scholarly journals Cost-Effective Placement of Recharging Stations in Drone Path Planning for Surveillance Missions on Large Farms

Symmetry ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 1661
Author(s):  
Jean Louis Ebongue Kedieng Fendji ◽  
Israel Kolaigue Bayaola ◽  
Christopher Thron ◽  
Marie Danielle Fendji ◽  
Anna Förster
Keyword(s):  
Simulated Annealing ◽  
Path Planning ◽  
Optimal Solution ◽  
Cost Effective ◽  
Planning Problem ◽  
Area Of Interest ◽  
Energy Limitation ◽  
Large Farm ◽  
Feasible Solutions ◽  
The Cost

The energy limitation remains one of the biggest constraints in drone path planning, since it prevents drones from performing long surveillance missions. To assist drones in such missions, recharging stations have recently been introduced. They are platforms where the drone can autonomously land to recharge its battery before continuing the mission. However, the cost of those platforms remains a significant obstacle to their adoption. Consequently, it is important to reduce their number while planning the path of the drone. This work introduces the Single Drone Multiple Recharging Stations on Large Farm problem (SD-MRS-LF). A large farm is considered as an area of interest to cover with a set of candidate locations where recharging stations can be installed. The aim is to determine the path of the drone that minimizes the number of locations for recharging stations as well as the completion time of the surveillance mission. This path planning problem falls within the realm of computational geometry and is related to similar problems that are encountered in the field of robotics. The problem is complicated due to environmental constraints on farms such as wind speed and direction, which produce asymmetries in the optimal solution. A back-and-forth-k-opt simulated annealing (BFKSA) approach is proposed to solve the defined problem. The new approach is compared to the basic back-and-forth (BF) and a K-opt variant of the well-known simulated annealing (KSA) approach over a set of 20 random topologies in different environmental conditions. The results from computational experiments show that the BFKSA approach outperforms the others, in terms of providing feasible solutions and minimizing the number of recharges.

OPTIMAL FEASIBLE SOLUTIONS TO A ROAD FREIGHT TRANSPORTATION PROBLEM

2020 ◽  
Vol 5 (1) ◽  
pp. 456
Author(s):  
Tolulope Latunde ◽  
Joseph Oluwaseun Richard ◽  
Opeyemi Odunayo Esan ◽  
Damilola Deborah Dare
Keyword(s):  
Transportation Problem ◽  
Optimal Solution ◽  
Transportation Cost ◽  
North West ◽  
Life Problems ◽  
Basic Feasible Solutions ◽  
Feasible Solutions ◽  
Road Freight Transportation ◽  
The Cost ◽  
Cost Of Transportation

For twenty decades, there is a visible ever forward advancement in the technology of mobility, vehicles and transportation system in general. However, there is no "cure-all" remedy ideal enough to solve all life problems but mathematics has proven that if the problem can be determined, it is most likely solvable. New methods and applications will keep coming to making sure that life problems will be solved faster and easier. This study is to adopt a mathematical transportation problem in the Coca-Cola company aiming to help the logistics department manager of the Asejire and Ikeja plant to decide on how to distribute demand by the customers and at the same time, minimize the cost of transportation. Here, different algorithms are used and compared to generate an optimal solution, namely; North West Corner Method (NWC), Least Cost Method (LCM) and Vogel’s Approximation Method (VAM). The transportation model type in this work is the Linear Programming as the problems are represented in tables and results are compared with the result obtained on Maple 18 software. The study shows various ways in which the initial basic feasible solutions to the problem can be obtained where the best method that saves the highest percentage of transportation cost with for this problem is the NWC. The NWC produces the optimal transportation cost which is 517,040 units.

