Multi-objective Decision-making Problem Research Articles

Multi-objective optimal allocation of construction project risks, ant colony optimization algorithm

PurposeThe purpose of this paper is to introduce a new approach for finding the most appropriate risk allocation among construction contract parties. Although risk allocation is a strategic decision that can greatly affect the cost and time of the project, it is often made based on the personal judgments of employers. That is why owners usually find it a very time-consuming and expensive decision process, while most contractors feel that risk sharing is not done fairly. In this paper, a quantitative model for risk allocation is introduced to fulfill clients’ conflicting expectations in the risk allocation process.Design/methodology/approachBy defining conflicting expectation of owners in the form of three quantitative objectives (lowest cost, maximum reliability and minimum risk exposure), a multi-objective optimization algorithm was developed to select the most appropriate risk allocation. Using experts’ knowledge through fuzzy set theory, a multi-objective decision-making model is developed based on an ant colony optimization algorithm. The proposed model is able to find the optimum risk allocation at the lowest cost and highest reliability while protecting the client against risk exposure within multiple parties projects.FindingsThe proposed model has the ability to select the most appropriate risk allocation in multi-parties projects (such as public–private partnerships) and quantitatively measure the impact of each employer’s choice on project results in the form of cost and time. The results of implementing the proposed model in a case study project revealed that optimum risk allocation requires a balanced attitude, and the transfer of all risks to the other parties will not necessarily lead to the lowest cost. The client should bear more responsibilities in risk management to avoid extreme time delay and cost overrun.Research limitations/implicationsThe proposed model can be implemented in multi-parties projects (such as public–private partnership), while other methods introduced in previous studies can only be used for projects with two party (client and contractor).Practical implicationsBy implementing the proposed model in a real project in this article and comparing its results with previous works, it has been shown that the proposed model has a good performance. Using this model can help clients drastically reduce cost and time and ultimately successfully conclude risk allocation negotiations (which is the most difficult part of any contract negotiation).Originality/valueIn this paper, the risk allocation process is modeled in the form of a multi-objective decision problem. This method helps employers to measure their conflicting goals and choose the most appropriate risk allocation according to their objectives. Also, in this article, the conflicting expectations of the employer in the process of contract negotiations are introduced as three measurable goals, which gives the decision-maker the ability to balance his expectations and not miss an objective. In the final step, an optimization model is developed to select the best option, which can choose the most appropriate party to bear the risk in a short time and among an unlimited number of participants.

Adaptive (re)operations facilitate environmental flow maintenance downstream of multi-purpose reservoirs

Multi-purpose reservoirs support socioeconomic development by providing irrigation, domestic water supply, hydropower, and other services. However, impoundment of water impacts instream aquatic ecosystems. Thus, the concept of minimum environmental flows (MEFs) was established to restore the benefits of naturally flowing rivers by specifying minimum flow rates to be maintained downstream of dams. But varying legislative contexts under which multi-purpose reservoirs operate may not always necessitate MEF releases. To what extent the release of MEF affects other sectoral benefits remains an open-ended and possibly a site-specific inquiry. A related issue is − how does the order in which releases are prioritized influences sectoral performances? We analyse these issues for the Nagarjuna Sagar reservoir, one of the largest multipurpose reservoirs in southern India. We formulate two versions of a multi-objective decision problem. PF_MEF formulation prioritizes MEF releases over releases for water demand satisfaction, followed by hydropower releases. PF_nMEF formulation follows the regional legislative rule releasing first for demand satisfaction, followed by hydropower and MEF releases. The objectives include: annual demand deficits (minimize), hydropower production (maximize), reliability of maintaining MEF (maximize), and reliability of non-exceedance of high flows downstream (maximize). A dynamic rule using radial basis functions (RBFs) specifies releases as a function of reservoir storage and the Borg multi-objective evolutionary algorithm is used to identify the Pareto approximate solutions. The reliability of MEF satisfaction, annual demand deficits, and hydropower production attained across the Pareto-optimal strategies obtained for PF_MEF (PF_nMEF) formulation is 86–99 % (63–80%), 47–1063 (17.6–340) Mm3, and 3452–3703 (3408–3650) GWh, respectively. Results thus indicate that prioritizing MEF releases improves can meet MEF requirements without significant compromises in other objectives. We hypothesize that similar investigations may reveal how simple modification of release order may improve ability of other reservoirs to meet environmental goals.

