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We consider a dynamic duopoly game where firms choose both the timing and size of their investments. The existing real options literature predominantly consists of contributions where firms have a single option to invest. This paper relaxes this assumption by giving Firm A multiple options to undertake further investments with the purpose to expand whereas Firm B only holds the option to enter the market. In this asymmetric setting we get the surprising result that, in equilibrium, Firm B invests first. If Firm A invests first, Firm A and Firm B keep on being involved in preemption games for subsequent investments until Firm B enters the market, which leads to inefficiently early investments of Firm A. When Firm B invests first, then only one preemption game is played, which leads to Firm A being free to choose its unrestricted optimal investment moments. [ABSTRACT FROM AUTHOR]

Analysing and mitigating errors in production processes is a primary objective of companies in the automotive sector. Unfortunately, due to inaccurate or partially missing information, comparing errors is often very difficult, resulting from the experts' provision of incomplete pairwise comparison matrices. In the literature, several techniques have been developed to complete such matrices. These techniques merely estimate what the decision makers or experts would have entered according to known entries. In this article, we propose a new methodology based on the stochastic multi-objective acceptability analysis; we apply it to vary the missing entries of the pairwise comparison matrix, thus providing the probability that an alternative/criterion will attain a given rank. This approach gives a complete view of the possible outcomes because it represents all possible decision maker mindsets. We present a case study carried out in a multinational automotive industry where we apply our methodology for evaluating errors in the production process. [ABSTRACT FROM AUTHOR]

Generic data envelopment analysis (DEA) models are based on deterministic input and output. However, input and output vectors are often interrupted by random factors, such as measurement errors and data noise, in real economic situations. This study proposes a new chance-constrained network DEA model based on the modified directional distance function (DDF) and enhanced Russell measure (ERM) model for assessing government management and culture-led urban regeneration. In addition to exploring the randomness of data, this study integrates the advantages of both ERM and DDF and considers the inefficiency level from a non-oriented viewpoint, the direction vector, and each input and output simultaneously. Each input and output of the two production stages can use non-radials to measure efficiency. Results show that the urban-rural gap has gradually widened since 2015. To validate the legitimacy of the model, this study utilizes the bootstrapping method to verify the results of the stochastic network DEA model and the conventional two-stage network DEA approach. This study also considers different alpha values as basis for comparison to confirm whether the results obtained differ by uncertainty level. [ABSTRACT FROM AUTHOR]

Current data transactions are usually driven by data providers, and data buyers have little information about the data they will buy. In turn, this information asymmetry impacts the data providers' sales decisions. In this paper, we consider a data provider with private quality information to sell a data set to multiple potential data buyers, and the data provider signals quality information to the potential buyers by setting the price and sample set size. We study how the data provider decides the optimal trading strategy in terms of price and sample set size based on different characteristics of data buyers through a signalling game. It is found that the degree of information asymmetry and the cost of providing a sample set affect the data provider's choice of whether to signal data quality truthfully or to hide quality information. The joint signal of price and sample set is a better signalling option for data providers to achieve higher profits than the price signal alone. Tracing data trading back to the production process of datasets, the increased information asymmetry and inefficient data production make data providers more inclined to generate low-quality datasets, which adversely affects the data trading market. [ABSTRACT FROM AUTHOR]

In group decision making (GDM), there may exist some problems that need to assign alternatives to some predefined ordered categories, which are called ordinal classification-based GDM problems. To obtain classification results that can be accepted by most decision makers (DMs), it is necessary to implement a consensus reaching process for ordinal classification-based GDM problems. In this paper, we study consensus reaching models for a new type of ordinal classification-based GDM problem, in which DMs do not provide criteria weights and category cardinalities but provide indirect and imprecise heterogeneous preference information. To do so, a consistency verification method is first proposed to check whether each DM's preference information is consistent and then a minimum adjustment optimization model is developed to modify DMs' inconsistent preference information. Afterwards, we establish some optimization models to obtain each DM's possible categories for alternatives. Followed by this, we define the consensus levels of DMs and devise some optimization models to assist DMs in adjusting alternatives' classification results and DMs' preference information at the same time. Furthermore, a maximum support degree-based method is provided to determine the consensual classification result for alternatives. Finally, a numerical application and some sensitivity analysis are provided to justify the proposed models. [ABSTRACT FROM AUTHOR]

