Search

Multiple stakeholders involved in the decision-making process have inherent interests that are sought to be maximized along with the collective goals specified by the organization as a whole. Due to the nature of these interests being diverse and often conflicting, an apparent dispute emerges which results in an even greater chaotic situation among stakeholders. Despite the introduction of several analytical and optimization tools to put the perspective of stakeholders into balance, there appears to be an inadequacy of frameworks that objectively incorporates the interests of stakeholders into a single metric. To advance this significant gap, this paper proposes a multiple stakeholder-based target-oriented robust optimization (MS-TORO) model which aggregates the interests of stakeholders into a single model with account for uncertainty. The conceptual and mathematical properties of the classical TORO model are used as a part of the MS-TORO framework to generate a satisficing solution with respect to the interests of multiple stakeholders. To demonstrate the applicability and validity of the proposed model, a hypothetical case study is performed in the decision process involving the post-departure aircraft rerouting problem. The system of the rerouting process involves multiple stakeholders each with inherent interests in an uncertain environment. Implementing the model provided solutions which satisfices the interests of multiple stakeholders as represented by the target metric minimizing the deviation from the performance targets of stakeholders. The proposed model not only confirmed the preferences of stakeholders in instances when a common route is selected but also showed non-biased solutions, thereby, adequately balancing interests. [ABSTRACT FROM AUTHOR]

Data envelopment analysis (DEA) is one of the widely used methods to measure the efficiency scores of decision making units (DMUs). Conventional DEA is unable to consider both uncertainty in data and decision makers' (DMs) judgments in the evaluations. This study, to address the shortcomings of the conventional DEA, proposes a new best worst method (BWM)- robust credibility DEA (BWM-RCDEA) model to estimate the efficiency scores of DMUs considering DMs' preferences and uncertain data, simultaneously. First, to handle uncertainty in input and output variables, fuzzy credibility model has been applied. Additionally, uncertainty in constructing fuzzy sets is modeled using robust optimization with fuzzy perturbation degree. In this paper, two new types of RCDEA models are proposed: RCDEA model with exact perturbation in fuzzy inputs and outputs and RCDEA model with fuzzy perturbation in fuzzy inputs and outputs. In addition, to deal with flexibility of weights and incorporating DMs' judgement into the RCDEA model, a bi-objective BWM-RCDEA model is introduced. Finally, the proposed bi-objective model is solved using min–max approach. To illustrate the usefulness and capability of the proposed model, efficiency scores of 39 distribution companies in Iran is investigated and results are analyzed and discussed. Finally, based on the results, recommendations have been made for policy makers. [ABSTRACT FROM AUTHOR]

We consider the problem of a project manager of a matrix organization responsible for the timely completion of projects arriving over time and requiring the usage of a set of resources under his/her purview. A project is defined by a set of activities, precedence relations between activities, resource requirements, a customer due date, and a priority (a weight). Arriving projects are released to a flow control system that monitors the progress of activities and schedules the set of available activities, i.e., activities that are ready to be processed, as appropriate. A critical feature of such control systems is the decision process for choosing the next activity to seize a given resource. This is the focus of this paper. In the past several decades, various heuristic priority rules have been proposed in the literature to support this type of decision in differing settings such as the job shop problem and the deterministic resource-constrained project scheduling problem. A gap exists with respect to testing the various rules all together in the more realistic dynamic-stochastic multi-project environment when the objective is to minimize weighted project tardiness. The purpose of this paper is to fill this gap. Results show that the priority rule "Weighted Critical Ratio and Shortest Processing Time" (W (CR + SPT)) is the best performing rule with respect to minimizing weighted project tardiness. W (CR + SPT) is shown to be a variant of the family of "Modified Due Date" rules first introduced by Baker and Bertrand (J Oper Manage 1(3):37–42, 1982). Repeated application of Duncan's Multiple Range test demonstrates the robustness of our findings. For the environmental parameters (due date tightness, variation of expected activity durations, and utilization of resource), the W (CR + SPT) rule is dominant with respect to weighted project tardiness among the eleven priority rules tested. Only when the number of resources is very modest (either 1 or 3 resources) or under a purely parallel resource network is W (CR + SPT) not the dominant rule. In those cases, its variant W (CR + GSPT) is the best performing rule with respect to weighted project tardiness. [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]

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]

Supply Chain Projects (SCPs) are essential ways to boost supply chain resilience. Making sound Risk Response Decisions (RRDs) for SCPs matters for business growth. However, SCPs are mainly managed in Project Portfolios (PPs). Decision Interdependency (DI) between projects and dependent uncertain events hinder determining risk response budgets and project success degree. Existing studies cannot effectively resolve them. Thus, this paper proposes a novel three-stage method. First, Monte Carlo Simulation (MCS) is applied to calculate the occurrence probabilities of scenarios formed by dependent uncertain events. In the second stage, considering the DI, the Stackelberg game is utilized to find the optimal risk response budget under each scenario. The final budget is determined by the conception of stratification theory. Third, a fuzzy RRD optimization model is constructed to select strategies within budgets. Finally, an illustrative case is given to prove the feasibility of the proposed method. Its advantages and reliability are validated by sensitivity and comparison analyses, which show: (1) optimal budgets exist; (2) the performance of the proposed method is better than the methods ignoring DI and dependent uncertain events; (3) the dependency of uncertain events affects RRDs; (4) focusing on DI can provide win–win situations for SCPs. This paper offers theoretical and managerial insights on RRDs. [ABSTRACT FROM AUTHOR]