Case Article—Optimizing Transportation and Equity in a District Zoning Problem for ORville Public Schools

This case asks students to solve a multiobjective optimization decision problem applied to school district zoning. In the case, residential areas in a school district must be assigned to middle schools having capacity limits. The operational considerations on the decision are the weighted distances traveled between students’ residences and their assigned middle school campuses. The ethical considerations pertain to equity in education and are measured using the numbers of economically disadvantaged students assigned to each middle school. This problem, including its two objectives, is motivated by a real zoning problem faced by a school district. Publicly available data from the school district and its rezoning efforts are used to populate the case. To complete the case, students model and solve an optimization problem both with and without the ethical considerations included. In doing so, they observe how ethical considerations can change the outputs generated by the optimization model. Evidence is provided from student surveys to indicate how completion of the case impacts perceptions of the importance of and understanding of how to include ethical considerations in optimization modeling. Results indicate statistically significant increases in those perceptions. Supplemental Material: The Teaching Note and DataAndMetricsDemo Excel workbook are available at https://www.informs.org/Publications/Subscribe/Access-Restricted-Materials .

Novel machine learning-driven multi-objective optimization method for EDM trajectory planning of distorted closed surfaces

Highly distorted closed surfaces pose significant challenges for machining trajectory planning due to their intricate surface constraints and closed structures. Despite these challenges, components with such features are prevalent in industries like aerospace. This paper presents a machine learning-driven multi-objective optimization method for electrical discharge machining (EDM) trajectory planning of highly distorted closed surfaces. The method transforms the structural design of forming electrodes and trajectory planning into a multi-objective decision problem. And a discrete point trajectory planning method, guided by surface average curvature, is employed to determine the optimal position and orientation of the electrode. Additionally, an elite dataset, generated using the Monte Carlo method and Arena's Principle, is utilized to train an artificial neural network (ANN). This network predicts hyperparameters for the nonlinear optimization problem. Based on the proposed method, a multi-objective optimization model is formulated for an integral shrouded blisk, considering minimization of iteration count, axial motion, and maximization of machining surface quality. The Pareto front is utilized to obtain the optimal EDM trajectory. Experimental results demonstrate a 17.38 % reduction in the overall machining cycle duration using this trajectory, and the surface roughness and profile accuracy satisfy the design specifications, which proves the effectiveness of this method.

Research on the traction characteristic evaluation of urban rail vehicles based on entropy weight TOPSIS

This paper aims to investigate the influence of different speed at the end of the constant power section of the traction characteristic curve (TCC) on the vehicle dynamic performance and evaluate the TCC. A dynamics co-simulation model is developed in SIMPACK and MATLAB for dynamic analysis of the urban rail vehicle (URV) under different traction characteristics. Then it is used to investigate how acceleration distance and vehicle dynamic performance varies with the speed at the end of the constant power section of the TCC under different starting torques. Moreover, the entropy weight TOPSIS method which is widely used to solve the multi-objective decision-making problem is chosen to evaluate the traction characteristics based on various dynamic performance indexes over 25 test sections and acceleration distance. The results show that under traction conditions, the starting torque has effects on the lateral and vertical accelerations of car body, lateral and vertical wheel-rail contact forces, derailment coefficient and wheel unloading factor. The higher the torque, the greater the value of each index, that means the poorer the vehicle ride characteristics and running safety. For a given starting torque, the dynamic performance indexes increase with the increase of the speed at the end of the constant power section from 50 km/h to 80 km/h during the vehicle speed-up process. The maximum growth rate of the lateral and vertical accelerations of car body, lateral and vertical wheel-rail contact forces are 17%, 8%, 8% and 2%, respectively, when the speed at the end of the constant power section increases from 50 km/h to 80 km/h. Last, the comprehensive evaluation of traction characteristics using entropy-weight TOPSIS method reveals when the starting torque is 800 [Formula: see text], 1000 [Formula: see text], 1200 [Formula: see text] and 1400 [Formula: see text], the corresponding optimal speed at the end of the constant power section is 80 km/h, 80 km/h, 70 km/h and 50 km/h, respectively. The result of the study can provide theoretical support for traction characteristic design and traction control for URVs.

