Cross Warehouse Scheduling of Logistics Distribution and Cycle Re-Claimer Based on Heuristics Algorithm

2014 ◽  
Vol 644-650 ◽  
pp. 2606-2610 ◽  
Author(s):  
Rong Hua Ma
Keyword(s):  
Heuristic Algorithm ◽  
Programming Model ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Simulation Method ◽  
New Method ◽  
Logistics Distribution ◽  
Suboptimal Solution ◽  
Taboo Search Algorithm ◽  
Heuristics Algorithm

In the uncertain environment, the cycle re-claimer and logistics distribution cross warehouse scheduling is important and it should be optimized. The mathematical programming model is constructed, and the proposed two-stage heuristic algorithm is proposed, the optimal solution of heuristic algorithm is used as the initial value. And the taboo search algorithm is designed to improve the initial solution. In order to verify the availability of the method, the Monte Carlo simulation method is used for numerical experiment. The experiment results show that the new method can solve the suboptimal solution which close to the optimal solution in fast, and the taboo searching algorithm can improve the solution of heuristic algorithm, it has significantly improvement performance for new method, and it has good application value in practice.

Direct Gravitational Search Algorithm for Global Optimisation Problems

2016 ◽  
Vol 6 (3) ◽  
pp. 290-313 ◽  
Author(s):  
Ahmed F. Ali ◽  
Mohamed A. Tawhid
Keyword(s):  
Heuristic Algorithm ◽  
Hybrid Algorithm ◽  
Search Algorithm ◽  
Direct Method ◽  
Gravitational Search Algorithm ◽  
Optimal Solution ◽  
Slow Convergence ◽  
Gravitational Search ◽  
Law Of Gravity ◽  
Number Of Iterations

AbstractA gravitational search algorithm (GSA) is a meta-heuristic development that is modelled on the Newtonian law of gravity and mass interaction. Here we propose a new hybrid algorithm called the Direct Gravitational Search Algorithm (DGSA), which combines a GSA that can perform a wide exploration and deep exploitation with the Nelder-Mead method, as a promising direct method capable of an intensification search. The main drawback of a meta-heuristic algorithm is slow convergence, but in our DGSA the standard GSA is run for a number of iterations before the best solution obtained is passed to the Nelder-Mead method to refine it and avoid running iterations that provide negligible further improvement. We test the DGSA on 7 benchmark integer functions and 10 benchmark minimax functions to compare the performance against 9 other algorithms, and the numerical results show the optimal or near optimal solution is obtained faster.

Taboo Search Algorithm for Optimizing Shunting Scheme of Through Wagon Flow Delivering and Fetching Operation

2012 ◽  
Vol 178-181 ◽  
pp. 1908-1914
Author(s):  
Jin Bao Xie ◽  
Ying Han ◽  
Long Han ◽  
Rui Na Yang
Keyword(s):  
Processing Time ◽  
Search Algorithm ◽  
Fitness Function ◽  
Optimal Solution ◽  
Two Dimensional ◽  
Taboo Search ◽  
Taboo Search Algorithm ◽  
Iteration Number ◽  
Simulation Results ◽  
Predetermined Number

In order to improve the efficiency of selecting the optimal scheme from the delivering and fetching shunting schemes of through wagon flow to and from the enterprise dedicated lines which are located in radial shape, the taboo search algorithm is applied in the process finding the optimal solution. The objective function is directly used as the fitness function, and the solution generated by exchanging the wagon group delivering order of one dedicated line with another is taken as the new solution; the taboo list is two-dimensional array, and a fixed value is given as the taboo length; if the evaluation value of the current optimal solution is superior to the historical one, the taboo rules will be defied and the current optimal solution be directly accepted; once the iteration number reaches the predetermined number, the calculating process will be terminated. The simulation results verify the effectiveness of the Taboo search algorithm to the problem, and show that the bigger the number of the dedicated lines, the more the number of equivalent schemes and the higher the searching efficiency is. And the smaller the number of the equivalent schemes, the bigger the search scope is. If the number of the dedicated lines is not more than 8, the processing time by computer is not longer than 15 milliseconds.

The Kalman Filter Cuckoo Search Algorithm to Solve the TSP Problem

2015 ◽  
Vol 733 ◽  
pp. 918-921
Author(s):  
Juan Li ◽  
Ting Zhang
Keyword(s):  
Kalman Filter ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Benchmark Problems ◽  
Cuckoo Optimization Algorithm ◽  
Logistics Distribution ◽  
Routing Optimization ◽  
Effectiveness And Efficiency

TSP problem optimization is a combinatorial optimization model studied which is NP hard, and it has been solved by a lot of algorithms. A new improved cuckoo optimization algorithm (KF-CS) has been put forward to solve the routing optimization problem of logistics distribution vehicle. Kalman Filter Cuckoo search (KF-CS) is a new intelligent algorithm which used to estimate the state of a stochastic phenomenon which has Gaussian distribution. The problem of travelling salesman was experimented. To demonstrate the effectiveness and efficiency of the proposed algorithm, the benchmark problems from TSPLIB were tested and compared with PSO, DE, ACO and standard CS. The results showed that the KF-CS algorithm achieved shorter distances in all cases within fewer generations, and it has obvious effects to find the optimal solution frequency and time.

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.

