Optimal Decision Model Research Articles

Low-carbon route optimization model for multimodal freight transport considering value and time attributes

As the international community increasingly focuses on climate change, optimizing low-carbon transportation routes in the multimodal freight transport system has become a pressing issue. However, due to the variability in cargo properties and the influence of various factors on transportation route decisions, formulating a low-carbon and economical multimodal freight transport plan remains a significant challenge. To address the issue, this study considered cargoes with different attributes in terms of both value and time attributes. Triangular fuzzy numbers were employed to represent the uncertain demand for cargo, with confidence levels introduced for clarification. A low-carbon route decision optimization model for multimodal freight transport was established to minimize the combined transportation carbon emission and time costs. The catastrophe adaptive genetic algorithm, based on Monte Carlo sampling, was employed to solve the model using arithmetic examples. Finally, parameter sensitivity analysis revealed that adjustments to carbon tax values and changes in the proportion of electric trucks and green electricity supply had the most significant impact on the low-carbon route decision-making plan for multimodal freight transport. For low value-added and timeliness-strong cargo, a 60 % increase in carbon tax value shifted the mode of transportation from road to railway. When the carbon tax increased by more than 140 %, the transportation mode shifted from railway to waterway. Additionally, when the proportion of electric trucks and green electricity supply both exceeded 80 %, the transportation mode between some city nodes shifted from railway to road. When these proportions increased beyond 90 %, road transportation became the predominant mode.

Stochastic Decision-Making Optimization Model for Large Electricity Self-Producers Using Natural Gas in Industrial Processes: An Approach Considering a Regret Cost Function

In the context of high energy costs and energy transition, the optimal use of energy resources for industrial consumption is of fundamental importance. This paper presents a decision-making structure for large consumers with flexibility to manage electricity or natural gas consumption to satisfy the demands of industrial processes. The proposed modelling energy system structure relates monthly medium and hourly short-term decisions to which these agents are subjected, represented by two connected optimization models. In the medium term, the decision occurs under uncertain conditions of energy and natural gas market prices, as well as hydropower generation (self-production). The monthly decision is represented by a risk-constrained optimization model. In the short term, hourly optimization considers the operational flexibility of energy and/or natural gas consumption, subject to the strategy defined in the medium term and mathematically connected by a regret cost function. The model application of a real case of a Brazilian aluminum producer indicates a measured energy cost reduction of USD 3.98 millions over a six-month analysis period.

Bidding Strategy for the Alliance of Prosumer Aggregators in the Distribution Market

Photovoltaic energy storage system (PV-ESS) prosumer aggregators are characterized by a large number but small scale in the distribution system and are not competitive enough to participate in market transactions. For this reason, a prosumer aggregator alliance is proposed to participate in the distribution market bidding strategy. Firstly, based on the framework for prosumer aggregator alliances participating in distribution market trading, a bilevel bidding model is constructed. The upper level represents the optimal decision-making model for the prosumer aggregators, while the lower level constitutes the distribution market-clearing model. Secondly, the additional benefits obtained by the alliance are distributed more fairly using the improved Shapley value based on the PV self-consumption rate. Given the problem that the traditional diagonalization algorithm (DA) has an excessive number of iterations when solving the game equilibrium problem of multiple subjects, the DA is improved by optimizing the initial value of the inputs. Finally, case studies are conducted based on the improved IEEE-33 bus distribution system to validate the feasibility and economic viability of the proposed strategy. The case study results show that forming cooperative alliances to participate in market bidding can significantly increase overall profits. The improved DA reduces the number of bids and computation time by 75% and 80%, respectively. Additionally, the improved Shapley value facilitates compensation for some of the aggregators.

