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In most project portfolio selection (PPS) situations, the presence of multiple attributes and decision-maker preference is inevitable. As Multi-criteria Decision Analysis (MCDA) methods provide a framework well-suited to deal with these challenges in PPS problems, the use of MCDA methods in real-life PPS problems has increased in recent years. This paper provides a comprehensive literature review of the use of different MCDA methods and their individual or combined utilization with other modeling techniques to support PPS problems. First, we summarize how MCDA methods are used in different modeling approaches. Second, we examine the mathematical models that are generally used to combine MCDA with mathematical programming techniques to solve PPS problems with resource constraints. Third, we present the drawbacks of combined utilization and discuss recent advances. Finally, we visualize the summary of the reviewed papers as a decision tree to assist researchers and practitioners in the use of MCDA methods in a specific PPS context and propose some future research directions. [ABSTRACT FROM AUTHOR]

The COVID 19 has brought unprecedented changes in the way we communicate. There is a greater accent on Virtual communication. This paper aims to establish a relationship between Emotional intelligence and the effectiveness of Virtual communication on Decision making. This empirical study is based on a sample drawn from 296 working professionals at five different levels of organizational hierarchy. A standardized questionnaire (ɑ = 0.824) was used to collect the responses of Emotional intelligence, Virtual communication, and Decision-making effectiveness. Hierarchical regression using PROCESS Macro model 1 was used to identify the moderating effect of Emotional intelligence on Virtual communication and Decision making effectiveness. Since the p-value (p ≤.007) is found significant, Emotional intelligence acts as a moderator that affects the strength of the relationship between Virtual communication effectiveness and Decision making. Validation of Task Technology fit theory is the theoretical implication of the study. Manipulation of individual dimensions in the model can reduce the dependence on technology for task completion with enhanced performance effectiveness. The findings are relevant to educators, consultants, and any professional who need to adapt Virtual communication platforms on an ongoing basis. Since work-life balance is projected as a constraint in this study, policymakers can consider policy amendments to reduce the stress caused due to Virtual communication which intrudes into their personal space. This empirical study is the first of its kind to benchmark the organizational practice of Emotional intelligence training to enhance Virtual communication and Decision making effectiveness during unprecedented times of pandemic. [ABSTRACT FROM AUTHOR]

Industrial relocation (IR) is a business strategy consisting of moving operations locations. The purpose of this paper is to present how to assess, with multi-attribute decision-making (MADM), alternatives for IR. With MADM, IR strategies can be assessed not only based on a single attribute, as costs, or profits. This paper presents the application of MADM in a real case of IR. Four leading methods of MADM were applied: analytic hierarchy process (AHP), multi-attribute utility theory (MAUT), multi-attribute value theory (MAVT), and technique of order preference by similarity to ideal solution (TOPSIS). Results of AHP, MAUT, MAVT, and TOPSIS were quite similar, indicating the decision for the company not to relocate. A joint comparison of results with compatibility indices and correlation coefficients is the major novelty presented by this paper to the field of Operations Research, known as MADM. [ABSTRACT FROM AUTHOR]

Organisations need to develop long-term strategies to ensure they incorporate innovation for environmental sustainability (IES) to remain competitive in the market. This can be challenging given the high level of uncertainty regarding the future (e.g., following the COVID pandemic). Supplier selection is an important decision that organisations make and can be designed to support IES. While the literature provides various criteria in the field of IES strategies, it does not identify the criteria which can be utilised to assist organisations in their supplier selection decisions. Moreover, the literature in this field does not consider uncertainty related to the occurrence of possible future events which may influence the importance of these criteria. To address this gap, this paper develops a novel criteria decision framework to assist supplier evaluation in organisations, taking into consideration different events that may occur in the future. The framework that combines three decision-making methods: the stratified multi-criteria decision-making method, best worst method, and technique for order of preference by similarity to ideal solution. The framework, proposed in this paper, can also be adopted to enable effective and sustainable decision making under uncertainty in various fields. [ABSTRACT FROM AUTHOR]

