Incorporating Decision Maker Research Articles

Designing a sustainable plastic bottle reverse logistics network: A data-driven optimization approach

Management of recovery options for plastic beverage containers involves some challenges. Most materials of used containers are recycled and are used to produce new products. The locations of collection facilities are the strategic decisions, affecting the amount collected and the total cost of a Reverse Logistics Network (RLN). In this study, a multi-objective (MO) optimization model is introduced to configure a plastic beverage container RLN, considering economic, environmental, and social objectives. This study also implements a scenario-based possibilistic approach to handle the uncertainty of the parameters. Furthermore, a data-driven fuzzy optimization framework is developed to consider the overlapping and multi-clustered characteristics of historical data samples. The application of the proposed method is demonstrated by considering a network in Vancouver, Canada. The numerical results reveal that the optimal configuration of the RLN resulting from the proposed MO model exhibits significant sensitivity to fluctuations in costs, demands, and the prioritization of the objective functions. Additionally, the proposed data-driven framework can incorporate decision makers' preferences when tuning the conservatism degree of uncertain parameters and the preference level of different objectives of the MO model. Moreover, the developed data-driven algorithm can reduce over-conservatism by 14% and guarantee the feasibility of optimal solutions compared to other data-driven strategies.

A novel model for merger analysis and target setting: A CSW-Inverse DEA approach

The inverse data envelopment analysis (DEA) is an advanced complementary method for efficiency analysis using the classical DEA approach. One of the inverse DEA (InvDEA) method applications is the mergers and acquisitions problem. It can be used for analyzing any under evaluation mergers and acquisitions. The current article is the first attempt to propose a novel inverse structure for the DEA model using the multiplier forms. This eventuates the possibility of incorporating decision maker preferences within the merger analysis.Moreover, compared with the existing models in the literature, the proposed novel models are capable of analyzing multiple merger scenarios simultaneously in a single method based on the common set of weights (CSW) rather than a series of models for studying multiple scenarios of mergers and acquisitions. Practically, this property enables decision-makers to consider and analyze multiple mergers and find possible potentials at the same time. The applicability of the proposed model is investigated by using a real-world dataset in banking.

Science and technology resource allocation evaluation of research institutions considering the risk preference of decision makers

With the increasing emphasis on research institutions in China, there is a growing imperative to evaluate the efficacy of resource allocation in science and technology (S&T) investments. However, limited research has been conducted on the efficacy of resource allocation in research institutions while considering decision makers’ risk preference behavior. In order to address this issue, the present study employs a prospective cross-efficiency evaluation model that incorporates decision makers’ risk preferences. This model is utilized to assess the efficiency of S&T resource allocation in research institutions across 13 industries in China. After conducting empirical analysis, it has been observed that decision makers’ risk preferences significantly influence the efficiency outcomes of S&T resource allocation in research institutions. Finally, this study proposes appropriate measures and recommendations for S&T resource allocation in research institutions.

Ship weather routing featuring w-MOEA/D and uncertainty handling

The paper presents a new version of evolutionary multi-objective weather routing (WR) for ships taking into account uncertainties of weather forecasts in route optimization. The method applies authors’ w-MOEA/D algorithm: MOEA/D framework incorporating Decision Maker’s (DM) preferences by means of w-dominance relation. Owing to this, DM preferences are taken into account throughout optimization, allowing the process to focus on the part of vast objective’s space. Only the part of Pareto front being of interest to DM is generated, thus the process converges faster, without sacrificing quality of the final set. All of the above is essential for the WR method, which pursues three objectives while trying to meet multiple constraints and handling uncertainty of weather data. The final method has been implemented as a part of client–server system architecture, whose client part has been installed on board of a m/v Monte da Guia (MdG) vessel navigating between the Portuguese coast and the Azores. The method has then been verified in the course of computer simulations and its results have been compared with real MdG GPS routes. The comparison shows that the presented method is able to find routes that bring progress in terms of the objectives’ while satisfying the constraints.