An Intelligent Decision-Making Approach for Multi-Ship Traffic Conflict Mitigation from the Perspective of Maritime Surveillance

Potential multi-ship conflict situations in coastal or near-shore port areas have always been one of the important factors affecting ship navigation safety and a key target of maritime traffic regulatory authorities. In recent years, with the continuous development and integration of various emerging technologies in the maritime field, maritime traffic supervision has also shown a trend of intelligent and autonomous development. The traditional supervision method dominated by human experience is evolving towards data and model-driven practices. In order to solve the problem of ship navigation safety supervision under multi-ship conflict scenarios, it is urgent to build an intelligent conflict mitigation decision-making model. Therefore, this paper designs a novel risk mitigation decision-making model for multi-ship conflict scenarios from the perspective of maritime supervision. The model proposed in this paper first extracts high-density ship clusters based on AIS (Automatic Identification System) data and uses the MCD (Mean Core Density) and PRM (Proportion of Relative Motion) as feature indicators to further mine potential multi-ship conflict scenarios. Finally, a global optimization decision-making model is constructed to effectively mitigate conflict risks. Experimental verification shows that the intelligent decision-making model for the mitigation of maritime traffic conflict proposed in this paper can autonomously identify conflict scenarios and make reasonable decisions in real time. It can effectively ensure the navigation safety of ships in multi-ship conflict scenarios and further improve the supervision level of maritime departments.

Ordinal Clustering Based Homogeneous Road Segments in Asphalt Pavement Maintenance and Rehabilitation Optimized Decision-Making

An increase in the number of work zones on roads and highways will result in more costs from the increased frequency of maintenance and rehabilitation (M&R) equipment transfers. The number of M&R decision-making segments can affect decision-making efficiency and the number of work zones. Fixed segmentation and traditional dynamic homogeneous segmentation techniques, such as the cumulative difference approach (CDA) and K-means algorithms, cannot determine the optimal number of decision-making segments. To address this issue, this paper proposes a method to develop a multi-year asphalt pavement M&R plan that incorporates homogeneous road segmentation based on an ordinal clustering approach (OCA). The proposed method first applies an ordinal clustering approach to the survey units to identify homogeneous segments. These segments are then incorporated into a multi-year asphalt pavement M&R optimization decision-making model to determine the M&R plan. Data from 2022 covering 15.9 km of continuous asphalt pavement on the Donglv Highway in Shanxi Province were selected for analysis. These results demonstrate: 1) the OCA exhibits superior decision-making accuracy compared with the CDA; 2) compared with the M&R plans for survey units and CDA for homogeneous segments, the proposed method's M&R plan can be resolved faster and see fewer work zones, all achieved with a similar level of investment and meeting performance improvement; and 3) the M&R plan generated using the proposed method requires lower M&R investment but achieves higher performance compared with the actual M&R plan. These findings validate the effectiveness of the proposed method in producing improved M&R plans.

EDOM-MFIF: an end-to-end decision optimization model for multi-focus image fusion

EDOM-MFIF: an end-to-end decision optimization model for multi-focus image fusion

Simulation of Optimization Decision Model for Low-carbon Economy Schemes Based on Machine Learning Algorithms

Low-carbon economy (LCE) is a shift from high-carbon intensive industries and activities to low-emission or zero-emission industries and activities, which requires a lot of investment in new technologies and infrastructure, as well as changes in policies and regulations to promote sustainability and reduce emissions. In this article, the back propagation neural network (BPNN) algorithm in machine learning (ML) is applied to the construction of low-carbon economic scheme optimization decision model, and the performance of the model is simulated and tested. The results show that BPNN algorithm has higher accuracy and reliability in predicting the development level of LCE, which is 23.8% higher than Support Vector Machine (SVM) algorithm. Through the comparative study of BPNN algorithm and SVM algorithm in forecasting the development level of LCE, the advantages of BPNN algorithm in forecasting accuracy and application value are verified. BPNN algorithm can better adapt to and deal with the complex data patterns in the field of LCE, improve the accuracy and reliability of prediction, and provide strong technical support and guarantee for the growth of LCE.

