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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.

Inaction inertia is the phenomenon in which people are less likely to accept an opportunity after having previously missed a relatively superior one. This research explores how framing quantity promotions as either a freebie (e.g. “buy 1, get 1 free”) or a price bundle (e.g. “buy 2, get 50% off”) influences inaction inertia. Relevant mediators are also identified.

Three experiments, two using imaginary scenarios and one using an incentive-compatible design, test the hypotheses.

Consumers who miss a freebie quantity promotion express higher inaction inertia than consumers who miss a price bundle promotion. The cause of this difference is higher perceived regret and greater devaluation that result from missing a superior freebie (vs price bundle) promotion.

Future research should examine how factors influencing perceived regret and devaluation moderate the quantity promotional frame effect on inaction inertia.

The findings provide insights into which quantity promotional frames practitioners should use to reduce inaction inertia.

This study's comprehensive theoretical framework predicts quantity promotional frame effects on inaction inertia and identifies relevant internal mechanisms. The findings are evidence that inaction inertia is caused by both perceived regret and devaluation in certain contexts. Furthermore, this study identifies the conditions in which a price bundle promotional frame is more beneficial than a freebie promotional frame.

The purpose of this paper is to explore the decision‐making implications of “regrets of the heart” versus “regrets of the head” in economic decision making.

The phenomenon in three empirical studies is examined. Study 1 is a protocol analysis of people's “regrets of the heart” and “regrets of the head”. Study 2 uses the same recall prompt and examined decision makers' choices in an ultimatum bargaining game. Study 3 tests regrets of heart versus the head in an interactive face to face negotiation setting.

Overall, it is found that people who were prompted to recall a time in which they regretted “not following their heart” were more likely to recall situations in which they experienced a loss or lost opportunity compared to people who recalled a time when they regretted “not following their head”. Recalling a regret of the heart prompts decision makers and negotiators to put a greater value on maintaining relationships and avoid loss in an interpersonal exchange situation.

These findings contribute to the literature on how emotions affect economic decision making and provide a more nuanced examination of regret.

Focusing on “regrets of the head” may lead to greater economic gains in economic decisions.

This article examines a different type of regret and demonstrates how this type of regret impacts economic decision‐making behavior.

The purpose of this paper is to present a stochastic p-robust data envelopment analysis (DEA) model for decision-making units (DMUs) efficiency estimation under uncertainty. The main contribution of this paper consists of the development of a more robust system for the estimation of efficiency in situations of inputs uncertainty. The proposed model is used for the efficiency measurement of a commercial Iranian bank.

This paper has been arranged to launch along the following steps: the classical Charnes, Cooper, and Rhodes (CCR) DEA model was briefly reviewed. After that, the p-robust DEA model is introduced and then calculated the priority weights of each scenario for CCR DEA output oriented method. To compute the priority weights of criteria in discrete scenarios, the analytical hierarchy analysis process (AHP) is used. To tackle the uncertainty of experts’ opinion, a synthetic technique is applied based on both robust and stochastic optimizations. In the sequel, stochastic p-robust models are proposed for the estimation of efficiency, with particular attention being paid to DEA models.

The proposed method provides a more encompassing measure of efficiency in the presence of synthetic uncertainty approach. According to the results, the expected score, relative regret score and stochastic P-robust score for DMUs are obtained. The applicability of the extended model is illustrated in the context of the analysis of an Iranian commercial bank performance. Also, it is shown that the stochastic p-robust DEA model is a proper generalization of traditional DEA and gained a desired robustness level. In fact, the maximum possible efficiency score of a DMU with overall permissible uncertainties is obtained, and the minimal amount of uncertainty level under the stochastic p-robustness measure that is required to achieve this efficiency score. Finally, by an example, it is shown that the objective values of the input and output models are not inverse of each other as in classical DEA models.

This research showed that the enormous decrease in maximum possible regret makes only a small addition in the expected efficiency. In other words, improvements in regret can somewhat affect the expected efficiency. The superior issue this kind of modeling is to permit a harmful effect to the objective to better hedge against the uncertain cases that are commonly ignored.

The purpose of this paper is to explore the role of emotions that consumers experience following service failures and to assess the effects of each of these emotions on important behavioral outcomes.

This paper extends the work of Wetzer et al. (2007) and draws upon the existing literature to test a series of research hypotheses tying emotions to four important behavioral outcomes primarily using stepwise regression.

