Scheduling of Yard Truck Considering Loading and Unloading Simultaneously in an Underground Container Logistics System

2021 ◽  
pp. 036119812110478
Author(s):  
Yinping Gao ◽  
Daofang Chang ◽  
Jun Yuan ◽  
Chengji Liang
Keyword(s):  
Genetic Algorithm ◽  
Container Terminals ◽  
Mixed Integer ◽  
Operating Efficiency ◽  
Adaptive Genetic Algorithm ◽  
Integer Programming Problem ◽  
Logistics System ◽  
Container Logistics ◽  
Loading And Unloading ◽  
Mixed Integer Programming Problem

With the rapid growth of containers and scarce of land, the underground container logistics system (UCLS) presents a logical alternative for container terminals to better protect the environment and relieve traffic pressure. The operating efficiency of container terminals is one of the competitive edges over other terminals, which requires UCLS to be well integrated with the handling process of the storage yard. In UCLS, yard trucks (YTs) serve different handling points dynamically instead of one fixed handling point, and yard cranes (YCs) perform loading and unloading simultaneously. To minimize the total time of handling all containers in UCLS, the mixed integer programming problem is described and solved using an adaptive genetic algorithm (AGA). The convergence speed and accuracy of AGA are demonstrated by comparison with conventional genetic algorithm (GA). Additionally, AGA and CPLEX are compared with different scale cases. Experimental results show that the proposed algorithm is superior to CPLEX in resulted solutions and calculation time. A sensitivity analysis is provided to obtain reasonable numbers of YTs for scheduling between handling points and the storage yard in UCLS.

Chaotic Genetic Algorithm for Mixed Integer Programming Problem

2014 ◽  
Vol 651-653 ◽  
pp. 2273-2277
Author(s):  
Ya Ming Wang ◽  
Z. Zhang ◽  
Jun Bao Zheng ◽  
L.L. Tong
Keyword(s):  
Genetic Algorithm ◽  
Integer Programming ◽  
Programming Problem ◽  
Mixed Integer Programming ◽  
Stability And Convergence ◽  
Mixed Integer ◽  
Search Process ◽  
Integer Programming Problem ◽  
Genetic Search ◽  
Mixed Integer Programming Problem

This paper proposed a chaotic genetic algorithm (CGA) to solve the mixed integer programming problem (MIPP). The basic idea of this algorithm is to overcome the deficiency of genetic algorithm (GA) by introducing chaotic disturbances into the genetic search process. Two typical MIPP problems are used to evaluate the performances of the proposed CGA. Experimental results show that performances of the algorithm have been improved by the chaotic disturbances, such as, search ability, precision, stability and convergence speed or calculation efficiency. The proposed CGA algorithm is suitable for solving complicated practical MIPP problem.

scholarly journals A Mixed Integer Programming Poultry Feed Ration Optimisation Problem Using the Bat Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-6 ◽  
Author(s):  
Godfrey Chagwiza ◽  
Chipo Chivuraise ◽  
Christopher T. Gadzirayi
Keyword(s):  
Integer Programming ◽  
Mixed Integer Programming ◽  
Moringa Oleifera ◽  
Bat Algorithm ◽  
Mixed Integer ◽  
Poultry Feed ◽  
Integer Programming Problem ◽  
Optimum Solution ◽  
Mixed Integer Programming Problem ◽  
Number Of Iterations

In this paper, a feed ration problem is presented as a mixed integer programming problem. An attempt to find the optimal quantities of Moringa oleifera inclusion into the poultry feed ration was done and the problem was solved using the Bat algorithm and the Cplex solver. The study used findings of previous research to investigate the effects of Moringa oleifera inclusion in poultry feed ration. The results show that the farmer is likely to gain US$0.89 more if Moringa oleifera is included in the feed ration. Results also show superiority of the Bat algorithm in terms of execution time and number of iterations required to find the optimum solution as compared with the results obtained by the Cplex solver. Results revealed that there is a significant economic benefit of Moringa oleifera inclusion into the poultry feed ration.

The Optimal Controlled Partitioning Strategy for Power System Based on Master-Slave Problem

2013 ◽  
Vol 385-386 ◽  
pp. 999-1006
Author(s):  
Wei Wang ◽  
Ting Yu ◽  
Tian Jiao Pu ◽  
Ai Zhong Tian ◽  
Ji Keng Lin
Keyword(s):  
Large Scale ◽  
Optimization Method ◽  
Mixed Integer ◽  
Master Problem ◽  
Decomposition Algorithms ◽  
Integer Programming Problem ◽  
Complete Model ◽  
New Approach ◽  
Mixed Integer Programming Problem ◽  
Partitioning Strategy

Controlled partitioning strategy is one of the effective measures taken for the situation when system out-of-step occurs. The complete splitting model, mostly solved by approximate decomposition algorithms, is a large-scale nonlinear mixed integer programming problem. A new alternate optimization method based on master-slave problem to search for optimal splitting strategy is proposed hereby. The complete model was converted into master-slave problems based on CGKP (Connected Graph Constrained Knapsack Problem). The coupling between master problem and slave problem is achieved through load adjustment. A better splitting strategy can be obtained through the alternating iteration between the master problem and the salve problem. The results of the examples show that the method can obtain better splitting strategy with less shed load than other approximate algorithms, which verifies the feasibility and effectiveness of the new approach presented.

