Multi-objective Decision-making Problem Research Articles

A New Decision-Making GMDH Neural Network: Effective for Limited and Fuzzy Data.

This paper presents a new approach to solve multi-objective decision-making (DM) problems based on neural networks (NN). The utility evaluation function is estimated using the proposed group method of data handling (GMDH) NN. A series of training data is obtained based on a limited number of initial solutions to train the NN. The NN parameters are adjusted based on the error propagation training method and unscented Kalman filter (UKF). The designed DM is used in solving the practical problem, showing that the proposed method is very effective and gives favorable results, under limited fuzzy data. Also, the results of the proposed method are compared with some similar methods.

Supporting strategy selection in multiobjective decision problems under uncertainty and hidden requirements

Decision-makers are often faced with multi-faceted problems that require making trade-offs between multiple, conflicting objectives under various uncertainties. The task is even more difficult when considering dynamic, non-linear processes and when the decisions themselves are complex, for instance in the case of selecting trajectories for multiple decision variables. These types of problems are often solved using multiobjective optimization (MOO). A typical problem in MOO is that the number of Pareto optimal solutions can be very large, whereby the selection process of a single preferred solution is cumbersome. Moreover, preference between model-based solutions may not be determined only by their objective function values, but also in terms of how robust and implementable these solutions are. In this paper, we develop a methodological framework to support the identification of a small but diverse set of robust Pareto optimal solutions. In particular, we eliminate non-robust solutions from the Pareto front and cluster the remaining solutions based on their similarity in the decision variable space. This enables a manageable visual inspection of the remaining solutions to compare them in terms of practical implementability. We illustrate the framework and its benefits by means of an epidemic control problem that minimizes deaths and economic impacts, and a screening program for colorectal cancer that minimizes cancer prevalence and costs. These examples highlight the general applicability of the framework for disparate types of decision problems and process models.

Comparing chair time required for systemic agent drug reaction (DR) management to allocated chair time for systemic protocol.

40 Background: Chemotherapy scheduling and determining associated nursing resources are complex multiobjective decision problems. Management of DRs are thought to take additional chair and nurse time and should be accounted for in appointment scheduling in order to having an efficiently running clinic. BC Cancer – Victoria is a tertiary care regional site of BC Cancer in Victoria, British Columbia, Canada and currently schedules patients using a regimen-based resource intensity model (Greene et al, 2012). Methods: DRs were tracked in real time over 3 months, where BC Cancer ID, reaction, protocol, agent, cycle number and time of reaction were recorded. Our electronic medical record was interrogated to determine start and end of chair time. We then compared the actual chair time required to manage patients with a DR to scheduled chair time. Results: Thirty reactions occurred, including DRs to paclitaxel (43%), rituximab (13%), docetaxel (10%), nivolumab (7%), oxaliplatin (7%), pertuzumab (7%), bendmustine (3%), and daratumumab (3%). The reactions most commonly occurred with cycles 1 or 2. The averages for total chair time during DR, time from arrival to reaction, and time from reaction to discharge were 281 min (+79.5), 106 min (+ 55), and 171 min (+ 94), respectively. An average of 449 min/month extra were required for DR management above scheduled chair time. Conclusions: The agents precipitating DRs were common and predictable. An average of 7 hours and 29 minutes per month above scheduled chair time was required to manage DRs. This information could be used to more efficiently schedule chair and nursing time. This also highlights the need to minimize the number of DRs as management is resource intensive.[Table: see text]

Learning-based multi-objective evolutionary algorithm for batching decision problem

This study investigates a multi-objective batching decision problem that arises in batch annealing operations in the iron and steel industry. The problem concerns selecting coils from a set of waiting coils to be annealed to form batches so as to maximize two conflicting objectives: product quality and equipment utilization. In this study, a multi-objective optimization model is formulated, and a data-driven method is developed to determine the technological parameters for the model. To improve the efficiency and effectiveness of the solution process, we propose a learning-based multi-objective evolutionary algorithm (LBMOEA) with novel evolution operators and a learning-based solution space reduction strategy. To more quickly solve the problem, a clustering method is adopted to achieve a parallel mechanism in the LBMOEA. In computational experiments on 20 randomly generated instances, the results demonstrate that the above evolution operators and strategy are effective, and that the clustering method can reduce the average time cost by 58.76%. For 15 practical production instances, the results illustrate that the LBMOEA with the clustering method is superior to the non-dominated sorting genetic algorithm II (NSGA-II) and multi-objective evolutionary algorithm based on decomposition (MOEA/D), and shows good potential for application in practical production.