The method of pairwise comparisons is frequently applied for ranking purposes. This article aims to rank top women tennis players based on their win/lose ratios. Incomplete pairwise comparison matrices (PCMs) were constructed from data obtained from the WTA (Women's Tennis Association) homepage. The database contains head-to-head results from the period between 1973 and 2022 for 28 players who had the position No. 1 in the official rankings of WTA. The weight vector was calculated from the incomplete PCM with the logarithmic least squares method and the eigenvector method. The results are not surprising: Serena Williams, Steffi Graf, and Martina Navratilova stand in the first three positions, and Martina Hingis, Kim Clijsters, and Justine Henin follow them. We also tested the frequently used probability-based Bradley-Terry method and found high rank-correlation values. Using graph representations, the results gave us a deeper insight into the properties of incomplete PCMs. Special attention was given to the nontransitive triads. A data modification was necessary to remove ties in order to apply the commonly used tests. The results indicate that ordinally nontransitive triads are not significant in the data we analysed. [ABSTRACT FROM AUTHOR]

Consumers tend to perceive a service of higher value when more consumers, under the influence of others, make the same choice. In this article, we investigate the impact of social interactions in service operations under various price, waiting time target, and capacity decisions. The service is delivered through an M/M/1 queue system which attempts to achieve a waiting time target through a volume-based capacity allocation policy. Consumers perceive a positive (negative) value of the service if the queue length is longer (shorter) than the base queue length – an anchor point in their value perception. We examine the relationship of the intrinsic value of the service and the consumer's value perception (in terms of the queue length anchor point) with the price, the waiting time target, and the capacity decisions. We also quantify the potential gain or loss in profit when the influence of social interactions is ignored. Theoretical results are derived to guide the price, waiting time target, and capacity decisions to exploit the influence of social interactions. It is found that there exist delicate dynamics among key parameters and variables, when ignored, that could lead to undesirable outcomes. The study offers new insights to balance the demand-inducing effect and the congestion-aggravating effect in a service system under the influence of social interactions. [ABSTRACT FROM AUTHOR]

Portfolio selection for Stock evaluation and selection by an investor is multi-criteria decision-making (MCDM) problem of finding the best portfolio among an efficient set of portfolios, which should be tackled by using the appropriate techniques. The Analytic Hierarchy Process (AHP) is amongst the most widely used MCDM methods, which is often used in operation management. The main feature of AHP is, that, it firstly reduces the complex decision problems in hierarchical structures of objectives, criteria, sub-criteria, and alternatives and then, uses a fundamental scale to construct pairwise comparisons. The Spherical Fuzzy Sets (SFS) have many advantages in handling the uncertainty and vagueness of ordinary and 2D Fuzzy Sets (FS)s. Therefore, taking the advantage of AHP and SFSs in this paper, we modify the Fuzzy Analytic Hierarchy Process (FAHP) into Spherical fuzzy AHP (SFAHP). We introduce the concept of Spherical Fuzzy Preference Relation (SFPR) and develop an automatic algorithm to construct a consistent SFPR from an inconsistent one. The validity of the proposed approach is tested through an illustrative application of portfolio selection on the Pakistan Stock Exchange to make a prototype of our results. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid expansion of internet technology, the number of online food delivery services has grown. Despite the ease, a poor online food delivery platform (OFP) produces all kinds of problems for individuals. A comprehensive and reasonable evaluation of online food delivery is of great significance. Therefore, this study establishes an OFP evaluation model based on the probabilistic double hierarchy linguistic EDAS (PDHL-EDAS) method for multiple attribute group decision-making (MAGDM). Then the CRITIC model is introduced to derive the objective weight and the cumulative prospect theory is leaded to obtaining the cumulative weight of PDHLTS. In furthermore, the benefits and applicability of the PDHL-EDAS method are illustrated by solving practical MAGDM problems concerning the evaluation of OFPs. Lastly, the proposed method is verified by comparison with other MAGDM methods, in addition to discussing potential future research directions. [ABSTRACT FROM AUTHOR]

