scholarly journals Integrated Optimization Strategy for Sustainable Shared Designated Driver Ferry Vehicle Scheduling

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Siqi Wang ◽  
Jingbo Yin ◽  
Rafi Ullah Khan
Keyword(s):  
Search Algorithm ◽  
Supply And Demand ◽  
Utilization Rate ◽  
Optimization Strategy ◽  
Test Question ◽  
Efficient System ◽  
Integrated Optimization ◽  
Stage Matching ◽  
Designated Driver ◽  
Adjustment Scheme

The focus of this study is on optimizing the schedule adjustment scheme of shared designated driver ferry vehicles to obtain a sustainable and energy-efficient system to pick up and drop off designated drivers to serve drunk customers. A two-stage matching model for driver and customer supply and demand matching and driver ferry vehicle dispatching is designed in order to optimize the total distance travelled and minimize the generalized deviation costs. A maximum residual time adjustment algorithm is designed to reduce the logarithm of new interference demand, and a tabu search algorithm is used to solve the schedule adjustment scheme for ferry vehicles. The validity of the model and the algorithm is verified by a multiperiod example constructed in the Solomon test question bank. The result of numerical experiments shows that the proposed model and algorithm can solve the disruption adjustment scheduling strategy of shared designated driver ferry vehicles. The integrated optimization strategy can effectively improve the utilization rate and the operation efficiency of the shared driver ferry vehicles to reduce operation cost and energy consumption.

scholarly journals Optimization strategy of wireless charger node deployment based on improved cuckoo search algorithm

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Yang Wang ◽  
Feifan Wang ◽  
Yujun Zhu ◽  
Yiyang Liu ◽  
Chuanxin Zhao
Keyword(s):  
Sensor Network ◽  
Sensor Node ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Sensor Nodes ◽  
Optimization Strategy ◽  
Multi Objective ◽  
Received Power

AbstractIn wireless rechargeable sensor network, the deployment of charger node directly affects the overall charging utility of sensor network. Aiming at this problem, this paper abstracts the charger deployment problem as a multi-objective optimization problem that maximizes the received power of sensor nodes and minimizes the number of charger nodes. First, a network model that maximizes the sensor node received power and minimizes the number of charger nodes is constructed. Second, an improved cuckoo search (ICS) algorithm is proposed. This algorithm is based on the traditional cuckoo search algorithm (CS) to redefine its step factor, and then use the mutation factor to change the nesting position of the host bird to update the bird’s nest position, and then use ICS to find the ones that maximize the received power of the sensor node and minimize the number of charger nodes optimal solution. Compared with the traditional cuckoo search algorithm and multi-objective particle swarm optimization algorithm, the simulation results show that the algorithm can effectively increase the receiving power of sensor nodes, reduce the number of charger nodes and find the optimal solution to meet the conditions, so as to maximize the network charging utility.

scholarly journals Real-Time Return Demand Prediction Based on Multisource Data of One-Way Carsharing Systems

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Dongbo Liu ◽  
Jian Lu ◽  
Wanjing Ma
Keyword(s):  
Prediction Model ◽  
Real Time ◽  
Supply And Demand ◽  
Optimization Strategy ◽  
Trajectory Data ◽  
Demand Prediction ◽  
Data User ◽  
User Characteristic ◽  
Destination Station ◽  
The One

One-way carsharing system has been widely adopted in the carsharing field due to its flexible services. However, as one of the main limitations of the one-way carsharing system, the imbalance between supply and demand needs to be solved. Predicting pick-up demand has been studied to achieve the goal, but using returned vehicles to reduce unnecessary relocation is also one of the important methods. Nowadays, trajectory data and other data are available for real-time prediction for return demand. Based on the return demand prediction, the relocation response can be more reasonable. Thus, the balance of demand and supply can be largely improved. The multisource data include trajectory data, user application log data, order data, station data, and user characteristic data. Based on these data, a return demand prediction model was used to predict whether the user will return the vehicle in 15 min in real time, and a destination station prediction model was applied to forecast which station the user will park at. Finally, a case study using ten stations’ one-week field data was conducted to test the benefit of the dynamic return demand prediction. The results showed that the return demand prediction improves the efficiency of the relocations by mitigating the condition that the station parking space is full or empty. The potential application of this study would effectively reduce unnecessary relocation and further formulate an active operation optimization strategy to reduce the system’s operational cost and improve the service quality of the system.

