scholarly journals Scheduling the periodic delivery of liquefied petroleum gas tank with time window by using artificial intelligence approaches: An example in Taiwan

2021 ◽  
Vol 104 (3_suppl) ◽  
pp. 003685042110403
Author(s):  
Yi-Chih Hsieh ◽  
Peng-Sheng You ◽  
Cheng-Sheng Chen
Keyword(s):  
Evolutionary Algorithms ◽  
Time Window ◽  
Random Sequence ◽  
Cooking Time ◽  
Liquefied Petroleum Gas ◽  
Swarm Optimization ◽  
Encoding Scheme ◽  
Delivery Problem ◽  
Long Time ◽  
Delivery Efficiency

Introduction In Taiwan, liquefied petroleum gas tank users have to call a gas company to deliver a full liquefied petroleum gas tank when their tank is out of gas. The calls usually congest in the cooking time and the customers have to wait for a long time for a full liquefied petroleum gas tank. Additionally, allocating manpower is difficult for the gas company. Objectives A strategy of periodic delivery for gas companies was presented to deliver liquefied petroleum gas tanks in advance and charge the gas fee based on the weight of returned tanks. Additionally, a new encoding scheme was proposed and embedded into three evolutionary algorithms to solve the nondeterministic polynomial-hard problem. The objective of the problem is to minimize the total traveling distance of the vehicle such that the delivery efficiency of tanks increases and the waiting time of customer decreases. Methods A new encoding scheme was presented to convert any random sequence of integers into a solution of the problem and embedded into three evolutionary algorithms, namely, immune algorithm, genetic algorithm, and particle swarm optimization, to solve the delivery problem. Additionally, the encoding scheme can be used to different frequency types of demand based on customers’ requests. Results Numerical results, including a practical example in Yunlin, Taiwan, were provided to show that the adopted approaches can significantly improve the efficiency of delivery. Conclusions The periodic delivery strategy and the new encoding scheme can effectively solve the practical problem of liquefied petroleum gas tank in Taiwan.

scholarly journals A New Hybrid Algorithm to Solve Winner Determination Problem in Multiunit Double Internet Auction

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Mourad Ykhlef ◽  
Reem Alqifari
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Evolutionary Algorithms ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Double Auction ◽  
Winner Determination Problem ◽  
Swarm Optimization ◽  
Winner Determination ◽  
Long Time

Solving winner determination problem in multiunit double auction has become an important E-business task. The main issue in double auction is to improve the reward in order to match the ideal prices and quantity and make the best profit for sellers and buyers according to their bids and predefined quantities. There are many algorithms introduced for solving winner in multiunit double auction. Conventional algorithms can find the optimal solution but they take a long time, particularly when they are applied to large dataset. Nowadays, some evolutionary algorithms, such as particle swarm optimization and genetic algorithm, were proposed and have been applied. In order to improve the speed of evolutionary algorithms convergence, we will propose a new kind of hybrid evolutionary algorithm that combines genetic algorithm (GA) with particle swarm optimization (PSO) to solve winner determination problem in multiunit double auction; we will refer to this algorithm as AUC-GAPSO.

Time Window from Visual Images to Visual Short-Term Memory: Consolidation or Integration?

2004 ◽  
Vol 51 (1) ◽  
pp. 45-51 ◽  
Author(s):  
Yuhong Jiang
Keyword(s):  
Short Term Memory ◽  
Time Window ◽  
Short Interval ◽  
Visual Images ◽  
Short Term ◽  
Temporal Window ◽  
Term Memory ◽  
Visual Short Term Memory ◽  
Probe Task ◽  
Long Time

Abstract. When two dot arrays are briefly presented, separated by a short interval of time, visual short-term memory of the first array is disrupted if the interval between arrays is shorter than 1300-1500 ms ( Brockmole, Wang, & Irwin, 2002 ). Here we investigated whether such a time window was triggered by the necessity to integrate arrays. Using a probe task we removed the need for integration but retained the requirement to represent the images. We found that a long time window was needed for performance to reach asymptote even when integration across images was not required. Furthermore, such window was lengthened if subjects had to remember the locations of the second array, but not if they only conducted a visual search among it. We suggest that a temporal window is required for consolidation of the first array, which is vulnerable to disruption by subsequent images that also need to be memorized.

