A novel multi-objective programming model of relief distribution for sustainable disaster supply chain in large-scale natural disasters

Uncertain multi-objective multi-commodity multi-period multi-vehicle location-allocation model for earthquake evacuation planning

Due to the climate change and the rapid progress of urbanization, extreme weather disasters such as urban rainstorm and waterlogging are frequent. Therefore, how to find the waterlogging points in the presence of disasters and how to optimize the distribution of urban emergency logistics and reduce the negative impact of disasters have become a hot and difficult issue for government departments and scholars. First of all, the idea and method of using the big data of microblogging to obtain urban rainstorm and waterlogging disasters and public sentiment are put forward. In addition,this thesis constructed the location-routing problem model of urban emergency logistics in the situation of rainstorm and waterlogging disaster, and found out the dynamic emergency distribution path of Nanjing in the situation of waterlogging disaster by using NSGA-III algorithm. Research shows that the risk management of urban rainstorm and waterlogging disasters, together with social media data, is a feasible way to obtain on-site data of disasters and carry out risk assessment of disasters. At the same time, the emergency logistics location-positioning model and algorithm can provide a reference for similar disaster emergency logistics distribution network and the conclusion can provide empirical reference for cities to cope with rainstorm and waterlogging disasters.

Finding of urban rainstorm and waterlogging disasters based on microblogging data and the location-routing problem model of urban emergency logistics

As an important aspect in disaster operations management, relief distribution has been challenged by lots of factors, such as unpredictable occurrence time, intensity and location of secondary disasters (e.g. aftershocks and landslides, which usually occur after an earthquake), and availability of vehicles. A multi-stage stochastic programming model is developed for disaster relief distribution with consideration of multiple types of vehicles, fluctuation of rental, and the state of road network. The state of road network is characterized using uncertain and dynamic road capacity. The scenario tree is employed to represent the uncertain and dynamic road capacity, and demonstrate the decision process of relief distribution. A progressive hedging algorithm (PHA) is proposed for solving the proposed model in large-scale size. Based on a real-world case of Yaan earthquake in China, numerical experiments are presented to study the applicability of the proposed model and demonstrate the effectiveness of the proposed PHA. Useful managerial insights are provided by conducting numerical analysis.

A multi-stage stochastic programming model for relief distribution considering the state of road network

A fuzzy bi-level optimization model for multi-period post-disaster relief distribution in sustainable humanitarian supply chains

The resource distribution in post-disaster is an important part of emergency resource scheduling. In this paper, we first design a multi-objective optimization model for multi-period dynamic emergency resource scheduling (ERS) problems. Then, using the framework of multi-objective evolutionary algorithm based on decomposition (MOEA/D), an MOEA is proposed to solve this model. In the proposed algorithm, new evolutionary operators are designed with the intrinsic properties of multi-period dynamic ERS problems in mind. The experimental results show that the proposed algorithm can get a set of better candidate solutions than the non-dominated sorting genetic algorithm II (NSGA-II).

A multi-objective evolutionary algorithm for multi-period dynamic emergency resource scheduling problems

Evolutionary algorithms (EAs) have been widely and successfully applied to solve multi-objective optimization problems, due to their nature of population-based search. Population update is a key component in multi-objective EAs (MOEAs), and it is performed in a greedy, deterministic manner. That is, the next-generation population is formed by selecting the first population-size ranked solutions (based on some selection criteria, e.g., non-dominated sorting, crowdedness and indicators) from the collections of the current population and newly-generated solutions. In this paper, we question this practice. We analytically present that introducing randomness into the population update procedure in MOEAs can be beneficial for the search. More specifically, we prove that the expected running time of a well-established MOEA (SMS-EMOA) for solving a commonly studied bi-objective problem, OneJumpZeroJump, can be exponentially decreased if replacing its deterministic population update mechanism by a stochastic one. Empirical studies also verify the effectiveness of the proposed stochastic population update method. This work is an attempt to challenge a common practice for the population update in MOEAs. Its positive results, which might hold more generally, should encourage the exploration of developing new MOEAs in the area.

Stochastic Population Update Can Provably Be Helpful in Multi-Objective Evolutionary Algorithms

In the current paper, we considered the Fisher information matrix from generalized Rayleigh distribution (GR) distribution in moving extremes ranked set sampling (MERSS). The numerical results show that the ranked set sample carry more information about $$\lambda$$ and $$\alpha$$ than a simple random sample of equivalent size. In order to give more insight into the performance of MERSS with respect to (w.r.t.) simple random sampling (SRS), a modified unbiased estimator and a modified best linear unbiased estimator (BLUE) of scale and shape $$\lambda$$ and $$\alpha$$ from GR distribution in SRS and MERSS are studied. The numerical results show that the modified unbiased estimator and the modified BLUE of $$\lambda$$ and $$\alpha$$ in MERSS are significantly more efficient than the ones in SRS. 

Sampling Information for Generalized Rayleigh Distribution with Application to Parameter Estimation

With the promulgation of the Paris Peace Treaty, governments are paying more and more attention to environmental protection, and the world has put forward higher requirements for energy conservation. New energy becomes a focal spot, and more people are willing to invest in the new energy industry. Therefore, the question “which company is the worthiest to invest?” bother every investor. BYD, for example, has been China's new energy vehicle sales champion for four consecutive years. From January to October this year, the company's sales of new energy vehicles reached 172,000 units, increasing its market share to 20 percent and continuing to rank first. The Tesla Model 3, which is owned by Tesla Motors, has sold 158,925 units in the United States, occupying almost half of the sales of new energy vehicles in the United States. This paper takes Tesla as an example to study the influencing factors of new energy stocks, and this paper selects the closing price of Tesla from 2017 to the end of 2021. It studies the corresponding daily USD to RMB exchange rate, Dow Jones index, P/E ratio and CPI data for analysis, using S&P linear regression. In conclusion, the stoke of Tesla is most sensitive to Dow Jones and least sensitive to exchange rate. New energy is an important area of future energy development in the world and the one with the greatest potential. The trend of new energy stocks reflects the stockholders' expectation and bullishness on the future prospect of new energy.

Factors Influencing Stocks in the New Energy Vehicle Industry based on Multiple Linear Regression Models

Subset selection, which aims to select a subset from a ground set to maximize some objective function, arises in various applications such as influence maximization and sensor placement. In real-world scenarios, however, one often needs to find a subset which is robust against (i.e., is good over) a number of possible objective functions due to uncertainty, resulting in the problem of robust subset selection. This paper considers robust subset selection with monotone objective functions, relaxing the submodular property required by previous studies. We first show that the greedy algorithm can obtain an approximation ratio with respect to the correlation and submodularity ratios of the objective functions; and then propose EPORSS, an evolutionary Pareto optimization algorithm that can utilize more time to find better subsets. We prove that EPORSS can also be theoretically grounded, achieving a similar approximation guarantee to the greedy algorithm. In addition, we derive the lower bound of the correlation ratio for the application of robust influence maximization, and further conduct experiments to validate the performance of the greedy algorithm and EPORSS.

Yawen Zhou

Papers

A multi-objective evolutionary algorithm for multi-period dynamic emergency resource scheduling problems

Stochastic Population Update Can Provably Be Helpful in Multi-Objective Evolutionary Algorithms

Sampling Information for Generalized Rayleigh Distribution with Application to Parameter Estimation

Factors Influencing Stocks in the New Energy Vehicle Industry based on Multiple Linear Regression Models

Robust Subset Selection by Greedy and Evolutionary Pareto Optimization