Genetic algorithmic approach for security personnel scheduling

Adamuthe, Amol C.; Mane, Sandeep U.; Thampi, G. T.

doi:10.1109/iccict.2012.6398098

Cited by 8 publications

(3 citation statements)

References 22 publications

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“…Table 4 shows the penalty cost. [1,3,7,8,10] lb= [2,4,5,6,9] nbd= [2,4,6,8,10,12,14] lbd= [1,3,5,7,9,11,13] eb= [3,7,8,4,5] wb= [1,2,6,9,10] ed= [4,6,10,7,9,11,13] wd= [1,2,3,5,8,12,14] srd= [2,4,6,8] International Journal of Computer Applications (0975 -8887) Volume 145 -No.11, July 2016 assi...…”

Section: Implementation Detailsmentioning

confidence: 99%

“…It also defined as set cover partitioning or set covering problem [2,3,7,8]. The formulation of the personal scheduling (Bus driver, Truck driver, Nurses in the hospital) problem may have different approaches for different application such as Nurse scheduling problem, Laboratory personal timetabling problem, Security Personal Scheduling Problem defined in [9,10,11]. The objective of this work is the assignment of staff to the particular period and minimization of personal cost.…”

Section: Introductionmentioning

confidence: 99%

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Bus Driver Scheduling Problem using TLBO and JAYA Algorithm

Patil¹

2016

IJCA

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Bus driver scheduling problem is one of most important and complex problem faced by many companies and bus terminals. This paper attempts to solve this problem using paramterless evolutionary algorithms, TLBO and JAYA algorithm. The objective of this paper is to assign the drivers to duty on a particular day and block duty by satisfying the constraints. Algorithms are tested on four randomly generated datasets. In the work solution is obtained with no zero constraint violations. JAYA algorithm gives better results than TLBO algorithm.

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Section: Implementation Detailsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bus Driver Scheduling Problem using TLBO and JAYA Algorithm

Patil¹

2016

IJCA

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“…Resource capacity of the project is 4 units. Given the network depicted in Figure 1, some of the feasible activity lists are L 1 = (1, 3, 2, 6,5,7,4,8,11,10,9,12), L 2 = (1, 2, 4, 3,6,7,5,11,8,10,9,12) and L 3 = (1, 2, 3, 6,4,5,7,8,11,9,10,12) etc. For example, the makespan of L 3 is 21.…”

Section: Resource Constrained Project Scheduling Problemmentioning

confidence: 99%

A Genetic-Local Search Algorithm Approach for Resource Constrained Project Scheduling Problem

Kadam

Mane²

2015

2015 International Conference on Computing Communication Control and Automation

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In scheduling Resource Constrained Project Scheduling Problem (RCPSP) is a well-known NP hard problem. Several metaheuristics have been applied to find near optimal solution for resource constrained project scheduling problem (RCPSP). In this paper, Genetic-Local search algorithm(GLSA) is proposed to tackle the single mode resource constrained project scheduling problem. The objective of Genetic-Local search algorithm is to minimize makespan of schedule. Genetic-Local search algorithm combines elements from evolutionary and local search procedure by using priority based crossover, neighbourhood mutation operation and neighbourhood search procedure. The algorithm treats the solution of the resource constrained project scheduling problem as activity list and serial schedule generation scheme is used to generate the solution. For solving case studies in PSLIB Library, Performance of GLSA is found out against other metaheuristics. The results show that Genetic-Local search algorithm is a high quality approach that outperforms all recent algorithms for the resource constrained project scheduling problem known by the authors of this paper for the instance sets J30, J60, J90 and J120 and it is competitive with other heuristics for the instance set J30, J60, J90 and J120.

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A Parallel Non-dominated Sorting Genetic Algorithm-II for Vehicle Routing Problem in Supply Chain

Patil,

Shingan

2024

Proceedings of International Conference on Computational Intelligence

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Genetic algorithmic approach for security personnel scheduling

Cited by 8 publications

References 22 publications

Bus Driver Scheduling Problem using TLBO and JAYA Algorithm

Bus Driver Scheduling Problem using TLBO and JAYA Algorithm

A Genetic-Local Search Algorithm Approach for Resource Constrained Project Scheduling Problem

A Parallel Non-dominated Sorting Genetic Algorithm-II for Vehicle Routing Problem in Supply Chain

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