AHP is a well-known multi-criteria procedure which has been investigated and developed by many researchers and practitioners. Some AHP modifications are designed for decision making under uncertainty. The goal of this paper is to present a new AHP approach which can be useful in the case of uncertain one-shot decisions and independent criteria. The method proposed in the article is based on scenario planning, features characteristic for the Hurwicz rule (i.e. the use of the optimism coefficient) and on a scenario set reduction. The novel procedure gives the possibility to generate a relatively small number of pairwise comparison matrices thanks to the reduction of the initial sets of scenarios. The modified version of AHP may be helpful when the decision maker’s knowledge about probabilities of the occurrence of particular scenarios is partial. Such a situation occurs in the case of innovative, innovation and risky projects for which historical data are not known. The idea of the suggested scenario-based AHP is to adjust the final choice not only to the decision makers’ preferences (concerning criteria for example), but also to their nature, attitude towards risk, predictions, expectations and fears. [ABSTRACT FROM AUTHOR]

The advent of Internet of Things and artificial intelligence in the era of Industry 4.0 has transformed decision-making within production systems. In particular, many decisions that previously required significant human activity are now made automatically with minimal human intervention via so-called digital twins (DTs). In the context of maintenance and reliability modeling, this naturally calls for new paradigms that can be seamlessly integrated within DTs for decision-making. The input data for time to failure needed in reliability computations are directly collected from the work center in a digital setting and often do not satisfy a known distribution. A neural network (NN) is proposed here to bypass this difficulty within the DT. Further, an algorithm inspired from machine learning is employed to solve the underlying semi-Markov decision process, whose transition model is captured via the NN. Numerical studies are carried out to demonstrate the usefulness of the approach. Finally, convergence properties of the algorithm are analyzed mathematically. [ABSTRACT FROM AUTHOR]

Optimization of conflicting functions is of paramount importance in decision making, and real world applications frequently involve data that is uncertain or unknown, resulting in multi-objective optimization (MOO) problems of stochastic type. We study the stochastic multi-gradient (SMG) method, seen as an extension of the classical stochastic gradient method for single-objective optimization. At each iteration of the SMG method, a stochastic multi-gradient direction is calculated by solving a quadratic subproblem, and it is shown that this direction is biased even when all individual gradient estimators are unbiased. We establish rates to compute a point in the Pareto front, of order similar to what is known for stochastic gradient in both convex and strongly convex cases. The analysis handles the bias in the multi-gradient and the unknown a priori weights of the limiting Pareto point. The SMG method is framed into a Pareto-front type algorithm for calculating an approximation of the entire Pareto front. The Pareto-front SMG algorithm is capable of robustly determining Pareto fronts for a number of synthetic test problems. One can apply it to any stochastic MOO problem arising from supervised machine learning, and we report results for logistic binary classification where multiple objectives correspond to distinct-sources data groups. [ABSTRACT FROM AUTHOR]

We consider a sequential decision making process such as energy trading or electrical production scheduling whose outcome depends on the future realization of a random factor, such as a meteorological variable. Assuming that the decision maker has access to a dynamically updated probabilistic forecast (predictive distribution) of the random factor, we propose several stochastic models for the evolution of the probabilistic forecast of a given quantity, and show how these models may be calibrated from ensemble forecasts, commonly provided by weather centers. We then show how these stochastic models can be used to determine optimal decision making strategies to maximize a specific gain functional. Applications to wind energy trading are given. [ABSTRACT FROM AUTHOR]

We investigate how in the context of Corporate Venture Capital (CVC), the investment decisions affect the likelihood of their subsequent exit strategies. We use OLS and probit regression as well as Weibull distribution of residual values, given its reliability and validity for studying lifetime analysis. Based on a sample of 8722 VC-backed ventures with the first investment dates between 1999 and 2018 in United States (US) and United Kingdom (UK), the results show that the presence of CVCs positively affects the funding amounts and the duration of the investment. CVC funds are more generous and more patient than Independent Venture Capital (IVC) funds regarding their investments in ventures. Moreover, the findings provide evidence that the exit strategies are directly influenced by the funding amounts and the duration of the investment which are influenced, in turn, by the fund type. Greater funding increases the likelihood of IPO exit which is reduced by longer investment duration. Our results are robust to alternative estimation methods, namely two-stage treatment-effects regressions. These results help the various stakeholders (VC funds, investors, ventures) make crucial decisions regarding investment amounts and duration, and exit. [ABSTRACT FROM AUTHOR]