A subjective-objective integrated multi-objective decision-making method for reservoir operation featuring trade-offs among non-inferior solutions themselves

The key to formulating a multi-objective reservoir operation scheme is coordinating the high/low priorities and achieving balance among various targets accordingly in the decision-making process. However, the traditional decision-making methods are either completely subjective or neglect the decision-maker's preferences, making it essential to optimize these methods to address such unsatisfactory aspects. Therefore, through drawing on some universally acknowledged economic theories and studying the geometric relationships among the Pareto solutions to such multi-objective decision-making problems, the concepts of “replacement rate”, “profit-loss sensitivity ratio”, and “preference equilibrium degree” are introduced, deduced, and elaborated on in this paper. On the basis of them, the preference decision method (PDM), entrusted with strict mathematical and physical connotations, is constructed and derived in detail, especially for addressing the bi-objective and tri-objective scenarios. The PDM can quantify the complex internal feedback relationship among various targets, providing objective support for decision-makers in the subjective trade-offs of the interests of multiple subjects and achieving the unity of subjective and objective. Furthermore, the proposed method is applied to the Wujiang cascade reservoirs in China, through which the benefits of power generation, water supply and ecological protection are enabled to be considered simultaneously. The application shows that the PDM can effectively shrink the Pareto set, which greatly reduces the difficulty of decision-making, and the obtained results prove feasible and satisfactory. In addition, the PDM is based entirely on the Pareto set itself, making it simple to utilize, and can be extended into solving other multi-objective decision-making problems.

Genetic algorithm optimised Hadamard product method for inconsistency judgement matrix adjustment in AHP and automatic analysis system development

The analytic hierarchy process (AHP) is an important method to solve the multi-objective decision-making weight problem. However, due to the subjective judgement and selection preference of decision-makers, the consistency of the judgement matrix is inevitably poor. Hence, to improve the reliability of decision-making results, it is necessary to adjust the consistency of the judgement matrix. We propose a genetic algorithm optimised Hadamard product (GAOHP) to judge the consistency of the matrix. This method converts the original matrix consistency adjustment problem into an optimisation solution problem and uses a meta-heuristic algorithm to search the global optimisation quickly. Our method, which significantly improves the computing efficiency and realises that the judgement matrix after adjustment satisfies the basic consistency, has fully retained the judgement intention of the decision-makers. It offers the dual advantages of preserving the original intention of decision-makers and greater efficiency of operation. Finally, we developed an automatic analysis system based on MATLAB app designer to realise the rapid adjustment of the consistency of judgement matrix, which is conducive to providing an analysis system with simple and stable operation for AHP analysis.

An Improved Multi-Objective Deep Reinforcement Learning Algorithm Based on Envelope Update

Multi-objective reinforcement learning (MORL) aims to uniformly approximate the Pareto frontier in multi-objective decision-making problems, which suffers from insufficient exploration and unstable convergence. We propose a multi-objective deep reinforcement learning algorithm (envelope with dueling structure, Noisynet, and soft update (EDNs)) to improve the ability of the agent to learn optimal multi-objective strategies. Firstly, the EDNs algorithm uses neural networks to approximate the value function and update the parameters based on the convex envelope of the solution boundary. Then, the DQN structure is replaced with the dueling structure, and the state value function is split into the dominance function and value function to make it converge faster. Secondly, the Noisynet method is used to add exploration noise to the neural network parameters to make the agent have a more efficient exploration ability. Finally, the soft update method updates the target network parameters to stabilize the training procedure. We use the DST environment as a case study, and the experimental results show that the EDNs algorithm has better stability and exploration capability than the EMODRL algorithm. In 1000 episodes, the EDNs algorithm improved the coverage by 5.39% and reduced the adaptation error by 36.87%.

Solving Aggregate Production Planning Problems: An Extended TOPSIS Approach

Aggregate production planning (APP) was developed for solving the problem of determining production, inventory, and workforce levels to meet fluctuating demand requirements over a planning horizon. In this work, multiple objectives were considered to determine the most effective means of satisfying forecasted demand by adjusting production rates, hiring and layoffs, inventory levels, overtime work, back orders, and other controllable variables. An extended technique for order preference via the similarity ideal solution (TOPSIS) approach was developed. It was formulated to solve this complicated, multi-objective APP decision problem. Compromise (ideal solution) control minimized the measure of distance, providing which of the closest solutions has the shortest distance from a positive ideal solution (PIS) and the longest distance from a negative ideal solution (NIS). The proposed method can transform multiple objectives into two objectives. The bi-objective problem can then be solved by balancing satisfaction using a max–min operator for resolving the conflict between the new criteria based on PIS and NIS. Finally, an application example demonstrated the proposed model’s applicability to practical APP decision problems.