As online shopping flourished, consumers in their shopping can refer to rich product descriptions and a large amount of review information. For the scenario of consumer online choice decision among candidate products characterized by limited attributes, we refer to it as an online multi-attribute decision-making problem. To address the challenge of online choice decision support for consumers, we propose a data-driven analytic hierarchy process (AHP). The data-driven AHP includes extracting attributes of candidate products, calculating attribute values, attribute-weight learning, interaction-based preference revision process, and product ranking. In particular, we develop an Exp-strategy for attribute-weight learning, which helps learn the attribute weights of consumers who provide reviews as a reference for an end consumer. This learning method can handle dynamic online reviews without the problem of information overload. In addition, we design the interaction-based preference revision process to help the end consumer identify his attribute weights and make a choice decision. [ABSTRACT FROM AUTHOR]

This study investigates what it takes to win a soccer game in three major soccer leagues, (i) English Premier League (EPL), (ii) Spain's La Liga and (iii) Major League Soccer (MLS) at the USA. To do so, a set of supervised machine learning models were trained and used to predict the outcome of soccer games and investigate the importance of predictors (features) contributing to the outcome. A total of 91 game and team related features (for both home and away teams, and the differences between them) that cover match statistics, attempt types, card situations, pass types and squad information were taken into consideration. The best prediction performance was achieved by a random forest (RF) model with a classification accuracy of 86.31% of the combined dataset. Relative importance percentages of the game and/or team features for each league are investigated to identify the most crucial factors of the winning/losing teams. Furthermore, each league's characteristics are discussed and compared through the lenses formed by the intuitive experiment results. For instance, the home field advantage in the MLS games has been shown to be two times more important than this advantage in the La Liga and EPL games. [ABSTRACT FROM AUTHOR]

In a fast-changing environment, state in the future is difficult to predict. Traditional approaches are unable to support decision-makers to find out optimal alternative effectively when the probability of future's environmental state is unknown or uncertain. In this study, we propose a stochastic decision tree acceptability analysis (SDTAA), which aims to manage this decision-making problem effectively. In SDTAA, state probability space with random distribution is utilized to capture unknown or uncertain state probabilities and stochastic values or ordinal values are used to model uncertain attributes values. Then, by computing rank acceptability, holistic expected value and value variance of each alternative, SDTAA can help decision makers find the optimal alternative effectively when state probability is uncertain, unknown or missing. In addition, Monte Carlo simulation based algorithms are proposed to calculate the rank acceptability, holistic expected value and value variance. A numerical example is presented to illustrate the SDTAA method. [ABSTRACT FROM AUTHOR]

In a group decision-making process, contradictory pairwise comparisons may exist in individual preferences or the group preferences even though the consensus level is reached. To avoid such a contradictory phenomenon, this study presents an approximate transitivity-based consistency threshold for reciprocal preference relations to ensure the reliability of the ranking of alternatives with reciprocal preference relations in group decision making. The natural inconsistency or intransitivity of reciprocal preference relations is analyzed and verified by numerical experiments. Then, using the results of numerical experiments, approximate transitivity-based consistency thresholds are introduced based on the objectives of minimising the Type I error, Type II error and total error in statistics. Moreover, a transitivity checking process regarding individual reciprocal preference relations and group reciprocal preference relations is incorporated in the group decision-making process. A transitivity-checking integrated group decision-making model is given for application. An example about the station selection for high-speed railway line is provided to show the necessity of the approximate transitivity-based consistency threshold in checking the transitivity of reciprocal preference relations for group decision making. [ABSTRACT FROM AUTHOR]