A Multiple Level of Detail Approach to the Tactical Movement Problem

2012 ◽  
Vol 6 (6) ◽  
pp. 749-756 ◽  
Author(s):  
Peter Beasley ◽  
◽  
P. Ross McAree
Keyword(s):  
Optimal Path ◽  
Optimal Solution ◽  
Cost Effective ◽  
Multiple Level ◽  
Practical Application ◽  
Robotic Excavator ◽  
The World ◽  
The Cost ◽  
Robotic Agent ◽  
Movement Problem

The tactical movement problemis considered to be one in which a robotic agent is required to move around its world to complete a task. This agent has manipulation abilities which allow it to perform work on its local surroundings. The coupled optimisation of the agent movements and manipulations is thus of key importance to minimise the cost of completing the task. The driving practical application in this paper is one of cost effective excavation in a mining environment. The agent is a mining shovel and it has the ability to manipulate the world through excavation actions. The problem becomes one of determining the optimal path that the shovel should take and the dig operations that should be completed at each point along the path. An initial solution is presented to automatically generate an optimized dig plan for a large robotic excavator. A wavelet based detail reduction approach is used which allows a near optimal solution of the problem to be generated in practically useful timeframes.

A Decision-Making Tool for the Optimization of Empty Containers' Return in the Liner Shipping: Optimization by Using the Genetic Algorithm

2018 ◽  
Vol 10 (3) ◽  
pp. 39-56
Author(s):  
Naima Belayachi ◽  
Fouzia Amrani ◽  
Karim Bouamrane
Keyword(s):  
Decision Making ◽  
Lower Cost ◽  
Optimal Solution ◽  
Maritime Transportation ◽  
Initial Population ◽  
Transportation Sector ◽  
Empty Containers ◽  
Feasible Solutions ◽  
The Cost ◽  
Decision Making Tool

This article describes how in the maritime transportation sector, containerization represents one of the most remarkable improvements. In fact, the different shipping companies provide great efforts, whose purpose is to reduce the cost of this transport. However, these companies are facing a problem of empty containers, which are not available at some ports of Maritime Transport Network (MTN) to meet the clients' demands. This problem is simply a consequence of the imbalance in the distribution of containers through the MTN due to the set of containers that do not return to the origin port. This work offers a decision-making tool to this problem by proposing an optimal return of empty containers. The proposed application is based on evolutionary heuristics. Its principle is to find an optimal solution from a set of several feasible solutions generated during an initial population in order to enable the search of empty containers at lower cost.

Handoff Cost Minimization and Planning of Heterogeneous Integrated Overlay Networks

2012 ◽  
pp. 49-64
Author(s):  
Ayan Paul ◽  
Madhubanti Maitra ◽  
Swarup Mandal ◽  
Samir Kumar Sadhukhan
Keyword(s):  
Overlay Networks ◽  
Cost Minimization ◽  
Cost Effective ◽  
Overlay Network ◽  
Vertical Handoff ◽  
Planning Problem ◽  
Network Resources ◽  
Planning Approach ◽  
Technology Market ◽  
The Cost

The wireless technology market has witnessed a complete paradigm shift as multiple standards and protocols are emerging almost every day. Each and every standard has its limitations and merits, which can be either masked or complemented by some other standards. The demands from the service providers are now sky-high and for the complete commercialization, it is expected that even with scarce network resources all kind of services would be provided, especially in a cost effective manner. This burning issue compels a service provider to roll out some integrated wireless networks to exploit the virtues of each. This chapter formulates the planning problem of an overlay network integrating particularly, 3G, WiMAX, and WLAN. The issue of planning is to establish proper connectivity amongst the three network standards which is unique in its nature. In the proposed planning approach, the authors have endeavored to minimize total cost for vertical handoff generated in the overlay network as well as the cost for wire line connection amongst the various network gateways of the overlay hierarchy. In this work, the authors have focused on the initial planning phase. For validating the novel planning problem, the chapter has taken recourse to simulated annealing (SA) and a well cited meta-heuristic H-II. The authors have also presented comparison of the performances of SA and H-II with a variant of distance based planning (DBP) scheme in this domain.

scholarly journals A Two-Stage Method for Target Searching in the Path Planning for Mobile Robots

