Production Scheduling for Dispatching Ready Mixed Concrete Trucks Using Bee Colony Optimization

Srichandum,

doi:10.3844/ajeassp.2010.7.14

Cited by 40 publications

(30 citation statements)

References 13 publications

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“…For solving constrained optimization problems, a constraint handling method was incorporated with the algorithm (Srichandum and Rujirayanyong, 2010).…”

Section: K-means Clustering For Missing Attribute Value Predictionmentioning

confidence: 99%

Predicting Missing Attribute Values Using k-Means Clustering

Suguna¹,

Thanushkodi²

2011

Journal of Computer Science

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Problem statement: Predicting the value for missing attributes is an important data preprocessing problem in data mining and knowledge discovery tasks. Several methods have been proposed to treat missing data and the one used more frequently is deleting instances containing at least one missing value of a feature. When the dataset has minimum number of missing attribute values then we can neglect the instances. But if it is high, deleting those instances may neglect the essential information. Some methods, such as assigning an average value to the missing attribute, assigning the most common values make good use of all the available data. However the assigned value may not come from the information which the data originally derived from, thus noise is brought to the data. Approach: In this study, k-means clustering is proposed for predicting missing attribute values. The performance of the proposed approach is analyzed with nine different methods. The overall analysis shows that the k-means clustering can predict the missing attribute values better than other methods. After assigning the missing attributes, the feature selection is performed with Bees Colony Optimization (BCO) and the improved Genetic KNN is applied for finding the classification performance as discussed in our previous study. Results: The performance is analyzed with four different medical datasets; Dermatology, Cleveland Heart, Lung Cancer and Wisconsin. For all the datasets, the proposed k-means based missing attribute prediction achieves higher accuracy of 94.60 %, 90.45 %, 87.51 % and 95.70 % respectively. Conclusion: The greater classification accuracy shows the superior performance of the k-means based missing attribute value prediction.

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“…For solving constrained optimization problems, a constraint handling method was incorporated with the algorithm (Srichandum and Rujirayanyong, 2010).…”

Section: K-means Clustering For Missing Attribute Value Predictionmentioning

confidence: 99%

Predicting Missing Attribute Values Using k-Means Clustering

Suguna¹,

Thanushkodi²

2011

Journal of Computer Science

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“…Among the introduced methods, Genetic Algorithms is the most promising heuristic solution in the RMCDP literature [1,3,5,[7][8][9][10]. Other heuristic methods also have been tested in this context, such as Ant Colony [11], Particle Swarm Optimization (PSO) [12,13], Bee Colony Optimization (BCO) [14] and Tabu Search (TS) [14]. Despite developments in implementing heuristic methods in RMCDP, the solution structure of most of the mentioned techniques is pretty much same.…”

Section: Introductionmentioning

confidence: 99%

Using Benders Decomposition for Solving Ready Mixed Concrete Dispatching Problems

Maghrebi¹,

Periaraj²,

Waller³

et al. 2014

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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Large scale dispatching problems are technically characterized as classical NP-hard problems which means that they cannot be solved optimally with existing methods in a polynomial time. Benders decomposition is recommended for solving large scale Mixed Integer Programming (MIP). In this paper we use the Bender Decomposition technique for reformulating the Ready Mixed Concrete Dispatching Problem (RMCDP). Benders decomposition involves separating the original RMCDP formulation into the master (lower bound) and sub-problems (upper bound). The master problem only deals with integer variables and the sub problem is usually a linear programming problem. Benders optimally cuts and Benders feasibility cuts are added to the master problem upon solving the sub-problem at each iteration.. The proposed method is tested on a single real instance and results are reported.

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“…Thongwan et al (2011), Mahi and Izabatene (2011), Mosavi (2011, Asfaw andSaiedi, (2011), Norozi et al (2010), Srichandum and Rujirayanyong (2010), Yedjour et al (2010), Kangrang et al (2009), Renaud et al (2002, Gendreau et al (1996;1999) and Kalantari et al (1985). This study proposes the developed genetic algorithm (GA) for PDTSPTC, called "Hybrid Encoding Genetic Algorithm (HEGA)".…”

Section: Introductionmentioning

confidence: 99%

Application of Hybrid Encoding Genetic Algorithm on Pickup and Delivery Traveling Salesman Problem with Traffic Conditions

Patvichaichod¹

2011

Journal of Computer Science

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Problem statement:This study deals with the pickup and delivery traveling salesman problem with traffic conditions (PDTSPTW), an extension of the pickup and delivery traveling salesman problem (PDTSP) where each customer to be served is associated with two quantities of product to be collected and delivered. Almost PDTSP problems uses distance between each point of customers as Euclidean Distance and are not concerned with other parameters to find minimal cost. Approach: The PDTSPTC concerns more parameters, such as street network and vehicle speed, which results it closer to the real world condition. The study also proposes the developed genetic algorithm called "Hybrid Encoding Genetic Algorithm (HEGA)". The concept is to combine binary encoding and integer encoding together, causing the in complexity of the algorithm structure and the ease of implementation. Results: the HEGA can save the travel cost about 26-57%. It is obviously seen that HEGA in the PDTSPTC test problems result the fast convergence that is about 12-43% and in all, the computational time is under 6 seconds. Conclusion: The HEGA performs quite well when testing a test problem. Computation times are small in all case. The PDTSPTC is closer to real-world conditions than PDTSP. This problem can be applied in logistics immediately if the distance of streets, traffic conditions (average vehicle speed in each street) and vehicle conditions (fuel consumption rate and vehicle capacity) are known.

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Production Scheduling for Dispatching Ready Mixed Concrete Trucks Using Bee Colony Optimization

Cited by 40 publications

References 13 publications

Predicting Missing Attribute Values Using k-Means Clustering

Predicting Missing Attribute Values Using k-Means Clustering

Using Benders Decomposition for Solving Ready Mixed Concrete Dispatching Problems

Application of Hybrid Encoding Genetic Algorithm on Pickup and Delivery Traveling Salesman Problem with Traffic Conditions

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