The tendency of strategic hackers to attack specific industries brings new challenges for information security management. This paper examines the interaction between firms in a specific industry and a strategic hacker by considering industry-specific characteristics including the intrinsic vulnerability, intentions of the hacker, competition between firms, and similarity of security technologies. We find that firms in an overly dangerous industry should consider reforming their business mode to reduce the intrinsic vulnerability rather than investing heavily in security protection. Moreover, we distinguish the hacker as profit-seeking and fame-seeking and find that different intentions generate different hacker's behaviour. Furthermore, keep exerting effort is still a better strategy for the firms when the competition becomes more intense even the threat of the hacker reduces. Besides, the technical similarity enhances the hacker's incentive to exert attack effort while induces a free-riding problem for competitive firms. Accordingly, we introduce a social planner to regulate the security decisions of competitive firms, and identify that the supervision of a social planner could partly alleviate the free-riding behaviour, but will only be accepted by competitive firms when facing a less or highly competitive environment. Our results imply that introducing a social planner to enforce security protection may not be advisable for all industries. Finally, we extend our model to discuss two additional cases, including the case of sequential game and the case of asymmetric condition. [ABSTRACT FROM AUTHOR]

Sustainable warehousing is essential for organisations to achieve overall supply chain sustainability. Warehousing facilities have the greatest potential for reducing socio-environmental impact. Yet, both research and practice have given relatively less attention to considering all aspects of sustainability in warehouses. In order to address this gap, this study proposes combining both input from professionals and from a literature survey of triple-bottom-line theory in order to develop a sustainable warehouse criteria framework, thus contributing to sustainable organisational warehouse evaluation. The method supporting the evaluation of this framework is based on the integration of a multicriteria AHPSort traffic light visualisation technique and novel post-optimal analysis. Furthermore, the authors deployed this framework and integrated methodology in an Indian manufacturing company to evaluate and classify seven of their warehouses for decision making. The traffic light visualisation technique presents and conveys the results better than numbers. Finally, the new post-optimal analysis provides recommendations for cost efficient improvements. The findings of this study present valuable insights and guidelines for industrial managers and practitioners, especially those from the Indian manufacturing industry, for sustainable warehouse decision-making, and for improving their overall corporate sustainability performance. [ABSTRACT FROM AUTHOR]

With fast-moving technological innovation, technology firms are confronted with the issue of how to dispose of the old technology-based product. Regarding the old technology-based product's response strategy to a prevalent new technology-based product, firms usually choose from the three options of stopping, maintaining, and improving. We consider a multiproduct monopolist that manufactures traditional, tangible goods derived by old and new technologies, respectively, and build a two-period analytical model to form the response strategy for the old technology-based product. We also investigate the action sequence about whether to stop or improve the old product before or after network externality arises in the market, and determine the level of improvement effort exerted on the old product. The results show that the firm should maintain the old product if the new product is significantly superior to the old one. Furthermore, when the new product is superior to the old one, the firm has an incentive to stop or improve the old product based on the quality difference between old and new products and the marginal production cost of the old product. Finally, we prove that the improvement of old products can increase demands for both old and new products under specific conditions. [ABSTRACT FROM AUTHOR]

Consensus thresholds play a significant role in consensus checking and reaching in group decision-making problems. The existing studies contribute very few guidelines and leave the task of determining consensus thresholds to decision makers. This paper presents an effective process to determine the thresholds in the setting of group decision-making with preference relations (PRs). The process employs a concept called random consensus indices which refer to the expected values of consensus indices of randomly generated PRs. Instead of determining the thresholds directly, decision makers are only required to express that, to what extent, the information provided by the authorized experts should be better than the random case. From the view of decision makers, the process is independent to specific consensus measures and thus could be applied easily. The process is implemented with different types of PRs and discussed in distinct situations. Besides, for the applications of frequently considered scenarios, the values of random consensus indices are provided and the consensus thresholds are recommended. [ABSTRACT FROM AUTHOR]

