Integrated energy system (IES) plays a vital role in achieving energy revolution and the goals of carbon peak and carbon neutrality. The optimal planning of IES is of great significance to improve the overall efficiency of the system and promoting its sustainable development. Focusing on this issue, this paper proposes a planning framework integrating multi-objective optimization with fuzzy multi-criteria decision making (MCDM). In this framework, IES planning is modeled as a multi-objective optimization problem that, for the first time, simultaneously minimizes energy consumption, carbon emissions, and economic costs. Thereafter, the optimization problem is solved by a multi-objective state transition algorithm based on decomposition (MOSTA/D), which generates a Pareto set that realizes multiple conflicting objective tradeoffs. Furthermore, to comprehensively evaluate the Pareto optimal solutions, an evaluation criteria system is established from various perspectives, and a novel MCDM approach is proposed. This approach combines the analytic network process-entropy weighting technique, which takes into account the correlation between criteria as well as subjective preference and objective information, with fuzzy TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) for scientifically ranking and selecting solutions under uncertainty. The simulation results of an IES planning case study demonstrate that the optimal scheme determined by the proposed method achieves the best overall benefit for IES, with significant annual economic cost savings, primary energy savings, and carbon dioxide emission reduction rates of 2.27%, 40.36%, and 56.25%, respectively, proving effectiveness and superiority of the proposed method. 

Multi-objective optimization and decision making for integrated energy system using STA and fuzzy TOPSIS

The development of artificial intelligence and the recent COVID-19 pandemic as well as the emergence of other diseases has led to dramatic changes in the overall supply chain development. The choice of a suitable supplier will be the key to ensuring sustainable development of the company and the normal operation of the overall supply chain. However, the evaluation data of the supplier selection contains both qualitative and quantitative data simultaneously. In addition, the evaluation information provided by experts often includes some incomplete and hesitant information. These reasons lead to the complexity of supplier selection. Traditional supplier selection calculation methods ignore the objective weight considerations and thus lead to biased assessment results. The main goal of this study is to overcome the limitations of conventional supplier selection methods, fully consider the subjective and objective weights of the evaluation criteria and deal with incomplete information for providing more correct supplier ranking results. A stepwise weight assessment ratio analysis (SWARA) method, the 2-tuple linguistic representation method, and the combined compromise solution (CoCoSo) were applied in this study to solve the problem of supplier selection. To verify the rationality and correctness of the proposed method, the third-party logistics supplier selection was used as the illustrated example in the numerical validation section. The simulation results confirm that the proposed method can effectively deal with supplier selection with unclear information and can provide more reasonable supplier ranking results.

/pdf/integrating-subjective-objective-weights-consideration-and-a-rnr5xm5c.pdf

Integrating Subjective-Objective Weights Consideration and a Combined Compromise Solution Method for Handling Supplier Selection Issues

This article aims to introduce and investigate the analytical fuzzy solution of the incommensurate non-homogeneous system of fuzzy linear fractional differential equations (INS-FLFDEs) using trivariate Mittag-Leffler functions. Entries of the coefficient matrix of the given system are treated as real numbers, initial-values are triangular fuzzy numbers (TFNs), and the forcing function is a fuzzy set (or a bunch) of real function. We extract the potential solution in the form of a fuzzy bunch of real functions (FBoRFs) rather than the solution of fuzzy-valued functions. We formulate the fuzzy initial value problem as a set of classical initial value problems by taking the forcing function from the class of FBoRFs and the initial value from the collection of TFNs (as a special case). The solution of this system is in the form of a trivariate Mittag-Leffler function. We interpret this solution as an element of the fuzzy solution set and assign the minimum value of membership that takes from the forcing function and the initial value in the fuzzy set. The originality of the proposed technique is that the uncertainty is smaller compared to the uncertainty extracted from other techniques. In addition, generalized derivatives increase the order and dimension of the system. Therefore, the proposed technique is better in terms of complexity because it reduces the order and dimension of the system. Finally, to grasp the proposed technique, we solve the electrical network and multiple mass-spring systems as applications and analyze their graphs to visualize and support theoretical results. 

Incommensurate non-homogeneous system of fuzzy linear fractional differential equations using the fuzzy bunch of real functions

A Novel Combinational Response Mechanism for Dynamic Multi-Objective Optimization

Unmanned aerial vehicles (UAVs) require pre-planned flight paths that are energy-efficient, safe and smooth across their wide range of application scenarios. In this study, a novel UAV path planning method is proposed. Firstly, the UAV path planning under numerous obstacles is modelled as a continuous constrained optimization problem. The cost function is formulated as a linear combination of length, height variation, and smoothness, while the constraints include obstacle avoidance, height limitation, and the maneuverability of UAV. Subsequently, a novel constrained state transition algorithm with adaptive fuzzy penalty (AFSTA) is proposed to solve the optimization problem. In AFSTA, a novel adaptive fuzzy penalty function is designed to leverage expert knowledge to establish a reasonable mapping relationship from the fitness value and the degree of constraint violation to the penalty factor for a candidate solution. Meanwhile, the state transition algorithm (STA) is used as the search engine for both global and local search. Experimental results illustrate that the proposed method can find energy-efficient, safe, and maneuverable flight paths successfully with the superiority over other state-of-the-art metaheuristic algorithms. 

A novel state transition algorithm with adaptive fuzzy penalty for multi-constraint UAV path planning

Dynamic multi-objective optimization and fuzzy AHP for copper removal process of zinc hydrometallurgy

It is necessary to change the parameters of PID controller if the parameters of plants change or there are disturbances. Particle swarm optimization algorithm is a powerful optimization algorithm to find the global optimal values in the problem space. In this paper, the particle swarm optimization algorithm is used to identify the model of the plant and the parameter of digital PID controller online. The model of the plant is identified online according to the absolute error of the real system output and the identified model output. The digital PID parameters are tuned based on the identified model and they are adaptive if the model is changed. Simulations are done to validate the proposed method comparing with the classical PID controller.

Adaptive Optimal Digital PID Controller

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Dynamic multi-objective optimization and fuzzy AHP for copper removal process of zinc hydrometallurgy

Adaptive Optimal Digital PID Controller