Optimal Decision Model Research Articles

Construction and analysis of optimal decision-making model for vegetable sales based on entropy weight-TOPSIS algorithm and gray prediction

Construction and analysis of optimal decision-making model for vegetable sales based on entropy weight-TOPSIS algorithm and gray prediction

Utilizing Tax Mechanisms to Foster Research and Development Activities in Enterprises in Poland

This paper intends to present different models for the decision-making process in mining enterprises. In the beginning, the most significant parameters of optimization problems, along with criteria optimization models are introduced, while both quantitative and qualitative optimization criteria are identified next in the article. The paper deals then with introducing the main issues related to optimal decision models in the mining industry through linear programming and the problem of re-optimizations. The analysis continues by presenting the mathematical formulation of linear programming, giving general considerations on applicability in the mining industry, and providing an example of a linear programming problem in the mining industry. Hence, the rest of the paper is dedicated to some numerical examples, first of a model of the optimal planning of the exploitation of the panels regarding the production of copper and gold, and second, giving a numerical example of the linear programming problem in the mining industry, to demonstrate its validity in investment decision-making for mining exploitation.

Econometric Models for Optimal Decisions in Mining Industry

This paper intends to present different models for the decision-making process in mining enterprises. In the beginning, the most significant parameters of optimization problems, along with criteria optimization models are introduced, while both quantitative and qualitative optimization criteria are identified next in the article. The paper deals then with introducing the main issues related to optimal decision models in the mining industry through linear programming and the problem of re-optimizations. The analysis continues by presenting the mathematical formulation of linear programming, giving general considerations on applicability in the mining industry, and providing an example of a linear programming problem in the mining industry. Hence, the rest of the paper is dedicated to some numerical examples, first of a model of the optimal planning of the exploitation of the panels regarding the production of copper and gold, and second, giving a numerical example of the linear programming problem in the mining industry, to demonstrate its validity in investment decision-making for mining exploitation.

Research on the decision-making model of power purchase in electricity spot market oriented to market force suppression

Abstract In this study, for the electricity spot market under the market power suppression scenario, the market power suppression effect of contractual decomposed electricity is quantitatively evaluated by first reconstructing different electricity consumption loads using the combined MLR method and then combining the market power assessment model with the Lerner index. In addition, a contract for difference based on surveillance price is designed to inhibit market members from utilizing market power by adjusting the space of competitive electricity and the space of contracted electricity to avoid the market risk of rising electricity prices. Finally, a dynamic power purchase optimization decision model is constructed with CVaR as the risk metric and the profit of the power selling company and customer satisfaction as the objectives, and the MOSADE algorithm is used to solve the model and explore the optimal power purchase scheme. The analysis results show that the risk of the spot market can be avoided by bilateral contracts, option contracts, and unit power generation, and the price of electricity sales can be reduced. The expected returns of the four combinations are 21.37M$, 20.43M$, 19.24M$, 18.88M$, and 17.49M$ under the five risk coefficients, respectively. The CVaR values are −23.14, 2.84, 8.83, 10.75, 12.58. Higher than all the other cases, and their selling price for electricity is lower than all the different cases.

Carsharing equitable relocation problem: A two-stage stochastic programming approach with learning-embedded endogenous uncertainty in demand

Uncertain demand may exacerbate the imbalance of the supply–demand for a one-way carsharing system and complicate vehicle relocation decisions. To consider the effect of the uncertainty, this study proposes a two-stage stochastic nonlinear programming model, integrating long-term and short-term decisions and maximizing the profit of the carsharing companies. Specifically, in the first stage, tactical decisions of fleet sizing and initial vehicle distribution are determined before the realization of the uncertain demand. Operational decisions of both operator-based and user-based relocation are optimized in the second stage. Moreover, this paper first studies the user-based relocation incentives, which affect the distribution of uncertain demand with an endogenous relationship. A learning-embedded optimization method is introduced to learn such a distribution, enabling the decision-making optimization model to achieve higher performance under the guidance of the demand uncertainty. Second, we envision an equitable relocation issue that considers an uneven distribution of the unsatisfied demand with two different equity criteria measured from the aspects of stations and OD pairs, respectively. Third, the large problem scale, the nonlinear objective function and constraints, and the endogenous demand uncertainty constitute the nontrivial challenges to the feasible solution. For solving the problem efficiently, we linearize the nonlinear terms and develop a dedicated two-phase solution algorithm with a learning-embedded trust-region method in phase I to solve the continuous relaxation problem and a mixed-integer linear programming guided iterative rounding in phase II to obtain the integer solutions of carsharing operations. The solution algorithm adaptively bridges the learning and optimization process via the trust-region method with flexible sample generation. Finally, we conduct numerical experiments based on a real-world one-way carsharing system in Beijing to demonstrate the effectiveness and applicability of the proposed method and reveal some insights for the carsharing service.

