Integrating Tier-1 module suppliers in car sequencing problem

[EN] The objective of this study is to develop a car assembly sequence that is mutually agreed between car manufacturers and Tier-1 module suppliers such that overall modular supply chain efficiency is improved. In the literature so far, only constraints of car manufacturers have been considered in the car sequencing problem. However, an assembly sequence from car manufacturer imposes a module assembly sequence on Tier-1 module suppliers since their assembly activities are synchronous and in sequence with assembly line of that car manufacturer. An imposed assembly sequence defines a certain demand rate for Tier-1 module suppliers and has significant impacts on operational cost of these suppliers which ultimately affects the overall modular supply chain efficiency. In this paper, a heuristic approach has been introduced to generate a supplier cognizant car sequence which does not only provide better operational conditions for Tier-1 module suppliers, but also satisfies constraints of the car manufacturer.Jung, E. (2021). Integrating Tier-1 module suppliers in car sequencing problem. International Journal of Production Management and Engineering. 9(2):113-123. https://doi.org/10.4995/ijpme.2021.14985OJS11312392Benoist, T., Gardi, F., Megel, R., Nouioua, K. 2011. LocalSolver 1.x: a black-box local-search solver for 0-1 programming. 4OR - A Quarterly Journal of Operations Research, 9(299). https://doi.org/10.1007/s10288-011-0165-9Boysen, N., Fliedner, M., Scholl, A. 2009. Sequencing mixed-model assembly lines: survey, classification and model critique. European Journal of Operational Research, 192, 349-373. https://doi.org/10.1016/j.ejor.2007.09.013Doran, D. 2002. Manufacturing for synchronous supply: a case study of Ikeda Hoover Ltd. Integrated Manufacturing Systems, 13(1), 18-24. https://doi.org/10.1108/09576060210411477Drexl, A., Kimms, A. 2001. Sequencing JIT mixed-model assembly lines under station-load and part-usage constraints. Management Science, 47,(3), 480-491. https://doi.org/10.1287/mnsc.47.3.480.9777Estellon, B., Gardi, F. 2006. Car sequencing is NP-hard: a short proof. Journal of the Operational Research Society, 64, 1503-1504. https://doi.org/10.1057/jors.2011.165Estellon, B., Gardi, F., Nouioua, K. 2006. Large neighborhood improvements for solving car sequencing problems. RAIRO - Operations Research, 40(4), 355-379. https://doi.org/10.1051/ro:2007003Estellon, B., Gardi, F., Nouioua, K. 2008. Two local search approaches for solving real-life car sequencing problems. European Journal of Operational Research, 191(3), 928-944. https://doi.org/10.1016/j.ejor.2007.04.043Fredriksson, P., Gadde, L.E. 2005. Flexibility & rigidity in customization and build-to-order production. Science Direct Industrial Marketing Management, 34, 695-705. https://doi.org/10.1016/j.indmarman.2005.05.010Gagne, C., Gravel, M., Price, W. 2006. Solving real car sequencing problems with ant colony optimization. European Journal of Operational Research, 174(3), 1427-1448. https://doi.org/10.1016/j.ejor.2005.02.063Gottlieb, J., Puchta, M., Solnon., C. 2003. A study of greedy, local search and ant colony optimization approaches for car sequencing problems. In Applications of Evolutionary Computing, Lecture Notes in Computer Science, 2611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36605-9_23Hellingrath, B. 2008. Key principles of flexible production and logistics networks. Build to Order: The Road to the 5-Day Car, Springer-Verlag, London, 177-180. https://doi.org/10.1007/978-1-84800-225-8_10Larsson, A. 2002. The development and regional significance of the automotive industry: supplier parks in Western Europe. International Journal of Urban and Regional Research, 26(4), 767-784. https://doi.org/10.1111/1468-2427.00417Monden, Y. 1998. Toyota production systems: an integrated approach to just-in-time, 3rd edition. Industrial Engineering & Management Press, NorcossNiemann, J., Seisenberger, S., Schlegel, A., Putz, M. 2019. Development of a method to increase flexibility and changeability of supply contracts in the automotive industry. 52nd CIRP Conference on Manufacturing Systems, Ljubljana, Slovenia, June 12-14. https://doi.org/10.1016/j.procir.2019.03.045Parrello, B.D., Kabat, W.C., Wos, L. 1986. Job-shop scheduling using automated reasoning: a case study of the car-sequencing problem. Journal of Automated Reasoning, 2(1), 1-42. https://doi.org/10.1007/BF00246021Regin, J.C., Puget, J.F. 1997. A filtering algorithm for global sequencing constraints. In: Smolka G. (eds) Principles and Practice of Constraint Programming-CP97. Lecture Notes in Computer Science, 1330. Springer, Heidelberg. https://doi.org/10.1007/BFb0017428Solnon, C., Cung, V.D., Nguyen A., Artigues, C. 2008. The car sequencing problem: overview of state-of-the-art methods and industrial casestudy of the ROADEEF'2005 challenge problem. European Journal of Operational Research, 191, 912-927. https://doi.org/10.1016/j.ejor.2007.04.03

The Use of Persistent Explorer Artificial Ants to Solve the Car Sequencing Problem

