Set Of Decisions Research Articles

A fuzzy multiobjective linear programming approach for green multiechelon distribution network design

PurposeDistribution network design involves a set of strategic decisions in supply chains because of their long-term impacts on the total logistics cost and environment. To incorporate a trade-off between financial and environmental aspects of these decisions, this paper aims to determine an optimal location, among candidate locations, of a new logistics center, its capacity, as well as optimal network flows for an existing distribution network, while concurrently minimizing the total logistics cost and gas emission. In addition, uncertainty in transportation and warehousing costs are considered.Design/methodology/approachThe problem is formulated as a fuzzy multiobjective mathematical model. The effectiveness of this model is demonstrated using an industrial case study. The problem instance is a four-echelon distribution network with 22 products and a planning horizon of 20 periods. The model is solved by using the min–max and augmented ε-constraint methods with CPLEX as the solver. In addition to illustrating model’s applicability, the effect of choosing a new warehouse in the model is investigated through a scenario analysis.FindingsFor the applicability of the model, the results indicate that the augmented ε-constraint approach provides a set of Pareto solutions, which represents the ideal trade-off between the total logistics cost and gas emission. Through a case study problem instance, the augmented ε-constraint approach is recommended for similar network design problems. From a scenario analysis, when the operational cost of the new warehouse is within a specific fraction of the warehousing cost of third-party warehouses, the solution with the new warehouse outperforms that without the new warehouse with respective to financial and environmental objectives.Originality/valueThe proposed model is an effective decision support tool for management, who would like to assess the impact of network planning decisions on the performance of their supply chains with respect to both financial and environmental aspects under uncertainty.

On Kemeny Optimization Scheme for Fuzzy Set of Relations

The present paper investigated the aggregation of individual preferences into a group fuzzy preference relation for a fuzzy set of decision-makers (DMs). This aggregation is based on the Kemeny optimization scheme. It was proven that this group relation is a Type-2 fuzzy relation (T2FR). The decomposition approach was used to analyze the group T2FR. It is shown that the group T2FR can be decomposed according to secondary membership grades into a finite collection of Type-1 fuzzy relations. Each of them is a group fuzzy relation for a crisp set of DMs, which is the corresponding α-cut of the original fuzzy set of DMs. Illustrative examples are given.

Assessing sustainability of food supply chains by using a novel method integrating group multi-criteria decision-making and interval Type-2 fuzzy set

Assessing sustainability of food supply chains by using a novel method integrating group multi-criteria decision-making and interval Type-2 fuzzy set

The ethics of using COVID-19 host genomic information for clinical and public health decision-making: A survey of US health professionals

Several genetic variants linked to COVID-19 have been identified by host genomics researchers. Further advances in this research will likely play a role in the clinical management and public health control of future infectious disease outbreaks. The implementation of genetic testing to identify host genomic risk factors associated with infectious diseases raises several ethical, legal, and social implications (ELSIs). As an important stakeholder group, health professionals can provide key insights into these ELSI issues. In 2021, a cross-sectional online survey was fielded to US health professionals. The survey explored how they view the value and ethical acceptability of using COVID-19 host genomic information in three main decision-making settings: (1) clinical, (2) public health, and (3) workforce. The survey also assessed participants' personal and professional experience with genomics and infectious diseases and collected key demographic data. A total of 603 participants completed the survey. A majority (84%) of participants agreed that it is ethically acceptable to use host genomics to make decisions about clinical care and 73% agreed that genetic screening has an important role to play in the public health control of COVID-19. However, more than 90% disagreed that it is ethically acceptable to use host genomics to deny resources or admission to individuals when hospital resources are scarce. Understanding stakeholder perspectives and anticipating ELSI issues will help inform policies for hospitals and public health departments to evaluate and perhaps adopt host genomic technologies in an ethically and socially responsible manner during future infectious disease outbreaks.

