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The IUP Journal of Operations Management :
Minimization of Makespan and Total Tardiness in a Flow Shop Scheduling Using Artificial Neural Network
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The paper makes an attempt to minimize the makespan and total tardiness in the flow shop scheduling using Artificial Neural Network (ANN). A feed forward back propagation neural network is implemented for the optimal solution of the problem. The network has been trained with the optimal sequences for different jobs on five machines problem. The trained network is further considered for solving the problem of flow shop scheduling. The analysis of all the algorithms that are implemented is carried out using Nawaz, Enscore and Ham (NEH) heuristic for 5 to 10 jobs with 5 machines under Sequence Dependent Set-up Time (SDST) environment with due dates and weights allotted to each job. With the help of NEH heuristic, different sequences have been generated for the considered problem. The number of sequences obtained is assembled in 270 × 10 matrix for the input of neural network. The network is trained with Baygon regression training algorithm and finally the results obtained from the neural network are found to be 93% accurate.

 
 

In the present global competitive market, different factors affect the management of demand and capacity. Production planning and control is considered as one of the prime factors which minimizes the total production time of any product/service along with achieving some critical objectives such as higher customer satisfaction, good profitability, faster delivery speed, greater reliability and minimum cost. Scheduling plays an important role in achieving the objectives of the production planning and control. Scheduling refers to the act of assigning tasks to resources or assigning resources to tasks. Scheduling decisions occur when there are more outstanding requests than the number of available resources. Flow shop scheduling problem is considered as one of the general production scheduling problems in which n jobs must be processed by m machines in the same order. During the last several decades, most of the work has been applied to the permutation flow-shop problem with an objective of minimizing makespan, Cmax, which is found to be an Non-deterministic Polynomial-time (NP) complete problem of combinatorial optimization problem. According to Jain and Meeran (2002), makespan minimization provides a useful area for analysis because it is an important model in scheduling theory and it is usually very difficult to find its optimal solution. The minimization of makespan scheduling problems involving both earliness and tardiness costs has received significant attention in recent years. These types of problems became important with the advent of the Just-In-Time (JIT) concept, where early or tardy delivery is highly discouraged because early jobs may cause inventory problems while tardy jobs may generate contractual penalties and loss of trustworthiness, causing damages to the company’s image and loss of clients. Several approaches have already been proposed to solve the bi-objective scheduling problem. In this paper, an attempt is made to minimize the makespan and total tardiness in the flow shop scheduling using Artificial Neural Network (ANN).

 
 

Operations Management Journal, Minimization of Makespan and Total Tardiness, Flow Shop Scheduling Using Artificial Neural Network