Compromise Solution Research Articles

Integrated usage of artificial intelligence, blockchain and the internet of things in logistics for decarbonization through paradox lens

The objective of this study was to identify, assess, and prioritize resolution strategies to address paradoxes associated with the integrated use of artificial intelligence (AI), blockchain, and the Internet of Things (IoT) in logistics for decarbonization. To achieve these objectives, a four-phase mixed-methods approach was employed, involving the identification, prioritization, and verification of resolution strategies for their significance in managing paradoxes. A panel of 10 experts with experience in adopting AI, blockchain, and IoT in logistics participated in the study. The study identified ten key resolution strategies: education & training, coordination & collaboration, government regulations & support, standardized data format and protocol, modular architecture, interoperability standard, process optimization, security auditing, management participation, and man–machine interactions. Using an integrated multicriteria decision-making approach, the study presented compromised rank of these strategies based on their effectiveness in addressing paradoxes. The most significant resolution strategy identified was modular architecture. This was followed by a second compromise solution set of interoperability standards, process optimization, management participation, coordination & collaboration, and man–machine interactions, and a third compromise solution set including education & training, government regulation & support, standardized data format, and security auditing. The derived solutions were then verified by logistics domain experts. This study provided the first empirical investigation of paradox theory related to the adoption of AI, blockchain, and IoT for decarbonization in logistics. Overall, the study enhanced the understanding of competing demands, tensions, and complexities involved in adopting digital technologies for decarbonization using paradox theory.

Stepwise Structural Simplification of the Dihydroxyanthraquinone Moiety of a Multitarget Rhein-Based Anti-Alzheimer Lead to Improve Drug Metabolism and Pharmacokinetic Properties.

Multitarget compounds have emerged as promising drug candidates to cope with complex multifactorial diseases, like Alzheimer's disease (AD). Most multitarget compounds are designed by linking two pharmacophores through a tether chain (linked hybrids), which results in rather large molecules that are particularly useful to hit targets with large binding cavities, but at the expense of suffering from suboptimal physicochemical/pharmacokinetic properties. Molecular size reduction by removal of superfluous structural elements while retaining the key pharmacophoric motifs may represent a compromise solution to achieve both multitargeting and favorable physicochemical/PK properties. Here, we report the stepwise structural simplification of the dihydroxyanthraquinone moiety of a rhein-huprine hybrid lead by hydroxy group removal-ring contraction-ring opening-ring removal, which has led to new analogs that retain or surpass the potency of the lead on its multiple AD targets while exhibiting more favorable drug metabolism and pharmacokinetic (DMPK) properties and safety profile. In particular, the most simplified acetophenone analog displays dual nanomolar inhibition of human acetylcholinesterase and butyrylcholinesterase (IC50 = 6 nM and 13 nM, respectively), moderately potent inhibition of human BACE-1 (48% inhibition at 15 µM) and Aβ42 and tau aggregation (73% and 68% inhibition, respectively, at 10 µM), favorable in vitro brain permeation, higher aqueous solubility (18 µM) and plasma stability (100/96/86% remaining in human/mouse/rat plasma after 6 h incubation), and lower acute toxicity in a model organism (zebrafish embryos; LC50 >> 100 µM) than the initial lead, thereby confirming the successful lead optimization by structural simplification.

MDB-KCP: persistence framework of in-memory database with CRIU-based container checkpoint in Kubernetes

As the demand for container technology and platforms increases due to the efficiency of IT resources, various workloads are being containerized. Although there are efforts to integrate various workloads into Kubernetes, the most widely used container platform today, the nature of containers makes it challenging to support persistence for memory-centric workloads like in-memory databases. In this paper, we discuss the drawbacks of one of the persistence support methods used for in-memory databases in a Kubernetes environment, namely, the data snapshot. To address these issues, we propose a compromise solution of using container checkpoints. Through this approach, we can perform checkpointing without incurring additional memory usage due to CoW, which is a problem in fork-based data snapshots during snapshot creation. Additionally, container checkpointing induces up to 7.1 times less downtime compared to the main process-based data snapshot. Furthermore, during database recovery, it is possible to achieve up to 11.3 times faster recovery compared to the data snapshot method.

