Study on Patrol Service Platform Settings and Scheduling Based on Genetic Algorithm

2012 ◽  
Vol 182-183 ◽  
pp. 1713-1718
Author(s):  
Huai Hui Liu ◽  
Hai Feng Qiao ◽  
Nai Peng Li ◽  
Hong Bao Yao
Keyword(s):  
Genetic Algorithm ◽  
Graph Theory ◽  
Road Traffic ◽  
Police Force ◽  
Allocation Model ◽  
Service Platform ◽  
Security Forces ◽  
Scheme Optimization ◽  
Set Up ◽  
Theory Combination

In order to achieve rapid ease and rescue incidents in regional emergency, dispatching police scientific and reasonable , putting forward the model of emergency police force allocation to set up patrol service platform. Based on graph theory construction of the topological structure of the original problem, we use genetic algorithm to solve the problem .Through the solving ,we can get the best setting scheme of patrol service platform, to meeting patrol police within the specified time to achieve the area of possible emergencies of any emergency in the situation that given the number of police. Under meeting the hypothesis conditions using MATLAB software combined with genetic algorithm, we can get the police force allocation scheme optimization finally. Therefore, based on the graph theory combination of genetic algorithm and construction of city road traffic emergency police force allocation model, we can realize the security forces rescue scheduling, further realize the scientific and efficient rescue.

scholarly journals Genetic Algorithm for the Retailers’ Shelf Space Allocation Profit Maximization Problem

2021 ◽  
Vol 11 (14) ◽  
pp. 6401
Author(s):  
Kateryna Czerniachowska ◽  
Karina Sachpazidu-Wójcicka ◽  
Piotr Sulikowski ◽  
Marcin Hernes ◽  
Artur Rot
Keyword(s):  
Genetic Algorithm ◽  
Profit Maximization ◽  
Allocation Rule ◽  
Maximization Problem ◽  
Shelf Space ◽  
Allocation Model ◽  
Space Allocation ◽  
Shelf Space Allocation ◽  
To Receive

This paper discusses the problem of retailers’ profit maximization regarding displaying products on the planogram shelves, which may have different dimensions in each store but allocate the same product sets. We develop a mathematical model and a genetic algorithm for solving the shelf space allocation problem with the criteria of retailers’ profit maximization. The implemented program executes in a reasonable time. The quality of the genetic algorithm has been evaluated using the CPLEX solver. We determine four groups of constraints for the products that should be allocated on a shelf: shelf constraints, shelf type constraints, product constraints, and virtual segment constraints. The validity of the developed genetic algorithm has been checked on 25 retailing test cases. Computational results prove that the proposed approach allows for obtaining efficient results in short running time, and the developed complex shelf space allocation model, which considers multiple attributes of a shelf, segment, and product, as well as product capping and nesting allocation rule, is of high practical relevance. The proposed approach allows retailers to receive higher store profits with regard to the actual merchandising rules.

Reforms in the Police Forces

2020 ◽  
pp. 160-177
Author(s):  
Joshua N. Aston
Keyword(s):  
Police Force ◽  
System P ◽  
Set Up ◽  
High Level ◽  
Police System ◽  
Police Forces

The Indian police force or the police system is an organization that is absolute in its institution. Thus it has been a big challenge to bring in a long-lasting and meaningful reforms in the system. This chapter discusses various reforms and recommendations by a number of committees set up in order to reform the Indian police system. There have been many high-level committees established, headed by eminent jurists, reformers, bureaucrats, and so on, to bring reforms in the system.

Dynamic testing resource allocation modeling for multi-release software using optimal control theory and genetic algorithm

2020 ◽  
Vol 37 (6/7) ◽  
pp. 1049-1069
Author(s):  
Vijay Kumar ◽  
Ramita Sahni
Keyword(s):  
Genetic Algorithm ◽  
Optimal Control ◽  
Resource Allocation ◽  
Real Life ◽  
Theoretic Approach ◽  
Numerical Illustration ◽  
Data Set ◽  
Allocation Model ◽  
Content Type ◽  
Software Firms

PurposeThe use of software is overpowering our modern society. Advancement in technology is directly proportional to an increase in user demand which further leads to an increase in the burden on software firms to develop high-quality and reliable software. To meet the demands, software firms need to upgrade existing versions. The upgrade process of software may lead to additional faults in successive versions of the software. The faults that remain undetected in the previous version are passed on to the new release. As this process is complicated and time-consuming, it is important for firms to allocate resources optimally during the testing phase of software development life cycle (SDLC). Resource allocation task becomes more challenging when the testing is carried out in a dynamic nature.Design/methodology/approachThe model presented in this paper explains the methodology to estimate the testing efforts in a dynamic environment with the assumption that debugging cost corresponding to each release follows learning curve phenomenon. We have used optimal control theoretic approach to find the optimal policies and genetic algorithm to estimate the testing effort. Further, numerical illustration has been given to validate the applicability of the proposed model using a real-life software failure data set.FindingsThe paper yields several substantive insights for software managers. The study shows that estimated testing efforts as well as the faults detected for both the releases are closer to the real data set.Originality /valueWe have proposed a dynamic resource allocation model for multirelease of software with the objective to minimize the total testing cost using the flexible software reliability growth model (SRGM).

Finite Characterization of the Coarsest Balanced Coloring of a Network

2020 ◽  
Vol 30 (14) ◽  
pp. 2050212
Author(s):  
Ian Stewart
Keyword(s):  
Graph Theory ◽  
Adjacency Matrix ◽  
Universal Cover ◽  
Running Time ◽  
Set Up ◽  
Balanced Coloring ◽  
Refinement Algorithm ◽  
Partition Refinement

Balanced colorings of networks correspond to flow-invariant synchrony spaces. It is known that the coarsest balanced coloring is equivalent to nodes having isomorphic infinite input trees, but this condition is not algorithmic. We provide an algorithmic characterization: two nodes have the same color for the coarsest balanced coloring if and only if their [Formula: see text]th input trees are isomorphic, where [Formula: see text] is the number of nodes. Here [Formula: see text] is the best possible. The proof is analogous to that of Leighton’s theorem in graph theory, using the universal cover of the network and the notion of a symbolic adjacency matrix to set up a partition refinement algorithm whose output is the coarsest balanced coloring. The running time of the algorithm is cubic in [Formula: see text].

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