The aim of this paper is to solve an agricultural technology packages selection problem by considering multiple dimensions which influence a maize producer's preferences. The decision-making process is aided by a new multicriteria method for eliciting scale constants in additive models: flexible and interactive tradeoff (FITradeoff). This method works with partial information, obtained from the decision maker (DM), and thus reduces the time that the DM has to spend on the process for eliciting his/her preferences as he/she may avoid answering difficult questions. The decision-making process makes use of a decision support system (DSS), in which the DM interactively gives preference statements in a structured manner. The DSS gives flexibility to the DM, in such way that he/she gives as much information as he/she is willing to. Graphical visualization is provided at each step in order to help the DM's analyses. Throughout the description of an application, some insights are provided including a discussion of the advantages and features of the FITradeoff method. [ABSTRACT FROM AUTHOR]

Global corporate giants are keen to adopt Industry 4.0 (I4.0) owing to its continuous, impactful, and evident benefits. However, implementing I4.0 remains a significant challenge for many organizations, mainly due to the absence of a systematic and comprehensive framework. The risk assessment study is key to the flawless execution of any project is a proven fact. This paper aims to develop a KPIs-based sustainable integrated model to assess and evaluate risks associated with the I4.0 implementation. This research paper has developed the I4.0 risks evaluation model through fifteen expert interventions and an extensive systematic literature review. This research, based on sixteen KPIs evaluates six risks impacting the organization's decision to adopt I4.0. Initially, the Fuzzy Decision-Making Trial and Evaluation Laboratory method is used to map the causal relationship among the KPIs. Further, the additive ratio assessment with interval triangular fuzzy numbers method is used to rank the risks. The study revealed that information technology infrastructure and prediction capabilities are the most crucial prominence and receiver KPIs. Simultaneously, technological and social risks are found to be highly significant in the I4.0 implementation decision-making process. The developed model meticulously supports the manufacturer's, policymaker, and researchers' viewpoint toward I4.0 implementation in the present and post COVID-19 pandemic phases in manufacturing companies. The comprehensive yet simple model developed in this study contributes to the larger ambit of new knowledge and extant literature. The integrated model is exceptionally based on the most prominent risks and a wider range of KPIs that are further analyzed by aptly fitting two fuzzy MCDM techniques, which makes the study special as it perfectly takes care of the uncertainties and vagueness in the decision-making process. Hence, this study is pioneering and unique in context to I4.0 risks prioritization aiming to accelerate I4.0 adoption. [ABSTRACT FROM AUTHOR]

Analytical Hierarchy Process (AHP) has evolved since its inception and has contributed mainly to multi-criteria decision-making (MCDM) to solve complex problems. It is a widespread technique that uses group decision-making approach to solve problems in business operations, mathematics, and numerous other scientific fields. While its applications are numerous, a handful of researchers have studied the growth trajectory of AHP. Whilst these seminal works have discussed the evolution of AHP in different contexts and using contemporary techniques, we complement them by presenting and reflecting on the unchartered and pressing need of charting the managerial and theoretical evolution of AHP, over the last decade. We do this through a systematic and thematic review of the extant literature. This study uses the Scopus database to extract papers published between 2012 and 2021 in decision science with an overlap with operations research and information systems. The study follows Tranfield et al. (2003)'s guidelines to understand the past trends of AHP along three key dimensions and offers future directions. The study also showcases key trends using historiography and uses data visualization techniques to understand the use of AHP with other MCDM techniques. [ABSTRACT FROM AUTHOR]

In the context of local analytic hierarchy process-group decision making (AHP-GDM), this paper presents a theoretical framework and a semi-automatic procedure for reducing incompatibility between the actors involved in the decision making process and the collective position. The row geometric mean is employed as the prioritisation procedure and the geometric compatibility index (GCOMPI ) as the incompatibility measure; individual pairwise comparison matrices are considered as the input of the reduction process, whilst the collective vector is the output. The reduction is attained by slightly modifying, in relative terms, the judgements of the collective pairwise comparison matrix, irrespective of the method used to obtain it, that further improve the GCOMPI . The resulting judgements of the collective matrix and the associated collective priorities are close to the initial collective values. The procedure does not modify the judgements of the initial individual matrices and this simplifies the process of reaching consensus. A simulation analysis is utilised to study the performance of the algorithm along with an illustrative numerical example. The analysis proves that the proposed algorithm is easy to implement and efficient, it provides mathematically closed results and significantly reduces the GCOMPI associated with the precise consistency consensus matrix which is one of the AHP-GDM tools. The framework allows the procedure to be adapted to specific interests. [ABSTRACT FROM AUTHOR]