The Role of Energy Policies for Air Pollution Control in the Po Valley

This paper examines the use of integrated assessment modelling to select policies aimed at reducing air pollution in response to growing concerns about the deterioration of air quality, especially in highly populated and industrialized areas. The study focuses on the Po Valley region in northern Italy, renowned for its intricate environmental difficulties arising from industrial operations, extensive farming, and topographic characteristics. The goal of the multi-objective decision problem is to find the best plans for reducing emissions that will minimize the average annual concentrations of PM2.5 while also considering the costs of putting these plans into action and incorporating end-of-pipe, energy, and fuel switch measures. Results highlight the trade-offs between air quality improvement and associated costs, presenting a Pareto curve of optimal solutions.

Credibilistic portfolio optimization with higher-order moments using coherent triangular fuzzy numbers

Financial portfolio formation is usually a multi-objective decision-making problem concerning return and risk on the investment. In this study, we make use of an extension of regular triangular fuzzy numbers, known as coherent triangular fuzzy numbers, to describe portfolio returns in a credibility-based framework. The paper’s novelty lies in proposing and deriving a crisp equivalent for computing the coherent triangular fuzzy number-based credibilistic semivariance, credibilistic skewness, and credibilistic semikurtosis. Utilizing these analytical expressions, along with additional formulations found in existing literature, we present three multi-objective portfolio optimization models involving the practical constraints related to investment decisions. All the proposed analytical expressions, when used with the returns of the portfolio as a whole, help to overcome the computationally expensive process of simulating results using the returns of individual assets. The proposed models differ with respect to different risk measures, viz. semivariance, Mean-Absolute-Semi-Deviation (MASD), and Conditional Value-at-Risk (CVaR). These models are solved using an adaptation of an efficient Multi-Objective Genetic Algorithm (MOGA) specifically designed to solve portfolio optimization problems with practical constraints. Data from the National Stock Exchange (NSE) in Mumbai, India and the New York Stock Exchange (NYSE) in New York, USA, are used to demonstrate the effectiveness of the proposed portfolio optimization models and solution methodology. All the proposed models are compared with respect to each other and the benchmarks considered in this study to bring out the performance stability.

Application of Methods of Decentralized Systems in Management in Lean Manufacturing

Abstract The emergence and subsequent popularization of lean manufacturing have become one of the most significant for improving the efficiency and productivity of operations. The use of lean manufacturing tools and methods leads to the elimination of waste in the organization. Traditional information systems that allow organizations to share information about resources while managing process performance and traceability have a number of disadvantages such as security, interoperability, and transparency. Currently, distributed ledger technology (block-chain) is widely used for this purpose. This article presents a study of decentralized management of the implementation of a distributed ledger infrastructure, which is selected based on the characteristics of the production system. This study proposes a framework that analyzes lean production methods using simulation and data envelopment analysis (DEA) to accommodate the underlying multi-objective decision-making problem. The current study examines the impact of the simultaneous application of RCA technology, lean manufacturing methods, and distributed ledger technology on the total time, costs, and time of production processes.

Solving an energy resource management problem with a novel multi-objective evolutionary reinforcement learning method

Microgrids have become popular candidates for integrating diverse energy sources into the power grid as means of reducing fossil fuel usage. Energy Resource Management (ERM) is a type of Unit Commitment problem, where a player operates a microgrid with diverse renewable generators integrated with an external supplier. Calculating the economic dispatch of each committed unit on a planning horizon is an NP-hard problem, and therefore, finding an exact solution is difficult. This paper presents a multi-objective solution to the ERM problem from the perspective of battery operation and external supplier dispatch. First, a novel multi-objective decision problem modeling is proposed that considers three objectives: cost, greenhouse gas emissions, and battery degradation. This framework involves a learning agent that controls the depth of discharge of a Lithium-Ion battery. To address the proposed problem, a new multi-objective algorithm called Multi-Objective Evolutionary Policy Search (MEPS) is introduced. The proposed algorithm uses NeuroEvolution of Augmenting Topologies structure to evolve artificial neural networks for estimating action-preference values considering multi-objective rewards. The MEPS performance is evaluated on both standard and newly-proposed benchmark problems, using the hypervolume as the evaluation metric. When compared to standard deep reinforcement learning, results showed that MEPS provides cost-effective, environmentally friendly, and efficient energy storage management solutions. Furthermore, MEPS effectively solves the proposed ERM problem by finding neural networks with a small number of nodes and connections, which are suitable for use in embedded control systems. Overall, MEPS proved to be a promising multi-objective approach in the transition to clean energy resources.

