A Two-Stage Stochastic Network Model and Solution Methods for the Dynamic Empty Container Allocation Problem

1998 ◽  
Vol 32 (2) ◽  
pp. 142-162 ◽  
Author(s):  
Raymond K. Cheung ◽  
Chuen-Yih Chen
Keyword(s):  
Network Model ◽  
Allocation Problem ◽  
Stochastic Network ◽  
Two Stage ◽  
Empty Container ◽  
Solution Methods

A Two Stage Method for the Multiple Traveling Salesman Problem

2020 ◽  
Vol 11 (3) ◽  
pp. 79-91
Author(s):  
Azcarie Manuel Cabrera Cuevas ◽  
Jania Astrid Saucedo Martínez ◽  
José Antonio Marmolejo Saucedo
Keyword(s):  
Traveling Salesman Problem ◽  
Traveling Salesman ◽  
Difficulty Level ◽  
Median Problem ◽  
Two Stage ◽  
Multiple Traveling Salesman Problem ◽  
Solution Methods ◽  
The Many ◽  
The Traveling Salesman Problem

The variation of the traveling salesman problem (TSP) with multiple salesmen (m-TSP) has been studied for many years resulting in diverse solution methods, both exact and heuristic. However, the high difficulty level on finding optimal (or acceptable) solutions has opposed the many efforts of doing so. The proposed method regards a two stage procedure which implies a modified version of the p-Median Problem (PMP) alongside the TSP, making a partition of the nodes into subsets that will be assigned to each salesman, solving it with Branch & Cut (B&C), in the first stage. This is followed by the routing, applying an Ant Colony Optimization (ACO) metaheuristic algorithm to solve a TSP for each subset of nodes. A case study was reviewed, detailing the positioning of five vehicles in strategic places in the Mexican Republic.

scholarly journals Resource allocation problem in project management

2019 ◽  
Vol 97 ◽  
pp. 01003 ◽  
Author(s):  
Irina Burkova ◽  
Boris Titarenko ◽  
Amir Hasnaoui ◽  
Roman Titarenko
Keyword(s):  
Resource Allocation ◽  
Project Management ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Maximum Flow ◽  
Allocation Problem ◽  
Real Problem ◽  
Resource Allocation Problem ◽  
Flow Problems ◽  
Solution Methods

Resource allocation problems in project management are notoriously complex. Therefore the development of efficient algorithms for solving various specific cases is a real problem. This paper shows a specific case of the problem, where a program has a particular structure. The resource allocation problem in such a program is reduced to classical Johnson’s problem or job-shop scheduling problem. Effective solution methods, by way of reducing to maximum flow problems, are suggested for some types of resources. For other cases, heuristic rules are developed, with a description of the situations in which these rules allow good enough solutions to be obtained.

scholarly journals Efficacy-specific Herbal Group Detection from Traditional Chinese Medicine Prescriptions via Hierarchical Attentive Neural Network Model

2020 ◽  
Author(s):  
Li Chen ◽  
Xinglong Liu ◽  
Siyuan Zhang ◽  
Hong Yi ◽  
Yongmei Lu ◽  
...  
Keyword(s):  
Neural Network ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Network Model ◽  
Neural Network Model ◽  
Experimental Results ◽  
Frequent Patterns ◽  
Two Stage ◽  
Second Stage ◽  
Group Detection

Abstract Background: Mining massive prescriptions in Traditional Chinese Medicine (TCM) accumulated in the lengthy period of several thousand years to discover essential herbal groups for distinct efficacies is of significance for TCM modernization, thus starting to draw attentions recently. However, most existing methods for the task treat herbs with different surface forms orthogonally and determine efficacy-specific herbal groups based on the raw frequencies an herbal group occur in a collection of prescriptions. Such methods entirely overlook the fact that prescriptions in TCM are formed empirically by different people at different historical stages, and thus full of herbs with different surface forms expressing the same material, or even noisy and redundant herbs.Methods: We propose a two-stage approach for efficacy-specific herbal group detection from prescriptions in TCM. For the first stage we devise a hierarchical attentive neural network model to capture essential herbs in a prescription for its efficacy, where herbs are encoded with dense real-valued vectors learned automatically to identify their differences on the semantical level. For the second stage, frequent patterns are mined to discover essential herbal groups for an efficacy from distilled prescriptions obtained in the first stage.Results: We verify the effectiveness of our proposed approach from two aspects, the first one is the ability of the hierarchical attentive neural network model to distill a prescription, and the second one is the accuracy in discovering efficacy-specific herbal groups.Conclusion: The experimental results demonstrate that the hierarchical attentive neural network model is capable to capture herbs in a prescription essential to its efficacy, and the distilled prescriptions significantly could improve the performance of efficacy-specific herbal group detection.

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