Layout Optimization of Urban Cultural Space Construction Based on Forward Three-Layer Neural Network Model.

The change of urban cultural space layout is a multi-variable, multi-objective, and restricted research process. The optimization of urban cultural space construction and layout is a multi-objective decision-making problem that needs to be solved urgently. Based on the forward three-layer neural network theory, this paper constructs an optimization model for the construction and layout of urban cultural space evaluation of the layout of cultural space. This paper first analyzes the feasibility of combining the forward three-layer neural network model with the optimization and adjustment of cultural space layout structure. Taking the three-layer feedforward network as an example, the structure optimization model based on the forward three-layer neural network is selected, and the established model is used to reflect the internal environment of the objective world. Structure and perform dynamic simulation. In the process of simulation modeling, from the aspects of system description, model structure, logical analysis, reasoning, and interpretation, two effective computer dynamic simulation methods, namely, forward three-layer neural network model and system dynamics SD model, were carried out for theoretical comparison and identification. The experimental results show that the feasibility and calculation error of the application of the optimization model are relatively good, reaching 0.897 and 6.21%, respectively. The number of newly added cultural spaces and the expansion speed show an increasing trend, expanding at an average annual speed of about 35 km2, effectively increasing the quality of regional planning and construction layout.

Application of Multi-Criteria Decision-Making Analysis to Rural Spatial Sustainability Evaluation: A Systematic Review.

The rational allocation of spatial resources is an important factor to ensure the sustainable development of rural areas, and effective pre-emptive spatial evaluation is the prerequisite for identifying the predicament of rural resource allocation. Multi-criteria decision-making analysis has advantages in solving multi-attribute and multi-objective decision-making problems, and has been used in sustainability evaluation research in various disciplines in recent years. Previous studies have proved the value of spatial evaluation using multi-criteria decision analysis in guiding rural incremental development and inventory updates, but systematic reviews of the previous literature from a multidisciplinary perspective and studies of the implementation steps of the evaluation framework are lacking. In the current paper, the research is reviewed from the two levels of quantitative statistics and research content, and through vertical and horizontal comparisons based on three common operating procedures: standard formulation, weight distribution, and ranking and verification. Through the results, the application status and characteristics of the MCDA method in related research are determined, and five research foci in the future are proposed.

The Combination of Internet of Things Technology Based on Probability Model Network and Mass Education

With the continuous development of the Internet of things, educational informatization has become a hot spot in the application of education. Internet of things technology, combined with various subject fields of education, can better achieve subject teaching objectives and teaching assistance. In the multimode teaching optimization model, weight distribution is a complex multiobjective decision-making problem. In this paper, a Bayesian network based on probability model is proposed, which is combined with the large entropy criterion to determine the comprehensive weight. The combination of Bayesian network and Bayesian statistics can make full use of the information of domain knowledge and sample data. Bayesian network uses arc to represent the dependence between variables and probability distribution table to represent the strength of dependence. It organically combines prior information with sample knowledge to promote the integration of prior knowledge and data, which is particularly effective when sample data is sparse or difficult to obtain. Bayesian network is used to associate objective attributes and influencing factors to self-study the target weight. By grasping the characteristics of information technology and the nature of university citizenship curriculum, this paper further analyzes the internal relationship between them, promotes the deep integration of the two, and improves the effectiveness of university citizenship curriculum teaching. By grasping the characteristics of information technology and the nature of the school civics curriculum, we promote a deeper integration of the two. It is my education that is more advanced.

A prior information‐based coverage path planner for underwater search and rescue using autonomous underwater vehicle (AUV) with side‐scan sonar

The coverage path planning (CPP) technique attracts growing interest in studies on underwater search and rescue (SAR) conducted with an autonomous underwater vehicle (AUV) equipped with a side-scan sonar (SSS). In SAR missions, prior information is crucial. Aiming at the underwater SAR mission with prior information, a new coverage path planner (SAR-A*) is proposed. The ultimate goal is to generate a feasible path for completely covering the task area and preferentially visiting more valuable cells with fewer turns. First, the whole task area is decomposed into hexagon cells as waypoints to be visited for complete coverage. Second, the probability of discovering the target is obtained according to the target presence probability and the SSS detection ability. Under the assumption of prior target information, the target presence probability is modelled as a two-dimension Gaussian distribution based on predicted target locations or trajectories. Then, an optimal next-waypoint selection process is formulated as a multi-objective decision-making problem and solved by the weighted metric method. Finally, simulation and experimental results demonstrate that the generated path can improve the cumulative probability of discovering the target with fewer turns.

