scholarly journals Multi-UAV Coverage Path Planning Based on Hexagonal Grid Decomposition in Maritime Search and Rescue

Mathematics ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 83
Author(s):  
Sung-Won Cho ◽  
Jin-Hyoung Park ◽  
Hyun-Ji Park ◽  
Seongmin Kim
Keyword(s):  
Simulation Analysis ◽  
Search And Rescue ◽  
Mixed Integer ◽  
Hexagonal Grid ◽  
Coverage Path Planning ◽  
Search Area ◽  
Proposed Model ◽  
Maritime Search And Rescue ◽  
Grid Based ◽  
Multi Uav

In the event of a maritime accident, surveying the maximum area efficiently in the least amount of time is crucial for rescuing survivors. Increasingly, unmanned aerial vehicles (UAVs) are being used in search and rescue operations. This study proposes a method to generate a search path that covers all generated nodes in the shortest amount of time with multiple heterogeneous UAVs. The proposed model, which is a mixed-integer linear programming (MILP) model based on a hexagonal grid-based decomposition method, was verified through a simulation analysis based on the performance of an actual UAV. This study presents both the optimization technique’s calculation time as a function of the search area size and the various UAV routes derived as the search area grows. The results of this study can have wide-ranging applications for emergency search and rescue operations.

Coverage path planning for maritime search and rescue using reinforcement learning

2021 ◽  
Vol 241 ◽  
pp. 110098
Author(s):  
Bo Ai ◽  
Maoxin Jia ◽  
Hanwen Xu ◽  
Jiangling Xu ◽  
Zhen Wen ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Search And Rescue ◽  
Coverage Path Planning ◽  
Maritime Search And Rescue

Coverage Path Planning with Proximal Policy Optimization in a Grid-based Environment

2020 ◽  
Author(s):  
Aleksandr Ianenko ◽  
Alexander Artamonov ◽  
Georgii Sarapulov ◽  
Alexey Safaraleev ◽  
Sergey Bogomolov ◽  
...  
Keyword(s):  
Path Planning ◽  
Coverage Path Planning ◽  
Policy Optimization ◽  
Grid Based

scholarly journals Allocation and Scheduling of Handling Resources in the Railway Container Terminal Based on Crossing Crane Area

2021 ◽  
Vol 13 (3) ◽  
pp. 1190
Author(s):  
Gang Ren ◽  
Xiaohan Wang ◽  
Jiaxin Cai ◽  
Shujuan Guo
Keyword(s):  
Programming Model ◽  
Container Terminal ◽  
Simultaneous Optimization ◽  
Mixed Integer ◽  
Gantry Crane ◽  
Optimization Scheme ◽  
Long Distance ◽  
Proposed Model ◽  
Hybrid Heuristic Algorithm ◽  
Searching Ability

The integrated allocation and scheduling of handling resources are crucial problems in the railway container terminal (RCT). We investigate the integrated optimization problem for handling resources of the crane area, dual-gantry crane (GC), and internal trucks (ITs). A creative handling scheme is proposed to reduce the long-distance, full-loaded movement of GCs by making use of the advantages of ITs. Based on this scheme, we propose a flexible crossing crane area to balance the workload of dual-GC. Decomposing the integrated problem into four sub-problems, a multi-objective mixed-integer programming model (MIP) is developed. By analyzing the characteristic of the integrated problem, a three-layer hybrid heuristic algorithm (TLHHA) incorporating heuristic rule (HR), elite co-evolution genetic algorithm (ECEGA), greedy rule (GR), and simulated annealing (SA) is designed for solving the problem. Numerical experiments were conducted to verify the effectiveness of the proposed model and algorithm. The results show that the proposed algorithm has excellent searching ability, and the simultaneous optimization scheme could ensure the requirements for efficiency, effectiveness, and energy-saving, as well as the balance rate of dual-GC.

scholarly journals An Optimized Mathematical Model for Items Supplies Planning of a Logistic System

2016 ◽  
Vol 10 (10) ◽  
pp. 133
Author(s):  
Mohammad Ali Nasiri Khalili ◽  
Mostafa Kafaei Razavi ◽  
Morteza Kafaee Razavi
Keyword(s):  
Mathematical Model ◽  
Mixed Integer ◽  
Logistics System ◽  
Proposed Model ◽  
Multiple Objective Decision Making ◽  
System Requirements ◽  
Purchasing Logistics ◽  
The Cost ◽  
The Mathematical Model ◽  
First Time