Incorporating decision makers’ attitudes towards risk and opportunity into network-level pavement maintenance optimisation

ABSTRACT Decision makers have different attitudes towards risks and opportunities of maintenance and rehabilitation (M&R) strategies. However, most existing pavement management studies simply assumed the neutral attitudes of decision makers. The available risk-based network-level M&R optimisation research equated risk with uncertainty which is actually different. Hence, this study aims to develop a method to quantitatively incorporate decision makers’ attitudes towards risk and opportunity into network-level pavement maintenance planning. Quantitative criteria were developed and incorporated into the maintenance optimisation model. A multi-objective optimisation (MOO) model was established to explore the trade-offs between expected returns, risks, and opportunities. The proposed methods were applied to a real-world highway network as a demonstration. The results show that budget increases can simultaneously reduce expected total costs and downside risks and increase upside potential by up to 0.41%, 5.26%, and 0.92%, respectively, for each 1% increase in current year’s budget, but their marginal effects are diminishing. Risk reduction requires compromising the expected performance and upside potential of the M&R strategy. The solutions derived from the mean-semivariance model dominate those from the mean-variance model. The outcomes of this study provide decision-makers with ways to incorporate their attitudes into maintenance optimisation, thereby reducing risk exposure and exploiting potential opportunities.

A novel best‐worst‐method two‐stage data envelopment analysis model considering decision makers' preferences: An application in bank branches evaluation

AbstractData envelopment analysis (DEA) model has been applied for evaluating bank branches and recognizing efficient and inefficient branches can help bank managers to provide appropriate strategies to improve the inefficient branches' performance. Conventional DEA models are based on the ‘black box’ approach. However, the process of providing services in banks is made up of interactive and interdependent processes. Additionally, some managers tend to incorporate their preferences in evaluation process. In this paper, best worst method (BWM) is used for incorporating decision maker (DM) preferences in two‐stage DEA model. First, BWM model is applied to obtain the weights of inputs, intermediate measures and outputs based on DM's judgement. Second, generated weights are imposed on two‐stage DEA model as additional constraints and a novel bi‐objective BWM‐two stage DEA model is introduced. Finally, the proposed bi‐objective BWM‐two stage DEA model is solved using min‐max approach. To illustrate the capability of proposed model, 45 Agricultural Bank (Agribank) branches in West Azerbaijan province of Iran are evaluated. The branches' processes are considered as two stages ‘production’ and ‘profitability’ and efficiency of branches are calculated in each stage. According to the efficiencies of each sub‐stage, branches are divided to four groups and recommendations are made for each group.

W-dominance: Tradeoff-inspired dominance relation for preference-based evolutionary multi-objective optimization

Abstract The paper presents a method of incorporating decision maker preferences into multi-objective meta-heuristics. It is based on tradeoff coefficients and extends their applicability from bi-objective to multi-objective. The method assumes that a decision maker specifies a priori each objective's importance as a weight interval. Based on this, w-dominance relation is introduced, which extends Pareto dominance. By replacing reference points with weight intervals the method eliminates the need for any knowledge concerning expected solutions. Instead, decision maker reflects his context-independent policy regarding objectives. The proposed w-dominance was incorporated into selected multi-objective metaheuristics. Following this, three new metrics were designed. The metrics include prescreening true Pareto Front and final population according to w-dominance relation. Based on preliminary tests, Vector Angle Evolutionary Algorithm (VaEA) was selected as the best match for w-dominance. W-dominance-extended VAEA (wVAEA) was compared in a series of simulations with four state-of-the-art reference point-based multi-objective algorithms. The results show that wVaEA outperforms the four representative algorithms for selected benchmark problems.

A novel best worst method robust data envelopment analysis: Incorporating decision makers’ preferences in an uncertain environment

Data Envelopment Analysis (DEA) has been widely applied in measuring the efficiency of Decision-Making Units (DMUs). The conventional DEA has three major drawbacks: a) it does not consider Decision Makers’ (DMs) preferences in the evaluation process, b) DMUs in this model are flexible in weighting the criteria to reach the maximum possible efficiency, and c) it ignores the uncertainty in data. However, in many real-world applications, data are uncertain as well as imprecise and managers want to impose their opinions in decision-making procedure. To address these problems, this paper develops a novel multi-objective Best Worst Method (BWM)-Robust DEA (RDEA) for incorporating DMs’ preferences into DEA model in an uncertain environment. The proposed model tries to provide a new efficiency score which is more reliable and compatible with real problems by taking the advantages of the BWM to apply experts’ opinions and RDEA to model the uncertainty This bi-objective BWM-RDEA model is solved utilizing amin-max technique and so as to illustrate its usefulness, this model is implemented for assessing Iranian airlines.

