Two-plant production model with customers’ demand dependent on warranty period of the product and carbon emission level of the manufacturer via different meta-heuristic algorithms

• Published in: Neural computing & applications Vol. 33; no. 21; pp. 14263 - 14281
• Main Authors: Manna, Amalesh Kumar, Benerjee, Tanmoy, Mondal, Sankar Prasad, Shaikh, Ali Akbar, Bhunia, Asoke Kumar
• Format: Journal Article
• Language: English
• Published: London Springer London 01.11.2021; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Carbon; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Customers; Data Mining and Knowledge Discovery; Heuristic methods; Image Processing and Computer Vision; Inspection; Manufacturers; Original Article; Parameter sensitivity; Probability and Statistics in Computer Science; Carbon emission; WQPSO; AQPSO; Imperfect production; GA; Warranty policy; GQPSO
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-021-06073-9

In the developed countries, most of the customers are aware about the harmful effects of purchased products that pollute the environment. On the other hand, the aim of the manufacturer is to reduce by controlling the pollution rates. Motivating from this situation, a two-plant production model in single manufacturing system with warranty period of the product and carbon emission level dependent demand is developed. The manufacturer inspects every produced item in both plants before sending it to the market. After inspection, a part of imperfect items is reworked to reproduce these items into perfect items and also to reduce the loss of the manufacturer. The objective of the model is to determine the optimal values of the product’s warranty period and the production period in each plant in order to maximize the average profit of the manufacturer subject to some constraints. To illustrate and validate the model, two numerical examples are considered and solved by four meta-heuristic algorithms. Finally, to examine the effects of some significant model parameters on the optimal policy, sensitivity analyses are performed.