Search Results (9)

With the rapid development of urban rail transit, train operation control is required to meet increasingly stringent demands in terms of energy consumption, comfort, punctuality, and precise stopping. The optimization and tracking control of speed profiles are two critical issues in ensuring the performance of automatic train operation systems. However, conventional model predictive control (MPC) methods are highly dependent on parameter settings and show limited adaptability, while heuristic optimization approaches such as the whale optimization algorithm (WOA) often suffer from premature convergence and insufficient robustness. To overcome these limitations, this study proposes an optimized model predictive controller using the multi-objective whale optimization algorithm (MPC-MOWOA) for urban rail train tracking control. In the improved optimization algorithm, a nonlinear convergence mechanism and the Tchebycheff decomposition method are introduced to enhance convergence accuracy and population diversity, which enables effective optimization of the initial parameters of the MPC. During real-time operation, the MPC is further enhanced by integrating a fuzzy satisfaction function that adaptively adjusts the softening factor. In addition, the control coefficients are corrected online according to the speed error and its rate of change, thereby improving adaptability of the control system. Taking the section from Lvshun New Port to Tieshan Town on Dalian Metro Line 12 as the study case, the proposed control algorithm was deployed on a TMS320F28335 embedded processor platform, and hardware-in-the-loop simulation experiments (HILSEs) were conducted under the same simulation environment, a unified train dynamic model, consistent operating conditions, and an identical evaluation index system. The results indicate that, compared with the Fuzzy-PID control method, the proposed control strategy reduces the integral of time-weighted absolute error nearly by 39.6% and decreases energy consumption nearly by 5.9%, while punctuality, stopping accuracy, and comfort are improved nearly by 33.2%, 12.4%, and 7.1%, respectively. These results not only verify the superior performance of the proposed MPC-MOWOA, but also demonstrate its capability for real-time implementation on embedded processors, thereby overcoming the limitations of purely MATLAB-based offline simulations and exhibiting strong potential for practical engineering applications in urban rail transit. Full article

A critical process underpinning the sustainability of the global economy is the reliable and efficient supply of crude oil from tank farms to international markets. The crude oil terminal serves as the central component of this process. The availability of crude oil in tank farms and the scheduling of oil carriers are key operational decisions made at the oil terminals. This study introduces a novel multi-objective mixed-integer programming (MIP) model for Crude Oil Terminal Scheduling (COTS). The model is an extension of our earlier mathematical modeling framework on COTS. The primary objective of the proposed COTS model is to enhance customer satisfaction by reducing the deviation between actual loading dates and customers’ preferred loading dates. Secondary objectives include managing crude oil inventory levels and reducing the frequency of tank service changes. The model’s results support decision-makers in both scheduling oil carriers and allocating tank storage capacities while enabling dynamic adjustments of tank operations across multiple crude oil types. To address multiple objectives, in this work, several solution approaches, namely the weighted sum (WS) approach, the hierarchical optimization (HO) approach, and the weighted Tchebycheff (WT) approach, are developed. Novel ordering and scaling methods in handling the multiple objectives for COTS using HO and WT are proposed in this paper. The validity of the proposed model and the effectiveness of its solutions are demonstrated through a numerical case study. Based on the numerical analysis, it is estimated that the Pareto-based approaches like HO and WT increase the solution time by ≈400% in the presence of four objectives. However, the Pareto approaches provide a spectrum of operational points to the decision-maker. Finally, key findings and future research paths are discussed. Full article

Emerging in-vehicle applications seek to improve travel experiences, but the rising number of vehicles results in more computational tasks and redundant content requests, leading to resource waste. Efficient compute offloading and content caching strategies are crucial for the Internet of Vehicles (IoV) to optimize performance in time latency and energy consumption. This paper proposes a joint task offloading and content caching optimization method based on forecasting traffic streams, called TOCC. First, temporal and spatial correlations are extracted from the preprocessed dataset using the Forecasting Open Source Tool (FOST) and integrated to predict the traffic stream to obtain the number of tasks in the region at the next moment. To obtain a suitable joint optimization strategy for task offloading and content caching, the multi-objective problem of minimizing delay and energy consumption is decomposed into multiple single-objective problems using an improved Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) via the Tchebycheff weight aggregation method, and a set of Pareto-optimal solutions is obtained. Finally, the experimental results verify the effectiveness of the TOCC strategy. Compared with other methods, its latency is up to 29% higher and its energy consumption is up to 83% higher. Full article

(1) Background: In this study on Robotic Process Automation (RPA), the feasibility of sustainable RPA implementation was investigated, considering user requirements in the context of this technology’s stakeholders, with a strong emphasis on sustainability. (2) Methods: A multi-objective mathematical model was developed and the Weighted Sum and Tchebycheff methods were used to evaluate the efficiency of the implementation. An enterprise case study was utilized for data collection, employing investigation hypotheses, questionnaires, and brainstorming sessions with company stakeholders. (3) Results: The results underscore the significance of user requirements within the RPA landscape and demonstrate that integrating these requirements into the multi-objective model enhances the implementation assessment. Practical guidelines for RPA planning and management with a sustainability focus are provided. The analysis reveals a solution that reduces initial costs by 21.10% and allows for an efficient and equitable allocation of available resources. (4) Conclusion: This study advances our understanding of the interplay between user requirements and RPA feasibility, offering viable guidelines for the sustainable implementation of this technology. Full article