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6919
Author(s):  
Tao Song ◽  
Xiang Huo ◽  
Xinkai Wu
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Search Time ◽  
Optimal Solution ◽  
Corner Detection ◽  
Planning Problem ◽  
Complete Coverage ◽  
Two Stage ◽  
Set Up ◽  
Target Searching

The path planning for target searching in mobile robots is critical for many applications, such as warehouse inspection and caring and surveillance for elderly people in the family scene. To ensure visual complete coverage from the camera equipped in robots is one of the most challenging tasks. To tackle this issue, we propose a two-stage optimization model to efficiently obtain an approximate optimal solution. In this model, we first develop a method to determine the key locations for visual complete coverage of a two-dimensional grid map, which is constructed by drawing lessons from the method of corner detection in the image processing. Then, we design a planning problem for searching the shortest path that passes all key locations considering the frequency of target occurrence. The testing results show that the proposed algorithm can achieve the significantly shorter search path length and the shorter target search time than the current Rule-based Algorithm and Genetic Algorithm (GA) in various simulation cases. Furthermore, the results show that the improved optimization algorithm with the priori known frequency of occurrence of the target can further improve the searching with shorter searching time. We also set up a test in a real environment to verify the feasibility of our algorithm.

scholarly journals An Effective Heuristic-Based Approach for Partitioning

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Xibin Zhao ◽  
Hehua Zhang ◽  
Yu Jiang ◽  
Songzheng Song ◽  
Xun Jiao ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Embedded System ◽  
Simulated Annealing Algorithm ◽  
Optimal Solution ◽  
Local Optimal Solution ◽  
Annealing Algorithm ◽  
The Cost ◽  
Combined Algorithm ◽  
Search Capability

As being one of the most crucial steps in the design of embedded systems, hardware/software partitioning has received more concern than ever. The performance of a system design will strongly depend on the efficiency of the partitioning. In this paper, we construct a communication graph for embedded system and describe the delay-related constraints and the cost-related objective based on the graph structure. Then, we propose a heuristic based on genetic algorithm and simulated annealing to solve the problem near optimally. We note that the genetic algorithm has a strong global search capability, while the simulated annealing algorithm will fail in a local optimal solution easily. Hence, we can incorporate simulated annealing algorithm in genetic algorithm. The combined algorithm will provide more accurate near-optimal solution with faster speed. Experiment results show that the proposed algorithm produce more accurate partitions than the original genetic algorithm.

scholarly journals BOOSTING SIMULATED ANNEALING WITH FITNESS LANDSCAPE PARAMETERS FOR BETTER OPTIMALITY

2015 ◽  
pp. 107-112
Author(s):  
Sunanda Gupta ◽  
Sakshi Arora
Keyword(s):  
Simulated Annealing ◽  
Probability Function ◽  
Fitness Landscape ◽  
Optimal Solution ◽  
Search Space ◽  
Solution Quality ◽  
Acceptance Probability ◽  
Evaluation Strategies ◽  
Fitness Distance Correlation ◽  
The Cost

Multi Dimensional Knapsack problem is a widely studied NP hard problem requiring extensive processing to achieve optimality. Simulated Annealing (SA) unlike other is capable of providing fast solutions but at the cost of solution quality. This paper focuses on making SA robust in terms of solution quality while assuring faster convergence by incorporating effective fitness landscape parameters. For this it proposes to modify the ‘Acceptance Probability’ function of SA. The fitness landscape evaluation strategies are embedded to Acceptance Probability Function to identify the exploitation and exploration of the search space and analyze the behavior on the performance of SA. The basis of doing so is that SA in the process of reaching optimality ignores the association between the search space and fitness space and focuses only on the comparison of current solution with optimal solution on the basis of temperature settings at that point. The idea is implemented in two different ways i.e. by making use of Fitness Distance Correlation and Auto Correlation functions. The experiments are conducted to evaluate the resulting SA on the range of MKP instances available in the OR library.

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