A Linear Programming approach for optimal scheduling of workers in a Transport Corporation

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2017 by IJETT Journal
Volume-45 Number-10
Year of Publication : 2017
Authors : Rama.S, Srividya S, Deepa Bellatti
DOI :  10.14445/22315381/IJETT-V45P291


Rama.S, Srividya S, Deepa Bellatti "A Linear Programming approach for optimal scheduling of workers in a Transport Corporation", International Journal of Engineering Trends and Technology (IJETT), V45(10),482-487 March 2017. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

The scheduling of workers is very important in any organization as an excess or scarce of workers accounts for the loss of the company both in measures of time and money. Proper scheduling of workers will enhance the outcome of the company. In this paper the scheduling of drivers for a transport corporation in a metropolitan city has been considered. A mathematical model has been created by using the linear programming techniques. By this model the minimum number of drivers needed for each shift in a day has been calculated and this reduces the amount spends for the reserved drivers. Extending this model to a private transport agency, where the number of drivers required for each day varies and also each day has four shifts. Hence seven sub problems have been solved and the results are tabulated. By Linear programming techniques the real life problem has been mathematically formulated and solved analytically to get the optimal solution. As the number of variables increases, the problem becomes more complex and therefore the computational technique using MATLAB software has been applied. Here the constraints are obtained by taking the maximum number of drivers required for each shift and the objective is to minimize the allocation of drivers for each day. Mathematically both the constraints and objective function are assumed to be linear.


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Linear Programming, Workers Scheduling, Optimal Solution, MATLAB, Minimization.