Chang Chunguang

Study on multi-destination emergency scheduling model under dynamic continuous consumption

With the purpose to enhance efficiency and accurate of emergency dispatch, realize reasonable distribution material quantity, reduce life and property loss caused by paroxysmal emergency events, a dynamic continuous consumption emergency scheduling model is established. Some features of emergency scheduling goals are analyzed. The objective function includes minimizing earliest emergency start time and the least disaster relief participated places. Simultaneously, satisfactory degree of disaster area is also considered in the objective function. Two main categories of constraints, namely, continuous consumption, supply and demand quantity are considered. GA is employed to solve the model, and the basic implement step of GA is given in detail. To employ GA easily, the constraints are disposed with penalty function idea so that they are introduced to the fitness value. To validate the validity of GA, the multi disaster places emergency scheduling problem for dynamic and continuous consumption is abstracted. The experiment shows that by multi running, GA can get multi efficient typical solutions so as to provide significant support for decision-making of emergency scheduling. The global search capacity of GA is strong, and it is suitable to solve complex optimization problems such as emergency scheduling problem under dynamic and continuous consumption condition.

Emergency goods scheduling model and algorithm during initial stage of disaster relief

During initial stage of disaster relief, real time requirement and limited relief source quantity are the main features of relief work. To improve the efficiency and precision of emergency goods scheduling, a model is established for the multi-depot and multi disaster places, single category material requirement emergency scheduling problem. The objective of the model includes minimizing task complete time and relief participated vehicles. The model is under the constraints of the relief vehicle number and the specified time by disaster places. Genetic algorithm is adopted to optimize the routes, to determine the number of participated vehicles and routes of varied emergency relief centers, thus the minimum emergency time and vehicles can be obtained. Finally an example is employed to validate the validity of the model and algorithm.

The rationality of comprehensive evaluation and a holistic solution

To improve maneuverability of comprehensive evaluation, and promote its systematism, the rationality of comprehensive evaluation and a holistic solution is analyzed. Four comprehensive evaluation basic processes including determining the evaluated objects, establishing evaluation index system, determining the weight parameters and selecting the comprehensive evaluation model are induced, and the logic process figure is given for comprehensive evaluation. Seventeen basic issues during the process of comprehensive evaluation were analyzed. The basic and related issues are attributed to the rationality of comprehensive evaluation. A holistic solution based on the design and optimization of evaluation flow and primary analysis framework were set up. The basic analyzing frame is established, which includes constructing evaluation design and optimization theory frame, initial evaluation for selecting satisfaction conditions of flow scheme, establishing the feature information table of evaluation method, the second evaluating for the flow scheme set by technology condition selecting and reliability optimization, studying on combination optimization model of effective evaluation flow scheme conclusions, designing and implementing of independently generated knowledge base for comprehensive evaluation flow schemes. Above integrated solution idea will benefit for improving the maneuverability of comprehensive evaluation.

A Feature Reduction Method by Grey Theory and Rough Set

In varied assessment work, feature reduction is a key cycle which benefits for reducing redundancy. It will improve the assessment efficiency, and reduce much burdensome statistical work. Grey theory and variable rough set theory are introduced to propose a feature reduction method. For the empty values of assessment features in past years, the grey theory based predicting method for imperfection information is proposed to dispose empty values. For each continuous feature, based on approximation precision, a continuous feature dispersing method for chronological series is proposed to obtain varied dispersed intervals. Based on the differentiating matrix, a variable precision rough set based feature reduction method is designed, and the antinoise capability is improved. The obtained assessment result by feature reduction is almost the same with that by all features, and the strong error tolerance capability is validated.

Demand Feature Identifying for Emergency Goods under Earthquake Rescue Logistics by Vague Set Based BP Neural Network

During the preliminary stage of rescue for earthquake disaster, some important information needs to be provided by identifying the key demand features such as quantity, necessary degree and urgent degree for emergency goods. To dispose uncertain information, especially unknown information for identifying above features, vague set is introduced into the conventional BP neural network. The relation among supportive degree, counteractive degree and unknown degree are analyzed. For disposing unknown information for vague set, the method for transforming vague set information into fuzzy set information is proposed. A series of rules for transforming vague values into fuzzy values are presented. Hereby, the fuzzy membership degree function is given. The structure of four-layer multi output VBP neural network is designed, and its implement steps are studied. To validate the validity of VBP neural network, it is applied to identify demand feature for emergency goods under earthquake. The result by VBP neural network is compared with those by conventional BP neural network and the VBP neural network which is based on conventional fuzzy membership degree transforming formula. The result shows that, the test precision by above VBP neural network is higher than those by the other two methods. As a novel learning method, VBP neural network is more suitable for training samples with uncertain information.