Automatic Data Clustering Using Parameter Adaptive Harmony Search Algorithm and Its Application to Image Segmentation

2016 ◽  
Vol 25 (4) ◽  
pp. 595-610 ◽  
Author(s):  
Vijay Kumar ◽  
Jitender Kumar Chhabra ◽  
Dinesh Kumar
Keyword(s):  
Data Clustering ◽  
Euclidean Distance ◽  
Search Algorithm ◽  
Harmony Search ◽  
Real Life ◽  
Optimal Number ◽  
Optimization Strategy ◽  
Number Of Clusters ◽  
Automatic Data ◽  
Parameter Adaptive

AbstractIn this paper, the problem of automatic data clustering is treated as the searching of optimal number of clusters so that the obtained partitions should be optimized. The automatic data clustering technique utilizes a recently developed parameter adaptive harmony search (PAHS) as an underlying optimization strategy. It uses real-coded variable length harmony vector, which is able to detect the number of clusters automatically. The newly developed concepts regarding “threshold setting” and “cutoff” are used to refine the optimization strategy. The assignment of data points to different cluster centers is done based on the newly developed weighted Euclidean distance instead of Euclidean distance. The developed approach is able to detect any type of cluster irrespective of their geometric shape. It is compared with four well-established clustering techniques. It is further applied for automatic segmentation of grayscale and color images, and its performance is compared with other existing techniques. For real-life datasets, statistical analysis is done. The technique shows its effectiveness and the usefulness.

scholarly journals Eagle Strategy based Crow Search Algorithm for solving Unit Commitment Problem

2018 ◽  
Vol 12 (1) ◽  
pp. 17 ◽  
Author(s):  
Rachid Habachi ◽  
Achraf Touil ◽  
Abdelkabir Charkaoui ◽  
Abdelwahed Echchatbi
Keyword(s):  
Local Search ◽  
Unit Commitment ◽  
Search Algorithm ◽  
Optimization Strategy ◽  
Unit Commitment Problem ◽  
Spinning Reserve ◽  
Two Stage ◽  
Commitment Problem ◽  
Local Optimizer ◽  
Eagle Strategy

<p>Eagle strategy is a two-stage optimization strategy, which is inspired by the observation of the hunting behavior of eagles in nature. In this two-stage strategy, the first stage explores the search space globally by using a Levy flight; if it finds a promising solution, then an intensive local search is employed using a more efficient local optimizer, such as hillclimbing and the downhill simplex method. Then, the two-stage process starts again with new global exploration, followed by a local search in a new region. One of the remarkable advantages of such a combina-tion is to use a balanced tradeoff between global search (which is generally slow) and a rapid local search. The crow search algorithm (CSA) is a recently developed metaheuristic search algorithm inspired by the intelligent behavior of crows.This research article integrates the crow search algorithm as a local optimizer of Eagle strategy to solve unit commitment (UC) problem. The Unit commitment problem (UCP) is mainly finding the minimum cost schedule to a set of generators by turning each one either on or off over a given time horizon to meet the demand load and satisfy different operational constraints. There are many constraints in unit commitment problem such as spinning reserve, minimum up/down, crew, must run and fuel constraints. The proposed strategy ES-CSA is tested on 10 to 100 unit systems with a 24-h scheduling horizon. The effectiveness of the proposed strategy is compared with other well-known evolutionary, heuristics and meta-heuristics search algorithms, and by reported numerical results, it has been found that proposed strategy yields global results for the solution of the unit commitment problem.</p><p> </p>

scholarly journals Collaborative Optimization of Car-flow Organization for Freight Trains Based on Adjacent Technical Stations

2021 ◽  
Vol 33 (1) ◽  
pp. 117-128
Author(s):  
Yijing Yang ◽  
Xu Wu ◽  
Haonan Li
Keyword(s):  
Genetic Algorithm ◽  
Dwell Time ◽  
Search Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Average Dwell Time ◽  
Collaborative Optimization ◽  
Utilization Rate ◽  
Organization Problem ◽  
Scheduling Method ◽  
Taboo Search Algorithm