Eco-friendly container transshipment route scheduling problem with repacking operations

Currently, a huge amount of cargo is transported via containers by liner shipping companies. Under stochastic demand, repacking operations and carbon reduction, which may lead to an increase in effectiveness and environmental improvement, have been rarely considered in previous literature. In this paper, we investigate a container transshipment route scheduling problem with repacking operations under stochastic demand and environmental protection. The problem is a combinatorial optimization problem. Lacking historical data, a chance-constrained programming model is proposed to minimize the total operating and environment-related costs. We choose two distribution-free approaches, i.e., approximation based in Markov’s Inequality and Mixed Integer Second-Order Conic Program to approximate the chance constraints. As the loses induced by unfulfilled demand are not taken into account in the above model, a scenario-based model is developed considering the loses. Risk-neutral model may provide solutions that perform poorly while considering uncertainty. To incorporate decision makers’ perspectives, therefore, we also propose a risk-averse model adopting a risk aversion measure called Conditional Value-at-Risk to meet different preferences. Finally, we conduct computational experiments based on real data to compare the performances of the modeling methods and illustrate the impacts by testing different risk levels and confidence levels.

Condition-Based Multiobjective Maintenance Decision Making for Highway Bridges Considering Risk Perceptions

This paper proposes a novel optimal condition-based life-cycle highway bridge maintenance framework incorporating decision makers’ risk perceptions. The inspection and repair planning are formulated as a multiobjective optimization problem, i.e., simultaneously minimizing the expected maintenance cost and expected failure cost. Monte Carlo simulation is employed to calculate the distribution of maintenance and failure costs. To be consistent with bridge maintenance practices, both preventative and essential maintenance are considered, where the maintenance action is dependent on the load rating factor of individual bridge components. Utility theory and cumulative prospect theory are used to model decision makers’ risk preference among the Pareto front solutions. The cost-benefit implications of the Pareto front in terms of maintenance and failure costs, in conjunction with the preference solutions provided by utility theory and cumulative prospect theory, assist in the selection of the optimal bridge maintenance solution. The developed framework is illustrated on an in-service highway bridge.

Ambiguity on uncertainty and the equity premium

This paper considers an asset pricing model with a multiple-priors recursive utility incorporating decision makers’ concern with ambiguity on drift and jumps of driving process. Based on our empirical evidence, given a small relative risk aversion (RRA) coefficient (e.g., RRA = 2), the model can well explain the equity premium puzzle, since the ambiguity aversion, as a complementary aversion of the risk aversion, can increase the equity premium and decrease the risk-free rate. This paper documents that ambiguity on uncertainty is a resolution of the equity premium puzzle.

An integrated supplier selection approach incorporating decision maker’s risk attitude using ANN, AHP and TOPSIS methods

Purpose The purpose of this paper is to develop a novel integrated supplier selection approach incorporating decision maker’s risk attitude using the artificial neural network (ANN), analytic hierarchy process (AHP) and technique for order of preference by similarity to ideal solution (TOPSIS) methods. Design/methodology/approach In the proposed approach, the ANN model is used to classify decision maker’s risk attitude; the fuzzy AHP method is used to determine the relative weights of evaluation criteria; and the fuzzy TOPSIS method is used to evaluate ratings of suppliers. Finally, experiments are conducted to verify the feasibility and efficiency of the proposed integrated approach. Findings Experiments results show that the proposed integrated approach is effective and efficient to help decision makers to select suitable suppliers according to their risk attitudes. Originality/value The aim of this paper is to develop a novel integrated supplier selection approach incorporating decision maker’s risk attitude using the ANN, AHP and TOPSIS methods. The decision maker’s risk attitude toward procurement transaction is originally considered in supplier selection process.

