Abstract
We consider a multi-model assembly line production planning problem. We assume an environment where orders with several different model types with varying quantities are received by contract manufacturers in a Make-to-Order (MTO) environment. The models are similar enough in such a way that they share some common critical raw materials/parts and are to be produced on an already balanced multi model assembly line. Due to MTO and contracts, there are significant costs associated with earliness and tardiness in addition to inventory and production costs, capacity, and other operational constraints. The challenge is to be able to make quick and accurate decisions regarding whether or not to accept an order and provide a due date along with raw material procurement and production plan that can be followed. This problem is closely related to Available to Promise (ATP) systems. Increased revenue and profitability are expected with the better management of ATP systems by reducing the amount of missed market opportunities and improving operational efficiency. This study aims to develop an effective solution method for this problem, which will minimize earliness, tardiness, lost sales, inventory holding, FGI, subcontracting, overtime, and raw material costs. We present the results of a detailed review of related literature. This study fills the gap in the literature of assembly line planning problems that covers Available-to-Promise by considering shipping decisions on critical raw materials required for production in a make-to-order environment. After determining a gap in the literature, a novel mathematical model has been developed to solve the problem on hand. While this developed mathematical model offers an acceptable calculation time for problems where the production time required to meet the total demand does not exceed the total inhouse production time, it does not offer a fast solution method for the problems where the total inhouse production time is insufficient to meet the total demand. For this reason, a heuristic algorithm has been developed that provides faster results with near-optimal solutions for this type of problem. We present the results of our experimentation with both models.
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References
Asl, A.J., Solimanpur, M., Shankar, R.: Multi-objective multi-model assembly line balancing problem: a quantitative study in engine manufacturing industry. Opsearch 56(3), 603–627 (2019)
Reginato, G., Anzanello, M.J., Kahmann, A., Schmidt, L.: Mixed assembly line balancing method in scenarios with different mix of products. Gestão & Produção 23(2), 294–307 (2016)
Bard, J.F., Shtub, A., Joshi, S.B.: Sequencing mixed-model assembly lines to level parts usage and minimize line length. Int. J. Prod. Res. 32(10), 2431–2454 (1994)
Xiaobo, Z., Ohno, K.: Algorithms for sequencing mixed models on an assembly line in a JIT production system. Comput. Ind. Eng. 32(1), 47–56 (1997)
Akkan, C.: Finite-capacity scheduling-based planning for revenue-based capacity management. Eur. J. Oper. Res. 100(1), 170–179 (1997)
McMullen, P.R.: JIT sequencing for mixed-model assembly lines with setups using tabu search. Prod. Plan. Control 9(5), 504–510 (1998)
Oğuz, C., Salman, F.S., Yalçın, Z.B.: Order acceptance and scheduling decisions in make-to-order systems. Int. J. Prod. Econ. 125(1), 200–211 (2010)
Manavizadeh, N., Tavakoli, L., Rabbani, M., Jolai, F.: A multi-objective mixed-model assembly line sequencing problem in order to minimize total costs in a Make-To-Order environment, considering order priority. J. Manuf. Syst. 32(1), 124–137 (2013)
Sadjadi, S.J., Makui, A., Dehghani, E., Pourmohammad, M.: Applying queuing approach for a stochastic location-inventory problem with two different mean inventory considerations. Appl. Math. Model. 40(1), 578–596 (2016)
Rabbani, M., Manavizadeh, N., Shabanpour, N.: Sequencing of mixed models on U-shaped assembly lines by considering effective help policies in make-to-order environment. Scientia Scientia Iranica. Trans. E Ind. Eng. 24(3), 1493–1504 (2017)
Nazar, K.A., Pillai, V.M.: Mixed-model sequencing problem under capacity and machine idle time constraints in JIT production systems. Comput. Ind. Eng. 118, 226–236 (2018)
Tanhaie, F., Rabbani, M., & Manavizadeh, N. (2020). Sequencing mixed-model assembly lines with demand management: problem development and efficient multi-objective algorithms. Engineering Optimization, 1–18.
Tanhaie, F., Rabbani, M., Manavizadeh, N.: 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. Opsearch 57(2), 320–346 (2020)
Robinson, A.G., Carlson, R.C.: Dynamic order promising: real-time ATP. Int. J. Integr. Supply Manage. 3(3), 283–301 (2007)
Kalantari, M., Rabbani, M., Ebadian, M.: A decision support system for order acceptance/rejection in hybrid MTS/MTO production systems. Appl. Math. Model. 35(3), 1363–1377 (2011)
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Yüksel, M. et al. (2022). Applying Available-to-Promise (ATP) Concept in Multi-Model Assembly Line Planning Problems in a Make-to-Order (MTO) Environment. In: Durakbasa, N.M., Gençyılmaz, M.G. (eds) Digitizing Production Systems. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-90421-0_55
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