Applying available-to-promise (ATP) concept in mixed-model assembly line sequencing problems in a Make-To-Order (MTO) environment: problem extension, model formulation and Lagrangian relaxation algorithm

F. Tanhaie (), M. Rabbani () and N. Manavizadeh ()
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F. Tanhaie: University of Tehran
M. Rabbani: University of Tehran
N. Manavizadeh: Khatam University

OPSEARCH, 2020, vol. 57, issue 2, No 4, 320-346

Abstract: Abstract Mixed-model assembly line is known to be a special case of production lines where variety of product models similar to product characteristics are assembled. This article addresses available-to-promise (ATP) in mixed-model assembly line sequencing problems in a Make-To-Order environment in two stages. First, the customers are prioritized based on their corresponding profit values and a decision support system for order acceptance/rejection based on ATP is developed. By implementing this concept and developing a mathematical model, delivery quantity and date in a planning horizon are determined based on the inventories in the stock. The second stage is solving a mixed binary mathematical model to sequence accepted orders suitably according to demands due dates that guarantees the orders are not released too late or too early. The problem simultaneously considers following objectives: minimizing the total tardiness and earliness costs based on the determined priority of orders and minimizing the utility work and idle time of workers in the production line. An algorithm based on Lagrangian relaxation is developed for the problem, and tested in terms of solution quality and computational efficiency. To validate the performance of the proposed algorithm, various test problems in small size are solved using the CPLEX solver, and compared with the Lagrangian relaxation method. Finally, the proposed model is solved in large size problems to analyze the model performance. The drawback of the CPLEX is that it could not solve large problem instances in reasonable time. For the small sized problem, there is approximately 1% duality gap for the Lagrangian relaxation method. The maximum duality gap in the Lagrangian relaxation method for the large sized problem is always kept below 4% while the average computing time is very reasonable. Therefore, according to the results obtained from test problems, the developed Lagrangian relaxation method proved to be the suitable method for this problem.

Keywords: MMAL; Sequencing; MTO; ATP; Decision support system; Lagrangian relaxation algorithm (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s12597-019-00436-6

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