Deterministic Agent-Based Path Optimization by Mimicking the Spreading of Ripples

A deterministic and nature-inspired algorithm for the fuzzy multi-objective path optimization problem

Increasing evaluation indexes have been involved in the network modeling, and some parameters cannot be described precisely. Fuzzy set theory becomes a promising mathematical method to characterize such uncertain parameters. This study investigates the fuzzy multi-objective path optimization problem (FMOPOP), in which each arc has multiple crisp and fuzzy weights simultaneously. Fuzzy weights are characterized by triangular fuzzy numbers or trapezoidal fuzzy numbers. We adopt two fuzzy number ranking methods based on their fuzzy graded mean values and distances from the fuzzy minimum number. Motivated by the ripple spreading patterns on the natural water surface, we propose a novel ripple-spreading algorithm (RSA) to solve the FMOPOP. Theoretical analyses prove that the RSA can find all Pareto optimal paths from the source node to all other nodes within a single run. Numerical examples and comparative experiments demonstrate the efficiency and robustness of the newly proposed RSA. Moreover, in the first numerical example, the processes of the RSA are illustrated using metaphor-based language and ripple spreading phenomena to be more comprehensible. To the best of our knowledge, the RSA is the first algorithm for the FMOPOP that can adopt various fuzzy numbers and ranking methods while maintaining optimality.

A Method to Assess and Reduce Pollutant Emissions of Logistic Transportation under Adverse Weather

With the increase of vehicle activities and adverse weather under the background of modernization and global warming, more attention should be paid to vehicle emissions reduction in such circumstances for environmental protection and sustainable transportation. Different from some typical measures, e.g., relevant government policies, improvement of vehicle hardware technologies, and optimization of traffic management, this paper develop a new method based on emergency path optimization to evaluate and reduce pollutant emissions of logistic transportation under adverse weather. Firstly, we establish a calculation model of pollutant emissions (LT-PE model) to calculate the amount of vehicle pollutant emissions produced under adverse weather. Then, a co-evolving path optimization (CEPO) method-based ripple-spreading algorithm is proposed in order to reduce pollutant emissions. To validate the effectiveness of the proposed method, this paper selects fruit logistics transportation affected by typhoon in China’s Hainan Island as a case study. The results show that total vehicle pollutant emissions from the fruit transportation of 35 farms under the typhoon are 28.1% more than when there is no typhoon. The proposed method can reduce pollutant emissions by 21.2% compared with the traditional methods under typhoon.