Distributed algorithm for social cost minimization with second-order nonlinear dynamics over weight-unbalanced digraphs

In this paper, the emerging social cost minimization framework of second-order nonlinear systems over weight-unbalanced digraphs is studied. It provides a general framework containing several important classes of problems. Therein each local cost function depends on a global decision variable consisting of all the agents’ decisions not limited to the form of aggregative terms. Under the partial decision and cost function information setting, distributed algorithms are designed based on the state feedback and left eigenvector estimation mechanism to overcome the challenges posed by second-order nonlinear systems and weight-unbalanced digraphs. The asymptotic convergence of the algorithm is demonstrated via the Lyapunov stability theory. Finally, numerical simulations of the vehicle monitoring problem are provided to support the algorithm design.

Automatic speech recognition of Portuguese phonemes using neural networks ensemble

The automatic speech recognition based on detection of phonemes provides advantages for online recognition of a speech represented by a sound signal. The development of a system for automatic speech recognition is multidisciplinary. It covers several areas of research, such as linguistics, signal processing and computational intelligence. In this work, the process starts with a speech signal pre-processing to extract the main features of the speech signal at a given instant of time. Inspired by the “divide and conquer” principle, we bridge the complexity gap of automatic speech recognition by devising models based on an ensemble of neural network experts, allowing to divide the huge decision space regarding speech recognition so that each expert takes care only of a delimited area of this decision space. This novel application of this strategy improves the precision, sensitivity and accuracy of the recognition process. Each included expert decides regarding each one of the pre-processed input samples. The decision set thus obtained is weighted. So, the expert with the highest weight for the output will determine the sample final classification. After that, a dynamic post-processing step, implemented as a recurrent neural network, is executed. It aims at mitigating the oscillatory effect that occurs during the recognition of classes with similar characteristics. In this work, two ensembles are investigated. The first is based on the clustering of similar phonetics classes while the second takes care of the imbalanced distribution of samples in the training set. The proposed model achieves 7.63% improvement in terms of accuracy with respect to the best so far related model for automatic speech recognition.

Grit Description in High/Vocational School Student of Bandung City

According to student development tasks for high/vocational school students, selecting and preparing a career is an essential aspect of their development. Therefore, these students must engage in career planning, and one of the factors that significantly influences career planning and maturity is their individual personality or level of Grit. Several components related to career development include career maturity, personality effectiveness, career decision-making, and goal setting. One specific personality factor that aligns with the positive psychology approach is Grit, which encompasses effort, diligence, consistency of interest, and adaptability to various situations. In this research, fifty high/vocational school students from Bandung City were selected as the sample for the study. The researchers used the Grit Scale developed by Duckworth as a measuring instrument to assess the students’ level of Grit. The research method employed in this study is descriptive quantitative to provide an overview and description of the Grit personality among the students. The findings of the research indicate that the overall level of Grit in high/vocational school students from Bandung City is low.
 Keywords: grit, career development, high/vocational school student

Distributed zeroth-order optimization: Convergence rates that match centralized counterpart

Zeroth-order optimization has become increasingly important in complex optimization and machine learning when cost functions are impossible to be described in closed analytical forms. The key idea of zeroth-order optimization lies in the ability for a learner to build gradient estimates by queries sent to the cost function, and then traditional gradient descent algorithms can be executed replacing gradients by the estimates. For optimization over large-scale multi-agent systems with decentralized data and costs, zeroth-order optimization can continue to be utilized to develop scalable and distributed algorithms. In this paper, we aim at understanding the trend in performance transitioning from centralized to distributed zeroth-order algorithms in terms of convergence rates, and focus on multi-agent systems with time-varying communication networks. We establish a series of convergence rates for distributed zeroth-order subgradient algorithms under both one-point and two-point zeroth-order oracles. Apart from the additional node-to-node communication cost due to the distributed nature of algorithms, the established rates in convergence are shown to match their centralized counterpart. We also propose a multi-stage distributed zeroth-order algorithm that better utilizes the learning rates, reduces the computational complexity, and attains even faster convergence rates for compact decision set.

