Scheduling Optimization of Offshore Oil Spill Cleaning Materials Considering Multiple Accident Sites and Multiple Oil Types
Abstract
:1. Introduction
- An optimization model for dispatching multilocation emergency supplies in response to small offshore oil spills that considers the total dispatching cost and oil spill recovery time is established.
- The interrelationships between the decision-making environment and the groundbreaking consideration of multisite cleanup of small oil spills are critical for an oil spill emergency response.
- Considering the timing of different types of oil spill recovery, this study uses the corresponding batch delivery time window to adjust the emergency operation and transportation of oil spill emergency supplementary resources.
- An improved genetic algorithm (IGA) is proposed to optimize the scheduling model of oil spills and sewage disposal materials in multiple offshore locations. This improves the efficiency and convergence speed of the calculation.
2. Background of the Problem
- (1)
- Materials for blocking oil spills, mainly for the containment boom, are used for containment, oil spill diversion, and potential oil spill prevention.
- (2)
- Materials for the recovery of oil spills, mainly for the oil collector, are used to recover water oil spills and oil and water mixtures.
- (3)
- Other items mainly include adsorption materials and chemical and biological treatment agents, which are used to reduce damage caused by oil spills and accelerate the recovery of damaged waters.
3. Model Building
3.1. Model Assumptions
- (1)
- In the case of general or small offshore oil spill accidents, the demand for emergency supplies is minimal, and the supply of shore-based points can meet the demand. In this case, emergency time is the most important factor. Various oil spill emergency materials, such as oil spill dispersants, oil booms, and oil absorption felt, are available in shore-based material storage. These materials are compatible in nature and can be loaded and transported together. In this study, the materials are packed and processed to form the decontamination-resource package when the demand for spilled oil materials is counted, and the quantity is measured in buckets during calculation. The total amount of shore-based storage completely meets the total demand.
- (2)
- The type and quantity of emergency supplies needed at each accident point should be determined by the actual emergency situation known by the ship–shore communication system and the Geographical Information System.
- (3)
- The loading and unloading times of materials account for a small proportion of the entire material scheduling process. The distance from the transport of materials from the emergency base to the cleaning vessel is extremely short, which has a limited impact on emergency efficiency. The cost and time of these factors are ignored to simplify the analysis.
- (4)
- The vessels from the emergency center to the accident spot transport goods bear the roles of wind and waves. However, the entire process of emergency response and the environment do not change. The differences can remain stable despite the different carriers in different emergency bases, with accident points back and forth between the speed rates. A specific rate calculation method can be referenced.
- (5)
- Territorial management is implemented between emergency bases. Loading between cleaning vessels is prohibited. The loading and unloading of supplies are not allowed to change vessels. Only one oil spill recovery vessel is needed for each accident point to complete the cleaning task. Cooperative operation of multiple vessels is unnecessary.
- (6)
- The loading capacity of the cleaning vessel and the demand for all types of oil spill materials can be arranged by a unified unit, and the materials at different accident points are forbidden from being mixed in the same transport ship.
- (7)
- The loading capacity of the cleaning vessel is sufficient, and the sum of the demands of each customer on each distribution path does not exceed the cargo capacity of the ship. The needs of each site must be met, and only one cleanup vessel can perform one mission.
- (8)
- All cleaning vessels must return to the dock for standby after completing cleaning tasks.
- (9)
- All the functions constructed in the model are continuously differentiable convex functions. Under the condition of effectively controlling oil spill pollution and related constraints, the total dispatching cost of emergency oil spill materials, considering the time window problem, is minimized as the emergency target [45,46].
3.2. Associated Symbols and Definitions
3.3. Establishment of Scheduling Model
4. Research Methods
4.1. Genetic Algorithm (GA)
4.2. Improved Genetic Algorithm (IGA)
4.2.1. Chromosome Coding
4.2.2. Fitness Function
4.2.3. Genetic Operators
- (1)
- Choice
- (2)
- Crossover and mutation operators
4.3. Algorithm Step
5. Implementation of Simulation Experiment and Analysis
5.1. Example Description
5.2. Experimental Results and Discussion
6. Conclusions and Future Research
- (1)
- In comparison to the SA and regular GA, the IGA produces superior results and arrives at the best answer faster in the evolutionary process.