Infectious waste disposal is a crucial concern in many areas. Not only is the waste obnoxious, but it can also pose a vital risk to human health. Disposal of infectious waste incurs higher costs than general waste disposal and must abide by stricter regulations. In this paper, the infectious waste disposal is formulated as a multi-objective optimization model. The objectives encompass economic, social, and environmental concerns. To save cost, waste transshipment facilities to function as consolidation points are proposed and integrated in the model. The economic objective includes construction and operational costs of the transshipment and disposal facilities. The social objective considers the communities surrounding the disposal facilities, while carbon dioxide emission is used as the measure in the environmental objective. The model is reformulated based on the lexicographic weighted Tchebycheff method to ensure that the Pareto frontier of the solutions is obtained. Then the model is applied to a health region in Thailand. Daily and every-other-day waste collection intervals are compared to examine additional benefits. Certain sensitivity of the solutions is also analyzed. After comparing several solutions, a compromise among all three objectives is suggested. It is composed of three transshipment and two disposal facilities, each with 1000 kg capacity. Moreover, if the solution is executed with the every-other-day waste collection interval, the overall costs can be saved. A sensitivity analysis of the solution on fuel price found that the solution was not very sensitive against an increase in the fuel price, in that when the fuel price increased by 20% the overall costs only increased by 7%. Lastly, when the daily infectious wastes are doubled, all the objective function values rise, ranging from 56% to 163%. The new solution suggests an increase in the number of the disposal facilities to four, but a decrease of the transshipment ones to only two. Full article

Decomposition-based evolutionary algorithms are popular with solving multi-objective optimization problems. It uses weight vectors and aggregate functions to keep the convergence and diversity. However, it is hard to balance diversity and convergence in high-dimensional objective space. In order to discriminate solutions and equilibrate the convergence and diversity in high-dimensional objective space, a two-archive many-objective optimization algorithm based on D-dominance and decomposition (Two Arch-D) is proposed. In Two Arch-D, the method of D-dominance and adaptive strategy adjusting parameter are used to apply selection pressure on the population to identify better solutions. Then, it uses the two archives’ strategy to equilibrate convergence and diversity, and after classifying solutions in convergence archive, the improved Tchebycheff function is used to evaluate the solution set and retain the better solutions. For the diversity archive, the diversity is maintained by making any two solutions as far apart and different as possible. Finally, the Two Arch-D is compared with other four multi-objective evolutionary algorithms on 45 many-objective test problems (including 5, 10 and 15 objectives). Good performance of the algorithm is verified by the description and analysis of the experimental results. Full article

In this paper, a mathematical model for an extended multi-objective portfolio selection (EMOPS) problem is explored with liquidity considered as another objective function besides the risk and return. The model is mathematically formulated in an uncertain environment. The concerned uncertainty is dealt with by employing the fuzzy numbers in the risk matrix and return. While the fuzzy EMOPS model is converted into the corresponding deterministic case based on the $\alpha —$level sets of the fuzzy numbers, a weighted Tchebycheff method is implemented by defining relative weights and ideal targets. The merit of the suggested method is the applicability in many real-world situations. At the end, some numerical illustration is exhibited for the utility of the suggested EMOPS problem. Finally, it is concluded that the suggested method is simple to learn and to implement in real-life situations for the decision maker. Full article

In this paper, we study an unmanned-aerial-vehicle (UAV) based full-duplex (FD) multi-user communication network, where a UAV is deployed as a multiple-input–multiple-output (MIMO) FD base station (BS) to serve multiple FD users on the ground. We propose a multi-objective optimization framework which considers two desirable objective functions, namely sum uplink (UL) rate maximization and sum downlink (DL) rate maximization while providing quality-of-service to all the users in the communication network. A novel resource allocation multi-objective-optimization-problem (MOOP) is designed which optimizes the downlink beamformer, the beamwidth angle, and the 3D position of the UAV, and also the UL power of the FD users. The formulated MOOP is a non-convex problem which is generally intractable. To handle the MOOP, a weighted Tchebycheff method is proposed, which converts the problem to the single-objective-optimization-problem (SOOP). Further, an alternative optimization approach is used, where SOOP is converted in to multiple sub-problems and optimization variables are operated alternatively. The numerical results show a trade-off region between sum UL and sum DL rate, and also validate that the considered FD system provides substantial improvement over traditional HD systems. Full article

Failure Mode and Effect Analysis (FMEA) is a useful risk assessment tool used to identify, evaluate, and eliminate potential failure modes in numerous fields to improve security and reliability. Risk evaluation is a crucial step in FMEA and the Risk Priority Number (RPN) is a classical method for risk evaluation. However, the traditional RPN method has deficiencies in evaluation information, risk factor weights, robustness of results, etc. To overcome these shortcomings, this paper aims to develop a new risk evaluation in FMEA method. First, this paper converts linguistic evaluation information into corresponding interval-valued intuitionistic fuzzy numbers (IVIFNs) to effectively address the uncertainty and vagueness of the information. Next, different priorities are assigned to experts using the interval-valued intuitionistic fuzzy priority weight average (IVIFPWA) operator to solve the problem of expert weight. Then, the weights of risk factors are subjectively and objectively determined using the expert evaluation method and the deviation maximization model method. Finally, the paper innovatively introduces the interval-valued intuitionistic fuzzy weighted averaging (IVIFWA) operator, Tchebycheff Metric distance, and the interval-valued intuitionistic fuzzy weighted geometric (IVIFWG) operator into the ratio system, the reference point method, and the full multiplication form of MULTIMOORA sub-methods to optimize the information aggregation process of FMEA. The extended IVIF-MULTIMOORA method is proposed to obtain the risk ranking order of failure modes, which will help in obtaining more reasonable and practical results and in improving the robustness of results. The case of the Middle Route of the South-to-North Water Diversion Project’s operation risk is used to demonstrate the application and effectiveness of the proposed FMEA framework. Full article