A heuristic two-phase solution approach for the multi-objective dial-a-ride problem

Networks ◽  
2009 ◽  
Vol 54 (4) ◽  
pp. 227-242 ◽  
Author(s):  
Sophie N. Parragh ◽  
Karl F. Doerner ◽  
Richard F. Hartl ◽  
Xavier Gandibleux
Keyword(s):  
Two Phase ◽  
Solution Approach ◽  
Multi Objective ◽  
Phase Solution

scholarly journals A two-phase solution approach for a manufacturing-distribution problem with rework, outsourcing, and multi-shipment policy

2020 ◽  
Vol 18 (4) ◽  
pp. 505-509
Author(s):  
Chiu Peter ◽  
Peng-Cheng Sung ◽  
Victoria Chiu
Keyword(s):  
Cost Minimization ◽  
Algebraic Approach ◽  
Solution Procedure ◽  
Batch Size ◽  
Real Problem ◽  
Two Phase ◽  
Solution Approach ◽  
Straightforward Solution ◽  
The Cost ◽  
Phase Solution

In a recent study, a manufacturing batch-size and end-product shipment problem with outsourcing, multi-shipment, and rework was investigated using mathematical modeling and derivatives in its solution procedure. This study demonstrates that a simplified two-phase algebraic approach can also solve the problem and decide the cost-minimization policies for batch-size and end-product shipments. Our proposed straightforward solution approach enables the practitioners in the production planning and controlling filed to comprehend and efficiently solve the best replenishing batch-size and shipment policies of this real problem.

REAL-LIFE VEHICLE ROUTING WITH TIME WINDOWS FOR VISUAL ATTRACTIVENESS AND OPERATIONAL ROBUSTNESS

2012 ◽  
Vol 29 (04) ◽  
pp. 1250017 ◽  
Author(s):  
B. L. HOLLIS ◽  
P. J. GREEN
Keyword(s):  
Vehicle Routing ◽  
Search Algorithm ◽  
Time Windows ◽  
Real Life ◽  
Second Phase ◽  
Two Phase ◽  
Guided Local Search ◽  
Solution Approach ◽  
Robust Solutions ◽  
Phase Solution

This paper describes an algorithm for producing visually attractive and operationally robust solutions to a real-life vehicle routing problem with time windows. Visually attractive and operationally robust solutions are usually synonymous with compact, nonoverlapping routes with little or no intra-route cross over. The visual attractiveness of routes, for practical routing applications, is often of paramount importance. We present a two phase solution approach. The first phase is inspired by the sequential insertion algorithm of Solomon (1987) and includes a range of novel enhancements to ensure visually attractive solutions are produced in the face of a range of nonstandard real-life constraints: A constrained and heterogeneous vehicle fleet; tight time windows and banned delivery windows; multiple route capacity measures; driver breaks; minimum route volumes; vehicle-location compatibility rules; nonreturn to base routes; peak hour traveling times; vehicle type dependent service times; and replenishment back at the depot. The second phase is based on the guided local search algorithm of Kilby et al. (1999). It uses an augmented objective function designed to seek solutions which strike a balance between minimizing traditional cost measures, whilst maximizing the visual attractiveness of the solution. Our two phase solution approach is particularly adept at producing solutions that both aggressively minimize the total number of routes, a feature that we believe has been missing in algorithms presented in equivalent literature, as well as minimizing traditional cost measures whilst delivering a very high degree of visual attractiveness. The algorithm presented has been successfully implemented and deployed for the real-life, daily beverage distribution problem of Schweppes Australia Pty. Ltd. for a range of capital cities throughout Australia.

scholarly journals A novel two-phase cycle algorithm for effective cyber intrusion detection in edge computing

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Yiguang Gong ◽  
Yunping Liu ◽  
Chuanyang Yin
Keyword(s):  
Intrusion Detection ◽  
Optimal Parameter ◽  
Edge Computing ◽  
Cloud Services ◽  
Second Phase ◽  
Pareto Optimal ◽  
Two Phase ◽  
Multi Objective ◽  
Phase Cycle ◽  
Positive Rate

AbstractEdge computing extends traditional cloud services to the edge of the network, closer to users, and is suitable for network services with low latency requirements. With the rise of edge computing, its security issues have also received increasing attention. In this paper, a novel two-phase cycle algorithm is proposed for effective cyber intrusion detection in edge computing based on a multi-objective genetic algorithm (MOGA) and modified back-propagation neural network (MBPNN), namely TPC-MOGA-MBPNN. In the first phase, the MOGA is employed to build a multi-objective optimization model that tries to find the Pareto optimal parameter set for MBPNN. The Pareto optimal parameter set is applied for simultaneous minimization of the average false positive rate (Avg FPR), mean squared error (MSE) and negative average true positive rate (Avg TPR) in the dataset. In the second phase, some MBPNNs are created based on the parameter set obtained by MOGA and are trained to search for a more optimal parameter set locally. The parameter set obtained in the second phase is used as the input of the first phase, and the training process is repeated until the termination criteria are reached. A benchmark dataset, KDD cup 1999, is used to demonstrate and validate the performance of the proposed approach for intrusion detection. The proposed approach can discover a pool of MBPNN-based solutions. Combining these MBPNN solutions can significantly improve detection performance, and a GA is used to find the optimal MBPNN combination. The results show that the proposed approach achieves an accuracy of 98.81% and a detection rate of 98.23% and outperform most systems of previous works found in the literature. In addition, the proposed approach is a generalized classification approach that is applicable to the problem of any field having multiple conflicting objectives.

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