Study on Vehicle Logistics Routing Path Problem with Time Windows Based on Particle Swarm Optimization

2014 ◽  
Vol 687-691 ◽  
pp. 5161-5164
Author(s):  
Lian Zhou Gao
Keyword(s):  
Particle Swarm Optimization ◽  
World Economy ◽  
Time Window ◽  
Time Windows ◽  
Particle Swarm ◽  
Modern Logistics ◽  
Swarm Optimization ◽  
Logistics Industry ◽  
Search Speed ◽  
Simulation Results

As the development of world economy, how to realize the reasonable vehicle logistics routing path problem with time window constrain is the key issue in promoting the prosperity and development of modern logistics industry. Through the research of vehicle logistics routing path 's demand, particle swarm optimization with a novel particle presentation is designed to solve the problem which is improved, effective and adept to the normal vehicle logistics routing. The simulation results of example indicate that the algorithm has more search speed and stronger optimization ability.

scholarly journals Minimization of Logistics Cost and Carbon Emissions Based on Quantum Particle Swarm Optimization

2018 ◽  
Vol 10 (10) ◽  
pp. 3791 ◽  
Author(s):  
Daqing Wu ◽  
Jiazhen Huo ◽  
Gefu Zhang ◽  
Weihua Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Supply Chain ◽  
Carbon Emissions ◽  
Time Window ◽  
Quantum Particle ◽  
Particle Swarm ◽  
Transport Cost ◽  
Distribution Centers ◽  
Swarm Optimization ◽  
Quantum Particle Swarm Optimization

This paper aims to simultaneously minimize logistics costs and carbon emissions. For this purpose, a mathematical model for a three-echelon supply chain network is created considering the relevant constraints such as capacity, production cost, transport cost, carbon emissions, and time window, which will be solved by the proposed quantum-particle swarm optimization algorithm. The three-echelon supply chain, consisting of suppliers, distribution centers, and retailers, is established based on the number and location of suppliers, the transport method from suppliers to distribution centers, and the quantity of products to be transported from suppliers to distribution centers and from these centers to retailers. Then, a quantum-particle swarm optimization is described as its performance is validated with different benchmark functions. The scenario analysis validates the model and evaluates its performance to balance the economic benefit and environmental effect.

scholarly journals Performance of Resource Allocation in Device-to-Device Communication Systems Based on Evolutionally Optimization Algorithms

2018 ◽  
Author(s):  
Hsu-Tan Tan ◽  
Bor-An Chen ◽  
Yung-Fa Huang
Keyword(s):  
Particle Swarm Optimization ◽  
Evolutionary Algorithms ◽  
Communication Systems ◽  
Particle Swarm ◽  
Optimal Solution ◽  
System Capacity ◽  
Swarm Optimization ◽  
Device To Device ◽  
Simulation Results ◽  
Capacity Performance

In this study, the resource blocks (RB) are allocated to user equipment (UE) according to the evolutional algorithms for long term evolution (LTE) systems. Particle Swarm Optimization (PSO) algorithm is one of the evolutionary algorithms, based on the imitation of a flock of birds foraging behavior through learning and grouping the best experience. In previous work, the Simple Particle Swarm Optimization (SPSO) algorithm was proposed for RB allocation to enhance the throughput of Device-to-Device (D2D) communications and improve the system capacity performance. In simulation results, with less population size of M = 10, the SPSO can perform quickly convergence to sub-optimal solution in the 100th generation and obtained sub-optimum performance with more 2 UEs than the Rand method. Genetic algorithm (GA) is one of the evolutionary algorithms, based on Darwinian models of natural selection and evolution. Therefore, we further proposed a Refined PSO (RPSO) and a novel GA to enhance the throughput of UEs and to improve the system capacity performance. Simulation results show that the proposed GA with 100 populations, in 200 generations can converge to suboptimal solutions. Therefore, with comparing with the SPSO algorithm the proposed GA and RPSO can improve system capacity performance with 1.8 and 0.4 UEs, respectively.