Resource scheduling optimization for industrial operating system using deep reinforcement learning and WOA algorithm

Industrial operating systems (IOS) are essential for supporting smart manufacturing, particularly in managing and utilizing heterogeneous production resources through resource instantiation scheduling (RIS) technique. However, RIS faces the challenge of efficiently selecting optimal resource service compositions from numerous options with varying quality of service. To boost the solving of the RIS problem and improve the quality of the solution, this paper proposes a novel hybrid algorithm, named DWOA, based on the whale optimization algorithm (WOA) and deep reinforcement learning (DRL). It first incorporates the DRL algorithm to learn experience from the historical data regarding exploration and exploitation in the WOA search process and train an optimal behavior decision model. Subsequently, utilizing the trained model, the DWOA can effectively guide the search agent in achieving a better balance between global exploration and local exploitation, thereby enhancing its convergence speed and solution quality. The effectiveness and efficiency of the DWOA approach are evaluated by the CEC2017 benchmark functions and RIS problems with various scales, compared with 11 state-of-the-art methods. The experimental results indicate that our method converges faster and produces better solutions for RIS problems.

Research on Decision Optimization of Supply Chain Quality Information Disclosure Considering Stigma Level

With the frequent occurrence of online public opinion events, the problem of product stigma is becoming increasingly serious. Enterprises must use effective quality information disclosure strategies to reduce losses affecting market sales and profit. Therefore, this paper aims to address the supply chain structure composed of one product manufacturer and one component manufacturer under the influence of stigma. It constructs a decision optimization model under three scenarios: no information disclosure, the product manufacturer disclosures information, and the component manufacturer disclosures information, and uses Stackelberg game theory to solve and analyze the model. Furthermore, we use numerical examples to verify the model results, and provide management suggestions for enterprises. The research results show that enterprises suffering from product stigma should actively implement information disclosure strategies to reduce their profit losses, and the lower the stigma level, the better the effect of information disclosure will be; when the stigma level becomes more serious, enterprises should take timely steps to reduce the sales price of products, the sales price of components, and the efforts to disclose information; for industries that value confidentiality of product information, although the implementation of information disclosure by the component manufacturer can require less effort for information disclosure, the two enterprises will suffer higher economic losses.

Analysis on the synergy between markets of electricity, carbon, and tradable green certificates in China

China has integrated tradable green certificates and carbon markets with its electricity sector to promote renewable energy and reduce carbon emissions. However, the impact mechanism of decision-making behaviors of involved entities in these integrated markets on the market equilibrium is still unclear, hence limiting the further development of renewable energy industries and carbon reduction progress to some extent. Therefore, conducting in-depth research to inform policy makers about the impacts of entities' decision-making behaviors on market equilibrium within the context of integrated multiple markets is crucial. This study aims to analyze the characteristics of entities' decision-making behaviors and equilibrium states within the collaborative dynamics of carbon, electricity, and green certificate markets. Specifically, an optimal decision-making model for entities given the multi-market coupling was developed by employing mathematical programming methods to analyze market equilibriums across various market combinations and policy parameters. The conclusions underscore equilibrium price shifts and decision-making characteristics among entities across multiple markets. The result shows that the carbon market could increase power prices and decrease green certificate prices. To ensure sustainable energy development, policymakers should consider the decision-making behavior of entities under multi-market coupling, which can promote the low-carbon transformation of the Chinese energy industry.

Optimal Maintenance Decision Method for a Sensor Network Based on Belief Rule Base considering Attribute Correlation

Optimal maintenance decision for a sensor network aims to intelligently determine the optimal repair time. The accuracy of the optimal maintenance decision method directly affects the reliability and safety of the sensor network. This paper develops a new optimal maintenance decision method based on belief rule base considering attribute correlation (BRB-c), which is designed to address three challenges: the lack of observation data, complex system mechanisms, and characteristic correlation. This method consists of two sections: the health state assessment model and the health state prediction model. Firstly, the former is accomplished through a BRB-c-based health assessment model that considers characteristic correlation. Subsequently, based on the current health state, a Wiener process is used to predict the health state of the sensor network. After predicting the health state, experts are then required to establish the minimum threshold, which in turn determines the optimal maintenance time. To demonstrate the proposed method is effective, a case study for the wireless sensor network (WSN) of oil storage tank was conducted. The experimental data were collected from an actual storage tank sensor network in Hainan Province, China. The experimental results validate the accuracy of the developed optimal maintenance decision model, confirming its capability to efficiently predict the optimal maintenance time.