Experimental investigation and optimization of performance, emission, and vibro-acoustic parameters of SI engine fueled with n-propanol and gasoline blends using ANN-GA coupled with NSGA3-modified TOPSIS hybrid approach

In the present study, performance, emission, and vibro-acoustic studies were conducted on a spark ignition (SI) engine fueled with gasoline and an n-propanol blend at variable compression ratio (CR), speed, and propanol blend fraction (PBF). Experimental data were used to model an artificial neural network (ANN) trained with a genetic algorithm (GA). ANN predictive responses were employed to establish regression relationships between brake power (BP), brake specific fuel consumption (BSFC), brake thermal efficiency (BTE), oxides of nitrogen (NOx), carbon monoxide (CO), hydrocarbon (HC), resultant vibration acceleration (RVA), and sound pressure level (SPL) with operating parameters using response surface methodology (RSM). These models served as objective functions in the non-dominated sorting genetic algorithm-3 (NSGA3), a multi-objective optimization (MOO) technique, to optimize responses and obtain non-dominated solutions. These solutions were filtered using a modified technique for order preference by similarity to the ideal solution (TOPSIS) to obtain a compromised optimal solution. ANN-GA model outcomes showed high accuracy, with coefficient of determination (R2) and root mean square error (RMSE) values ranging from 0.979 to 0.993 and 0.0381 to 0.0643, respectively. NSGA3 coupled with modified TOPSIS identified optimal operating conditions at 1271.77 RPM, a CR of 11.96, and a PBF of 33.26 %.

A novel interval rough model for optimizing road network performance and safety

This paper introduces a novel Integrated Interval Rough Pivot Pairwise Relative Criteria Importance Assessment (IRN PIPRECIA) model combined with Interval Rough Combined Compromise Solution (IRN CoCoSo), marking a significant advancement in sustainable traffic flow management for commercial vehicles. This innovative merger is a first in literature, methodologically enhancing the evaluation of road sections based on critical parameters including passenger car equivalent (PCE) 85%, AADT, road conditions, and accident data. Our model systematically fills the research gap in holistic traffic performance analysis, providing a unique tool for prioritizing road safety and efficiency. The key scientific contribution is the model’s ability to integrate causal and consequential traffic factors into a single framework, offering a novel multi-criteria decision-making (MCDM) approach. Results, validated through various verification models, show the integrated model’s effectiveness in real-world scenarios, confirming its robustness and stability. With strong engineering application potential, our work supports urban planners and traffic managers in making informed, sustainable decisions. The model extends beyond traditional traffic analysis, promising a shift towards more adaptive, data-driven infrastructure management. Future research will aim to refine the criteria basis and explore real-time decision-making through advanced MCDM applications.

A new Fermatean fuzzy multi‐objective indefinite quadratic transportation problem with an application to sustainable transportation

AbstractIn this work, a Fermatean fuzzy (FF) multi‐objective indefinite quadratic transportation problem (TP) is introduced. Due to some unavoidable reasons, real‐life transportation parameters such as supply, demand and costs are indeterminate in nature and cannot be expressed in crisp terms. We represent these parameters using FF numbers, an extension of fuzzy numbers, which are capable of representing indeterminacy efficiently. A multi‐objective indefinite quadratic TP where each objective is a product of two linear factors (cost functions) is considered. Defuzzification of FF numbers is accomplished by the introduction of ‐cut for the first time. The obtained crisp TP is solved using the intuitionistic fuzzy programming approach and FF programming approach to arrive at a compromise solution. To substantiate the work, solution methodology based on defuzzification using the ranking function is also deliberated. The applicability of the model is demonstrated through a sustainable TP, which simultaneously minimizes transportation cost with depreciation cost and packaging cost with wastage cost. The resulting value of the objective functions and the aspiration levels are compared to depict the efficacy of the proposed method over the ranking function method. The concluding section summarizes the work, and future avenues along with some limitations of the work are also specified.