Motivated Cognition in Cooperation

Successful cooperation is tightly linked to individuals’ beliefs about their interaction partners, the decision setting, and existing norms, perceptions, and values. This article reviews and integrates findings from judgment and decision-making, social and cognitive psychology, political science, and economics, developing a systematic overview of the mechanisms underlying motivated cognition in cooperation. We elaborate on how theories and concepts related to motivated cognition developed in various disciplines define the concept and describe its functionality. We explain why beliefs play such an essential role in cooperation, how they can be distorted, and how this fosters or harms cooperation. We also highlight how individual differences and situational factors change the propensity to engage in motivated cognition. In the form of a construct map, we provide a visualization of the theoretical and empirical knowledge structure regarding the role of motivated cognition, including its many interdependencies, feedback loops, and moderating influences. We conclude with a brief suggestion for a future research agenda based on this compiled evidence.

Bayesian predictive decision synthesis

Abstract Decision-guided perspectives on model uncertainty expand traditional statistical thinking about managing, comparing, and combining inferences from sets of models. Bayesian predictive decision synthesis (BPDS) advances conceptual and theoretical foundations, and defines new methodology that explicitly integrates decision-analytic outcomes into the evaluation, comparison, and potential combination of candidate models. BPDS extends recent theoretical and practical advances based on both Bayesian predictive synthesis and empirical goal-focused model uncertainty analysis. This is enabled by the development of a novel subjective Bayesian perspective on model weighting in predictive decision settings. Illustrations come from applied contexts including optimal design for regression prediction and sequential time series forecasting for financial portfolio decisions.

Maximum slacks-based measure of efficiency in network data envelopment analysis: A case of garment manufacturing

The conventional slacks-based measure (SBM) model is widely applied to measure the relative efficiency of a set of decision making units (DMUs) because the measured efficiency is not affected by weakly efficient frontiers. This model seeks the target point on the production frontier that is farthest to the assessed DMU to calculate efficiency, which requires more effort for the DMU to reach to become efficient. This paper develops a model to calculate the maximum SBM efficiency for general network production systems. Different from the conventional minimum SBM efficiency that is always less than the radial efficiency for inefficient DMUs, the maximum SBM efficiency can be greater than the radial efficiency. The measured efficiency satisfies the monotonicity property. The efficiencies of the component divisions of the network system can also be calculated at the same time. A garment manufacturing company whose major operations comprise cutting and sewing is studied using the developed model. The results show that the average maximum SBM efficiency is 35 % higher than the average minimum SBM efficiency and the sewing operation contributes more to the system efficiency than the cutting operation does.

Uncertainty quantification in machine learning for engineering design and health prognostics: A tutorial

On top of machine learning (ML) models, uncertainty quantification (UQ) functions as an essential layer of safety assurance that could lead to more principled decision making by enabling sound risk assessment and management. The safety and reliability improvement of ML models empowered by UQ has the potential to significantly facilitate the broad adoption of ML solutions in high-stakes decision settings, such as healthcare, manufacturing, and aviation, to name a few. In this tutorial, we aim to provide a holistic lens on emerging UQ methods for ML models with a particular focus on neural networks and the applications of these UQ methods in tackling engineering design as well as prognostics and health management problems. Towards this goal, we start with a comprehensive classification of uncertainty types, sources, and causes pertaining to UQ of ML models. Next, we provide a tutorial-style description of several state-of-the-art UQ methods: Gaussian process regression, Bayesian neural network, neural network ensemble, and deterministic UQ methods focusing on spectral-normalized neural Gaussian process. Established upon the mathematical formulations, we subsequently examine the soundness of these UQ methods quantitatively and qualitatively (by a toy regression example) to examine their strengths and shortcomings from different dimensions. Then, we review quantitative metrics commonly used to assess the quality of predictive uncertainty in classification and regression problems. Afterward, we discuss the increasingly important role of UQ of ML models in solving challenging problems in engineering design and health prognostics. Two case studies with source codes available on GitHub are used to demonstrate these UQ methods and compare their performance in the life prediction of lithium-ion batteries at the early stage (case study 1) and the remaining useful life prediction of turbofan engines (case study 2).