Multi-Objective Optimization for Seismic Structural Investigation and Maintenance Strategies of Bridges in Service

With the continuous updating of seismic design codes for Bridges, the new regulations have higher requirements for the seismic structure and mechanical properties of old Bridges. In order to make the decision of seismic structure strengthening more scientifically, with the goal of optimal overall performance, minimum economic cost and minimum environmental impact, the optimal model of seismic strengthening sustainable strategy is established. Firstly, according to the results of the seismic measures of the existing Bridges, the corresponding reinforcement measures are proposed for the structural problems that do not meet the seismic measures. Then the utility evaluation of the three objectives of the existing bridge was carried out. At last, NSGA-Ⅱ algorithm was used to build a multi-objective maintenance decision optimization model of the bridge network. Combined with the weight of the decision-maker's preference, TOPSIS algorithm was used to obtain the optimal solution of the target, and decision-making suggestions were provided for the relevant departments. The results show that there are many structural problems in the new evaluation of the old bridge, and the maintenance of the structural problems are more feasible and effective than the improvement of the traditional mechanical properties. Combined with the comprehensive evaluation utility and reinforcement objective function, the maintenance decision can be better under different objectives. The reinforcement maintenance decision model has the ability to improve the comprehensive performance and sustainability of the bridge to the maximum extent within the limited budget.

Multi-Time Scale Cloud-Edge Collaborative Scheduling Strategy for Distribution Network Considering Spatiotemporal Characteristics of Demand Response

The increasing penetration rate of distributed resources in the distribution network has brought about significant volatility and uncertainty problems. Demand response (DR) can flexibly change the energy consumption method of the user to balance supply and demand. This paper first considers the spatial distribution characteristics of DR resources to schedule DR resources to construct a distributed resource cloud-edge collaborative scheduling framework. Based on this, the distribution network scheduling requirements are combined with the multi-time scale characteristics of DR. A three-stage cloud-edge collaborative optimization scheduling strategy for distributed resources in the distribution network is proposed, which allocates the multi-time scale scheduling tasks of DR resources to the cloud and edge. Secondly, taking the cloud and edge as the optimization platform, a three-stage optimization decision-making model of the distribution network is established. In the day-ahead stage, the global optimization decision is made by combining cloud-centralized optimization with edge-independent optimization. In the intraday stage, edge-rolling optimization is carried out. In the real-time stage, the edge-distributed calculation is based on the consensus algorithm. Finally, the effectiveness and economy of the proposed model and strategy are verified by an example analysis.

Research on Decision Optimization and the Risk Measurement of the Power Generation Side Based on Quantile Data-Driven IGDT

In an environment marked by dual carbon goals and substantial fluctuations in coal market prices, coal power generation enterprises face an urgent imperative to make scientifically informed decisions regarding production management amidst significant market uncertainties. To tackle this challenge, this paper proposes a methodology for optimizing electricity generation side market decisions and assessing risks using quantile data-driven information-gap decision theory (QDD-IGDT). Initially, a dual-layer decision optimization model for electricity production is formulated, taking into account coal procurement and blending processes. This model optimizes the selection of spot coal and long-term contract coal prices and simplifies the dual-layer structure into an equivalent single-layer model using the McCormick envelope and Karush–Kuhn–Tucker (KKT) conditions. Subsequently, a quantile dataset is generated utilizing a short-term coal price interval prediction model based on the quantile regression neural network (QRNN). Interval constraints on expected costs are introduced to develop an uncertainty decision risk measurement model grounded in QDD-IGDT, quantifying decision risks arising from coal market uncertainties to bolster decision robustness. Lastly, case simulations are executed by using real production data from a power generation enterprise, and the dual-layer decision optimization model is solved by employing the McCormick–KKT–Gurobi approach. Additionally, decision risks associated with coal market uncertainties are assessed through a one-dimensional search under interval constraints on expected cost volatility. The findings demonstrate the effectiveness of the proposed research methodology in cost optimization within the context of coal market uncertainties, underscoring its validity and economic efficiency.