A critical issue for ensuring long-term sustainability of pension funds is how the Public Service Pension Fund Management Board (PSPFMB) makes entrusting decisions to choose investment trust corporations (ITCs). To design a more comprehensive performance measure, this study uses the additive network data envelopment analysis (DEA) approach to measure efficiency scores of 34 ITCs, including operating performance, equity fund performance, and bond fund performance. We further propose a method to effectively forecast the future efficiency scores of each ITC by using a trend analysis technique. Our mechanism for ITC evaluation helps improve the deficiency of old decision-making processes that merely look at ITCs' past performances. Our proposed approach offers a theoretical contribution to the additive DEA literature by through its practicality and by integrating measurement and prediction procedures. For a practical implication with additive network DEA, we provide a more feasible method for selecting ITCs that will run the Public Service Pension Fund. [ABSTRACT FROM AUTHOR]

Although developing renewable energy sources is the best way to reduce carbon dioxide emissions and deal with climate change, fossil fuels are currently the most important energy sources. To counter climate change and reduce carbon dioxide emissions from fossil fuels, scientists are developing new carbon capture and storage (CCS) technologies. In this paper, we discuss existing and future CCS technologies, compare the values of three CCS investment strategies in the real options framework under uncertainties in technology, policy and market, and consider the effect of learning curves on investment values. Generally, uncertainty can increase investment valuation, but we find that uncertainty in CCS technology reduces its investment valuation. Finally, we calculate the probability that the optimal investment strategy will be implemented. [ABSTRACT FROM AUTHOR]

Rank aggregation has recently become a common approach for combining individual rankings into a consensus and for quantifying and improving performance in various applications, such as elections, web page rankings, and sports. During the past few years, rankings from many sources have become increasingly high-dimensional and partial. In this study, we develop a rank aggregation method by constructing a directed weighted competition graph. We introduce the concept of "ratio of out- and in-degrees (ROID)" to transform high-dimensional partial rankings into a single consensus. Moreover, we provide a novel effectiveness measure for the aggregate ranking according to its deviations from the ground truth ranking. The proposed method is compared with four typical methods with synthetic rankings. The results indicate that our method outperforms the other four by a significant margin and can be particularly efficient in aggregating high-dimensional rankings. The empirical results validate the effectiveness and feasibility of our method. [ABSTRACT FROM AUTHOR]

Multicriteria decision making (MCDM) methods are widely used in decision making practice. However, these methods are heuristic: they are based on some rules, the effectiveness and validity of which cannot be strictly proved, and it is unknown exactly how trustworthy their outputs reflect Decision-maker's judgements. Such methods can generate different outputs, using the same inputs, and it is impossible to select the more correct method from the available ones – this is the subject of the decision-making paradox. In this article, we focus on the possibility to construct a non-heuristic MCDM method, whose accuracy and reliability can be verified. We base the proposed method on two new ideas on how to construct a pairwise comparison (PC) matrix for a multi-criteria decision-making problem with several criteria and alternatives, and how to correctly derive information from this matrix. We show that it is possible (i) to estimate the objective uncertainty in the PC-matrix information with respect to each alternative and (ii) to estimate the reliability of the rankings. The non-heuristic MCDM method based on these ideas allows to verify the accuracy and reliability of Decision-maker's judgements on MCDM problems, and has clear anti-reversal guarantees. The software implementing the method is freely available online at . [ABSTRACT FROM AUTHOR]

Academics and practitioners have developed different constructs to quantify corporate governance quality. Despite the limitations of the existing measures, they are still being commonly used. The literature finds that the relationship between performance and corporate governance quality can be positive, non-existing or even negative. To resolve this puzzle, we introduce a multi-criteria decision analysis (MCDA) approach to construct an alternative corporate governance quality synthesising companies' practices and mechanisms through an exhaustive pair comparison procedures based on outranking relationships analysis. Our approach compares the aggregate quality with a well-known corporate governance index, ASSET4 ESG in Thomson Reuters Datastream, using data for the US firms. Using this MCDA approach based on PROMETHEE methods and econometric analysis, we obtain consistently a negative and strong link between firm performance and corporate governance quality. The findings are of particular interest to both scholars and decision makers including providers of corporate governance indices and rating agencies. [ABSTRACT FROM AUTHOR]