A practical guide to multi-objective reinforcement learning and planning

Real-world sequential decision-making tasks are generally complex, requiring trade-offs between multiple, often conflicting, objectives. Despite this, the majority of research in reinforcement learning and decision-theoretic planning either assumes only a single objective, or that multiple objectives can be adequately handled via a simple linear combination. Such approaches may oversimplify the underlying problem and hence produce suboptimal results. This paper serves as a guide to the application of multi-objective methods to difficult problems, and is aimed at researchers who are already familiar with single-objective reinforcement learning and planning methods who wish to adopt a multi-objective perspective on their research, as well as practitioners who encounter multi-objective decision problems in practice. It identifies the factors that may influence the nature of the desired solution, and illustrates by example how these influence the design of multi-objective decision-making systems for complex problems.

Maximization of Strategic Crops Production in Iraq with Fuzzy Goal Programming

The need to increase agricultural production has become a challenging task for most countries. Generally, many resource factors affect the deterioration of production level, such as low water level, desertification, soil salinity, low on capital, lack of equipment, the impact of export and import of crops, lack of fertilizers, pesticide, and the ineffective role of agricultural extension services which are significant in this sector. The main objective of this research is to develop fuzzy goal programming (GP) model to improve agricultural crop production, leading to increased agricultural benefits (more tons of produce per acre) based on the minimization of the main resources (water, fertilizer, and pesticide) to determine the weight in the objectives function subject to different constraints (land area, irrigation, labor, fertilizer, pesticide, equipment, and seed). Fuzzy GP (FGP) and GP were utilized to solve multi-objective decision-making (MODM) problems. From the results, this research has successfully presented a new alternative method that introduced multi-interval weights in solving a multi-objective FGP and GP model problem in a fuzzy manner, in the current uncertain decision-making environment for the agricultural sector. The significance of this research lies in the fact that some of the farming zones have resource limitations while others adversely impact their environment due to misuse of resources.

An Offloading Algorithm based on Markov Decision Process in Mobile Edge Computing System

In this paper, an offloading algorithm based on Markov Decision Process (MDP) is proposed to solve the multi-objective offloading decision problem in Mobile Edge Computing (MEC) system. The feature of the algorithm is that MDP is used to make offloading decision. The number of tasks in the task queue, the number of accessible edge clouds and Signal-Noise-Ratio (SNR) of the wireless channel are taken into account in the state space of the MDP model. The offloading delay and energy consumption are considered to define the value function of the MDP model, i.e. the objective function. To maximize the value function, Value Iteration Algorithm is used to obtain the optimal offloading policy. According to the policy, tasks of mobile terminals (MTs) are offloaded to the edge cloud or central cloud, or executed locally. The simulation results show that the proposed algorithm can effectively reduce the offloading delay and energy consumption.

MULTI-OBJECTIVE TRANSPORTATION PROBLEM UNDER TYPE-2 TRAPEZOIDAL FUZZY NUMBERS WITH PARAMETERS ESTIMATION AND GOODNESS OF FIT

The problem of transportation in real-life is an uncertain multi-objective decision-making problem. In particular, by taking into account the conflicting objectives, Decision-Makers (DMs) are looking for the best transport set up to determine the optimum shipping quantity subject to certain capacity constraints on each route. This paper presented a Multi-Objective Transportation Problem (MOTP) where the objective functions are considered as Type-2 trapezoidal fuzzy numbers (T2TpFN), respectively. Demand and supply in constraints are in multi-choice and probabilistic random variables, respectively. Also considered the “rate of increment in Transportation Cost (TC) and rate of decrement in profit on transporting the products from ith sources to jth destinations due to” (or additional cost) of each product due to the damage, late deliveries, weather conditions, and any other issues. Due to the presence of all these uncertainties, it is not possible to obtain the optimum solution directly, so first, we need to convert all these uncertainties from the model into a crisp equivalent form. The two-phase defuzzification technique is used to transform T2TpFN into a crisp equivalent form. Multi-choice and probabilistic random variables are transformed into an equivalent value using Stochastic Programming (SP) approach and the binary variable, respectively. It is assumed that the supply and demand parameter follows various types of probabilistic distributions like Weibull, Extreme value, Cauchy and Pareto, Normal distribution, respectively. The unknown parameters of probabilistic distributions estimated using the maximum likelihood estimation method at the defined probability level. The best fit of the probability distributions is determined using the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC), respectively. Using the Fuzzy Goal Programming (FGP) method, the final problem is solved for the optimal decision. A case study is intended to provide the effectiveness of the proposed work.