An investment decision framework for offshore CCUS project under interval-valued fermatean fuzzy environment

ABSTRACT Carbon Capture, Utilization and Storage (CCUS) is an indispensable technology for achieving a net-zero emission society. The offshore CCUS project is still in its infancy. To promote its sustainable development, developing a comprehensive framework for investment decision-making is very crucial. First, a comprehensive evaluation criteria system is established. Second, in order to characterize the ambiguity and uncertainty of information in the process of making decisions, the interval-valued fermatean fuzzy set (IVFFS) is introduced, and the extended variance method of IVFFS is proposed to systematically calculate the weights of experts. Then, the power weighted average (PWA) operator based similarity measure of IVFFSs is developed to aggregate different expert information. Meanwhile, the fuzzy-weighted zero-inconsistency (FWZIC) method and the method based on the removal effects of criteria (MEREC) are used to determine the criteria weights. In addition, considering the interactions between the criteria, we introduce the Hamacher operator into the measurement of alternatives and ranking according to the compromise solution (MARCOS) method to select the optimal alternative in the interval-valued fermatean fuzzy (IVFF) environment. The suggested framework is then used to analyse a case study. After that, sensitivity and comparative analyses are conducted to confirm its robustness and viability. This study creates a practical investment framework for offshore CCUS projects, identifies a number of investment-sensitive criteria and provides management insights. The proposed framework expands the methods and applications in the field of decision-making and provides a scientific approach for investment decision-making in offshore CCUS projects, which can be a useful reference for managers.

Comparison of the Results of the Edas and Vikor Methods in the Decision Support System for KIP Scholarship Recipients at Malikussaleh University

Many high school graduates do not go on to higher education due to the high cost of education. To address this issue, the government, educational institutions and related parties can work together to find solutions to reduce financial barriers and increase access to higher education. In finding prospective students who are eligible to receive scholarships, the government must be fair and precise in making decisions regarding KIP scholarship recipients. This research uses EDAS and VIKOR methods in the selection of prospective KIP scholarship recipients at Malikussaleh University. This study uses historical data of previous KIP scholarship recipients as test data, where there are 6 criteria used, namely parent status, parent income, number of parent dependents, parent employment, parent home ownership, and parent home area. The application of the EDAS method resulted in Fauzi (230510004) as the first rank with an AS value of 0.850; then Hernita Ceria (230180023) as the second rank with an AS value of 0.791; and Ipadayan (230340033) as the third rank with an AS value of 0.789. In the application of the VIKOR method results, Halimah Murni (230210176) as the first rank with a Q value of 0.000; then Nauval Alzabar (230110077) as the second rank with a Q value of 0.015; and Muhammad Bukhari (230110040) as the third rank with a Q value of 0.024. 4. The results of this study state that the EDAS and VIKOR methods are methods that have different types. The EDAS method ranks alternatives based on their distance from the average solution, resulting in a ranking of alternatives based on how close they are to the optimal solution. While the VIKOR method ranks alternatives and provides the best compromise solution that best matches the applied criteria.

Optimization of a damper system for noise and vibration reduction in a PMSM

PurposeThis paper aims to optimize passive damper system (PDS) design by configuring its parameters to improve its performance and behavior in permanent magnet synchronous machines (PMSM).Design/methodology/approachFirst, the principle and effectiveness of the PDS are recalled. Second, the impact of different PDS parameters on its operation is analyzed. Third, a multi-objective optimization is proposed to explore a compromise design of PDS. Finally, the transient finite element method simulation is performed to validate the optimized design, which can ensure an excellent noise reduction effect and weaker negative impacts.FindingsA suitable capacitance value in PDS is a key to realizing the damping effect. A larger copper wire can improve the noise reduction performance of PDS and reduce its Joule losses. A compromise solution obtained from a multi-objective optimization remains the excellent noise reduction and reduces Joule losses.Originality/valueThis paper explores the impact of PDS parameters on its operation and provides an orientation of PDS optimization, which is favorable to extend its application in different electrical machines.