Given that the values of the criteria in uncertain multi-criteria group decision-making (MGDM) problems take the form of fuzzy linguistic variables, this paper proposes a model based on hesitant fuzzy linguistic term sets (HFLTSs), named MGDM-HFLTS, to estimate investment alternatives for angel investors. To meet the challenges of complexity, lack of information and time pressure among several possible values in MGDM, the HFLTSs are introduced and revised. The HFLTSs, which are convenient and sufficiently flexible to reflect the decision-makers' preferences, are introduced to represent the hesitation or doubt originating from systematic comparisons of the assessment values of alternatives for each criterion during both preference elicitation and alternative evaluation phases. Then, context-free grammar is revised for computing with words to enhance and extend the applicability of HFLTSs according to a set of various membership degrees over which decision-makers hesitate when eliciting their preferences over alternatives. Subsequently, the most satisfactory alternative(s) is/are determined by the outranking relationship approach to integrate the degree of preference and entropy information. In addition, studies of evaluation criteria and their weights in angel investment decision-making are investigated. An illustrative example of an angel investment implemented by the proposed MGDM-HFLTS and its corresponding algorithm confirms the effectiveness and practicability of the proposed method. [ABSTRACT FROM AUTHOR]

Psychological measures of decision making could help researchers better understand and model individual differences in Multiple Criteria Decision Making. However, such measures have so far gained little traction in behavioral operational research. I investigate whether decision style, cognitive reflection, and tendency to maximise, together with the decision environment, can explain choice quality. 159 participants answered the psychological measures and made 26 choices in a setting with six alternatives and six criteria. According to the Bayesian analysis, low cognitive reflection and high need to explore alternatives were related to a higher chance of making errors in choices. This indicates that psychological measures have explanatory power in MCDM, and that the relationship to choice quality is not always in the expected direction. [ABSTRACT FROM AUTHOR]

In a multiple criteria decision-making problem, decision-makers often make judgments of importance, for example, that "rent is more important than apartment size" when choosing apartments. Even though linear models are heavily used in choice prediction, it has remained unclear whether criterion weights are connected to judgments of importance. A surprisingly common assumption is that a more important criterion tends to have a larger weight, as if weights and importance were equal, or at least heavily correlated. In the experiment, subjects provided pairwise judgments of importance for four criteria and made pairwise choices with apartments defined by these criteria. According to our results, Goldstein's (1990) idea of connecting judgments of importance to impact is more meaningful than connecting them to weights. Impact as the product of AHP weights and coefficient of variation is the best definition for impact, when measured by correlation to the original judgments of importance. [ABSTRACT FROM AUTHOR]

This paper emphasises the sensitivity of the data envelopment analysis (DEA) efficiency estimators to sampling variations of the production frontier and dimensionality of the production set. It has been proven that DEA yields asymptotic unbiased estimates. A DEA (smoothed) bootstrap method is widely being applied to address the inaccuracy of DEA estimators. The combination of DEA and a modified bootstrap expression enhances the statistical properties of DEA estimators without overcoming the inherent limitations of each of the two methods. This paper provides a non-resampling multi-parametric method to deal with the sensitivity of DEA estimators. The new method is applied to scaled samples, and the bias-corrected efficiency estimators are compared against their population counterparts. A comparative analysis among the standard bootstrap, the smoothed bootstrap, and the new method shows that the new method's estimations provide a better fit to the population than the estimations of the standard and smoothed bootstrap. [ABSTRACT FROM AUTHOR]

In 2012, Oscar Pistorius created history as the first amputee sprinter to compete in the Olympics. Other athletes achieved amazing feats long before the Paralympics were introduced, including gymnast George Eyser who won six medals at the 1904 Olympics with a wooden leg, and others who competed in both Games. An exciting challenge of considerable interest is to compare performances of Olympic and Paralympic athletes, so contributing to improving integration of the two competitions. We generalise the recent dynamic shrinkage method for class handicapping and apply it to competition results from equestrian individual dressage at the London 2012 Summer Games and cross country skiing at the Sochi 2014 Winter Games. Our analysis generates promising results and surprising revelations. It also offers a fair method for comparing performances by athletes from other diverse groups, with potential benefits of extra incentive and reward systems for motivating unified sporting participation in general settings. [ABSTRACT FROM AUTHOR]