Items supplies planning of a logistic system is one of the major issue in operations research. In this article the aim is to determine how much of each item per month from each supplier logistics system requirements must be provided. To do this, a novel multi objective mixed integer programming mathematical model is offered for the first time. Since in logistics system, delivery on time is very important, the first objective is minimization of time in delivery on time costs (including lack and maintenance costs) and the cost of purchasing logistics system. The second objective function is minimization of the transportation supplier costs. Solving the mathematical model shows how to use the Multiple Objective Decision Making (MODM) can provide the ensuring policy and transportation logistics needed items. This model is solved with CPLEX and computational results show the effectiveness of the proposed model.

A Least Cost Transmission Planning Model Considering Operation Cost

2014 ◽  
Vol 15 (2) ◽  
pp. 121-128
Author(s):  
Jorge Hans Alayo
Keyword(s):  
Test System ◽  
Mixed Integer ◽  
Transmission Planning ◽  
Integer Linear Programming Problem ◽  
Transmission Expansion ◽  
Planning Models ◽  
Proposed Model ◽  
Expansion Model ◽  
Operation Cost ◽  
Linearization Techniques

Abstract Existing transmission planning models consider basic aspects of the problem. In practice, a transmission utility needs to model other important details such as operation cost of the power system. In this article, a least cost transmission expansion model is proposed considering the operation cost in order to model the trade-off between building new transmission capacity and increasing the power system’s operation cost. The proposed model is transformed into a mixed integer linear programming problem using linearization techniques and solved with CPLEX. Finally, results of the model for the Garver test system and IEEE 24-bus test system are shown.

A Multi-Period Evacuation Vehicle Routing Problem Model

2014 ◽  
Vol 931-932 ◽  
pp. 578-582
Author(s):  
Sunarin Chanta ◽  
Ornurai Sangsawang
Keyword(s):  
Vehicle Routing ◽  
Public Transportation ◽  
Vehicle Routing Problem ◽  
Programming Model ◽  
High Water ◽  
Mixed Integer ◽  
Routing Problem ◽  
Problem Model ◽  
Proposed Model ◽  
Evacuation Routing

In this paper, we proposed an optimization model that addresses the evacuation routing problem for flood disaster when evacuees trying to move from affected areas to safe places using public transportation. A focus is on the situation of evacuating during high water level when special high vehicles are needed. The objective is to minimize the total traveled distance through evacuation periods where a limited number of vehicles is given. We formulated the problem as a mixed integer programming model based on the capacitated vehicle routing problem with multiple evcuation periods where demand changing by the time. The proposed model has been tested on a real-world case study affected by the severe flooding in Thailand, 2011.

How can mixed integer linear programming assist dairy manufacturers by integrating supply decisions and production planning?

2021 ◽  
Vol 11 (2) ◽  
pp. 178-193
Author(s):  
Juliana Emidio ◽  
Rafael Lima ◽  
Camila Leal ◽  
Grasiele Madrona
Keyword(s):  
Linear Programming ◽  
Supply Chain ◽  
Integer Linear Programming ◽  
Mixed Integer Linear Programming ◽  
Dairy Products ◽  
Raw Milk ◽  
Mixed Integer ◽  
Content Type ◽  
Proposed Model ◽  
By Products

PurposeThe dairy industry needs to make important decisions regarding its supply chain. In a context with many available suppliers, deciding which of them will be part of the supply chain and deciding when to buy raw milk is key to the supply chain performance. This study aims to propose a mathematical model to support milk supply decisions. In addition to determining which producers should be chosen as suppliers, the model decides on a milk pickup schedule over a planning horizon. The model addresses production decisions, inventory, setup and the use of by-products generated in the raw milk processing.Design/methodology/approachThe model was formulated using mixed integer linear programming, tested with randomly generated instances of various sizes and solved using the Gurobi Solver. Instances were generated using parameters obtained from a company that manufactures dairy products to test the model in a more realistic scenario.FindingsThe results show that the proposed model can be solved with real-world sized instances in short computational times and yielding high quality results. Hence, companies can adopt this model to reduce transportation, production and inventory costs by supporting decision making throughout their supply chains.Originality/valueThe novelty of the proposed model stems from the ability to integrate milk pickup and production planning of dairy products, thus being more comprehensive than the models currently available in the literature. Additionally, the model also considers by-products, which can be used as inputs for other products.

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