The evaluation of renewable energy resources in Turkey by integer multi-objective selection problem with interval coefficient

This paper aims to provide a scientific approach that indicates the need to focus on renewable energy potential to meet energy needs in Turkey. Turkey began to take advantage of renewable energy technologies a few years ago. Accordingly, the issue of determining the best renewable resources for the country has been brought to the agenda in recent years. The issue was considered to be a limited multi-objective optimization problem allowing us to achieve a reliable result. However, the parameter of each resource was considered as a range value, rather than traditionally expressed as an exact value, and a multi-objective decision problem was developed with an interval coefficient. This method allows us to obtain more accurate and reliable results without the need to resort to normalization methods used to eliminate unit differences. According to the results of this study, the most convenient alternatives for Turkey are hydro, wind, and solar power. The findings also support decision policies aimed at reaching targets for the electricity sector in 2023, as put forth by the Ministry of Energy and Natural Resources (MENR).

Solving the Multiobjective Transportation Decision-Making Problem Based on Improved S-Type Membership Function

Aiming at the problem of multiobjective transportation decision-making, a fuzzy compromise method with an improved S-type membership function is proposed. This method not only considers a single objective and evaluates it marginally but also evaluates the overall objective as a whole. First of all, a multiobjective transportation decision-making model is established. Then, each objective function is mapped to a function on the interval [0,1] through the S-type membership function, thereby transforming the multiobjective transportation linear programming model into a multiobjective transportation fuzzy compromise programming model, and its satisfaction is expressed by the global utility function. Finally, through two examples, the results of the example algorithm are compared with the results in the literature, highlighting the superiority of the method. The experimental results show that in the multiobjective transportation decision-making problem, the fuzzy compromise method of the S-type membership function has better flexibility and effectiveness.

Solving the Multiobjective Fractional Transportation Problem through the Neutrosophic Goal Programming Approach

Nowadays, the transportation problem is a multiobjective decision-making problem. It involves deciding to determine the ideal transportation setup that matches the decision maker’s preferences while taking into account competing objectives/criteria such as transportation cost, transportation time, and environmental and social concerns. This study presents a general framework of the multiobjective fractional transportation problem (MOFTP) to deal with such complex scenarios. This paper’s major goal is to propose a solution methodology to solve the MOFTP based on a neutrosophic goal programming (NGP) approach. By obtaining the optimal compromise solution using three memberships, namely, truth membership, indeterminacy membership, and falsity membership, the suggested technique gives a novel insight into solving the MOFTP. A real-world problem such as selling wind turbine blades’ problem and a numerical example are used to demonstrate the efficacy and superiority of the proposed method.

A value of information methodology for multiobjective decisions in quantitative set‐based design

AbstractEngineering complex systems is an exercise in sequential multiobjective decision making under uncertainty. One method for handling this complexity and uncertainty is set‐based design (SBD). SBD is a concurrent engineering and management methodology that develops, analyzes, and matures numerous design options, reducing risk and delivering higher value to the stakeholders and end users. SBD accomplishes this through controlled design space convergence which reduces uncertainty and prevents premature design decisions. While SBD has been the subject of numerous scholarly articles, there is limited research providing quantitative methodologies that inform decisions enabling design maturation and convergence. We present a value of information (VOI) based methodology for multiobjective decision problems, and demonstrate its applicability for SBD decisions. We apply Bayesian decision models and information value to inform multiobjective modeling and design maturation decisions. Research contributions include: 1) a framework integrating VOI into the SBD process, 2) a multiobjective VOI method assessing a higher‐resolution model's ability to reduce uncertainty, and 3) a means of informing modeling decisions by comparing multiple high resolutions models, given their usage cost and their potential to deliver information value. Finally, we demonstrate the inherent issues associated with premature decisions and traditional point‐based design approaches which run the risk of selecting an alternative that later proves infeasible.