Reliability service-based strategies for improving electricity retailer competitiveness using a novel heuristic algorithm

Liberalizing China's electricity sales market has led to fierce competition among electricity retailers. Providing customized electricity sales services is beneficial for electricity retailers to capture market share and gain a competitive edge. Therefore, a reliability service-based strategy is proposed to improve the electricity retailer competitiveness, which attracts industrial users by providing a continuous electricity supply. Specifically, the electricity retailer designs an electricity price package based on supply service reliability and a compensation model to cope with supply interruptions, considering the incremental cost approach and industrial users' different reliability needs. In addition, a user power shift model is constructed to evaluate user satisfaction, including electricity consumption, electricity cost, and reliability satisfaction. Furthermore, a decision-making optimization model for electricity retailers is formulated to maximize competitiveness and solved by the Growth Optimizer (GO). Experiments based on power grid company show that the proposed model can effectively improve the competitiveness of electricity retailers. Comparison with other heuristic algorithms also proves the superiority of the GO algorithm in terms of solution speed and quality.

Decision-making optimization model for the targeted sustainable maintenance of a complex road network

With the continuous expansion of highway construction scale, a complex road network can be divided into multiple subnetworks in accordance with any criteria, such as highway class, traffic volume, natural zoning, or administrative subdivision. To realize the targeted maintenance and local–whole optimization of a road network, this study proposes a new type of optimization model for road network maintenance decisions based on the idea of a bi-level programming model. The model fully considers the heterogeneity of a subnetwork, adopts different maintenance standards for targeted maintenance, establishes a whole network maintenance fund allocation model and a subnetwork unit maintenance decision-making (DM) model, and obtains the optimal maintenance scheme through the iterative calculation of a genetic algorithm. Finally, the model is applied to the maintenance DM of a highway network in Zhejiang Province and compared with the traditional DM model without subnetworks. Results show that the model developed in this study can better solve the maintenance DM problem of large-scale complex road networks and achieve the targeted maintenance scheme under limited maintenance funds. Hence, this model can help the sustainable development of road network maintenance.

Geospatial simulation and decision optimization towards identifying the layout suitability and priority for wind-photovoltaic-hydrogen-ammonia project: An empirical study in China

China is vigorously developing clean energy to achieve the low-carbon strategy. Fully utilizing renewable energy to build a clean production and consumption system has become the core path. The wind-photovoltaic-hydrogen-ammonia project could fully absorb green electricity and have significant environmental benefits. However, the prerequisite for developing this project is to solve the layout problem. Firstly, a three-step method for identifying the project's critical factors to determine the analysis input is proposed for the first time. Secondly, the suitability and priority of the layout are measured by constructing the geospatial simulation and decision optimization model. Then, the three northern regions of China are selected for empirical study. Geospatial simulation provides the suitability of the overall layout of the region and identifies seven possibilities. The decision technique that considers multiple factors provides priority among possibilities. Finally, the deep analysis demonstrated the reliability and progressiveness of the model. The scenario analysis provides the sensitivity of this project to wind and solar power and discusses the impact of decision preferences on outcomes. This study presents a theoretical model and analytical framework that decision-makers can use to plan clean energy projects strategically. Furthermore, the empirical findings from this study can serve as valuable guidance for practical implementation.