On the Optimal Precoder Design for Energy-Efficient and Secure MIMO Systems

2017 ◽  
Vol 3 (1) ◽  
pp. 1
Author(s):  
Tung T. Vu ◽  
Ha Hoang Kha
Keyword(s):  
Energy Efficiency ◽  
Mimo Systems ◽  
Search Algorithm ◽  
Multiple Input Multiple Output ◽  
Research Work ◽  
Computational Cost ◽  
Optimal Solution ◽  
Secrecy Rate ◽  
Highly Nonlinear ◽  
Precoder Design

In this research work, we investigate precoder designs to maximize the energy efficiency (EE) of secure multiple-input multiple-output (MIMO) systems in the presence of an eavesdropper. In general, the secure energy efficiency maximization (SEEM) problem is highly nonlinear and nonconvex and hard to be solved directly. To overcome this difficulty, we employ a branch-and-reduce-and-bound (BRB) approach to obtain the globally optimal solution. Since it is observed that the BRB algorithm suffers from highly computational cost, its globally optimal solution is importantly served as a benchmark for the performance evaluation of the suboptimal algorithms. Additionally, we also develop a low-complexity approach using the well-known zero-forcing (ZF) technique to cancel the wiretapped signal, making the design problem more amenable. Using the ZF based method, we transform the SEEM problem to a concave-convex fractional one which can be solved by applying the combination of the Dinkelbach and bisection search algorithm. Simulation results show that the ZF-based method can converge fast and obtain a sub-optimal EE performance which is closed to the optimal EE performance of the BRB method. The ZF based scheme also shows its advantages in terms of the energy efficiency in comparison with the conventional secrecy rate maximization precoder design.

scholarly journals Wind Farm Cable Connection Layout Optimization with Several Substations

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3615
Author(s):  
Adelaide Cerveira ◽  
Eduardo J. Solteiro Pires ◽  
José Baptista
Keyword(s):  
Energy Production ◽  
Wind Farm ◽  
Programming Model ◽  
Optimal Solution ◽  
Wind Farms ◽  
Green Energy ◽  
Fossil Energy ◽  
Proposed Model ◽  
Short Time ◽  
Cable Connection

Green energy has become a media issue due to climate changes, and consequently, the population has become more aware of pollution. Wind farms are an essential energy production alternative to fossil energy. The incentive to produce wind energy was a government policy some decades ago to decrease carbon emissions. In recent decades, wind farms were formed by a substation and a couple of turbines. Nowadays, wind farms are designed with hundreds of turbines requiring more than one substation. This paper formulates an integer linear programming model to design wind farms’ cable layout with several turbines. The proposed model obtains the optimal solution considering different cable types, infrastructure costs, and energy losses. An additional constraint was considered to limit the number of cables that cross a walkway, i.e., the number of connections between a set of wind turbines and the remaining wind farm. Furthermore, considering a discrete set of possible turbine locations, the model allows identifying those that should be present in the optimal solution, thereby addressing the optimal location of the substation(s) in the wind farm. The paper illustrates solutions and the associated costs of two wind farms, with up to 102 turbines and three substations in the optimal solution, selected among sixteen possible places. The optimal solutions are obtained in a short time.

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 Efficient Operative Cost Reduction in Distribution Grids Considering the Optimal Placement and Sizing of D-STATCOMs Using a Discrete-Continuous VSA

2021 ◽  
Vol 11 (5) ◽  
pp. 2175
Author(s):  
Oscar Danilo Montoya ◽  
Walter Gil-González ◽  
Jesus C. Hernández
Keyword(s):  
Objective Function ◽  
Reactive Power ◽  
Programming Model ◽  
Search Algorithm ◽  
Solution Space ◽  
Distribution Networks ◽  
Optimal Placement ◽  
Mixed Integer ◽  
Solution Vector ◽  
The Impact

The problem of reactive power compensation in electric distribution networks is addressed in this research paper from the point of view of the combinatorial optimization using a new discrete-continuous version of the vortex search algorithm (DCVSA). To explore and exploit the solution space, a discrete-continuous codification of the solution vector is proposed, where the discrete part determines the nodes where the distribution static compensator (D-STATCOM) will be installed, and the continuous part of the codification determines the optimal sizes of the D-STATCOMs. The main advantage of such codification is that the mixed-integer nonlinear programming model (MINLP) that represents the problem of optimal placement and sizing of the D-STATCOMs in distribution networks only requires a classical power flow method to evaluate the objective function, which implies that it can be implemented in any programming language. The objective function is the total costs of the grid power losses and the annualized investment costs in D-STATCOMs. In addition, to include the impact of the daily load variations, the active and reactive power demand curves are included in the optimization model. Numerical results in two radial test feeders with 33 and 69 buses demonstrate that the proposed DCVSA can solve the MINLP model with best results when compared with the MINLP solvers available in the GAMS software. All the simulations are implemented in MATLAB software using its programming environment.

The Location Problem of Given and Indivisible Different Units

1970 ◽  
Vol 2 (3) ◽  
pp. 341-356
Author(s):  
G. Jándy
Keyword(s):  
Exact Solution ◽  
Heuristic Algorithm ◽  
Network Flow ◽  
Location Problem ◽  
Optimal Solution ◽  
Type Problem ◽  
Distribution Problem ◽  
Enumeration Algorithm ◽  
Weighted Distribution ◽  
Partial Enumeration

In cases where certain simplifications are allowed, the location optimisation of given and indivisible different economic units may be modelled as a bi-value weighted distribution problem. The paper presents a heuristic algorithm for this network-flow-type problem and also a partial enumeration algorithm for deriving the exact solution. But it is also pointed out that an initial sub-optimal solution can quickly be improved with a derivation on a direct line only, if the exact solution is not absolutely essential. A numerical example is used to illustrate the method of derivation on a direct line starting with an upper bound given by a sub-optimal solution.

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