This paper proposes a collaborative optimization model of car-flow organization for freight trains based on adjacent technical stations to minimize the average dwell time of train cars in a yard. To solve the car-flow organization problems, a priority-based hump sequence, which depends on the cars available in two adjacent technical stations, is adopted. Furthermore, a meta-heuristic algorithm based on the genetic algorithm and the taboo search algorithm is adopted to solve the model, and the introduction of the active scheduling method improves the efficiency of the algorithm. Finally, the model is applied to the car-flow organization problem of two adjacent technical stations, and the results are compared with those from a single technical station without collaboration. The results demonstrate that collaborative car-flow organization between technical stations significantly reduces the average dwell time at the stations, thereby improving the utilization rate of railroad equipment. In addition, the results indicate that the hybrid genetic algorithm can rapidly determine the train hump and marshalling schemes.

scholarly journals FPGA-Based Implementation of an Optimization Algorithm to Maximize the Productivity of a Microbial Electrolysis Cell

Processes ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 1111
Author(s):  
José de Jesús Colín-Robles ◽  
Ixbalank Torres-Zúñiga ◽  
Mario A. Ibarra-Manzano ◽  
Víctor Alcaraz-González
Keyword(s):  
Optimization Algorithm ◽  
Search Algorithm ◽  
Golden Section ◽  
Electrolysis Cell ◽  
Microbial Electrolysis Cell ◽  
Optimization Strategy ◽  
Digital Architecture ◽  
Field Programmable ◽  
Productivity Rate ◽  
Microbial Electrolysis

In this work, the design of the hardware architecture to implement an algorithm for optimizing the Hydrogen Productivity Rate (HPR) in a Microbial Electrolysis Cell (MEC) is presented. The HPR in the MEC is maximized by the golden section search algorithm in conjunction with a super-twisting controller. The development of the digital architecture in the implementation step of the optimization algorithm was developed in the Very High Description Language (VHDL) and synthesized in a Field Programmable Gate Array (FPGA). Numerical simulations demonstrated the feasibility of the proposed optimization strategy embedded in an FPGA Cyclone II. Results showed that only 21% of the total logic elements, 5.19% of dedicated logic registers, and 64% of the total eight-bits multipliers of the FPGA were used. On the other hand, the estimated power consumption required by the FPGA-embedded optimization algorithm was only 146 mW.

scholarly journals Optimization Strategy of Wireless Charger Node Deployment Based on Improved Cuckoo Search Algorithm

2020 ◽  
Author(s):  
Yang Wang ◽  
feifan wang ◽  
Yujun Zhu ◽  
Yiyang Liu ◽  
Chuanxin Zhao
Keyword(s):  
Sensor Network ◽  
Sensor Node ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Sensor Nodes ◽  
Optimization Strategy ◽  
Multi Objective ◽  
Received Power

Abstract In wireless rechargeable sensor network, the deployment of charger node directly affects the overall charging utility of sensor network. Aiming at this problem, this paper abstracts the charger deployment problem as a multi-objective optimization problem that maximizes the received power of sensor nodes and minimizes the number of charger nodes. First, a network model that maximizes the sensor node received power and minimizes the number of charger nodes is constructed. Second, an Improved Cuckoo Search (ICS) algorithm is proposed. This algorithm is based on the traditional Cuckoo Search algorithm (CS) to redefine its step factor, and then use the mutation factor to change the nesting position of the host bird to update the bird ’s nest position, and then use ICS to find the ones that maximize the received power of the sensor node and minimize the number of charger nodes optimal solution. Compared with the traditional cuckoo search algorithm and multi-objective particle swarm optimization algorithm, the simulation results show that the algorithm can effectively increase the receiving power of sensor nodes, reduce the number of charger nodes and find the optimal solution to meet the conditions, so as to maximize the network charging utility.

scholarly journals The Current Situation and Optimization Strategy of Paper Books Utilization in University Library

2021 ◽  
Vol 5 (3) ◽  
Author(s):  
Weiguo Xie
Keyword(s):  
Information Service ◽  
Current Situation ◽  
Utilization Rate ◽  
Optimization Strategy ◽  
University Libraries ◽  
Construction Quality ◽  
University Library

Paper books and documents are the main media of information service in most university libraries. In order to study the main reasons for the decline of the utilization rate of paper books in university libraries, this paper takes a well-known university library in China as an example, discusses the current situation of its low utilization rate of paper books and the reasons for the problems, and puts forward relevant optimization strategies, so as to promote the improvement of the construction quality of university libraries in China.

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