An Optimized Decision Model for Electric Vehicle Aggregator Participation in the Electricity Market Based on the Stackelberg Game

With the growing popularity of charging pile infrastructure and the development of smart electronic devices and 5G communication technologies, the electric vehicle aggregator (EVA) as a bidding entity can aggregate numerous electric vehicle (EV) resources to participate in the electricity market. Moreover, as the number of grid-connected EVs increases, EVA will have an impact on the nodal marginal prices of electricity market clearing. Aiming at the bidding and offering problem of EVA participation in the day-ahead and intra-day electricity markets, based on the Stackelberg game theory, this paper establishes a bilevel optimization model for EVA participation in the two-stage electricity market as a price-maker. In the proposed bilevel model, the upper-level and lower-level models are constructed as an operational problem for EVA and a market-clearing problem for independent system operator (ISO), respectively. In the day-ahead stage, EVA is optimized to maximize its own expected benefits, and ISO aims to improve the social benefits. In the intra-day stage, EVA is optimized to maximize its self-interest, and the ISO aims to make it possible to minimize the cost of expenditures to maintain the system’s supply–demand balance. Karush–Kuhn–Tucker (KKT) conditions and dual theory are used to transform the nonlinear bilevel programming model into a mixed-integer single-level linear programming model. In order to verify the validity of the proposed bilevel model as well as to comparatively analyze the impact of EVA’s participation in the electricity market on the market clearing results. Two scenarios are set up where EVA is seen as the price-taker in Scenario 1 and EVA is seen as the price-maker in Scenario 2. ISO’s revenue under Scenario 2 increased by USD 2262.66 compared to Scenario 1. In addition, the EVA acts as an energy consumer in Scenario 1 with a charging cost of USD 26,432.95, whereas in Scenario 2, the EVA can profit by participating in the electricity market with a revenue of USD 26,432.95, at which point the EVA acts like a virtual power plant. The simulation examples verify that the proposed bilevel optimization model can improve the benefits of ISO and EVA at the same time, achieving mutual benefits for both parties. In addition, the simulation analyzes the impact of abandonment penalty price on ISO and EVA intra-day revenues. Comparing the scenarios where the abandonment penalty price is 0 with USD 10/MW, the ISO’s revenue in the intra-day market decreases by USD 197.5. Correspondingly, EVA’s reserve capacity is dispatched to consume wind power in the intra-day market, and its revenue increases by USD 197.5. The proposed two-stage bilevel optimization model can provide a reference for EVA to develop scheduling strategies in the day-ahead and intra-day electricity markets.

Pipeline reliability assessment and predictive maintenance considering multi-crack dependent degradation

Cracks due to corrosion are one of the main reasons for natural-gas pipeline leaks. Making the reliability assessment, prediction, and maintenance decision of pipelines based on measurable crack data is a central issue at present. The failure of pipelines is usually a result of the cumulative impact of multiple cracks. The interaction between adjacent cracks accelerates crack propagation, and greatly affects the degradation mechanism of pipelines. In this study, the reliability prediction and maintenance decisions were studied by considering the dependent degradation between multiple cracks in pipelines. Firstly, the initiation and propagation of pipeline cracks were modeled using a non-homogeneous Poisson process and a Gamma process, respectively. The interaction between cracks was defined to be a function of the random crack distance, which could be reflected by the change of shape parameters in the Gamma process. Secondly, the pipeline’s failure was defined as the competitive failure of the number of cracks, the maximum crack depth, and the total crack depth. The reliability prediction model of a pipeline under this failure mode was determined. A non-periodic combined maintenance policy considering both the pipeline condition and its predictive reliability was then proposed, and an optimal predictive maintenance decision model was constructed to minimize the long-term average cost rate. Finally, the effectiveness of the proposed model and policy was verified by a numerical experiment and a crack dataset of a transnational pipeline.

A decision-making optimization model for ship energy system integrating emission reduction regulations and scheduling strategies

The International Maritime Organization (IMO) has established various emission reduction regulations to limit ship emissions. Ships can choose among several energy system strategies to meet relevant regulatory requirements, and choosing the right energy strategy can maximize the ship's profitability, which is particularly important for shipping companies. In this paper, several shipping industry information such as emission reduction regulations, ship parameters, and energy strategy characteristics are integrated, and the potential connection between the ship scheduling approach and alternative energy system strategies is considered. Then, a more efficient energy strategy selection model is proposed based on life cycle analysis, which is solved by a designed dynamic programming method. Compared with the state of the art, this paper provides a novel way for shipping companies to link changes in ship scheduling behavior to energy strategy choices, which was ignored by previous studies. The proposed method is applied to a 5,000 Twenty-feet Equivalent Unit (TEU) container ship. The results show that, compared to the traditional method, the proposed model allows to select more appropriate energy system strategies and to increase profitability by nearly 30%. And traditional approaches underestimate the profitability of fuel switching and installing open-loop scrubbers, the proposed approach may prompt shipping companies more towards adopting the above two energy system strategies. In addition, the proposed model can help shipping companies effectively cope with fuel price fluctuations. Finally, compared to other carbon reduction policies, the incorporation of ships into the carbon trading system will encourage them to use Liquefied Natural Gas (LNG), thereby effectively reducing ship CO2 emissions.