Monte Carlo tree search-based deep reinforcement learning for flexible operation & maintenance optimization of a nuclear power plant

Nuclear power plants (NPPs) are required to operate on a flexible profitable production plan while guaranteeing high safety standards. Deep reinforcement learning (DRL) is an effective method to find the most profitable operation & maintenance (O&M) strategy to adopt in a complex system. However, profit-driven only DRL neglects safety-related issues. In this paper, we propose a DRL approach to solve single-objective sequential decision problems (SOSDPs) and multi-objective sequential decision problems (MOSDPs) to find O&M strategies that trade off reliability and profit. The combinatorial problem related with the training of the RL agent to search for the optimal solution is addressed by Monte Carlo tree search (MCTS), whose performance is compared with the traditionally adopted proximal policy optimization (PPO) & imitation learning (IL). A case study is considered for demonstration.

A Decision-Making Method for Design Schemes Based on Intuitionistic Fuzzy Sets and Prospect Theory

Conceptual design is a key link in the process of complex product design, and it is very important to select the appropriate design scheme; however, there are many types and inaccuracies of the evaluation data, and there is a problem of mutual influence between the evaluation criteria, which leads to unreliable decision making of the optimal solution. In order to solve this problem, a decision-making method based on intuitionistic fuzzy sets (IFS) and prospect theory is proposed. This method can be used for symmetric and asymmetric evaluation data. The evaluation data are classified according to different expression types and unified using intuitionistic fuzzy numbers. The intuitionistic fuzzy prospect value of decision information is calculated using prospect theory, and the prospect transformation of decision information is completed. At the same time, the Gray Relational Analysis (GRA) method and the Criteria Importance Though Intercriteria Correlation (CRITIC) method are used to calculate the subjective and objective weights of the technical and economic evaluation indexes of the product, and the combination weights are given; then, based on the evidence theory, the basic probability distribution of the evidence chain of all conceptual design schemes is synthesized, and the comprehensive prospect evaluation results of the schemes are obtained to complete the optimization of the conceptual design schemes. Finally, the effectiveness of the proposed method is verified by the conceptual design of the chip removal system of the deep hole machining machine tool. This work provides a promising method for decision makers to optimize the design scheme and provides insights into multi-objective decision-making problems.

Phase-wise evaluation and optimization of non-pharmaceutical interventions to contain the COVID-19 pandemic in the U.S.

Given that the effectiveness of COVID-19 vaccines and other therapies is greatly limited by the continuously emerging variants, non-pharmaceutical interventions have been adopted as primary control strategies in the global fight against the COVID-19 pandemic. However, implementing strict interventions over extended periods of time is inevitably hurting the economy. Many countries are faced with the dilemma of how to take appropriate policy actions for socio-economic recovery while curbing the further spread of COVID-19. With an aim to solve this multi-objective decision-making problem, we investigate the underlying temporal dynamics and associations between policies, mobility patterns, and virus transmission through vector autoregressive models and the Toda-Yamamoto Granger causality test. Our findings reveal the presence of temporal lagged effects and Granger causality relationships among various transmission and human mobility variables. We further assess the effectiveness of existing COVID-19 control measures and explore potential optimal strategies that strike a balance between public health and socio-economic recovery for individual states in the U.S. by employing the Pareto optimality and genetic algorithms. The results highlight the joint power of the state of emergency declaration, wearing face masks, and the closure of bars, and emphasize the necessity of pursuing tailor-made strategies for different states and phases of epidemiological transmission. Our framework enables policymakers to create more refined designs of COVID-19 strategies and can be extended to other countries regarding best practices in pandemic response.