VIKOR optimization decision model based on poset

The Vlsekriterijumska Optimizacija I Komprosmisno Resenie (VIKOR) method to some extent modifies the utility function to a value function that can consider different risk preferences. However, the weight and risk attitude parameters involved in the model are difficult to determine, which limits its application. To overcome this problem, a Poset-VIKOR model is proposed. A partial order set is a non-parametric decision-making method. Through the combination of partial order set and VIKOR model, the parameters can be “eliminated”, and a robust method that can run the model is obtained. This method uses the Hasse diagram to express the evaluation results, which can not only directly display the hierarchical and clustering information, but also show the robustness characteristics of the alternative comparison.

Emergency evacuation based on long range communication technology

The complexity and uncertainty of a fire in a large public building with complex structure leads to risks during emergency evacuation. Therefore, it is crucial to conduct intelligent emergency evacuation according to the situational development of the accident environment, the crowding degree of evacuation routes, as well as the reliability and flexibility of data transmission in large complex building structures. In this study, we develop an Intelligent Emergency Evacuation System (IEES) based on the Long Range (LoRa) communication technology and the Evacuation Decision Optimization Model (EDOM). The IEES comprises three parts, i.e., multi-sensor Fire Monitoring System (FMS), Central Control System (CCS), and Wireless Evacuation Indication System (WEIS). The LoRa communication technology is used for data transmission among the three parts of the IEES to assist large scale evacuation. The EDOM is developed to support the CCS based on the improved adaptive ant colony algorithm. To facilitate the evacuation decision optimization, the initial node database in the evacuation spatial network is established intelligently on the Building Information Modeling (BIM) platform. An on-site application in a subway station with three underground floors is conducted. The results demonstrate that the IEES can guide people in evacuating from hazardous locations according to the state of the fire. Moreover, the phenomena of crowd retention and congestion at a few certain evacuation exits are considerably reduced to achieve global optimization of the evacuation path and accomplish the objective of intelligent emergency evacuation. Furthermore, WEIS can maintain normal operation and have good visibility in the fire environment of heat radiation and thick smoke. The IEES can realize dual wireless indication of vision and sound perception, and its response time is slightly affected by the building structure and transmission distance. The electromagnetic signal of electrical equipment and the operational state of vehicles in and out of the subway station causes the response time to a relatively larger lag. The response time of the IEES is delayed up to 14 ms. Nonetheless, it fulfills the requirements of response time of evacuation indication signs stated in the Technical Standard for Fire Emergency Lighting and Evacuate Indicating System.

Balancing ventilation performance and energy conservation: An integrated multi-objective optimization and preference-based decision-making model for optimizing impinging jet ventilation

Balancing the requirements for thermal comfort and indoor air quality while minimizing energy consumption is a challenging trade-off problem for an indoor ventilation system. Multi-criteria decision-making (MCDM) technology is commonly employed to address this issue in response to changing outdoor weather conditions. However, conflicting ventilation performances, including thermal comfort and energy consumption, introduce complexities in identifying optimal ventilation operation parameters. This study therefore proposes an integrated multi-objective optimization and preference-based decision-making model to surmount these challenges. The former replaces the original alternatives, comprising all ventilation parameter combinations in the decision matrix, with Pareto-optimal sets, achieving improved trade-offs among all objectives without compromising individual performance. It mitigates undue influences on decision outcomes from inappropriate alternatives of ventilation parameter combinations when solely relying on MCDM techniques. Furthermore, this method maintains performance metrics with established standards in recommended thresholds, only compromising on the objectives without predefined criteria. This not only reduces the algorithm's complexity but also contributes to energy conservation. To expedite iterative processes, the proposed method incorporates the response surface methodology for predicting ventilation performances based on experimentally validated computational fluid dynamics simulations. Compared to the typical MCDM method, the proposed method has reduced energy consumption by up to 16.99% on average while still meeting thermal comfort and indoor air quality requirements. Additionally, this study identifies the optimal return vent height and operation modes that align with diverse decision preferences amidst changing weather conditions. The findings provide insights into accurately designing and controlling ventilation parameters in impinging jet ventilation, contributing to the practical applications across various ventilation scenarios.