Pairwise comparisons have been a long-standing technique for comparing alternatives/criteria and their role has been pivotal in the development of modern decision-making methods. The evaluation is very often done linguistically. Several scales have been proposed to translate the linguistic evaluation into a quantitative evaluation. In this paper, we perform an experiment to investigate, under our methodological choices, which type of scale provides the best matching of the decision-maker's verbal representation. The experiment aims to evaluate the suitability of eight evaluation scales for problems of different sizes. We find that the inverse linear scale provides the best matching verbal representation whenever the objective data are measured by means of pairwise comparisons matrices and a suitable distance between matrices is applied for computing the matching error. [ABSTRACT FROM AUTHOR]

The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Simple Additive Weighting (SAW) are among the most employed approaches for aggregating performances in Multi-Criteria Decision-Making (MCDM). TOPSIS and SAW are two MCDM methods based on the value function approach and are often used in combination with other MCDM methods in order to produce rankings of alternatives. In this paper, first, we analyse some common features of these two MCDM methods with a specific reference to the additive properties of the value function and to the sensitivity of the value function to trade-off weights. Based on such methodological insights, an experimental comparison of the results provided by these two aggregation methods across a computational test is performed. Specifically, similarities in rankings of alternatives produced by TOPSIS and SAW are evaluated under three different Minkowski distances (namely, the Euclidean, Manhattan and Tchebichev ones). Similarities are measured trough a set of statistical indices. Results show that TOPSIS, when used in combination with a Manhattan distance, produces rankings which are extremely similar to the ones resulting from SAW. Similarities are also Experimental results confirm that rankings produced by TOPSIS methods are closer to SAW ones when similar formal properties are satisfied. [ABSTRACT FROM AUTHOR]

DIKEDOC is a knowledge-based multicriteria methodology that is here proposed to organise dispersed knowledge about a complex problem when a decision process has not yet been activated, or is latent, and to generate an interaction space that produces new knowledge. An integrated use of logical and analytical tools is proposed, first for use at a technical level to organise any dispersed knowledge in a way that generates insights that can be communicated, and then in a participative context, to create an opportunity to interact, share personal points of view and experiences and to explore spaces of action, where such tools facilitate understanding, criticism and proposals. A pilot study was developed, by an interdisciplinary research team, in relation to the enhancement process of the "Ivrea, industrial city of the twentieth century" UNESCO site, which still needs to be activated after a long and complex decision process that led to the inclusion of the site in the World Heritage List. Several research activities and enhancement projects have been developed in the last few years, but a series of critical conditions have limited their implementation. A new perspective is now necessary to identify and control the uncertainties that have emerged, guide the incremental development of knowledge and foster relationships, decisions and policies. The paper presents DIKEDOC, a new knowledge organisation and problem description methodology, and the conducted pilot study, which led to the proposal of a constructive vision of decision aiding that logically and analytically "described" the space of action and its uncertainties. [ABSTRACT FROM AUTHOR]

In this paper a weighted complex network is used to detect communities of basketball players on the basis of their performances. A sparsification procedure to remove weak edges is also applied. In our proposal, at each removal of an edge the best community structure of the "giant component" is calculated, maximizing the modularity as a measure of compactness within communities and separation among communities. The "sparsification transition" is confirmed by the normalized mutual information. In this way, not only the best distribution of nodes into communities is found, but also the ideal number of communities as well. An application to community detection of basketball players for the NBA regular season 2020–2021 is presented. The proposed methodology allows a data driven decision making process in basketball. [ABSTRACT FROM AUTHOR]