Fuzzy decision-making approach of hobbing tool and cutting parameters

Machining performance, such as carbon emission, machining time, and quality, can be improved by selecting appropriate tools and cutting parameters for gear hobbing. However, the importance machining performance is usually difficult for users to quantify. To resolve the decision-making problem of hobbing tools and cutting parameters in fuzzy evaluation, a multi-objective fuzzy decision-making approach for hobbing parameters is proposed. It uses spectral clustering, the multi-objective weighted mean of vectors (MOINFO) algorithm and the fuzzy technique for ordering preference by similarity to the ideal solution (TOPSIS) algorithm. First, spectral clustering is used to confirm the initial range of the hobbing parameters based on past machining cases. MOINFO is then applied to search for nondominated hobbing parameters based on the optimization model, taking the carbon emission, cutting time, and quality as the objectives. Finally, the nondominated hobbing parameters are sorted by fuzzy TOPSIS according to a user evaluation, and the hobbing parameter ranking is used to guide hob selection and machining. Decision-making experiments on the hobbing parameters are performed with actual hobbing. The comprehensive evaluation index CEI result for the proposed approach is 0.19. Compared with other methods, the proposed approach exhibits certain advantages. These results demonstrate that the multi-objective decision-making problem of hobbing parameters under fuzzy evaluation can be solved using the proposed approach.

A new multi-objective decision-making method with diversified weights and Pythagorean fuzzy rough sets

In reality, there are many multi-objective decision-making problems with fuzziness and uncertainty, such as the choice of clinical treatment schemes. To effectively cope with such problems, it is urgent to scientifically handle fuzzy and uncertain information. Given the fact that the Pythagorean fuzzy set is a useful tool to tackle fuzzy information, and the rough set theory is superior in dealing with uncertain data, this paper aims to establish a novel decision-making method for scheme selection from the perspective of Pythagorean fuzzy rough sets. Pythagorean fuzzy numbers (PFNs) are first employed to express fuzzy preference information, and then multi-objective Pythagorean fuzzy information systems are introduced. Subsequently, we develop a new fuzzy rough set using an improved correlation coefficient method to deal with the uncertainty of PFNs. Furthermore, considering the diversified weights, we present a weight calculation method based on approximate accuracies. Meanwhile, a weighted fuzzy rough set model is constructed to achieve multi-objective information fusion. Accordingly, a multi-objective decision-making method is put forward with the help of overlap functions. Finally, we apply the built method to three cases from different decision scenarios to illustrate the applicability of the method. By some comparative analyses, the effectiveness and superiority of the method are also exhibited. Moreover, we discuss the establishment of decision support systems regarding scheme selection. On the whole, the work of this paper is helpful to support the solution of complex multi-objective decision-making problems.

Development of AHP-embedded Deng’s hybrid MCDM model in micro-EDM using carbon-coated electrode

Abstract The use of thin film-coated micro-tool electrode introduced a new technical solution to bring the best technical–economic efficiency in the field of non-conventional machining processes. Multi-objective optimization of process parameters in micro-electrical discharge machining (EDM) is very limited with the help of thin film-coated micro-tool, which brought benefits for the industry. In this research work, the technological parameters such as voltage (V), spindle rotation, and capacitance (C) in micro-EDM using carbon-coated electrode were optimized, and Z-coordinate depth of cut and tool wear rate were selected as response variables. Taguchi is combined with Deng’s method to solve this multi-objective decision problem. The results showed that Taguchi–Deng’s method is the right solution for multi-target decision in micro-EDM using the coated electrode. C is the strongest influence on optimal efficiency, and it is the smallest with V. The machining quality under optimal conditions is improved.

A novel multilevel decision-making evaluation approach for the renewable energy heating systems: A case study in China