Life beyond the grid: A Life-Cycle Sustainability Assessment of household energy needs

This paper presents a combined Life-Cycle Sustainability Assessment (LCSA) and Multi-Criteria Decision Analysis (MCDA) study on two off-grid houses in a rural village in Portugal to assess the sustainability of meeting electricity and heating needs in off-grid homes. The sustainability of meeting energy needs in the off-grid homes, compared to using the grid, was found to be dependent on the indicators under consideration, the technologies used in the homes, and household energy needs. We compared the current situation to four energy provisioning scenarios using Multi-Attribute Value Theory to rank alternatives based on their sustainability performance. The considered scenarios evaluated the impacts of extending and connecting the grid to the homes to allow for either: electricity consumption from the grid, or both consumption and injection of excess electricity generated to the grid. The results showed that meeting household electricity and heating needs in a sustainable way requires an analysis of the local context and available resources. Thus, a consideration of trade-offs is central to understanding the value of alternative possibilities for energy provisioning. This paper demonstrates that combining LCSA and MCDA procedures is useful for assessing sustainability impacts by incorporating decision maker’s preferences.

Incorporating decision makers’ preferences into DEA and common weight DEA models based on the best–worst method (BWM)

Incorporating decision makers’ (DMs’) judgments and preferences into DEA models is very important in some real-world problems. This paper presents an integrated data envelopment analysis (DEA)—best–worst method (BWM)—for considering DMs’ preferences in DEA and reducing flexibility in weights of inputs and outputs. First, the preferences vectors are designed using BWM, and then, a multi-objective DEA-BWM model is introduced. The proposed DEA-BWM model simultaneously maximizes the efficiency scores of DMUs and considers DMs’ preferences about weights of inputs and outputs. Finally, a goal programming model is suggested for extending the DEA-BWM model and finding common weights of inputs and outputs based on the DMs’ judgments. The proposed common weight DEA-BWM (CWDEA-BWM) model maximizes the efficiencies of DMUs, considers DMs’ preferences and uses a set of common weights. In order to illustrate the capability of proposed models, a numerical example is solved. Moreover, the proposed DEA-BWM and common weight DEA-BWM models are applied to evaluate 39 Iranian electricity distribution companies, and the results are analyzed and compared. The results indicate that the proposed DEA-BWM and CWDEA-BWM models are suitable for incorporating DMs’ preferences into DEA and fully ranking of DMUs.

A multi-objective reliability-based decision support system for incorporating decision maker utilities in the design of infrastructure

Infrastructure comprises the most fundamental facilities and systems serving society. Because infrastructure exists in economic, social, and environmental contexts, all lifecycle phases of such facilities should maximize utility for society, occupants, and designers. However, due to uncertainties associated with the nature of the built environment, the economic, social, and environmental (i.e., triple bottom line) impacts of infrastructure assets must be described as probabilistic. For this reason, optimization models should aim to maximize decision maker utilities with respect to multiple and potentially conflicting probabilistic decision criteria. Although stochastic optimization and multi-objective optimization are well developed in the field of operations research, their intersection (multi-objective optimization under uncertainty) is much less developed and computationally expensive. This article presents a computationally efficient, adaptable, multi-objective decision support system for finding optimal infrastructure design configurations with respect to multiple probabilistic decision criteria and decision maker requirements (utilities). The proposed model utilizes the First Order Reliability Method (FORM) in a systems reliability approach to assess the reliability of alternative infrastructure design configurations with regard to the probabilistic decision criteria and decision maker defined utilities, and prioritizes the decision criteria that require improvement. A pilot implementation is undertaken on a nine-story office building in Los Angeles, California to illustrate the capabilities of the framework. The results of the pilot implementation revealed that “high-performing” design configurations (with higher initial costs and lower failure costs) had a higher probability of meeting the decision maker’s preferences than more traditional, low initial cost configurations. The proposed framework can identify low-impact designs that also maximize decision maker utilities.