Anticipating an opponent's serve is a salient skill in tennis: a skill that undoubtedly requires hours of deliberate study to properly hone. Awareness of one's own serve tendencies is equally as important, and helps maintain unpredictable serve patterns that keep the returner unbalanced. This paper investigates intended serve direction with Bayesian hierarchical models applied on an extensive, and now publicly available data source of professional tennis players at Roland Garros. We find discernible differences between men's and women's tennis, and between individual players. General serve tendencies such as the preference of serving towards the Body on second serve and on high pressure points are revealed. [ABSTRACT FROM AUTHOR]

In this paper, we introduce an R software package for simulating plays and drives using play-by-play data from the National Football League. The simulations are generated by sampling play-by-play data from previous football seasons. The sampling procedure adds statistical rigor to any decisions or inferences arising from examining the simulations. We highlight that the package is particularly useful as a data-driven tool for evaluating potential in-game strategies or rule changes within the league. We demonstrate its utility by evaluating the oft-debated strategy of "going for it" on fourth down and investigating whether or not teams should pass more than the current standard. [ABSTRACT FROM AUTHOR]

This paper develops the performability and cost–benefit models for a two-stage intervened decision system with majority voting rule and binary input and output. The decision process of the system contains two stages: an inspection stage (stage 1) and a result submission stage (stage 2). During the first stage, each decision unit in the system will have multiple states and a supervisor will come to visit each unit and check its state for at most twice. The supervisor will conduct the first visit to each unit for certain. However, the behavior of the second visit to each unit will be determined by its state during the first visit. In addition, each decision unit may be removed from the system given certain states during each visit. Therefore the structure of system may change during the decision process. The units which are not removed during the first stage can submit the result at any time during the second stage. However, the performance of each remaining unit will be determined by the ending state of the first stage. Moreover, in order to improve the efficiency of the decision process, a check point is added to the second stage. The performability and cost–benefit models for this dynamic system are developed by considering the distribution of states at the end of the first stage. A three-step method will be proposed for model optimization. Some numerical examples for the three-step method will be presented. The proposed intervened decision system in this paper can be applied in many contexts such as financial investment, paper submission review and proposal evaluation, credit evaluation and loan application and product release and recall. [ABSTRACT FROM AUTHOR]

This paper considers a continuous-time queueing model with two types (classes) of customers each having their own dedicated server. The objective is to have a better grasp on the concept of a global first-come-first-served service discipline with presorting, i.e., all arriving customers are accommodated in one single FCFS queue, regardless of their type, with an exception of the first P customers. For the first P customers the FCFS rule holds only within the type, i.e., customers of different types can overtake each other in order to be served. Due to the global FCFS rule the model becomes non-workconserving and on the other hand we also have to keep track of the types of customers in the first P customers. The motivation of our work is the concept of a turn lane in road traffic, i.e., a lane reserved for vehicles making a specific turn at the next junction. This paper intends to be a step towards an analytic model to aid in the decision process of various policy makers of the optimal length of turn lanes. [ABSTRACT FROM AUTHOR]

The choice of an estimation method has received considerable attention in the Operations Research literature. In this paper we depart from the standard use of linex and double-linex loss functions which are widely used in parameter estimation and forecasting problems and we propose a non-standard use for them. Specifically, we propose to use the corresponding linex and double-linex error densities as models for the errors of a regression problem when more emphasis should be placed on over-estimation or under-estimation of errors. The new techniques are applied to synthetic as well real data concerning the role of management in production as well as to an application of forecasting volatility in intradaily data. [ABSTRACT FROM AUTHOR]

Capacities are a common tool in decision making. Each capacity determines a core, which is a polytope formed by additive measures. The problem of eliciting a single probability from the core is interesting in a number of fields: in coalitional game theory for selecting a fair way of splitting the wealth between the players, in the transferable belief model from evidence theory or for transforming a second order into a first order model. In this paper, we study this problem when the goal is to determine the centroid of the core of a capacity, and we compare four approaches: the Shapley value, the average of the extreme points, the incenter with respect to the total variation distance and the limit of a procedure of uniform contraction. We show that these four centroids do not coincide in general, we give some sufficient conditions for their equality, and we analyse their axiomatic properties. We also discuss how to define a notion of centrality measure indicating the degree of centrality of an additive measure in the core. Finally, we also analyse these four centroids in the more general context of imprecise probabilities. [ABSTRACT FROM AUTHOR]