Hazard Identification and Risk Assessment for Sustainable Shipyard Floating Dock Operations: An Integrated Spherical Fuzzy Analytical Hierarchy Process and Fuzzy CoCoSo Approach

Background: This study investigated the process of selecting sustainable safety protocols for floating dock operations in shipyards by identifying potential workplace risks in emergency situations. Thirteen occupational hazards for shipyard floating dock operations were identified through a literature review and expert discussions. Methods: We incorporated four risk elements (consequence: C, frequency: F, probability: P, and number of people at risk: NP) from the Fine–Kinney and Hazard Rating Number System (HRNS) approaches as the risk assessment criteria. We obtained the importance weights of the risk assessment criteria via the Spherical Fuzzy Analytical Hierarchy Process (SF-AHP) and extended the Combined Compromise Solution (CoCoSo) method within the fuzzy framework to prioritize occupational hazards. This study demonstrated the practicality and efficiency of the proposed emergency risk assessment model for shipyard floating dock operations through a case example of occupational risk assessment. Results: The analysis results show that H4 is the occupational hazard with the highest priority, with a score of 3.553. H4 represents the hazard associated with insufficient access to the entire pool area. The second and third most important hazards are the inability of cranes to move freely in and out of the berthing dock and the inability to dispatch emergency teams. These hazards, denoted H1 and H12, follow closely behind with scores of 3.391 and 3.344, respectively. H10 is deemed the least concerning hazard, with a score of 1.802. Conclusions: Professionals can handle complex and uncertain risk assessment data more flexibly using the proposed system, which excels in accurately organizing occupational hazards.

Play cards, salt, tobacco: trade restrictions of German Zollverein in 1833

Importance. The main objective of the study is to find the special regulations regarding some trade goods. The research is focused on the comprehensive study of restrictions of German Zollverein’s treaty, which had affected the local and foreign trade. The analysis has formed the complex of reasons which led to the special arrangements towards the important goods. The study of particular aspects of customs reforms shows the new perspectives in the research of this topic and fulfills the image of trade connections on German territories in 1820–30s.Materials and Methods. The main document for the research is “The treaty of customs unification” from March 22, 1833. There are also plenty of commercial press articles of that period studied, which overviewed the trade and customs changes and reforms. The analytics and synthesis methods give the possibility to have a full overview of the mentioned sources and to restore the main tendencies in trade restrictions of this epoch.Results and Discussion. The detailed analysis highlights the most important trade goods on the German area, which were the subjects of interests for many German governments. There were the monopoly goods or the sources of big custom duties, which filled the German treasuries for decades. The threat of losing this income due to established German Zollverein, which customs taxes were supposed to be summarized and parted, led to the compromise solution in regards to some goods. The play cards, salt, tobacco and alcohol production were supposed to stay under cautious control, the trade of them was specially mentioned on the extra conditions.Conclusion. Trade restrictions seemed to be the compromise arrangement in the customs treaty, which aimed to keep the state income safe. They were also based on law differences regarding mentioned goods and supposed to protect German industry from foreign competition.

Short-term stochastic multi-objective optimization scheduling of wind-solar-hydro hybrid system considering source-load uncertainties

Uncertainties in energy outputs (source side) and load side are simultaneously present and cannot be ignored in the actual operation of multi-energy systems. Adopting a reasonable uncertainty modeling method and understanding the impact of source-load uncertainties on optimization scheduling are key to formulating accurate and effective multi-energy scheduling plans. To address the uncertainties on both the source side and the load side in wind-solar-hydro hybrid systems, this paper proposes a multi-objective optimization scheduling model based on stochastic programming theory. The model aims to maximize the net profit of the system's power generation and minimize the fluctuation of the remaining load. It employs Vine-Copula coupled with Monte Carlo simulation and the deep learning method TimeGAN to generate joint wind and solar power output and load scenario sets. The generated source-load uncertainty scenarios are then reduced to representative scenarios using the K-Means clustering method, which are used as inputs for the scheduling model. The proposed model is applied to a wind-solar-hydro energy base in China, and the results show that: 1) The Vine-Copula-based source-side scenario generation method can quantitatively consider the correlations among meteorological factors. The relative errors of the generated scenarios' statistics compared to the original data are all less than 5%, and the relative errors of the correlation coefficients are less than 10%. 2) The TimeGAN-based load-side scenario generation method avoids the presupposition of the load probability distribution. Compared to the original data, the generated scenarios have R2 and Pearson correlation coefficients of 0.77 and 0.87, respectively. Additionally, TimeGAN shows significant advantages over traditional random sampling methods in simulating extreme scenarios. 3) Both source-side and load-side uncertainties significantly impact the optimization scheduling results of multi-energy systems, leading to increased fluctuation of the remaining load and decreased net profit. 4) The combined source-load uncertainties have a synergistic negative impact on the multi-objective optimization scheduling results. 5) The Pareto front of the optimization results is a concave function with low marginal benefits. Decision-makers should adopt a compromise solution as a guide for the operation of multi-energy systems.