scholarly journals Evolution of andrenine bees reveals a long and complex history of faunal interchanges between the Americas during the Mesozoic and Cenozoic

2021 ◽  
Author(s):  
Kelli S Ramos ◽  
Aline C Martins ◽  
Gabriel A R Melo
Keyword(s):  
North America ◽  
South America ◽  
Time Window ◽  
Distributional Pattern ◽  
Long Distance ◽  
Early Diversification ◽  
Palearctic Region ◽  
Long Time ◽  
The Times ◽  
Dated Phylogeny

Bees are presumed to have arisen in the early to mid-Cretaceous coincident with the fragmentation of the southern continents and concurrently with the early diversification of the flowering plants. Among the main groups of bees, Andreninae sensu lato comprise about 3000 species widely distributed with greatest and disjunct diversity in arid areas of North America, South America, and the Palearctic region. Here, we present the first comprehensive dated phylogeny and historical biogeographic analysis for andrenine bees, including representatives of all currently recognized tribes. Our analyses rely on a dataset of 106 taxa and 7952 aligned nucleotide positions from one mitochondrial and six nuclear loci. Andreninae is strongly supported as a monophyletic group and the recovered phylogeny corroborates the commonly recognized clades for the group. Thus, we propose a revised tribal classification that is congruent with our phylogenetic results. The time-calibrated phylogeny and ancestral range reconstructions of Andreninae reveal a fascinating evolutionary history with Gondwana patterns that are unlike those observed in other subfamilies of bees. Andreninae arose in South America during the Late Cretaceous around 90 Million years ago (Ma) and the origin of tribes occurred through a relatively long time-window from this age to the Miocene. The early evolution of the main lineages took place in South America until the beginning of Paleocene with North American fauna origin from it and Palearctic from North America as results of multiple lineage interchanges between these areas by long-distance dispersal or hopping through landmass chains. Overall, our analyses provide strong evidence of amphitropical distributional pattern currently observed in Andreninae in the American continent as result at least three periods of possible land connections between the two American landmasses, much prior to the Panama Isthmus closure. The andrenine lineages reached the Palearctic region through four dispersal events from North America during the Eocene, late Oligocene and early Miocene, most probably via the Thulean Bridge. The few lineages with Afrotropical distribution likely originated from a Palearctic ancestral in the Miocene around 10 Ma when these regions were contiguous, and the Sahara Desert was mostly vegetated making feasible the passage by several organisms. Incursions of andrenine bees to North America and then onto the Old World are chronological congruent with distinct periods when open-vegetation habitats were available for trans-continental dispersal and at the times when aridification and temperature decline offered favorable circumstances for bee diversification.

Using Particle Swarm Optimization Algorithm as an Optimization Tool Within Developed Neural Networks

2018 ◽  
pp. 215-244
Author(s):  
Goran Klepac
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Particle Swarm Optimization ◽  
Evolutionary Algorithms ◽  
Optimization Algorithm ◽  
Recommendation System ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Optimal Combination ◽  
Swarm Optimization

Developed neural networks as an output could have numerous potential outputs caused by numerous combinations of input values. When we are in position to find optimal combination of input values for achieving specific output value within neural network model it is not a trivial task. This request comes from profiling purposes if, for example, neural network gives information of specific profile regarding input or recommendation system realized by neural networks, etc. Utilizing evolutionary algorithms like particle swarm optimization algorithm, which will be illustrated in this chapter, can solve these problems.

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