In this study, we investigate the role of decision-making and coordination related to carbon reduction within humanitarian supply chain. Accordingly, a two-stage supply chain consisting of a single manufacturer and a single retailer has been designed, within which three strategies for carbon emission reduction have been considered, namely direct procurement of carbon emission right, investment in fixed carbon reduction targets, and investment in reducing carbon emissions per unit product. The game model under decentralized decision-making, centralized decision-making, and coordinative status has been established. The influences of both consumer carbon sensitivity coefficient and carbon trading price on investment decision based on carbon emission reduction within supply chains, as well as the optimal decision of supply chain operations, are all discussed in this paper. Our study shows that the choice of supply chain carbon reduction strategies depends on carbon trading price and fixed emission reduction target, both the wholesale price and selling price of products are positively correlated with carbon trading price, and both optimal production volume of supply chain and optimal expected profit of supply chain operations are negatively correlated with consumer carbon sensitivity coefficient. The price discount contract may realize coordination within a supply chain, but the value of discount price depends on respective negotiation ability. [ABSTRACT FROM AUTHOR]

Resource allocation and target setting is part of the strategic management process of an organization. In this paper, we address the important issue of "optimally" allocating additional resources to the different operating units. Three different managerial interpretations of this question are presented, differing on the assumptions on the expected output increases. In each case, using multiplier data envelopment analysis (DEA) models and common set of weights (CSW), a new procedure for resource allocation and target setting is proposed. The proposed approach is innovative in its use of CSW and multi objective optimization, both of which are consistent with the centralized decision making character of the problem. The validity and usefulness of the proposed CSW–DEA models is shown using different datasets. [ABSTRACT FROM AUTHOR]

This paper puts forward a new hybrid Grey Additive-Veto Model (GAVM) for selecting suppliers, which optimizes the choice by the decision-maker's (DM) preference. This study uses a Multi-Criteria Decision Making (MCDM) approach. The model proposed incorporates the uncertainty of real-world decision problems, through the Grey Numbers. Given the DM's compensatory rationality, the method reflects tradeoffs amongst different criteria. However, a compensatory model may not represent the DM faithfully and realistically. For this is proposed a performance veto condition. Additionally, the DM may consider that suppliers with a high uncertainty are not desirable or even that they are not acceptable. For this is also proposed width performance veto condition. Given practical implications, the need for an enriched analysis of the ranking is perceived because during the ranking process, there are some situations in which the DM is undecided (mistrust, hesitation) about the statement that a given supplier is better ranked than another supplier. This hesitation is mainly caused by uncertainty arising from the grey numbers, and from attitudes, functions and characteristics of vetoes that the DM has defined. As findings, this paper shows that GAVM is suited to dealing with multicriteria decisions with uncertain information, veto conditions and its potential applicability is illustrated by a numerical supplier selection example. In addition, the numerical example shows that the enriched analysis of the ranking proposed can be conducted to direct the DM's efforts to further enriching the insights for more assertive and coherent decision-making. [ABSTRACT FROM AUTHOR]

We study the embedding of the Hamiltonian Cycle problem, on a symmetric graph, in a discounted Markov decision process. The embedding allows us to explore the space of occupational measures corresponding to that decision process. In this paper we consider a convex combination of a Hamiltonian cycle and its reverse. We show that this convex combination traces out an interesting "H-curve" in the space of occupational measures. Since such an H-curve always exists in Hamiltonian graphs, its properties may help in differentiating between graphs possessing Hamiltonian cycles and those that do not. Our analysis relies on the fact that the resolvent-like matrix induced by our convex combination can be expanded in terms of finitely many powers of probability transition matrices corresponding to that Hamiltonian cycle. We derive closed form formulae for the coefficients of these powers which are reduced to expressions involving the classical Chebyshev polynomials of the second kind. For regular graphs, we also define a function that is the inner product of points on the H-curve with a suitably defined center of the space of occupational measures and show that, despite the nonlinearity of the inner-product, this function can be expressed as a linear function of auxiliary variables associated with our embedding. These results can be seen as stepping stones towards developing constraints on the space of occupational measures that may help characterize non-Hamiltonian graphs. [ABSTRACT FROM AUTHOR]

