Multi-criteria decision model for risk prioritisation involving multiple decision-makers: an application of composition of probabilistic preferences combined with FMEA in the supply chain

PurposeThe paper aims to propose a multi-criteria model for risk prioritisation associated to supply chain management involving multiple decision-makers.Design/methodology/approachThe model integrates the composition of probabilistic preferences (CPP) on the failure modes analysis and its effects (FMEA) criteria. First, the authors carried out a probabilistic transformation of the numerical evaluations of the multiple decision-makers on the FMEA criteria regarding the internal risks that affect the chain of clothing pole in the Agreste region of Pernambuco. Then, the authors proposed the use of the Kendall's concordance coefficient W to aggregate these evaluations.FindingsContrary to expectations, the two main risks to be investigated as a model suggestion was related to the context of supply chain suppliers and not related to the raw material costs. Besides, a simulation with the traditional FMEA was carried out, and comparing with the model result, the simulation is worth highlighting seven consistent differences along the two rankings.Research limitations/implicationsThe focus was restricted to the use of only internal chain risks.Practical implicationsThe proposed model can contribute to the improvement of the decisions within organisations that make up the chains, thus guaranteeing a better quality in risk management.Originality/valueEstablishing a more effective representation of uncertain information related to traditional FMEA treatment involving multiple decision-makers means identifying in advance the potential risks, providing a better supply chain control.

Hierarchical multilevel optimization with multiple-leaders multiple-followers setting and nonseparable objectives

Hierarchical multilevel multi-leader multi-follower problems are non-cooperative decision problems in which multiple decision-makers of equal status in the upper-level and multiple decision-makers of equal status are involved at each of the lower-levels of the hierarchy. Much of solution methods proposed so far on the topic are either model specific which may work only for a particular sub-class of problems or are based on some strong assumptions and only for two level cases. In this paper, we have considered hierarchical multilevel multi-leader multi-follower problems in which the objective functions contain separable and non-separable terms (but the non-separable terms can be written as a factor of two functions, a function which depends on other level decision variables and a function which is common to all objectives across the same level) and shared constraint. We have proposed a solution algorithm to such problems by equivalent reformulation as a hierarchical multilevel problem involving single decision maker at all levels of the hierarchy. Then, we applied a multi-parametric algorithm to solve the resulting single leader single followers problem.

Optimal Off-line Experimentation for Games

Many business situations can be called “games” because outcomes depend on multiple decision makers with differing objectives. Yet, in many cases, the payoffs for all combinations of player options are not available, but the ability to experiment off-line is available. For example, war-gaming exercises, test marketing, cyber-range activities, and many types of simulations can all be viewed as off-line gaming-related experimentation. We address the decision problem of planning and analyzing off-line experimentation for games with an initial procedure seeking to minimize the errors in payoff estimates. Then, we provide a sequential algorithm with reduced selections from option combinations that are irrelevant to evaluating candidate Nash, correlated, cumulative prospect theory or other equilibria. We also provide an efficient formula to estimate the chance that given Nash equilibria exists, provide convergence guarantees relating to general equilibria, and provide a stopping criterion called the estimated expected value of perfect off-line information (EEVPOI). The EEVPOI is based on bounded gains in expected utility from further off-line experimentation. An example of using a simulation model to illustrate all the proposed methods is provided based on a cyber security capture-the-flag game. The example demonstrates that the proposed methods enable substantial reductions in both the number of test runs (half) compared with a full factorial and the computational time for the stopping criterion.

A Qualitative Analysis of Oncology Patient Awareness of Medication Shortages and Their Preferences for How Shortages Should Be Managed

PURPOSE: Medication shortages in US hospitals are ongoing, widespread, and frequently involve antineoplastic and supportive medications used in cancer care. The ways shortages are managed and the ways provider-patient communication takes place are heterogeneous, but the related preferences of oncology patients are undefined. This study sought to qualitatively evaluate patient preferences. METHODS: A cross-sectional, semi-structured interview study was conducted from January to June 2019. Participants were adult oncology inpatients who received primary cancer care at the University of Chicago, had undergone treatment within 2 years, and had 1 or more previous hospitalizations during that period. Participants (n = 54) were selected consecutively from alternating hematology and oncology services. The primary outcome was thematic saturation across the domains of awareness of medication shortages, principle preferences regarding decision makers, preferences regarding allocation of therapy drugs, and allocation-related communication. RESULTS: Thematic saturation was reached after 39 participants completed the study procedures (mean age, 59.6 years [standard deviation, 14.5 years]; men made up 61.5% of the study population [mean age, 24 years]; response rate, 72.0%). In all, 18% of participants were aware of institutional medication shortages. Patients preferred having multiple decision makers for allocating medications in the event of a shortage. A majority of patients named oncologists (100%), ethicists (92%), non-oncology physicians (77%), and pharmacists (64%) as their preferred decision makers. Participants favored allocation of drugs based on their efficacy (normalized weighted average, 1.3), and they also favored prioritizing people who were already receiving treatment (1.8), younger patients (2.0), sicker patients (3.1), and those presenting first for treatment (5.3). Most participants preferred preferred disclosure of supportive care medication shortages (74%) and antineoplastic medication shortages (79%) for equivalent substitutions. CONCLUSION: In a tertiary-care center with medication shortages, few oncologic inpatients were aware of shortages. Participants preferred having multiple decision makers involved in principle-driven allocation of scarce medications. Disclosure was preferred when their usual medications needed to be substituted with equivalent alternatives. These preliminary data suggest that preferences do not align with current management practices for medication shortages.

Applying the TOPSIS Approach for Selecting the Best Small Project

Projects selection is a significant problem. However, regularly challenging task. It is difficult because there is typically more than one measurement of the effects on all projects, particularly when there are multiple decision-makers. In this paper, the authors consider a tangible presentation of projects choosing for a different number of projects through an experts opinions by a group decision-making method called TZOPSEIS (Technique for Order Preference by Similarity to an Ideal Solution). This method is characterized by its ability to measure the relative performance for each alternative in a simple mathematical form. Four criteria have been deliberated to select the finest one among five projects and rank them. The proposed manner is used in a case study in the Iraqi environment which is analyzed from diverse directions of vision to evaluate and select the best small project.