A many‐objective evolutionary algorithm incorporating decision maker's preference and its application to management of the electricity distribution network

AbstractThis paper proposes a step‐by‐step decision making approach that incorporates preferences of a decision maker into a many‐objective evolutionary algorithm. The algorithm is applied to a challenging many‐objective optimization problem (MaOP) involving integer objective values, tight constraints, heavy computational burden, and limited knowledge about the true Pareto front. The proposed method consists of five steps. (a) The entire Pareto front with high dimensions is roughly approximated by a small set of nondominated solutions. (b) On the basis of the approximated Pareto front, the decision maker specifies a preference. (c) Reference points are distributed through part of the objective space using the preference information. (d) The approximation accuracy of the Pareto front in the region of interest is improved by local search using the reference points. (e) The most preferred solution from a set of the nondominated solutions is chosen. The evolutionary algorithms employed in Steps (a) and (d) have an improved normalization procedure and selection mechanism for the MaOP involving integer objective value(s) and the tight constraints. Steps (b) and (e) use a graphical‐user‐interface‐based interactive tool we developed to efficiently narrow down to the preferred solution(s) among the high‐dimensional, nondominated solutions. This alleviates the cognitive burden on the decision maker and therefore aids the decision maker in handling MaOPs. In the computational experiment, we choose the real‐world application of reconfiguring an electricity distribution network. We demonstrate that the proposed approach can provide satisfying solutions to a decision maker.

Behavioral ordered weighted averaging operator and the application in multiattribute decision making

This paper introduces a new type of behavioral ordered weighted averaging (BOWA) operator, to incorporate decision maker’s gains and losses behavior tendency into the information aggregation process. The main characteristic of this BOWA operator is that it considers behavioral weights and ordered weights in the same formulation. We further provide a calculation method of the behavioral weights, in which various psychological preferences of different attribute types of the decision maker can be expressed intuitively. In addition, we discuss some particular cases of BOWA operator and its main properties. Finally, a numerical example is used to illustrate the use of the proposed method.

A robust optimization model for efficient and green supply chain planning with postponement strategy

This paper presents a bi-objective robust optimization model for the integrated production and distribution planning of a supply chain with the postponement strategy. The two objectives are economic (cost minimization) and environmental (greenhouse gas emissions minimization), respectively. The robust model is capable of incorporating decision makers' risk attitudes in addressing demand uncertainties under conditions of very limited historical data. The application of the proposed approach is examined using real data of an actual pharmaceutical supply chain as well as a benchmark problem in the existing literature. Our analysis and investigation focuses on exploring the extent to which the tradeoffs between economic and environmental objectives are influenced by the decision makers’ risk preferences and postponement strategy. We find that independent of the amount of greenhouse gas emissions and degree of demand variability, the postponement strategy can consistently provide cost savings for the supply chain.

Pragmatism in Decision Support System Research

Decision making on relief distribution is a complex multidisciplinary task in humanitarian logistics. It incorporates decision makers from different but related problem areas. The failure to perform assigned decision-making tasks in any area makes the entire system unstable and delays the relief distribution process. An organized, well-planned, and practical decision support system (DSS) can assist practitioners in making rapid decisions on delivering relief items. Hence, DSS researchers in humanitarian logistics require rigorous thinking, close and critical analysis, and the identification of challenges to conduct research or validate the generated knowledge properly. To perform such complex knowledge-based tasks, the philosophical understanding of DSS in the humanitarian context is necessary. After analyzing the commonly used philosophical paradigms, this research identifies the pragmatic approach as the adequate support for solving decision-making problems in relief distribution during large-scale disasters.

Multiobjective evolutionary algorithms based on target region preferences

Abstract Incorporating decision makers' preferences is of great significance in multiobjective optimization. Target region-based multiobjective evolutionary algorithms (TMOEAs), aiming at a well-distributed subset of Pareto optimal solutions within the user-provided region(s), are extensively investigated in this paper. An empirical comparison is performed among three TMOEA instantiations: T-NSGA-II, T-SMS-EMOA and T-R2-EMOA. Experimental results show that T-SMS-EMOA has the best overall performance regarding the hypervolume indicator within the target region, while T-NSGA-II is the fastest algorithm. We also compare TMOEAs with other state-of-the-art preference-based approaches, i.e., DF-SMS-EMOA, RVEA, AS-EMOA and R-NSGA-II to show the advantages of TMOEAs. A case study in the mission planning of earth observation satellite is carried out to verify the capabilities of TMOEAs in the real-world application. Experimental results indicate that preferences can improve the searching ability of MOEAs, and TMOEAs can successfully find nondominated solutions preferred by the decision maker.