Stripping path optimization decision-making of non-performing asset based on integration methods of SUMDII, fuzzy rough sets and PP

Aiming at the problem that manufacturing enterprises that rely more on asset projects currently lack effective means of divestiture of non performing assets, starting from incomplete information theory, this paper derives an optimal decision-making model for the divestiture path of non performing assets caused by the quality and safety crisis of manufacturing enterprises in an incomplete information environment, and uses the comprehensive method of SUMDII to establish a comprehensive integrated optimization model that reflects subjective evaluation and objective information. In addition, this study provides specific decision-making methods and implementation steps for optimizing the stripping path of non-performing assets. The empirical analysis results verify and demonstrate the feasibility, operability, accuracy, and applicability of the established model. The results show that the model designed in the study exhibits strong stability in sensitivity testing. When the parameter vectors are taken as (1,0,0,0), (1,1,0,0), (1,1,0,0), and (1,1,1,1), respectively, the ranking results corresponding to the first three parameter vectors are stable, all of which are A2 > A4 > A1 > A3. At the same time, the applied decision-making result of the model is A4 > A1 > A2 > A3, which is consistent with the best scheme evaluated by experts and superior to most comparative models. At the same time, in the analysis of decision-making characteristics, the research and design model has the most comprehensive review of decision-making elements, which is superior to other comparative models. It can be seen that the model designed by the research can lead to higher quality NPL divestiture schemes, which can help manufacturing enterprises improve asset quality and weaken the negative impact of the quality and safety crisis in manufacturing enterprises.

Optimization for Asphalt Pavement Maintenance Plans at Network Level: Integrating Maintenance Funds, Pavement Performance, Road Users, and Environment

As an infrastructure project, the cost and benefits of road maintenance should be measured through social costs. In order to fully consider user interests and make road maintenance decisions more reasonable, two types of user costs that may affect road maintenance benefits have been quantified through analysis of existing literature. At the same time, environmental issues have gradually become the focus of various industries, and in optimizing road maintenance decisions, the impact of environmental issues on decision making should also be considered. This article first analyzes and quantifies the user costs that affect the effectiveness of road maintenance. Secondly, based on the concept of sustainable development of roads, the optimization of road maintenance decisions is divided into two steps: the first step is to determine the minimum maintenance budget funds, and the second step is to determine the optimal plan. The specific optimization method is to use the 0-1 mathematical programming method to establish a network-level pavement maintenance decision optimization model based on a quantitative model. The most reasonable maintenance optimization plan is determined from four aspects: maintenance funds, maintenance performance improvement value, user benefit improvement, and reducing environmental impact. Finally, a provincial road network for a case study is selected. The applicability of the new model is verified through a case study. This study can help decision makers deal with asphalt pavement maintenance arrangements at the network level with four decision objectives: maintenance funds, pavement performance, road users, and environmental impacts.

Optimizing collaborative decision-making of multi-agent resources for large-scale projects: from a matching perspective