This study investigates how to design sensor systems in a way that responds to certain factors in the environment. This decision analysis problem focuses on sensor placement: how to place sensors to find an intruder that is affected by environmental elements. The sensors we use are of two types: the first type detects targets, and the second type detects elements in the environment. Techniques from the learning automata literature are used to develop a detection mechanism. The approach proposed in this study is dynamic, and can adjust to environmental variations. And its rate of detection exceeds static approaches, such as evenly spread sensor configuration. This work has implications for the design of any sensor system in which the physical environment shapes the probability of events occurring. [ABSTRACT FROM AUTHOR]

We consider two firms that order from a supplier and sell products to the markets with uncertain demand. The firms can transship stocks in between at an endogenous transfer price and set retail prices after learning actual market sizes. The relative timing of stock transshipment and retail pricing, both ex-post market size realization, gives rise to two decision models. The firms set retail prices and transship upon stock imbalance in the pro-pricing model, and, furthermore, transship stocks in between before retail pricing and demand satisfaction in the pro-transshipment model. We demonstrate that responsive pricing alone keeps the firms off stock imbalance and insulates them from the impacts of market uncertainty, and responsive stock redistribution contributes to a more efficient deployment of stocks to market selling, even absent volatility. Enhanced responsiveness in stocking and pricing benefits firms, but can hurt the supplier unless its production cost is sufficiently low. [ABSTRACT FROM AUTHOR]

This paper focuses on specific rank reversal phenomena in optimization methods (the least squares method, the chi-square method, etc.) designed to derive preference weights of alternatives from pairwise comparison matrices in the Analytic Hierarchy Process. It is preferable that the most irrelevant alternative had no effect on the ranking of the other alternatives. Unfortunately, it appears that, for many methods, most irrelevant alternatives tend to dictate the rank order of all the remaining alternatives. Respectively, adding some irrelevant alternative may turn the most important alternative into an unimportant one and conversely. We classify the optimization methods with respect to the dependence of irrelevant alternatives and specify all possible “dictatorial” methods, which provide the absolute dictate of very irrelevant alternatives, and all methods, which are free from the dictate of such alternatives. For the dictatorial methods, we propose “weight function” modifications, which prevent the influence of irrelevant alternatives. We show that without the modification, “dictatorial” methods can add confusion and false recommendations in the decision-making process even in the most ordinary decision-making situations. [ABSTRACT FROM AUTHOR]

Student selection is a complex decision-making process, in which several criteria need to be considered simultaneously. In this paper, we address this problem for a Brazilian university that has created an interdisciplinary degree in which several intermediate selection processes are required during the course, defining the final title degree. The university is currently using an aggregated score based on the performance of a student in the course. However, this method is facing difficulties in selecting the best students, because deficiencies in the way transferred, dropped and quit course credits are accounted for. As a possible alternative for the current method, we developed a hybrid ranking algorithm, called ELECTRE–TOPSIS (E–T). This method combines elements of the ELECTRE family and TOPSIS, two well-known multi-attribute analysis tools, to rank students based on objective criteria. Computational experiments and a case study were conducted to evaluate E–T. The results show that our approach provides quite competitive rankings in comparison with similar methods, through simultaneously eliminating ranking reversal and better balancing the formation time and the academic performance of the evaluated students. [ABSTRACT FROM PUBLISHER]

Identifying the practical benefits of theoretical methods is a challenge in several fields. The operational research approach, known as Soft, has achieved a field of study status and, from that, Problem Structuring Methods (PSM) have been consolidated as support tools in the group decision process. Thus, aiming to contribute to the improvement of the group decision process, this paper presents the results of an experiment involving 42 students of the Management Engineering major in a general decision process. Strategic Options Development and Analysis is a PSM that uses cognitive maps to express the group's understanding of a problem. Some practical implications on the use of cognitive maps are evaluated by establishing hypotheses. Such implications were drawn from the comparison of each participant's personal evaluation of the decision-making process to the final decision taken by the group along with observations and analysis of the groups that employed, and the ones that did not employ the cognitive mapping technique. [ABSTRACT FROM AUTHOR]