A multi-category emergency goods distribution model and its algorithm

When large scale emergency incident happens, the multi-category emergency goods distribution is a complex problem. To improve structure and efficiency of emergency goods distribution, a multi-objective non-linear optimization programming model for emergency goods distribution is established. Modeling precondition is analyzed in detail, some objectives such as minimizing the waiting time, the transportation cost, the preference of selecting emergency commodity distribution center (ECDC) and so on are taken into account. The constraints of the model including total demand quantity and structure of disaster area, the transportation quantity and time from each ECDC are considered. The penalty factor method is employed to transform above model into an easy one, then GA is employed. The encoding system for multi-category emergency goods distribution is introduced, and the basic implement steps of GA are given in detail. The typical instance abstracted from practice is adopted to validate the validity of above model and algorithm, the experiment result shows that above model can describe the practical demand of multi-category emergency goods distribution, and GA is suitable for solving some complex non-linear programming problems such as multi-category emergency goods distribution. Above model and algorithm will benefit for the emergency goods distribution practice.

CBR based expert system for house repair after earthquake

To improve practical efficiency and theory feasibility of house repair or reinforce measures so as to reduce life and property loss as could as possible after large scale earthquake, the two stage CBR implement mode is proposed to realize expert system for house repair or reinforce after earthquake. The first stage CBR system is employed to make out house repair strategies, and the second stage CBR systems is employed to make out house repair detail steps and instruction after earthquake. Thus, both the reasoning reaction velocity and the precision are ensured. In both stage CBR systems, four key technologies including case presentation, case retrieval, case adaptation and case maintenance are studied respectively. The system running result shows that, comparing with the conventional single state CBR system, both average case retrieved ratio and average case satisfactory degree by the two stage CBR system are obviously improved. The two stage CBR system is suitable for solving complex and multi disposing problems such as house repair after earthquake.

Energy consumption assessment by AIA based time series scatter degree method

To enlarge the difference of assessment values for energy consumption in equipment manufacturing industry among varied time, AIA based time series scatter degree method (AIA/TSSDM) is proposed so as to improve the energy consumption assessment performance. The comprehensive solution for energy consumption assessment by AIA/TSSDM is presented. AIA is introduced to adapt the conventional scatter degree method, and overcome the disadvantage of generating negative weights. In AIA, the scatter degree method oriented encoding system for antigens and antibodies is designed, the antibody density calculation is described. Several typical operators such as selecting, crossover and mutation are discussed, and the detail implement steps of AIA/TSSDM are designed. The energy consumption for equipment manufacturing industry in Shenyang city is take as research background, the above presented algorithm is applied. Comparing with conventional AHP, the difference of energy consumption assessment values for equipment manufacturing industry on varied years is enlarged. And solution diversity can be kept by introducing AIA in this algorithm so as to provide multi typical weight vectors.

An expert system for security diagnosing of construction producing by CBR

For improving systematization degree and efficiency of security diagnosing for construction producing, the flow of security diagnosing for construction producing system by CBR is designed. By analyzing the security influence factors for construction production systemically, the case presentation technology for security diagnosing for construction producing is designed, and the case retrieval, case adaptation and case maintenance technologies are respectively studied. To test performance, random selection and case maintenance experiment modes are respectively designed, the system running result shows that under the second mode, average case retrieval ratio increases more and average case adaptation ratio decreases more with the increasing of case base scale. The obvious advantage of CBR is validated, the more the system is employed, the better performance it will appear.

MDE based Memetic Algorithm using OpenMP and its application in engineering project scheduling problems with dynamic due dates

A multi-population DE based Memetic Algorithm using OpenMP is proposed, and applied to a kind of engineering project problems which can be modeled by single machine total weighted tardiness problems with dynamic due dates. The multi-population scheme and a best-survival individual synchronous updating strategy are introduced to simultaneously refresh the sub-populations in the same thread. The searching ability of the algorithm is improved by the differential evolutionary Memetic algorithm which is formulated by incorporating a local search method for the best individual in each sub-population into DE. The effectiveness of the algorithm is proved by a series of engineering project scheduling based simulation experiments.