PurposeIn recent decades, infrastructure has continued to develop as an important basis for social development and people's lives. Resource management of these large-scale projects has been immensely concerned because dozens of construction enterprises (CEs) often work together. In this situation, resource collaboration among enterprises has become a key measure to ensure project implementation. Thus, this study aims to propose a systematic multi-agent resource collaborative decision-making optimization model for large projects from a matching perspective.Design/methodology/approachThe main contribution of this work was an advancement of the current research by: (1) generalizing the resource matching decision-making problem and quantifying the relationship between CEs. (2) Based on the matching domain, the resource input costs and benefits of each enterprise in the associated group were comprehensively analyzed to build the mathematical model, which also incorporated prospect theory to map more realistic decisions. (3) According to the influencing factors of resource decision-making, such as cost, benefit and attitude of decision-makers, determined the optimal resource input in different situations.FindingsNumerical experiments were used to verify the effectiveness of the multi-agent resource matching decision (MARMD) method in this study. The results indicated that this model could provide guidance for optimal decision-making for each participating enterprise in the resource association group under different situations. And the results showed the psychological preference of decision-makers has an important influence on decision performance.Research limitations/implicationsWhile the MARMD method has been proposed in this research, MARMD still has many limitations. A more detailed matching relationship between different resource types in CEs is still not fully analyzed, and relevant studies about more accurate parameters of decision-makers’ psychological preferences should be conducted in this area in the future.Practical implicationsCompared with traditional projects, large-scale engineering construction has the characteristics of huge resource consumption and more participants. While decision-makers can determine the matching relationship between related enterprises, this is ambiguous and the wider range will vary with more participants or complex environment. The MARMD method provided in this paper is an effective methodological tool with clearer decision-making positioning and stronger actual operability, which could provide references for large-scale project resource management.Social implicationsLarge-scale engineering is complex infrastructure projects that ensure national security, increase economic development, improve people's lives and promote social progress. During the implementation of large-scale projects, CEs realize value-added through resource exchange and integration. Studying the optimal collaborative decision of multi-agent resources from a matching perspective can realize the improvement of resource transformation efficiency and promote the development of large-scale engineering projects.Originality/valueThe current research on engineering resources decision-making lacks a matching relationship, which leads to unclear decision objectives, ambiguous decision processes and poor operability decision methods. To solve these issues, a novel approach was proposed to reveal the decision mechanism of multi-agent resource optimization in large-scale projects. This paper could bring inspiration to the research of large-scale project resource management.

Research on decision-making of water diversion supply chain considering both social welfare and water quality utility

When water diversion projects become important part of the water network around the world, the effective operation and management of the projects play important roles in giving full play to the optimal allocation of water resources. For the operation and management of water transfer, the decision-making of water supply chain under the scenario of economic benefit, producer surplus, and water quality utility should be considered simultaneously. According to the idea of supply chain, this paper regards water transfer operation management as a water supply chain composed of water transfer companies, water supply companies, and consumers. From the perspective of social welfare and water quality utility, a comprehensive optimization and coordination decision model for water transfer is proposed. Taking the South-to-North Water Diversion Project as the research object, the cost-sharing contract is designed, and the Stackelberg game method is used to optimize the decision-making and coordination of the water supply chain. The results show that when the concern coefficient and the cost-sharing ratio are evaluated within a given feasible value region, the profits of both the water transfer company and the water supply company can be improved. The feasible value interval of the concern coefficient decreases with the increase in the cost-bearing proportion. When the concern coefficient increases, the profit of the water transfer company decreases, while profit of the water supply company, water quality, consumer surplus, water quality utility, and utility of the water transfer company increase gradually. The results provide valuable references for water transfer decision-making.

Multi-level predictive maintenance of smart manufacturing systems driven by digital twin: A matheuristics approach

Digital twin technology is gradually being applied to smart manufacturing systems and is providing valuable information for predictive maintenance of swarms of machines, but also raises the need for more accurate and real-time decision making. However, there is still a shortage of research in this area. This paper proposes a general multi-level predictive maintenance decision-making framework driven by digital twin, considering component dependencies, the variable time scale of decisions, and comprehensive maintenance resources, in which an optimal maintenance schedule can be obtained in real time and then fed back to the physical space, so as to realize closed-loop control. A maintenance decision-making optimization model is then formulated based on integer linear programming to minimize total maintenance costs while meeting required production capacity. Further, a novel matheuristics algorithm (i.e., the interoperation of metaheuristics and mathematical programming techniques) is introduced for various maintenance decision scenarios. Finally, a case study of an offshore oil and gas production system consisting of eight subsea Christmas trees is examined, and the effects of changes in production capacity, failure thresholds, and maintenance resources on the multi-level optimization of decision-making solutions are discussed.