This paper introduces a graphical method for valuing options on real asset investments that allow the investor to switch between different operating modes at a single point-in-time. The technique uses mixtures of truncated exponential functions to approximate both the probability density function for project value and the expressions for option value of each alternative. The distribution for project value is transformed into an expected cash flow function for the option under each mode of operation. After determining an optimal exercise strategy, these functions are used to determine the option value. The graphical method allows the option exercise strategy to be communicated effectively through a graphical representation of the expected cash flow functions. A comparison of this approach to the existing binomial lattice method is presented. The efficiency of the graphical method is comparable to the binomial lattice and in some cases accurate solutions can be obtained with less CPU time. Journal of the Operational Research Society (2010) 61, 1596-1606. doi: 10.1057/jors.2009.128 Published online 21 October 2009 Keywords: capital budgeting; decision analysis; decision support systems; finance; networks and graphs; probability

This paper describes an experimental method for determining the value of different types of information to military decision makers. The experimental method used a simple scenario and a set of serials constructed from cards, each presenting a single piece of information and presented sequentially. Each of a number of pairs of players were taken through the scenario and asked to judge how they would respond to the situation. This paper extends on the method presented in previous papers to consider the case of a decision in which the response can both increase and decrease as more information is presented. This allows a more general military problem to be considered, that of risk-taking behaviour in response to the possibility of chemical, biological, radiological or nuclear events. Journal of the Operational Research Society (2010) 61, 1561-1571. doi: 10.1057/jors.2009.130 Published online 25 November 2009 Keywords: decision analysis; military; gaming; regression

In 1959, LJ Savage attended a statistics seminar held in his honour at the University of London, confronting those present with a radically different approach to reasoning about uncertainty. Britain was well placed to respond to Savage, as very similar ideas had been laid out in Britain a full generation earlier, and in the next few decades, British and British-based practitioners and researchers championed a collection of techniques for thinking quantitatively about uncertainty (which we call 'Probabilistic Decision Analysis'), developing practice, and contributing to theoretic knowledge about the underlying psychology and mathematics. This effectively turned a collection of purely theoretical ideas into a practical modelling technology. In the first decade of the 21st century, some 50 years on, these ideas have made a noticeable influence on practice and thinking in various domains, but numerous challenges still remain. doi:10.1057/jors.2008.175 Keywords: decision analysis; probability; risk; history of OR

In order to accommodate the use of multi-criteria decision-aid tools for nuclear crisis management in the Belgian context, a stakeholder process was carried out, aiming to add in stakeholders' values and practical knowledge in the models developed for decision support. The main steps are described in this paper, following a case study concerning the management of contaminated milk. We argue in favour of a systematic interaction with relevant stakeholders in all phases of the decision analysis. Some difficulties arising from the complexity of the problem itself, as well as from the different points of view of relevant stakeholders are illustrated and discussed. Suggestions are given for resolving this in a decision-aid framework and an example is provided throughout the paper for a better understanding of the issues involved. Keywords: accidents; agriculture; decision analysis; stakeholders

The ratio scale measurement of utilities in multi-criteria decision analysis has been criticised due to theoretical and practical interpretation of the required assessments. Compared to the ratio scale models, for example, the '0-1-scale' models based on interval scale have been more generally accepted. On the other hand, under some conditions it has been shown that the results of multi-criteria decision support based on ratio scale comparisons are actually independent of the numerical magnitude of the ratio scale assessments. This paper reviews and interprets these results and proposes new solutions for the remaining problems, for which the numerical magnitude of the ratio scale utilities is required. Moreover, we interpret the scale-independent ratio scale models with respect to some other multi-criteria decision support models. The theoretical analysis is carried out by statistical models and illustrated by numerical examples. Keywords: cardinal utility; measurement scale; scale dependency; statistical inference; uncertainty