An optimal sequential investment decision model for generation-side energy storage projects in China considering policy uncertainty

Energy storage systems (ESS) are crucial for addressing the intermittent nature of renewable energy, and improving the flexibility of power systems. However, the uncertainties in the investment decision process pose a challenge for investment evaluation of ESS. This study develops a sequential investment decision model for ESS projects based on real options, aiming to derive the optimal investment timing and value of the project under electricity price and subsidy policy uncertainties. Considering the policy uncertainty caused by the possible retraction or provision of subsidies, sequential investments under four subsidy policies of stable, retraction, provision, and transformation are analyzed separately. The model is empirically and simulatively analyzed using a generation-side ESS project in Qinghai Province, China, as an example. The results show that: First, the sequential investment strategy promotes earlier project deployment compared to the lumpy investment strategy. Second, stochastic provision of subsidies incentivizes investment, but stochastic retraction of subsidies is a greater disincentive to investment, whereas stable subsidy policies provide the best incentives. However, local subsidies still need to increase to 0.15 yuan/kWh to trigger investment. Third, under subsidy recovery uncertainty, excessive subsidy levels discourage investment; meanwhile, under subsidy provision uncertainty, low subsidy levels become almost incentive-less.

Multi-Objective Optimization for Sustainable Pavement Maintenance Decision Making by Integrating Pavement Image Segmentation and TOPSIS Methods

To provide a low-carbon economy maintenance strategy is the most challenging problem faced by pavement management authorities under the restricted budget and significant environmental repercussions. The development of a multi-objective optimization model for pavement maintenance decision making is essential to formulate pavements. Nevertheless, the existing automatic detection can only recognize and classify pavement distress. However, few studies are able to accurately determine the precise dimensions of specific distresses such as cracks and potholes, especially combined with the actual size of the image. This limitation hinders the ability to provide specific maintenance recommendations and make optimal maintenance decisions. Therefore, this paper develops a comprehensive and effective multi-objective decision-making framework for pavement maintenance. This framework consists of four distinct components: (1) recognizing the dimensions of pavement distresses based on the pavement image segmentation technique; (2) compiling a list of viable pavement maintenance strategies; (3) assessing the costs and carbon emissions of these strategies; and (4) optimizing decisions on pavement maintenance. We used the U-Net algorithm to accurately recognize the dimensions of pavement distresses, while an improved entropy-weighted TOPSIS model was proposed to determine the optimal pavement maintenance strategy with the lowest cost and carbon emissions. The results indicated that the pavement distress dimension recognition model achieved a high accuracy of 96.88%, and the TOPSIS model identified the optimal maintenance strategy with a score of 99.16. This maintenance strategy achieved a substantial reduction of 30.80% in carbon emissions and a cost reduction of 20.81% compared to the highest values among all maintenance strategies. This study not only provides a scientifically objective method for making pavement maintenance decisions but also offers specific, quantifiable maintenance programs, marking a stride towards more environmentally friendly and cost-effective road maintenance. It also contributes to the sustainability of pavement maintenance.

A Pareto Dominance Principle for Data-Driven Optimization

Our paper proposes an effective way to make decisions based on data for uncertain situations. In simple terms, a data-driven decision is just a choice we make by looking at the available data. We express this choice as the best one according to a model we create from the data. The quality of this decision is judged by how well it performs in situations not seen during training. We also consider how often it disappoints in those situations. The challenge is that we do not know the exact probability of generating the data. An ideal data-driven decision should work well for any possible probability. However, such ideal decisions are usually not possible. Therefore, we look for decisions that work well on unseen data, considering the chances of disappointment. We prove that such effective decisions exist under certain conditions, allowing for practical applications. This approach holds regardless of whether the original problem is simple or complex, and it works even when the data are not uniformly collected. Our study also uncovers how the characteristics of the data-generating process influence the optimal decision-making model.