Study on Sustainable Development Oriented Community Public Hospital in China Based on Optimal Decision Making Model for Environment Renovation

Community public hospital provides public health and basic medical services, and their construction environment has an important impact on the health and well-being of the residents. Due to the long construction period of most community hospitals, the population expansion and the change in age structure have led to a mismatch between the needs of patients and the current situation, which is in urgent need of renovation. This paper aims to support the government and hospitals in making decisions by eliminating the blind spots in capital investment and benefit evaluation. In this study, environmental modification design variables are first established, and on this basis, the fuzzy Delphi method is used to understand the willingness for renovation on the part of patients, accompanying personnel, staff, healthcare workers as well as hospital administrators. Besides, the I-S model is used to identify the renovation projects that would maximize user satisfaction. Furthermore, the differences between patients and medical staff are compared. The ODM (Optimal Decision-making) model is used to evaluate the cost investment and benefits, which provides support for the decision-making of government and hospitals. The results clarify the key renovation design variables that can significantly improve user satisfaction and extend them into renovation strategies that can be landed, and identify renovation strategies with larger improvement scores and the range of renovation funds that maximize benefits to promote the sustainable development of community public hospitals. It can be seen that the ODM model can also be well applied to the renovation of community healthcare services. In addition, the reference classification and design variables should be adjusted according to the characteristics of China, taking into consideration the specificity of individual hospitals.

Multi-criteria decision-making optimization model for permeable breakwaters characterization

Permeable breakwaters have always been of interest due to their advantages over the traditional types. This study proposed a stochastic multi-criteria decision-making model to optimize the geometry of permeable breakwaters. A multi-objective optimization algorithm was conducted using the non-dominated sorting genetic algorithm-II (NSGA-II) coupling with the estimations made by a well-known machine learning (ML) model, the multi-layer perceptron neural network (MLP-NN) to achieve the objective. Considering the inherent uncertainties in the wave characteristics using the conditional value-at-risk (CVaR) method, the presented risk-based model could determine optimal tradeoffs between wave transmission, wave reflection, and rockfill materials volume. This CVaR-based multi-objective optimization model was experimentally applied to a permeable breakwater with maximum significant wave heights of 1–3.5 m at confidence levels of 50%, 75%, 90%, and 99% for risk analysis. To rank several alternatives between the Pareto-optimal solutions, a decisive so-called multi-criterion decision-making (MCDM) approach was employed, which coupled the fuzzy decision-making trial and evaluation laboratory (DEMATEL) method and analytical network process (ANP) procedure. Results indicated that the heavier permeable breakwater was the most appropriate for greater wave heights. To this extent, the relative rockfill materials height and width increased to 1.1 from 0.77 and 1.1 from 0.41, respectively, by increasing the specific wave height from 2 to 3.5 m.

An extended smart “predict, and optimize” (SPO) framework based on similar sets for ship inspection planning

This study aims to address one critical issue in ship inspection planning optimization, where the first step is to accurately predict ship risk, and the second step is to assign scarce port inspection resources, aiming to identify as much non-compliance from the inspected ships as possible. A traditional decision tree is first developed as the benchmark. Then, to go from a good prediction to a good decision, the structure and performance of the following optimization problem are integrated in the prediction model, which we denote by integrated decision trees. Three modes are proposed to develop integrated decision trees with different combination ways and degrees. Especially, we innovatively propose the concept of “similar set” in data sets, and use the similar sets to select the hyperparameter tuple leading to the best decision optimization problem in mode 1. Then, the structure of the decision problem is considered into the decision tree construction facilitated by similar sets in mode 2. Finally, similar sets are used to integrate the performance of the following decision optimization problem directly into the decision tree construction process in mode 3. Numerical experiments show that mode 3 can achieve the best performance in the decision optimization model. Conservative estimations show that the proposed models can save at least millions to tens of millions inspection cost in Hong Kong dollars for the Hong Kong port each year, and up to 837 million inspection cost in Hong Kong dollars all over the world per year.

Research on Pricing Decision Considering Advertising Benefit and Consumer Psychological Benefit

For perishable goods, the optimal pricing models studied by current scholars seldom consider price effect, psychological benefit and consumption time. Based on the current basic newsboy model, this paper focuses on the price characteristics and consumer psychology of perishable products under the background of single dealer, in order to build a complete optimal price decision model of perishable products. According to the two-stage ladder pricing of perishable products in the pricing stage, this paper constructs the optimal price decision model of dealers in different sales stages by representing consumer demand and dealer profit equation, so as to provide enterprises with the maximum optimal strategy. Moreover, sensitivity analysis of parameters in the model is carried out to ensure the stability of the model.