Location model for a remanufacturing reverse logistics network based on adaptive genetic algorithm

SIMULATION ◽  
2019 ◽  
Vol 95 (11) ◽  
pp. 1069-1084 ◽  
Author(s):  
Rui Yan ◽  
Bo Yan
Keyword(s):  
Genetic Algorithm ◽  
Network Model ◽  
Reverse Logistics ◽  
Closed Loop ◽  
Value Added ◽  
Open Loop ◽  
Demand Elasticity ◽  
Mixed Integer ◽  
Adaptive Genetic Algorithm ◽  
Loop Model

Energy saving and environmental protection are important issues of today. Concerning the environmental and social need to increase the utilization of used products, this paper introduces two remanufacturing reverse logistics (RL) network models, namely, the open-loop model and the closed-loop model. In an open-loop RL system, used products are recovered by outside firms, while in a closed-loop RL system, they are returned to their original producers. The open-loop model features a location selection with two layers. For this model, a mixed-integer linear program (MILP) is built to minimize the total costs of the open-loop RL system, including the fixed cost, the freight between nodes, the operation cost of storage and remanufacturing centers, the penalty cost of unmet or remaining demand quantity, and the government-provided subsidy given to the enterprises that protect the environment. This MILP is solved using an adaptive genetic algorithm with MATLAB simulation. For a closed-loop RL network model, a special demand function considering the relationship between new and remanufactured products is developed. Remanufacturing rate, environmental awareness, service demand elasticity, value-added services, and their impacts on total profit of the closed-loop supply chain are analyzed. The closed-loop RL network model is proved effective through the analysis of a numerical example.

scholarly journals An Adaptive Test Sheet Generation Mechanism Using Genetic Algorithm

2012 ◽  
Vol 2012 ◽  
pp. 1-18 ◽  
Author(s):  
Huan-Yu Lin ◽  
Jun-Ming Su ◽  
Shian-Shyong Tseng
Keyword(s):  
Genetic Algorithm ◽  
Computation Time ◽  
Mixed Integer ◽  
Selection Strategy ◽  
Integer Programming Problem ◽  
Test Sheet ◽  
Test Items ◽  
Adaptive Test ◽  
Time Series Approach ◽  
Assessment Requirements

For test-sheet composition systems, it is important to adaptively compose test sheets with diverse conceptual scopes, discrimination and difficulty degrees to meet various assessment requirements during real learning situations. Computation time and item exposure rate also influence performance and item bank security. Therefore, this study proposes an Adaptive Test Sheet Generation (ATSG) mechanism, where a Candidate Item Selection Strategy adaptively determines candidate test items and conceptual granularities according to desired conceptual scopes, and an Aggregate Objective Function applies Genetic Algorithm (GA) to figure out the approximate solution of mixed integer programming problem for the test-sheet composition. Experimental results show that the ATSG mechanism can efficiently, precisely generate test sheets to meet the various assessment requirements than existing ones. Furthermore, according to experimental finding, Fractal Time Series approach can be applied to analyze the self-similarity characteristics of GA’s fitness scores for improving the quality of the test-sheet composition in the near future.

scholarly journals Distributionally robust chance constrained svm model with $\ell_2$-Wasserstein distance

2021 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
Qing Ma ◽  
Yanjun Wang
Keyword(s):  
Integer Programming ◽  
Programming Problem ◽  
Numerical Experiments ◽  
Wasserstein Distance ◽  
Mixed Integer ◽  
Integer Programming Problem ◽  
Discrete Form ◽  
Svm Model ◽  
Distributionally Robust ◽  
Mixed Integer Programming Problem

<p style='text-indent:20px;'>In this paper, we propose a distributionally robust chance-constrained SVM model with <inline-formula><tex-math id="M1">\begin{document}$ \ell_2 $\end{document}</tex-math></inline-formula>-Wasserstein ambiguity. We present equivalent formulations of distributionally robust chance constraints based on <inline-formula><tex-math id="M2">\begin{document}$ \ell_2 $\end{document}</tex-math></inline-formula>-Wasserstein ambiguity. In terms of this method, the distributionally robust chance-constrained SVM model can be transformed into a solvable linear 0-1 mixed integer programming problem when the <inline-formula><tex-math id="M3">\begin{document}$ \ell_2 $\end{document}</tex-math></inline-formula>-Wasserstein distance is discrete form. The DRCC-SVM model could be transformed into a tractable 0-1 mixed-integer SOCP programming problem for the continuous case. Finally, numerical experiments are given to illustrate the effectiveness and feasibility of our model.</p>

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