Harnessing Vietnam's coastal potential: Prioritizing marine energy technologies with an objectively weighting decision-making approach

Vietnam's potential to harness marine energy for sustainable development is significant due to its long coastline, yet this potential remains underutilized as current renewable energy efforts focus mainly on wind and solar power. This study addressed the challenge of selecting optimal Marine Energy Technologies (METs) for Vietnam's specific environmental and socio-economic context. A streamlined methodology, integrating the Method based on the Removal Effects of Criteria (MEREC) and the Spherical Fuzzy Combined Compromise Solution (SF CoCoSo) methods, facilitated a multi-criteria analysis that distilled expert opinions into a decision-making framework. The study identified Point Absorbers (PAB) as the most promising MET for Vietnam, with Overtopping Devices (OTD) and Salinity Gradient Power (SGP) also ranking well based on key factors like readiness and cost. This study introduces a methodology that, with a more comprehensive data set including various marine energy technologies, could provide valuable insights for decision-makers in Vietnam's marine energy sector.

The need for renewal of the real estate cadastre on the territory of Vojvodina, Republic of Serbia

The aim of this paper is to perform ranking of five cadastral municipalities in the territory of the administrative municipality of Pancevo, in which land consolidation was not carried out, in order to determine the sequence for carrying out work on the renewal of the geodetic survey. The authors used the VIKOR method, as one of the multi-criteria optimization methods. The list of eight criterion functions (fi) and six sub-criteria (kij) was adopted for the multi-criteria optimization of the renewal of real estate cadastre in the research area. The multi-criteria ranking list of cadastral municipalities as alternative solutions, and the proposal of a compromise solution were determined for four different combinations of weights ωi, by which different relative importance of criterion functions is expressed. The obtained results, in a form of ranking lists for every combination of weights, allowed creating the hierarchy of the urgency of the commencement of works on the renewal of real estate cadastre. One of the problems in Serbian cadastre is that in all cadastral municipalities on the territory of Vojvodina, in which land consolidation has not been carried out, cadastral plans of the "old" graphical survey are still in use. The fact that real estate cadastre is still dealing with graphical survey data points to the need for the renewal of the real estate cadastre in the cadastral municipalities in which land consolidation was not carried out. The renewed, which would also mean contemporary and current, real estate cadastre represents a good foundation for making well-informed decisions relating to nature conservation, resolution of water-related issues, or enhancement of agricultural practices.

Multi-objective optimization of forest ecosystem services under uncertainty

Forest ecosystems deliver multiple services crucial for human wellbeing and welfare, with complex relationships among forest ecosystem services (FESs). Implementing sustainable forest management (SFM) policy requires insights into trade-offs, conflicts and synergies among key FESs, necessitating decision-support tools for multiple objectives. However, uncertainty in key parameters adds complexity. We formulated a tri-objective optimization problem concentrating on three vital ecosystem services in Nordic boreal forests: timber supply, climate change mitigation, and biodiversity conservation. Furthermore, future timber price uncertainty was integrated into the optimization model. Utilizing the TreeSim individual-tree growth and yield simulator, we simulated forest growth and yields, carbon stocks, and biodiversity values. Pareto-optimal solutions were found for four distinct management strategies using the epsilon-constrain method to solve the optimization problem. These management strategies differed in spatial restrictions on harvest location and timing, including considerations like harvest adjacency, green-up and maximum harvest opening area (MOA). The analysis revealed trade-offs among all three objectives, with variations influenced by management intensity. Furthermore, within the Pareto solutions for each strategy, a compromise solution was identified based on the knee-point method. The study demonstrated nuanced impacts of green-up constraints related to harvest practices, showing minor effects on harvesting, carbon sequestration, and biodiversity conservation when MOA was restricted to 35 hectares or less. However, more substantial impacts were observed with a MOA limited to 60 hectares or in a strategy without spatial restrictions. Our results underscore the importance of considering multiple objectives in forest management and policy under uncertain wood prices, preventing undesired effects from a singular or deterministic approach. This work provides insights into trade-offs and synergies, contributing to strategic planning and policy design.

