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Home
Research Domains
Engineering
Environmental Engineering
Air Pollution Modelling And Control
Research On Multi-objective Emergency Resource Scheduling Optimization In Chemical Industrial Parks.

Home
Research Domains
Engineering
Environmental Engineering
Air Pollution Modelling And Control
Research On Multi-objective Emergency Resource Scheduling Optimization In Chemical Industrial Parks.

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Research on multi-objective emergency resource scheduling optimization in chemical industrial parks.

Yuhang Wang^1,2, Mingguang Zhang^1,2, Jun Lu^1,2

¹College of Safety Science and Engineering, Nanjing Tech University, Nanjing, Jiangsu, China.

|September 30, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

This study optimizes emergency resource scheduling for chemical park accidents by balancing time, coverage, and fairness. An improved NSGA-II algorithm efficiently finds optimal solutions, crucial for dynamic multi-hazard scenarios.

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Area of Science:

Operations Research
Chemical Engineering
Emergency Management

Background:

Chemical parks pose risks of chain accidents due to hazardous sources.
Existing emergency resource scheduling lacks adaptability for dynamic scenarios and fairness quantification.
Dynamic cooperative optimization is needed for efficient emergency resource scheduling.

Purpose of the Study:

Develop a three-objective model for dynamic emergency resource scheduling.
Integrate time efficiency, demand coverage, and allocation fairness as optimization objectives.
Quantify fairness as an independent objective using a novel balance index.

Main Methods:

Constructed a three-objective mixed-integer planning model.
Proposed a standard deviation-based dynamic resource allocation balance index.

Employed an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II) for Pareto front optimization.

Utilized multi-warehouse collaboration and multi-resource coupling constraints.

Main Results:

The improved NSGA-II algorithm demonstrated superior convergence, diversity, and stability compared to weighted methods and MOGWO.
Case studies showed trade-offs between demand satisfaction and transportation time.
Achieving optimal fairness can potentially decrease overall demand satisfaction.
Resource demand significantly influences the number of feasible solutions.

Conclusions:

Fairness is a critical, irreplaceable factor in emergency scheduling decisions.
The proposed model and algorithm effectively address dynamic multi-hazard scenarios.
Decision-makers can select optimal solutions based on loss tolerance and composite scores.