A Genetic Algorithm for Solving a Capacitated p-Median Problem

Correa, Elon; Steiner, María Teresinha Arns; Freitas, Alex A.; Carnieri, Celso

doi:10.1023/b:numa.0000021767.42899.31

Cited by 100 publications

(64 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…For details of PFIH see Solomon (Solomon, 1987), Brandão and Vasconcelos (Brandão de Oliveira & Vasconcelos, 2010) and Thangiah, Osman and Sun (Thangiah, Osman, & Sun, 1994). After the initial population is built up, these chromosomes are sorted by fitness, and then a pair of chromosomes is randomly chosen for mating using the ranking-based selection of Correa et al (Correa et al, 2001), as shown in Eq. (35).…”

Section: Hgamentioning

confidence: 99%

Solving a multi-objective location routing problem for infectious waste disposal using hybrid goal programming and hybrid genetic algorithm

Wichapa¹,

Khokhajaikiat²

2018

10.5267/j.ijiec

View full text Add to dashboard Cite

Infectious waste disposal remains one of the most serious problems in the medical, social and environmental domains of almost every country. Selection of new suitable locations and finding the optimal set of transport routes for a fleet of vehicles to transport infectious waste material, location routing problem for infectious waste disposal, is one of the major problems in hazardous waste management. Determining locations for infectious waste disposal is a difficult and complex process, because it requires combining both intangible and tangible factors. Additionally, it depends on several criteria and various regulations. This facility location problem for infectious waste disposal is complicated, and it cannot be addressed using any standalone technique. Based on a case study, 107 hospitals and 6 candidate municipalities in UpperNortheastern Thailand, we considered criteria such as infrastructure, geology and social & environmental criteria, evaluating global priority weights using the fuzzy analytical hierarchy process (Fuzzy AHP). After that, a new multi-objective facility location problem model which hybridizes fuzzy AHP and goal programming (GP), namely the HGP model, was tested. Finally, the vehicle routing problem (VRP) for a case study was formulated, and it was tested using a hybrid genetic algorithm (HGA) which hybridizes the push forward insertion heuristic (PFIH), genetic algorithm (GA) and three local searches including 2-opt, insertion-move and interexchange-move. The results show that both the HGP and HGA can lead to select new suitable locations and to find the optimal set of transport routes for vehicles delivering infectious waste material. The novelty of the proposed methodologies, HGP, is the simultaneous combination of relevant factors that are difficult to interpret and cost factors in order to determine new suitable locations, and HGA can be applied to determine the transport routes which provide a minimum number of vehicles and minimum transportation cost under the actual situation efficiently in this case.

show abstract

Section: Hgamentioning

confidence: 99%

Solving a multi-objective location routing problem for infectious waste disposal using hybrid goal programming and hybrid genetic algorithm

Wichapa¹,

Khokhajaikiat²

2018

10.5267/j.ijiec

View full text Add to dashboard Cite

show abstract

“…This new kind of p-median problem is capacitated pmedian problem [13]. We present a formulation of p-median problem in an integer programming proposed in [14].…”

Section: Mobile Agent Location Problemmentioning

confidence: 99%

“…The main difference between a capacitated p-median problem and p-median problem is two constraints [13]:…”

Section: Mobile Agent Location Problemmentioning

confidence: 99%

See 1 more Smart Citation

Locating Performance Monitoring Mobile Agents in Scalable Active Networks

Hadad

Dehghan

Pedram

2005

Algorithmic Applications in Management

View full text Add to dashboard Cite

Abstract. The idea of active networks has been emerged in recent years to increase the processing power inside the network. The intermediate nodes such as routers will be able to host mobile agents and many management tasks can be handled using autonomous mobile agents inside the network. One of the important limitations, which should be considered in active networks, is the restricted processing power of active nodes. In this paper, we define an optimal location problem for monitoring mobile agents in a scalable active network as a p-median problem, which is indeed a kind of facility location problem. The agents are responsible to monitor and manage the performance of all of the network nodes such that the total monitoring traffic overhead is minimized. Then we proposed two methods of finding an appropriate sub set of intermediate nodes for hosting mobile agents. In our first method, we have not considered the limited processing power of active nodes, which host mobile agents. In our second method, we have solved the problem so that the processing loads of host nodes do not exceed a predefined threshold. Since p-median problems are NP-complete and the search space of these problems is very large, our methods are based on genetic algorithms. We have tested our two methods for finding mobile agents optimal locations on four network topologies with different number of nodes and compared the obtained location. By this comparison, we have shown the importance of considering processing load limitation for active nodes as a parameter in choosing them as hosts of mobile agents in a scalable active network. The proposed locations in our second method eliminates the probability of CPU overload in the active nodes hosting the mobile agents and reduces the processing time required for finding the optimal locations of mobile agents.

show abstract

“…The objective of developing such a classifier is to classify unlabeled dataset into classes. Many particle swarm optimization algorithms [21][22][23][24][25][26][27][28][29] have been developed that deals only with continuous variables. This is a significant limitation because many optimization problems are there which featuring discrete variables in the problem domain.…”

Section: Introductionmentioning

confidence: 99%

A Study on Rough Set Theory Based Dynamic Reduct for Classification System Optimization

Sengupta¹,

Das²

2014

IJAIA

View full text Add to dashboard Cite

In the present day huge amount of data is generated in every minute and transferred frequently. Although the data is sometimes static but most commonly it is dynamic and transactional. New data that is being generated is getting constantly added to the old/existing data. To discover the knowledge from this incremental data, one approach is to run the algorithm repeatedly for the modified data sets which is time consuming. Again to analyze the datasets properly, construction of efficient classifier model is necessary. The objective of developing such a classifier is to classify unlabeled dataset into appropriate classes. The paper proposes a dimension reduction algorithm that can be applied in dynamic environment for generation of reduced attribute set as dynamic reduct, and an optimization algorithm which uses the reduct and build up the corresponding classification system. The method analyzes the new dataset, when it becomes available, and modifies the reduct accordingly to fit the entire dataset and from the entire dataset, interesting optimal classification rule sets are generated. The concepts of discernibility relation, attribute dependency and attribute significance of Rough Set Theory are integrated for the generation of dynamic reduct set, and optimal classification rules are selected using PSO method, which not only reduces the complexity but also helps to achieve higher accuracy of the decision system. The proposed method has been applied on some benchmark dataset collected from the UCI repository and dynamic reduct is computed, and from the reduct optimal classification rules are also generated. Experimental result shows the efficiency of the proposed method.

show abstract

A Genetic Algorithm for Solving a Capacitated p-Median Problem

Cited by 100 publications

References 7 publications

Solving a multi-objective location routing problem for infectious waste disposal using hybrid goal programming and hybrid genetic algorithm

Solving a multi-objective location routing problem for infectious waste disposal using hybrid goal programming and hybrid genetic algorithm

Locating Performance Monitoring Mobile Agents in Scalable Active Networks

A Study on Rough Set Theory Based Dynamic Reduct for Classification System Optimization

Contact Info

Product

Resources

About