Optimization of the material handling costs and the machine reliability in cellular manufacturing system using cuckoo search algorithm

2018 ◽  
Vol 31 (8) ◽  
pp. 3743-3757 ◽  
Author(s):  
Bouchra Karoum ◽  
Youssef Bouazza Elbenani
Keyword(s):  
Material Handling ◽  
Manufacturing System ◽  
Cellular Manufacturing ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Cellular Manufacturing System ◽  
Machine Reliability ◽  
Material Handling Costs

scholarly journals Literature Review on Machine Reliability in Cellular Manufacturing System

2014 ◽  
Vol 11 (12) ◽  
pp. 1964-1968 ◽  
Author(s):  
A. Nouri Houshyar ◽  
Z. Leman ◽  
H. Pakzad Moghadam ◽  
R. Sulaiman
Keyword(s):  
Literature Review ◽  
Manufacturing System ◽  
Cellular Manufacturing ◽  
Cellular Manufacturing System ◽  
Machine Reliability

scholarly journals Cellular Manufacturing System with Dynamic Lot Size Material Handling

2016 ◽  
Vol 114 ◽  
pp. 012144 ◽  
Author(s):  
M.S.A. Khannan ◽  
A. Maruf ◽  
R. Wangsaputra ◽  
S. Sutrisno ◽  
T. Wibawa
Keyword(s):  
Material Handling ◽  
Manufacturing System ◽  
Cellular Manufacturing ◽  
Cellular Manufacturing System ◽  
Lot Size ◽  
Dynamic Lot Size

Multioptimization in a Cellular Manufacturing System Having Stochastic Parameters Considering Pricing

2015 ◽  
Vol 15 (3) ◽  
pp. 257-265
Author(s):  
Reza Salarian ◽  
Hamed Fazlollahtabar
Keyword(s):  
Mathematical Model ◽  
Manufacturing System ◽  
Cellular Manufacturing ◽  
Search Algorithm ◽  
Heuristic Method ◽  
The Novel ◽  
Cellular Manufacturing System ◽  
Solution Approach ◽  
Processing Times ◽  
Stochastic Parameters

AbstractA mathematical model is developed to formulate a cellular manufacturing system with uncertain parameters. In this work, the processing times and demands are stochastic and estimated via expected value and standard deviation after sampling process. The objectives of the proposed mathematical model are to configure machines’ layout in cells so that the inter-cell and intra-cell movements are minimized, the bottlenecks are breakthrough and the profit is increased. Finally, the profit is maximized due to decreasing production cost. The applicability of the proposed mathematical program is illustrated using numerical examples. With respect to the large amount of computational efforts in larger sized problem, a heuristic methodology is developed as solution approach. The properties of the proposed heuristic method are the novel search algorithm and the allocation methodology.

Optimal broadcast scheduling method for VANETs: An adaptive discrete firefly approach

2020 ◽  
Vol 39 (6) ◽  
pp. 8125-8137
Author(s):  
Jackson J Christy ◽  
D Rekha ◽  
V Vijayakumar ◽  
Glaucio H.S. Carvalho
Keyword(s):  
Intelligent Transportation System ◽  
Search Algorithm ◽  
Mac Protocol ◽  
High Mobility ◽  
Optimal Solution ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Traffic Density ◽  
Scheduling Problem ◽  
Broadcast Scheduling

Vehicular Adhoc Networks (VANET) are thought-about as a mainstay in Intelligent Transportation System (ITS). For an efficient vehicular Adhoc network, broadcasting i.e. sharing a safety related message across all vehicles and infrastructure throughout the network is pivotal. Hence an efficient TDMA based MAC protocol for VANETs would serve the purpose of broadcast scheduling. At the same time, high mobility, influential traffic density, and an altering network topology makes it strenuous to form an efficient broadcast schedule. In this paper an evolutionary approach has been chosen to solve the broadcast scheduling problem in VANETs. The paper focusses on identifying an optimal solution with minimal TDMA frames and increased transmissions. These two parameters are the converging factor for the evolutionary algorithms employed. The proposed approach uses an Adaptive Discrete Firefly Algorithm (ADFA) for solving the Broadcast Scheduling Problem (BSP). The results are compared with traditional evolutionary approaches such as Genetic Algorithm and Cuckoo search algorithm. A mathematical analysis to find the probability of achieving a time slot is done using Markov Chain analysis.

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.

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