with imprecise and uncertain data in order to determine the relative status of each branch.

In this study, the two-stage data envelopment analysis model with grey data is applied to assess the efficiency of bank branches in terms of operations. The result of grey two-stage data envelopment analysis model is a grey number as efficiency value of each branch. In the following, the branches are classified into three grey categories of performance by grey clustering method, and the complete grey ranking of branches are performed using “minimax regret-based approach” and “whitening value rating”.

The results show that after grey clustering of 22 branches based on grey efficiency value obtained from the grey two-stage DEA model, 6 branches are assigned to “excellent” class, 4 branches to “good” class and 12 branches to “poor” class. Moreover, the results of MRA and whitening value rating models are integrated, and a complete ranking of 22 branches are presented.

Grey clustering of branches based on grey efficiency value can facilitate planning and policy-making for branches so that there is no need to plan separately for each branch. The grey ranking helps the branches find their current position compared to other branches, and the results can be a dashboard to find the best practices for benchmarking.

Compared with traditional DEA methods which use deterministic data and consider decision-making units as black boxes, in this research, a grey two-stage DEA model is proposed to evaluate the efficiency of bank branches. Furthermore, grey clustering and grey ranking of efficiency values are used as a novel solution for improving the accuracy of grey two-stage DEA results.

This chapter is designed to outline how current methods in formal policy analysis have evolved to better respect limits to an analyst's knowledge. These limits are referred to as model uncertainty both in order to capture the idea that formal policy analysis is predicated on mathematically precise formulations that embody assumptions on the part of an analyst and because model uncertainty, which represents a recognition of the potential for these assumptions to produce unsound analyses, has been an active area of research in economics and statistics for the last 15 or so years. The argumentation in this chapter is not original and is admittedly selective. For Austrian economists, the paper will hopefully be of interest in indicating how empirical work is evolving in a way that better respects limits to a social scientist's knowledge. I certainly do not mean to suggest that these arguments should eliminate the objections that have been raised by some Austrian economists to formal empirical work. Rather, the intent of this chapter is to indicate the possibility of dialog and debate between Austrian and non-Austrian economists on the role of formal empirical work. In several contexts, I have introduced arguments concerning the limits of formal econometric analysis by Hayek and von Mises to both illustrate how the perspectives in this chapter relate to their views in order to suggest why, in my judgment, some of their skepticism is unwarranted.

This chapter reviews models of decision-making and choice under conditions of certainty. It allows readers to position the contribution of the other chapters in this book in the historical development of the topic area.

Bounded rationality is defined in terms of a strategy to simplify the decision-making process. Based on this definition, different models are reviewed. These models have assumed that individuals simplify the decision-making process by considering a subset of attributes, and/or a subset of choice alternatives and/or by disregarding small differences between attribute differences.

A body of empirical evidence has accumulated showing that under some circumstances the principle of bounded rationality better explains observed choices than the principle of utility maximization. Differences in predictive performance with utility-maximizing models are however small.

The chapter provides a detailed account of the different models, based on the principle of bounded rationality, that have been suggested over the years in travel behaviour analysis. The potential relevance of these models is articulated, model specifications are discussed and a selection of empirical evidence is presented. Aspects of an agenda of future research are identified.

The purpose of this paper is to provide a practical method to be used in team decision making when allocating resources.

The paper proposes the following hypothesis: the selected team consensus strategy from among all available strategies must have minimum sum of squares of monetary regrets. A general algebraic representation of the above hypothesis is developed.

This hypothesis can be interpreted as a Nash equilibrium involving mixed strategies when the entire problem is viewed in game theoretic framework. The paper provides an explanation in quantitative terms of the reasoning process pursued by five business college department chairs faced with three strategies, in an actual consensus decision making to illustrate the above hypothesis. By making observations of the behavior of decision makers in the selection of a budget allocation formula, the paper shows that the hypothesis holds true for the specific reasoning process pursued by the chairs in arriving at the consensus solution. However, the chairs' consensus solution is found to be a local solution vis‐à‐vis the global optimal solution found by solving the game theoretic model.

The authors plan to conduct further empirical testing of the hypothesis using allocation strategies found in diverse decision‐making environments involving diverse decision makers such as business executives, government officers, education administrators, and others.

If this hypothesis can be validated to be true, decision makers should propose for consideration only those rational strategies that have minimal or low variance in monetary regrets since these are the strategies that would most likely be selected in team decision making.

Team decision making involving resource allocation abounds in all organizations, at all levels and in diverse applications. The practical procedure proposed in this paper, based on analytical foundation of game theory, provides decision makers a viable tool for allocating resources that results in consensus of all rational parties involved.

This paper explores the role of neural networks for decision making in dynamic environments which are characterized by risks and uncertainties, and also provides experimental evidence from a simulated data. Theoretical support is derived from theories of affective balance, and self‐organized criticality. The simulation is conducted for a two‐person‐constant sum game. The findings of the experiment are helpful in extending to managerial decision making which involves varying degrees of uncertainties. Such decisions are affected by forces both internal and external to the company, and making judgments in such a fuzzy future is highly probabilistic. It is suggested, therefore that neural networks are better able to capture the interactive dynamics of variables operating in a managerial decision environment. In sum, the findings indicate that decisions in general and business decisions in particular can greatly benefit from the parallel computational capabilities of neural networks.

The purpose of this paper is to present a methodology which enables to measure and analyze the performance of sub-process and overall system of a cargo company.

Network data envelopment analysis method with fuzzy data is used for performance measurement which considers the sub-process efficiency simultaneously together with the overall efficiency and also the uncertainty included in input–output data.

A real-life application of the proposed model is presented for Turkey. The application results show the efficiency scores of ten branches according to each sub-process and also the overall system. Although the obtained results are case specific, the application results indicate that the inefficient branches can achieve efficiency either by decreasing circulation ratio input for human resources sub-process or by increasing closed complaint output or by decreasing open complaint output for customer relationship management sub-process.

The study presented provides insights into the performance measurement applications in cargo sector. The methodology presented provides the flexibility of removing or adding some new sub-processes and also decision-making units which enables the approach to be used for other performance evaluation problems.