A deep real options policy for sequential service region design and timing

There is a need for strategic flexibility in well-timed deployment decisions in the design and timing of mobility service regions, i.e. cast as ‘real options’. This problem becomes increasingly challenging considering the multiple interacting real options in such deployments. We propose a scalable machine learning (ML) based real options (RO) framework for multi-period sequential service region design and timing problem for mobility-on-demand (MoD) services, framed as a Markov decision process with non-stationary stochastic variables. A value function approximation policy from the literature uses multi-option least squares Monte Carlo simulation to get a policy value for a set of interdependent deployment decisions as deferral options (i.e., CR policy). The objective is to determine the optimal selection and timing of a set of zones to include in a service region (which to add now, which to defer to a later time to reconsider). However, prior work required explicit enumeration of all possible sequences of deployments (i.e. 5 options translate to 120 sequences). To address the combinatorial complexity arising from sequence enumeration, we propose a new variant ‘deep’ real options policy using an efficient recurrent neural network (RNN) based ML method (i.e., CR−RNN policy) to sample sequences to forego the need for enumeration, making the network design and timing policy tractable for large scale implementation. Experiments based on multiple service region scenarios in New York City demonstrate the efficacy of the proposed policy in substantially reducing the overall computational cost (i.e., time reduction associated with the RO evaluation of more than 90% of total deployment sequences is achieved), with zero to near-zero gap compared to the benchmark. We validate the model in a case study of sequential service region design for expansion of MoD services in Brooklyn, NYC, under service demand uncertainty. Results show that using the CR-RNN policy in determining optimal real options deployment strategy yields a similar performance (≈ 0.5% within the CR policy value) with significantly reduced computation time (about 5.4 times faster).

Random Forest-Based Restocking and Pricing Prediction for Vegetable Items

The intolerance to storage of vegetable commodities in supermarkets makes automatic pricing and replenishment decisions for vegetable commodities particularly important. This paper takes the measured data of a superstore as an example to formulate a set of effective pricing and replenishment decisions for vegetable commodities, which is a comprehensive consideration to ensure the balance of supply and demand, and to reduce the losses of the superstore and the loss rate of commodities. First of all, the sales of each category and single product in different time periods were counted, and the Pearson correlation coefficient was calculated to obtain the distribution pattern of the sales volume of each category and single product of vegetables. Then, the relationship between the total sales volume of and the cost-plus pricing of each vegetable category is analyzed, and a random forest model is established to predict the total replenishment volume and pricing strategy in the coming week. Finally, the replenishment quantity and pricing strategy of individual items are given to maximize the revenue of the superstore under the premise of trying to meet the market demand for each category of vegetable goods. The model established in the paper, which basically solves the given problem, has strong practicality and high computational efficiency.

Research on Product Decision of Real Estate Project Based on Big Data Analysis

The characteristics of product decision-making of real estate projects are determined by the particularity of real estate projects. Because the development and construction period of real estate projects is long, the investment is large, many parties participate, there are many risk factors in the development process, and after the development is completed, it is unrepeatable, and most of them aim at profit. Real estate regulation and control is a long-term work, and it is urgent to establish a set of operational analysis and decision support platform that runs through the whole chain of real estate and connects national and local levels, so as to realize real-time data aggregation, online calculation of indicators, rapid organization of content, rapid assembly of functions and flexible customization of services. In this paper, BD (Big Data) technology is used to collect real estate-related market data such as land, housing and finance, macro-statistical data such as population, society and economy, as well as real estate policy and public opinion information, to carry out system design and technology development, and to establish a real estate DSS(Decision Supporting System) based on BD. The system is helpful to carry out dynamic monitoring, analysis and evaluation of the real estate market, and effectively supports the product decision-making of real estate projects.