Renewable energy (RE) sources are important alternatives to mitigate the energy crisis and achieve sustainable development. Appropriate selection of RE system solutions is extremely crucial. Selection of the best RE technology requires the consideration of conflicting qualitative and quantitative evaluation criteria. Many evaluation criteria are judged with subjectivity and uncertainty. However, the description of uncertainty in the evaluation process remains a large research gap. Therefore, a novel combined evaluation method is developed to describe and visualize the uncertainty in the assessment process. The proposed evaluation method is tested for RE heating system selection. The RE systems are evaluated based on five dimensions and 15 evaluation indicators. This multidimensional indicator framework not only includes the three basic evaluation groups of energy, economy, and environment, but also extends to the performance of technology and policy. The combined weights of the evaluation indicators consist of objective weights and subjective weights. The objective weights are obtained by the Criteria Importance Through Intercriteria Correlation (CRITIC) method and subjective weights are calculated by the improved Fuzzy Analytic Hierarchy Process (FAHP). The set pair analysis (SPA) is introduced to assess the performance of different RE systems. It considers the uncertainty of indicator performance. A novel approach to visualizing the fuzziness of SPA evaluation is developed using the cloud model. Finally, the RE system ranking calculated by the proposed method is performed. The originality of this work is offering a promising method for RE selection and clarifying the degree of ambiguity in the evaluation process. It helps decision makers have an exact idea about the accuracy of the evaluation. It provides insights into multi-objective decision-making problems.

Multi-objective Reinforcement Learning – Concept, Approaches and Applications

Real-world decision-making tasks are generally complicated and require trade-offs between multiple, even conflicting, objectives. As the advent and great development of advanced information technology, it has evolved into using reinforcement learning (RL) algorithms to tackle the multi-objective decision making (MODM) problems. In this paper, we will first identify the basic concepts and factors when modelling the MODM tasks with reinforcement learning, and then review the traditional RL, such as Sarsa, Q-Learning, Policy Gradients, Actor-Critic, Monte-Carlo learning, and modern deep RL algorithms applied in this process. Furthermore, the specific practical scenarios described in MODM problems will be summarized through analyzing some typical articles. Finally, the future trends of multi-objective reinforcement learning will be discussed.

Improved multi-objective differential evolution algorithm based on a decomposition strategy for multi-objective optimization problems

Many real-world engineering problems need to balance different objectives and can be formatted as multi-objective optimization problem. An effective multi-objective algorithm can achieve a set of optimal solutions that can make a tradeoff between different objectives, which is valuable to further explore and design. In this paper, an improved multi-objective differential evolution algorithm (MOEA/D/DEM) based on a decomposition strategy is proposed to improve the performance of differential evolution algorithm for practical multi-objective nutrition decision problems. Firstly, considering the neighborhood characteristic, a neighbor intimacy factor is designed in the search process for enhancing the diversity of the population, then a new Gaussian mutation strategy with variable step size is proposed to reduce the probability of escaping local optimum area and improve the local search ability. Finally, the proposed algorithm is tested by classic test problems (DTLZ1-7 and WFG1-9) and applied to the multi-objective nutrition decision problems, compared to the other reported multi-objective algorithms, the proposed algorithm has a better search capability and obtained competitive results.

A selection scheme for energy storage to participate in primary frequency regulation of new energy

The choice of energy storage is the key content of the energy storage plan and design to participate in the primary frequency regulation of new energy. It involves many indicators such as safety, primary frequency modulation adaptability, economy, and environmental performance, and is a complex multi-objective decision-making problem. This paper analyses the characteristics of each index and constructs a two-level decision-making system. According to expert scores and practical application requirements, the weight of each decision-making index is obtained by the AHP method. The TOPSIS method is further used to select energy storage according to the weight of each index and related parameters.

A new interactive fuzzy programming with priority control for multi-objective decision-making problems

Fuzzy multiple objective linear programming (FMOLP) approaches have been used to be solved many applications of multi-objective decision-making (MODM) problems. Several methods have been proposed including max-min approaches, preemptive approaches, and weighted approaches. However, they have some limitations in use; some may not be able to obtain efficient solutions or can give only a few solutions. Some methods need to be solved in two steps or specify the target level, which may be difficult for decision-makers (DMs). In this research, a new single-phase interactive fuzzy programming approach with priority control that can find several efficient solutions is proposed. It is not necessary to specify the target value for each objective and it can solve with only one step for each solution. The DM can easily select the appropriate solutions from a set of efficient solutions. This method is different from existing single-phase approaches by controlling the satisfaction level of the last priority instead of using weight additive. Simple examples and a practical example of a perishable product supply chain network were tested to show the effectiveness of the proposed model. The performance of the proposed method was also compared with existing methods to verify and validate the model.