Li-ion battery state of health prediction through metaheuristic algorithms and genetic programming

Predicting the State of Health (SOH) of the Lithium-ion battery with higher accuracy and reduced cost is a challenging and crucial task for ensuring its reliability and safety. To achieve this, a two-stage prediction framework is proposed to find a concise expression of SOH using the health features of the battery through metaheuristic algorithms and genetic programming (GP).In Stage-I, three conflicting objectives are considered concurrently: the root-mean-square error (RMSE) of SOH prediction, the health features selected, and the expressional complexity of the battery capacity. The wrapper structure is utilized for SOH prediction, where a binary multi-objective grey wolf optimization (Binary MOGWO) algorithm is employed to select features and generate the Pareto set. Genetic programming is used to calculate the SOH value using symbolic regression models. In Stage-II, the final compromise solution is filtered from the Pareto set through decision-making approaches. The relationships between selected features and the capacity of the battery are investigated. The NASA Prognostics Center of Excellence battery dataset is chosen to verify the effectiveness of the proposed framework. The simulation results show that the features found through the framework can provide SOH predictions in the form of symbolic equations with higher accuracy, lower cost, and reduced complexity.

Bombay Stock Exchange Portfolio Ranking using Combined Compromise Solution (CoCoSo) Method

One of the decision problems in the financial domain is portfolio selection in investor’s point of view on the other hand portfolio management is in management point of view ,in both the cases ranking of the portfolio will be required.. While facing the complex market competitions, under the extremely competitive business environment financial institutions try their best to make an ultimate policy for portfolio selection to optimize the investor returns. Multi criteria decision making (MCDM) is one of the techniques which can be applied in better way to evaluate portfolio performance and finally decide ranking of the portfolio based on the multiple conflicting criteria of the indices. A portfolio may have many criterion such as low ,high, dividend, yearly return, price earnings ratio (P/E),price-to-book ratio(P/B) etc.. In this research work a popular MCDM method: A combined compromise solution (CoCoSo) is applied to obtain the rank of portfolio for further decision making process. Data of ten portfolios of Bombay Stock Exchange (BSE) namely BSE SENSEX, BSE GREENEX,BSE CARBONEX, BSE AUTO, BANKEX, BSE ENERGY, HEALTHCARE,IT, Power, Gas and Oil financial years: 2022-23 are collected for the study and to find out best portfolio. After applying COCOSO method BSE SENSEX is found to be better than other portfolios as first rank consistently for all three financial years

Rooftop solar potential in micro, small, and medium size enterprises: An insight into renewable energy tapping by decision-making approach

Rooftop solar micro-power plants mitigate pollution and transmission problems in crowded Indian cities. The Indian government is exploring alternate solutions, and the micro, small, and medium enterprises (MSME) sector is also coming forward to opt for sustainable solutions. This study is focused on the MSME segment in India, which constitutes approximately 63 million entities. Installing a solar system for which a rooftop could be the best place to tap solar energy from these entities is required. Checking the rooftop solar potential of any MSME entity requires a suitable technique to decide which sector to target first and the further order. A unique methodology based on multi-criteria decision-making (MCDM) methods is presented to rank 10 MSME alternatives with seven criteria. It includes the measurement of alternatives and ranking according to compromise solution (MARCOS) to rank MSME sectors in India with rooftop solar potential. Weights of importance are assigned with three objective techniques and combined with the Bonferroni operator. The ranks attained are compared with different MCDM methods, and sensitivity analysis is performed while changing weights. Kendall and Spearman rank correlation coefficients are utilized to access the correlation levels between various strategies. Finally, Garrett’s ranking technique is used to finalize the different ranks. The results reveal that the MARCOS method performed well, and “textiles” ranks first, followed by “auto and engineering products.” As a result, the leading industrial sectors will help improve energy utilization while concentrating on renewable energy resources.