A metric for assessing and optimizing data-driven prognostic algorithms for predictive maintenance

Prognostic Health Management aims to predict the Remaining Useful Life (RUL) of degrading components/systems utilizing monitoring data. These RUL predictions form the basis for optimizing maintenance planning in a Predictive Maintenance (PdM) paradigm. We here propose a metric for assessing data-driven prognostic algorithms based on their impact on downstream PdM decisions. The metric is defined in association with a decision setting and a corresponding PdM policy. We consider two typical PdM decision settings, namely component ordering and/or replacement planning, for which we investigate and improve PdM policies that are commonly utilized in the literature. All policies are evaluated via the data-based estimation of the long-run expected maintenance cost per unit time, using monitored run-to-failure experiments. The policy evaluation enables the estimation of the proposed metric. We employ the metric as an objective function for optimizing heuristic PdM policies and algorithms’ hyperparameters. The effect of different PdM policies on the metric is initially investigated through a theoretical numerical example. Subsequently, we employ four data-driven prognostic algorithms on a simulated turbofan engine degradation problem, and investigate the joint effect of prognostic algorithm and PdM policy on the metric, resulting in a decision-oriented performance assessment of these algorithms.

Mathematical Modeling of a Fuzzy Logic-Based Car Parking System

This paper presents the mathematical modeling of a fuzzy logic-based car parking system. Many people are facing problems parking vehicles in parking lots in most urban cities. The technology of fuzzy-based systems is necessary for all vehicle users to acquire parking slots in cities. A smart parking system helps in obtaining information about available parking spaces, organizing and processing it, and then placing the car in a certain position. The method adopted makes use of a fuzzy-based smart car parking system to develop the usage of small parking spaces for parking lots to allow the parking of vehicles based on their weight. A set of rules was used in the fuzzy logic to control two sets of parking decisions, namely the vehicle entry decision and the inpark decision, by bearing in mind the weight of vehicles and the availability of parking spaces in the parking mall. The results obtained show that the smart car parking system keeps the availability of parking lots in equilibrium, depending on the entry and exit of vehicles. In conclusion, it was discovered that the total number of vehicles granted entry at each time interval depends on the availability of parking lots on each designated floor.

Can We Detect Bias in Political Fact-Checking? Evidence from a Spanish Case Study

ABSTRACT Political fact-checkers evaluate the truthfulness of politicians’ claims. This paper contributes to an emerging scholarly debate on whether fact-checkers treat political parties differently in a systematic manner depending on their ideology (bias). We first examine the available approaches to analyze bias and then present a new approach in two steps. First, we propose a logistic regression model to analyze the outcomes of fact-checks and calculate how likely each political party will obtain a truth score. We test our model with a sample of fact-checks from Newtral, a prominent Spanish fact-checker. Our model would signal bias under two assumptions: (a) all political parties are on average equally accurate in their statements; (b) the verification method gives precise instructions and is implemented systematically. We investigate this second assumption with a series of interviews with Newtral fact-checkers. We show that standard verification protocols are loosely implemented and therefore fact-checks reflect a set of journalistic decisions, rather than a bias in the statistical sense. We call for a more rigorous definition of verification methods as a pre-requisite for an unbiased assessment of politician’s claims.

On complexity of finding strong-weak solutions in bilevel linear programming

We consider bilevel linear programs (BLPs) that model hierarchical decision-making settings with two independent decision-makers (DMs), referred to as a leader (an upper-level DM) and a follower (a lower-level DM). BLPs are strongly NP-hard. In general, the follower's rational reaction (i.e., a set that contains optimal solutions of the lower-level problem for a given leader's decision) is not a singleton. If we assume that, for a given leader's decision, the follower always selects a solution from the rational reaction set that is most (least) favorable to the leader, then we obtain the optimistic (pessimistic) model. It is known that the optimistic (pessimistic) model remains NP-hard even if an optimal pessimistic (optimistic) solution of the same BLP is known. One interesting generalization of these two approaches studied in the related literature is to consider the so-called α-strong-weak model, where parameter α∈[0,1] controls the leader's level of conservatism. That is, α provides the probability of the follower's “cooperation” in a sense that, the lower-level's rational decisions that are most and least favorable to the leader are picked with probabilities α and 1−α, respectively. In this note we show that for any fixed α∈(0,1) the problem of finding an optimal α-strong-weak solution remains strongly NP-hard, even when both optimal optimistic and pessimistic solutions for the same BLP are known.

Making It Magical

Making It Magical