This paper considers an integrated approach to two common problems in a precision agriculture framework: management zone delineation and selective harvest scheduling. Our model minimizes the total costs of harvest operations, establishing planning and scheduling for selective harvest of each selected management zone. Therefore, this tool provides important information for decision making of farmers in the field. Our integrated model is contrasted with the hierarchical approach commonly used in the literature for these cases, where the result of zoning problem is an input to schedule the harvest problem. Both problems were solved through a complete enumeration of all the potential management zones and demonstrated the advantages of our proposed model over the hierarchical approach. Our model reached an average reduction of 10% in harvest operations costs for different instances in a case study. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid development of Internet technology, the integration of platform economy and e-commerce has become a popular business model. Two-sided platforms have a specific impact on sales, customer experience and transaction efficiency of both sides. In the current severe situation caused by the coronavirus pandemic, both the traditional unilateral market platform and the emerging two-sided market platform are in urgent need of a change in operation mode to reduce the marketing cost. Inspired by the cooperation between Meituan, a two-sided platform, and WeChat, a social media platform, this paper investigates the two-sided platform's scalable decisions on when to cooperate and how to optimize the pricing and investment decisions. We analyze how the two-sided platform makes decisions by considering the changes of network externalities from the cooperation with the social network platform. Compared with the scenario of non-cooperation, we derive the conditions under which platform cooperation can increase demands and increase platforms' profits, and analyze how cooperation affects the optimal pricing strategies. We find that the cooperation leads to a larger demand and a higher total profit, but might lead to higher registration prices for the platform users. Furthermore, we adopt the Nash bargaining framework and introduce platform bargaining power parameters to obtain the optimal cooperation and sharing strategy. Finally, we show how to adjust the investment strategy of the two-sided platform under platform cooperation. [ABSTRACT FROM AUTHOR]

This paper aims at inspiring a change in the traditional approach of sales and operations planning (S&OP) of integrated steel industries that have reached a maturity planning level allowing the use of sophisticated technologies. Therefore, we propose a two-stage stochastic programming model suitable for reproducing the rolling horizon framework of tactical planning. Simulations of one year of operations in an integrated steel industry showed a potential improvement of about 15% on overall supply chain profit in comparison with a plan produced traditionally. Besides, we evaluated two scenarios to analyze the effects of a flexible plan when facing a tax raise and a capacity reduction. As a practical implication, the model provides a consensus view of the S&OP stages at once, supporting top managers to make better decisions. The two-stage modeling approach also reinforces the need for interaction of the sales team with procurement, production, and distribution teams. [ABSTRACT FROM AUTHOR]

In order to make sense of future uncertainty, managers have long resorted to creating scenarios that are then used to evaluate how uncertainty affects decision-making. The large number of scenarios that are required to faithfully represent several sources of uncertainty leads to major computational challenges in using these scenarios in a decision-support context. Moreover, the complexity induced by the large number of scenarios can stop decision makers from reasoning about the interplay between the uncertainty modelled by the data and the decision-making processes (i.e., how uncertainty affects the decisions to be made). To meet this challenge, we propose a new approach to group scenarios based on the decisions associated to them. We introduce a graph structure on the scenarios based on the opportunity cost of predicting the wrong scenario by the decision maker. This allows us to apply graph clustering methods and to obtain groups of scenarios with mutually acceptable decisions (i.e., decisions that remain efficient for all scenarios within the group). In the present paper, we test our approach by applying it in the context of stochastic optimization. Specifically, we use it as a means to derive both lower and upper bounds for stochastic network design models and fleet planning problems under uncertainty. Our numerical results indicate that our approach is particularly effective to derive high-quality bounds when dealing with complex problems under time limitations. [ABSTRACT FROM AUTHOR]