Intelligent Energy System and Power Metering Optimization Based on the Energy Plan Model

With the national carbon neutrality and carbon peaking policies proposed one after another, this paper proposes intelligent Energy system design and power metering based on the Energy Plan model. In this paper, the Energy PLAN model is proposed as the basic model for energy development planning during the 14th Five-Year Plan period, and the simulation calculation is carried out for the energy system in 2020. The research shows that there is a large gap between the peak and valley of power load, the peak time of power supply and demand does not match, and the response-ability of the energy supply department is poor. Thus, the problems to be solved in the development of energy are determined. In addition, the accuracy of the model is further verified by comparing the actual data with the simulation results of Energy PLAN, and the direction of energy structure reform is determined. By coupling the NSGA-II (Non-dominated Sorting Genetic Algorithms-II) algorithm with the development model, a dual-objective multi-time scale scheduling model is established, which aims at the minimum operating cost and the best energy and environmental benefit coefficient. Secondly, the proposed model is solved using the NSGA-II algorithm. In order to ensure the diversity of solutions and promote the Pareto front approach to the ideal Pareto front, the fuzzy dominance method is used to perform a fast non-dominated sorting of the population, and the maximum satisfaction method is used to select the Pareto optimal compromise solution. Example analysis shows that when the power demand level is L*-L*-L*, the power input under “H-M”, “M-L” and “H-L” scenarios is 106.02 × 103, 102.73 × 103 and 94.2 × 103 GWh, respectively. However, when the power demand level is H*-L*-L*, the power input is always 142.64 × 103 GWh. With the decrease in the transmission rate, the measurement data obtained by the user’s electric acquisition platform becomes less and less, and the performance of state estimation becomes worse. When λ=50%, the proposed model still maintains a high estimation accuracy.

A multi-objective optimisation approach with improved pareto-optimal solutions to enhance economic and environmental dispatch in power systems

This work implements the recently developed nth state Markovian jumping particle swarm optimisation (PSO) algorithm with local search (NS-MJPSOloc) awareness method to address the economic/environmental dispatch (EED) problem. The proposed approach, known as the Non-dominated Sorting Multi-objective PSO with Local Best (NS-MJPSOloc), aims to enhance the performance of the PSO algorithm in multi-objective optimisation problems. This is achieved by redefining the concept of best local candidates within the search space of multi-objective optimisation. The NS-MJPSOloc algorithm uses an evolutionary factor-based mechanism to identify the optimum compromise solution, a Markov chain state jumping technique to control the Pareto-optimal set size, and a neighbourhood’s topology (such as a ring or a star) to determine its size. Economic dispatch refers to the systematic allocation of available power resources in order to fulfill all relevant limitations and effectively meet the demand for electricity at the lowest possible operating cost. As a result of heightened public consciousness regarding environmental pollution and the implementation of clean air amendments, nations worldwide have compelled utilities to adapt their operational practises in order to comply with environmental regulations. The (NS-MJPSOloc) approach has been utilised for resolving the EED problem, including cost and emission objectives that are not commensurable. The findings illustrate the efficacy of the suggested (NS-MJPSOloc) approach in producing a collection of Pareto-optimal solutions that are evenly dispersed within a single iteration. The comparison of several approaches reveals the higher performance of the suggested (NS-MJPSOloc) in terms of the diversity of the Pareto-optimal solutions achieved. In addition, a measure of solution quality based on Pareto optimality has been incorporated. The findings validate the effectiveness of the proposed (NS-MJPSOloc) approach in addressing the multi-objective EED issue and generating a trade-off solution that is both optimal and of high quality. We observed that our approach can reduce ∼\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sim $$\\end{document}6.4% of fuel costs and ∼\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sim $$\\end{document}9.1% of computational time in comparison to the classical PSO technique. Furthermore, our method can reduce ∼\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sim $$\\end{document}9.4% of the emissions measured in tons per hour as compared to the PSO approach.