Optimizing Three-Dimensional Constrained Ordered Weighted Averaging Aggregation Problem with Bounded Variables

Tang, Hung Jen; Yang, Shen-Tai

doi:10.3390/math6090172

Cited by 4 publications

(3 citation statements)

References 28 publications

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“…In order to further optimize the results, after replacing the original several objective functions with the ratio form in the above equation, the genetic algorithm iteration is re-performed, and the entropy weighting method is also improved to process the data: a combination of subjective and objective weights is used, and based on the introduction of the COWA operator (Carlsson et al, 2003), the TOPSIS method ( Tang and Yang, 2018) and game theory (Kannan and Navneethakrishnan, 2020) to evaluate and rank the individual solutions of the Pareto optimal solution set. This requires a modification of Eq.…”

Section: (1)→8→12→18→21→27→30→[36]→ 31→27→21→14→12→4→(1) …… ……mentioning

confidence: 99%

Scheduling Optimization of Electric Trucks for Mines Based on Energy Recovery and Hybrid Genetic Algorithm

Yan,

Yu,

吴

2024

Preprint

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In the context of increasingly stringent fuel consumption regulations, new energy trucks have become one of the equipment for mining transportation. However, the batteries of new energy trucks consume a significant amount of energy, necessitating rational planning and scheduling to reduce energy consumption. This paper explores a mine transportation scheduling solution based on a hybrid genetic algorithm, with special consideration given to the energy recovery characteristics of electric trucks. Through an analysis of battery energy consumption and recovery during truck operation, and a comprehensive consideration of factors such as transportation time between various mine nodes, road slope and length, as well as node mining and queuing time, the constraints of transportation route selection and vehicle scheduling were examined. The study establishes a multi-objective optimization model that seeks a reasonable structure while taking into account maximizing transportation volume and minimizing costs.The Dijkstra algorithm, combined with the breadth-first search algorithm, is employed to calculate suitable paths. Subsequently, the non-dominated sorting genetic algorithm is utilized for vehicle task allocation through a unique genetic encoding method. Mathematical methods, including entropy weighting, are incorporated to address the multi-objective weighting problem of automatic truck scheduling. To enhance the realism of the solution, the model also accounts for time-varying changes in road congestion and road bifurcation points traversed by truck routes. Finally, simulation experiments validate that the established energy optimization model has a positive impact on mine operations by increasing mining transportation volume, reducing costs, and minimizing wasted time. Additionally, the proposed method exhibits robustness and practicality, demonstrating certain fault adjustment capabilities.

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Section: (1)→8→12→18→21→27→30→[36]→ 31→27→21→14→12→4→(1) …… ……mentioning

confidence: 99%

Scheduling Optimization of Electric Trucks for Mines Based on Energy Recovery and Hybrid Genetic Algorithm

Yan,

Yu,

吴

2024

Preprint

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“…They deal with the maximization constrained OWA problem with lower bounded variables and the minimization constrained OWA problem with upper bounded variables. For a three-dimensional case of these models, they present the explicitly optimal solutions theoretically and empirically [8].…”

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confidence: 99%

An Efficient Approach to Solve the Constrained OWA Aggregation Problem

Kim

Ahn

2022

Symmetry

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Constrained ordered weighted averaging (OWA) aggregation attempts to solve the OWA optimization problem subject to multiple constraints. The problem is nonlinear in nature due to the reordered variables of arguments in the objective function, and the solution approach via mixed integer linear programming is quite complex even in the problem with one restriction of which coefficients are all one. Recently, this has been relaxed to allow a constraint with variable coefficients but the solution approach is still abstruse. In this paper, we present a new intuitive method to constructing a problem with auxiliary symmetric constraints to convert it into linear programming problem. The side effect is that we encounter many small sub-problems to be solved. Interestingly, however, we discover that they share common symmetric features in the extreme points of the feasible region of each sub-problem. Consequently, we show that the structure of extreme points and the reordering process of input arguments peculiar to the OWA operator lead to a closed optimal solution to the constrained OWA optimization problem. Further, we extend our findings to the OWA optimization problem constrained by a range of order-preserving constraints and present the closed optimal solutions.

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“…Tang and Yang [4] deal with an ordered weighted averaging aggregation problem, denoted as COWA. For the three-dimensional case, they consider two variants: the maximization variant of the COWA problem with lower bounded variables and the minimization version with upper bounded variables.…”

mentioning

confidence: 99%