This paper presents a new Dynamic Multi-Attribute Decision-Making method based on Markovian property, which can predict the performance of each alternative in the future and at the same time allows modeling interrelationship among different periods. To this aim, the criteria and decision alternatives in different periods are determined at first, and the information of decision matrices over the decision-making horizon is gathered. To increase the robustness of the results, criteria weights are extracted using the Entropy method in each period and alternatives performance is evaluated using different Multi-Attribute Decision-Making methods. To attain the final rank of alternatives in each period, the results of different methods are aggregated by the Correlation coefficient and standard deviation method. Following this, the rank transformation matrices of alternatives during the evaluation horizon are extracted and the stable rank probability of alternatives is calculated based on limiting probability. Eventually, the overall rank of alternatives is determined using a linear assignment-based method. The proposed model has been used in the promotion of the sales staff in a private company to show the model effectiveness in a real-world problem. Results are compared with some well-known methods (five methods, to be exact). Finally, the trustworthiness and acceptability of the method are assessed based on features discussed in the literature. [ABSTRACT FROM AUTHOR]

A private placement is the sale of securities as a way of raising capital. Most private placements are offered to a small number of select investors, at a fixed rate that can be set by the issuer. The solicitation of investors can be simultaneous or sequential, and the chosen style may influence the associated cost. Sequential solicitation is generally more expensive over time since it is carried out gradually, although simultaneous solicitation can be more expensive if it involves a greater number of investors. In this paper, a theoretical model is developed that allows the two styles of solicitation to be compared. It is demonstrated that sequential solicitation generally involves a lower cost than simultaneous solicitation, although it does require a greater number of investors. Conditions are determined under which these two styles of solicitation are equivalent in cost and in the number of investors required. Specifically, it is obtained that, if the group of investors is homogeneous, then both styles of solicitation will have the same monetary cost and will require the same number of investors. [ABSTRACT FROM AUTHOR]

Generalized additive independence (GAI) models permit to represent interacting variables in decision making. A fundamental problem is that the expression of a GAI model is not unique as it has several equivalent different decompositions involving multivariate terms. Considering for simplicity 2-additive GAI models (i.e., with multivariate terms of at most 2 variables), the paper examines the different questions (definition, monotonicity, interpretation, etc.) around the decomposition of a 2-additive GAI model and proposes as a basis the notion of well-formed decomposition. We show that the presence of a bi-variate term in a well-formed decomposition implies that the variables are dependent in a preferential sense. Restricting to the case of discrete variables, and based on a previous result showing the existence of a monotone decomposition, we give a practical procedure to obtain a monotone and well-formed decomposition and give an explicit expression of it in a particular case. [ABSTRACT FROM AUTHOR]

Failure mode and effects analysis (FMEA) is known to be a proactive reliability analysis model broadly utilized to recognize and evaluate potential failure modes in various industries. The normal risk priority number (RPN) method, however, has suffers from a lot of criticisms, such as requirement of precise risk estimation, lack of scientific basis in computing RPN, and neglecting the weights of risk factors. Therefore, this paper devises a new FMEA model to evaluate and prioritize the risk of failure modes by integrating probabilistic linguistic term sets and TODIM (an acronym in Portuguese for interactive multi-criteria decision making) method. The probabilistic linguistic term sets are utilized to handle the intrinsic ambiguity existed in the risk assessments of FMEA team members, whilst an extended TODIM method is employed for determining the priority ranking of the individuated failure modes. Further, based on the technique for order of preference by similarity to ideal solution (TOPSIS), an objective weighting method is presented to derive the relative weights of risk factors. Finally, two illustrative examples are implemented and comparisons with other existing methods are performed to demonstrate the rationality and superiority of our proposed FMEA model. [ABSTRACT FROM AUTHOR]

The multicriteria Bipolar method can be extended and used to control multicriteria, multistage decision processes. In this extension, at each stage of the given multistage process two sets of reference points are determined, constituting a reference system for the evaluation of stage alternatives. Multistage alternatives, which are compositions of stage alternatives, are assigned to one of six predefined hierarchical classes and then ranked. The aim of this paper is to show the possibility of finding the best multistage alternative, using Bellman's optimality principle and optimality equations. Of particular importance is a theorem on the non-dominance of the best multistage alternative, proven here. The methodology proposed allows to avoid reviewing each multistage alternative, which is important in large-size problems. The method is illustrated by a numerical example and a brief description of the sustainable regional development problem. The problem can be solved by means of the proposed procedure. [ABSTRACT FROM AUTHOR]