- (2)
- The constructed multisite and multioil-type scheduling optimization model of oil spills and decontamination-related materials has universality. The designed hybrid GA has a high timeliness in solving the model, which can provide a scientific decision-making basis for solving small offshore multisite oil spill accidents.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Oil Samples | Density (20 °C)/(Kg∙m−3) | Viscosity (20 °C)/(MPa∙s) |
---|---|---|
A | 933.4 | 437.7 |
B | 926.9 | 581.2 |
C | 934.0 | 1722.3 |
Serial Number | X Coordinates | Y Coordinates | Required Materials /Drum | Amount of Dirty Oil Per Barrel | Occurrence Time of Oil Spills (AM) | Right Time Window (AM) | Cleaning Operation Time/Min | Oil Spill Species |
---|---|---|---|---|---|---|---|---|
0 | 100 | 0 | 0 | 0 | 4:00 | 10:00 | 0 | 0 |
1 | 1 | 40 | 18 | 12 | 4:24 | 8:24 | 60 | A |
2 | 4 | 60 | 18 | 16 | 4:16 | 8:54 | 80 | B |
3 | 6 | 110 | 25 | 10 | 5:23 | 7:23 | 60 | C |
4 | 50 | 130 | 19 | 20 | 5:18 | 8:38 | 100 | A |
5 | 70 | 30 | 22 | 13 | 4:19 | 9:19 | 60 | B |
6 | 90 | 5 | 19 | 10 | 5:01 | 6:41 | 80 | C |
7 | 120 | 14 | 18 | 12 | 4:20 | 8:20 | 60 | A |
8 | 150 | 35 | 16 | 14 | 5:21 | 10:01 | 80 | B |
9 | 160 | 190 | 14 | 7 | 4:15 | 6:15 | 60 | C |
10 | 127 | 170 | 19 | 10 | 5:24 | 9:36 | 48 | A |
11 | 140 | 149 | 24 | 11 | 4:15 | 9:35 | 40 | B |
12 | 63 | 157 | 20 | 8 | 5:22 | 7:52 | 30 | C |
Serial Number | Parameter | Value |
---|---|---|
1 | Population size | 200 |
2 | Evolution algebra | 100 |
3 | Crossover probability | Pa = 0.8 Pb = 0.5 |
4 | Mutation probability | Pu = 0.1 Pv = 0.002 |
5 | Generation gap | 0.9 |
6 | Number of cleaning vessels on standby/vessel | 10 |
7 | Maximum carrying capacity of a cleaning vessel/barrel | 100 |
8 | Speed of the cleaning vessel/km/h | 50 |
9 | Use cost of cleaning vessel/10,000 yuan | 100 |
10 | Transport cost per unit distance of cleaning vessel/yuan∙Km−1 | 70 |
11 | Penalty cost for breach of loading capacity (10,000 yuan∙barrel−1) | 5 |
12 | Penalties for violating the time window constraints (10,000 yuan∙min−1) | 1 |
Algorithm | Serial Number | Parameter | Value |
---|---|---|---|
GA | 1 | Population size | 200 |
2 | Evolution algebra | 100 | |
3 | Crossover probability | PC = 0.9 | |
4 | Mutation probability | Pm = 0.05 | |
5 | The generation gap | 0.9 | |
SA | 1 | Initial temperature | 3000 |
2 | Final temperature | 0.01 | |
3 | Temperature attenuation factor | 0.98 | |
4 | Markov chain length | 100 | |
5 | Tolerance | 1 | |
6 | Step length factor | 0.3 | |
7 | A metropolis procedure always accepts points | 0 |
Algorithm | Number of Cleaning Vessels Used | Total Cost of Clean-Up/Ten Thousand Yuan | Fuel Consumption /Ten Thousand Yuan | Penalty Cost for Breach of Loading Capacity/Ten Thousand Yuan | Penalty Costs for Time Window Violations/Ten Thousand Yuan | Scheduling Scheme for Cleaning Vessel Operation |
---|---|---|---|---|---|---|
IGA | 4 | 11,942,653 | 7,942,653 | 0 | 0 | The operation path of the NO. 1 cleaning vessel is: 0 ->1 -> 2 -> 5 -> 0 The operation path of the NO. 2 cleaning vessel is: 0 ->9 -> 10 -> 11 -> 8 -> 0 The operation path of the NO. 3 cleaning vessel is: 0 ->3 -> 12 -> 4 -> 0 The operation path of the NO. 4 cleaning vessel is: 0 ->6 -> 7 -> 0 |
GA | 4 | 12,287,934 | 8,287,934 | 0 | 0 | The operation path of the NO. 1 cleaning vessel is: 0 ->1 -> 3 -> 2 -> 0 The operation path of the NO. 2 cleaning vessel is: 0 ->5 -> 12 -> 4 -> 0 The operation path of the NO. 3 cleaning vessel is: 0 ->6 -> 7 -> 0 The operation path of the NO. 4 cleaning vessel is: 0 ->9 -> 10 -> 11 -> 8 -> 0 |
SA | 4 | 12,263,579 | 8,263,579 | 0 | 0 | The operation path of the NO. 1 cleaning vessel is: 0 ->3 -> 12 -> 4 -> 0 The operation path of the NO. 2 cleaning vessel is: 0 ->11 -> 9 -> 10 -> 0 The operation path of the NO. 3 cleaning vessel is: 0 ->6 -> 7 -> 8 -> 0 The operation path of the NO. 4 cleaning vessel is: 0 ->5 -> 2 -> 1 -> 0 |
Algorithm | Average Value/Ten Thousand Yuan | Standard Deviation | Operation Time/s |
---|---|---|---|
GA | 12,301,256 | 19.33456 | 287.3153 |
SA | 12,288,968 | 16.4226 | 1202.8636 |
IGA | 11,902,352 | 5.3426 | 221.2729 |
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Li, K.; Yu, H.; Xu, Y.; Luo, X. Scheduling Optimization of Offshore Oil Spill Cleaning Materials Considering Multiple Accident Sites and Multiple Oil Types. Sustainability 2022, 14, 10047. https://doi.org/10.3390/su141610047
Li K, Yu H, Xu Y, Luo X. Scheduling Optimization of Offshore Oil Spill Cleaning Materials Considering Multiple Accident Sites and Multiple Oil Types. Sustainability. 2022; 14(16):10047. https://doi.org/10.3390/su141610047
Chicago/Turabian StyleLi, Kai, Hongliang Yu, Yiqun Xu, and Xiaoqing Luo. 2022. "Scheduling Optimization of Offshore Oil Spill Cleaning Materials Considering Multiple Accident Sites and Multiple Oil Types" Sustainability 14, no. 16: 10047. https://doi.org/10.3390/su141610047