When a service failure occurs, customers experience any of a variety of negative emotions. The effect on behavioral outcomes depends on the specific emotion experienced by the consumer. The current research, which benefits by using retrospective experience sampling, finds that frustration is the predominant emotion experienced by customers following service failure, but that anger, regret and frustration affect behavioral outcomes. Uncertainty also plays a role.

Future research should investigate the antecedents of propensity for emotions and predisposition toward industries, as well as the consequences of word-of-mouth (WOM) praise and WOM activity. Additionally, emotions could be examined by service stage. Several other moderators could be investigated, including severity, complaining behavior, repeat occurrence, service importance, remedies and forgiveness, product vs process failures, tenure, gender and age.

The current research emphasizes the importance of understanding which emotion is being experienced by a customer following service failure to identify the behavioral outcomes that will be most impacted. The specific managerial implications depend upon the specific emotional response experienced by the customer and are discussed separately for anger, regret and frustration. Service personnel must be trained to recognize and address specific customer emotions rather than to provide a canned or generalized response.

To date, there has been little, if any, systematic research into the effects of multiple discrete negative emotions on multiple desirable behavioral outcomes. The current study examines six discrete emotions. Predominant emotions are differentiated from emotional intensity. The behavioral outcomes of reconciliation and reduced share-of-wallet are added to the traditional outcomes of repatronage intentions and negative WOM. While existing research tends to rely on a scenario approach, this study uses the retrospective experience sampling method. The authors distinguish between mixed emotions and multiple emotions. The relative effects of disappointment and regret are examined for each of the four outcomes. Finally, importance-performance map analysis was applied to the findings to prioritize managerial attention. Numerous managerial and research implications are identified.

The chapter outlines the principles underlying relative utility models, discusses the results of empirical applications and critically assesses the usefulness of this specification against commonly used random utility models and other context dependence models. It also discusses how relative utility can be viewed as a generalisation of context dependency.

In contrast to the conventional concept of random utility, relative utility assumes that decision-makers derive utility from their choices relative to some threshold(s) or reference points. Relative utility models thus systematically specify the utility against such thresholds or reference points.

Examples in the chapter show that relative utility model perform well in comparison to conventional utility-maximising models in some circumstances.

Examples of relative utility models are rare in transportation research. The chapter shows that several recent models can be viewed as special cases of relative utility models.

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 introduce robust optimization approaches to balance mixed model assembly lines with uncertain task times and daily model mix changes.

Scenario planning approach is used to represent the input data uncertainty in the decision model. Two kinds of robust criteria are provided: one is min‐max related; and the other is α‐worst scenario based. Corresponding optimization models are formulated, respectively. A genetic algorithm‐based robust optimization framework is designed. Comprehensive computational experiments are done to study the effect of these robust approaches.

With min‐max related robust criteria, the solutions can provide an optimal worst‐case hedge against uncertainties without a significant sacrifice in the long‐run performance; α‐worst scenario‐based criteria can generate flexible robust solutions: through rationally tuning the value of α, the decision maker can obtain a balance between robustness and conservatism of an assembly line task elements assignment.

This paper is an attempt to robust mixed model assembly line balancing. Some more efficient and effective robust approaches – including robust criteria and optimization algorithms – may be designed in the future.

In an assembly line with significant uncertainty, the robust approaches proposed in this paper can hedge against the risk of poor system performance in bad scenarios.

Using robust optimization approaches to balance mixed model assembly line.

Prior research consistently found maximizers to experience greater regret over their choice than satisficers. Moreover, research also found maximizers to be trapped in a “maximization-regret-maximization” cycle. This paper aims to assess the role of construal level theory in alleviating regret felt by maximizers.

The authors examine the construal level theory (CLT) in conjunction with the choice context (comparable and non-comparable choices). Three experimental studies tested our assertion that a match between CLT mindset and choice set relieves regret for maximizers.

The authors show maximizers experience similar levels of regret compared to satisficers when considering comparable options in a concrete mindset, and non-comparable options in an abstract mindset. However, maximizers experience heightened regret in comparison to satisficers when considering non-comparable (comparable) options in a concrete (abstract) mindset. Choice difficulty mediates our effect.

Future research is needed to replicate our results in real-life settings.

If marketers think that their product is likely to be compared with other comparable products, they should adopt product-specific information that focusses on how the product would be used. However, if marketers think that consumers will compare across non-comparable products, then they should focus on why their product is the most suitable to fulfil consumers’ needs.

This research represents the first attempt at reducing regret for maximizers and answers the call for an examination of the relationship between maximization and CLT. The research adds to the maximization literature by evidencing a CLT-based strategy that attenuates the negative experience of regret for maximizers.

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.