The leader–follower facility location problem consists of a leader and a follower who are competitors that locate new facilities sequentially. Traditional studies have generally assumed that the leader has partial or full advance information of the follower's response when making a decision. However, this assumption might be invalid or impracticable in practice. In this paper, we consider that the leader needs to locate a predetermined number of new facilities without knowing anything about the follower's response. By separating the scenarios in which the follower responds with different numbers of new facilities, a minimax regret model is proposed for the leader to minimise its maximum possible loss. Based on the structural characteristics of the proposed model, a set of solving procedures is provided that transforms the follower's nonlinear (fraction) programming model into a linear model. In the numerical experiments, the proposed model is compared with two other location models, a deterministic model and a risk model, and the efficiency of the linearisation in decreasing the computation time is verified. The results show that the proposed model is more applicable to the leader when there is no information about the number or probability distribution of the follower's new facilities. [ABSTRACT FROM AUTHOR]

Due to various disasters and catastrophic phenomena, we have seen different disruptions in goods supply in recent years. Such disruptions usually create a level of panic among the people. Besides this panic, the communications and interactions of people with each other affect their purchasing behavior. Our paper investigates the panic effect on the customers’ demand and retailer’s profit by considering social learning and updating customer's beliefs. Based on our findings, the retailer’s optimal strategy can be determined by the customers’ purchasing decisions. Our results show that in high level and low level of panic intensity exploitation of social learning tools and techniques is not recommended for the retailer, whether the warehouse capacity of the retailer is large or small. In the mid-range level of panic intensity, the retailer should exploit social learning tools and techniques to gain more profit. In situations with high-level customer information on stockpiling reasons and moderate panic intensity, employing social learning enhancement tools and techniques may not benefit the retailer. Finally, the conclusion and directions for future research are presented. [ABSTRACT FROM AUTHOR]

Artificial intelligence by principle is developed to assist but also support decision making processes. In our study, we explore how information retrieved from social media can assist decision-making processes for new product development (NPD). We focus on consumers' emotions that are expressed through social media and analyse the variations of their sentiments in all the stages of NPD. We collect data from Twitter that reveal consumers' appreciation of aspects of the design of a newly launched model of an innovative automotive company. We adopt the sensemaking approach coupled with the use of fuzzy logic for text mining. This combinatory methodological approach enables us to retrieve consensus from the data and to explore the variations of sentiments of the customers about the product and define the polarity of these emotions for each of the NPD stages. The analysis identifies sensemaking patterns in Twitter data and explains the NPD process and the associated steps where the social interactions from customers can have an iterative role. We conclude the paper by outlining an agenda for future research in the NPD process and the role of the customer opinion through sensemaking mechanisms. [ABSTRACT FROM AUTHOR]

The decision making of a suitable heating, ventilating and air conditioning system's set-point temperature is an energy and environmental challenge in our society. In the present paper, a general framework to define such temperature based on a dynamic adaptive comfort algorithm is proposed. Due to the fact that the thermal comfort of the occupants of a building has different ranges of acceptability, this method is applied to learn such comfort temperature with respect to the running mean temperature and therefore to decide the suitable range of indoor temperature. It is demonstrated that this solution allows to dynamically build an adaptive comfort algorithm, an algorithm based on the human being's thermal adaptability, without applying the traditional theory. The proposed methodology based on the K-Nearest-Neighbour algorithm was tested and compared with data from an experimental thermal comfort field study carried out in a mixed mode building in the south-western area of Spain and with the Support Vector Machine method. The results show that K-Nearest-Neighbour algorithm represents the pattern of thermal comfort data better than the traditional solution and that it is a suitable method to learn the thermal comfort area of a building and to define the set-point temperature for a heating, ventilating and air-conditioning system. [ABSTRACT FROM AUTHOR]