Ant Colony Optimisation is a widely researched meta-heuristic which uses the behaviour and pheromone laying activities of foraging ants to find paths through graphs. Since the early 1990’s this approach has been applied to problems such as the Travelling Salesman Problem, Quadratic Assignment Problem and Car Sequencing Problem to name a few. The ACO is not without its problems it tends to find good local optima and not good global optima. To solve this problem modifications have been made to the original ACO such as the Max Min ant system. Other solutions involve combining it with Evolutionary Algorithms to improve results. These improvements focused on the pheromone structures. Inspired by other swarm intelligence algorithms this work attempts to develop a new type of ant to explore different problem paths and thus improve the algorithm. The exploring ant would persist throughout the running time of the algorithm and explore unused paths. The Car Sequencing problem was chosen as a method to test the Exploring Ants. An existing algorithm was modified to implement the explorers. The results show that for the car sequencing problem the exploring ants did not have any positive impact, as the paths they chose were always sub-optimal

Modeling and Solution Methodologies for Mixed-Model Sequencing in Automobile Industry

The global competitive environment leads companies to consider how to produce high-quality products at a lower cost. Mixed-model assembly lines are often designed such that average station work satisfies the time allocated to each station, but some models with work-intensive options require more than the allocated time. Sequencing varying models in a mixed-model assembly line, mixed-model sequencing (MMS), is a short-term decision problem that has the objective of preventing line stoppage resulting from a station work overload. Accordingly, a good allocation of models is necessary to avoid work overload. The car sequencing problem (CSP) is a specific version of the MMS that minimizes work overload by controlling the sequence of models. In order to do that, CSP restricts the number of work-intensive options by applying capacity rules. Consequently, the objective is to find the sequence with the minimum number of capacity rule violations. In this dissertation, we provide exact and heuristic solution approaches to solve different variants of MMS and CSP. First, we provide five improved lower bounds for benchmark CSP instances by solving problems optimally with a subset of options. We present four local search metaheuristics adapting efficient transformation operators to solve CSP. The computational experiments show that the Adaptive Local Search provides a significant advantage by not requiring tuning on the operator weights due to its adaptive control mechanism. Additionally, we propose a two-stage stochastic program for the mixed-model sequencing (MMS) problem with stochastic product failures, and provide improvements to the second-stage problem. To tackle the exponential number of scenarios, we employ the sample average approximation approach and two solution methodologies. On one hand, we develop an L-shaped decomposition-based algorithm, where the computational experiments show its superiority over solving the deterministic equivalent formulation with an off-the-shelf solver. We also provide a tabu search algorithm in addition to a greedy heuristic to tackle case study instances inspired by our car manufacturer partner. Numerical experiments show that the proposed solution methodologies generate high-quality solutions by utilizing a sample of scenarios. Particularly, a robust sequence that is generated by considering car failures can decrease the expected work overload by more than 20\% for both small- and large-sized instances. To the best of our knowledge, this is the first study that considers stochastic failures of products in MMS. Moreover, we propose a two-stage stochastic program and formulation improvements for a mixed-model sequencing problem with stochastic product failures and integrated reinsertion process. We present a bi-objective evolutionary optimization algorithm, a two-stage bi-objective local search algorithm, and a hybrid local search integrated evolutionary optimization algorithm to tackle the proposed problem. Numerical experiments over a case study show that while the hybrid algorithm provides a better exploration of the Pareto front representation and more reliable solutions in terms of waiting time of failed vehicles, the local search algorithm provides more reliable solutions in terms of work overload objective. Finally, dynamic reinsertion simulations are executed over industry-inspired instances to assess the quality of the solutions. The results show that integrating the reinsertion process in addition to considering vehicle failures can keep reducing the work overload by around 20\% while significantly decreasing the waiting time of the failed vehicles

Mixed-model Sequencing with Stochastic Failures: A Case Study for Automobile Industry

In the automotive industry, the sequence of vehicles to be produced is determined ahead of the production day. However, there are some vehicles, failed vehicles, that cannot be produced due to some reasons such as material shortage or paint failure. These vehicles are pulled out of the sequence, and the vehicles in the succeeding positions are moved forward, potentially resulting in challenges for logistics or other scheduling concerns. This paper proposes a two-stage stochastic program for the mixed-model sequencing (MMS) problem with stochastic product failures, and provides improvements to the second-stage problem. To tackle the exponential number of scenarios, we employ the sample average approximation approach and two solution methodologies. On one hand, we develop an L-shaped decomposition-based algorithm, where the computational experiments show its superiority over solving the deterministic equivalent formulation with an off-the-shelf solver. Moreover, we provide a tabu search algorithm in addition to a greedy heuristic to tackle case study instances inspired by our car manufacturer partner. Numerical experiments show that the proposed solution methodologies generate high quality solutions by utilizing a sample of scenarios. Particularly, a robust sequence that is generated by considering car failures can decrease the expected work overload by more than 20\% for both small- and large-sized instances.Comment: 30 pages, 9 figure

Solving Many-Objective Car Sequencing Problems on Two-Sided Assembly Lines Using an Adaptive Differential Evolutionary Algorithm

The car sequencing problem (CSP) is addressed in this paper. The original environment of the CSP is modified to reflect real practices in the automotive industry by replacing the use of single-sided straight assembly lines with two-sided assembly lines. As a result, the problem becomes more complex caused by many additional constraints to be considered. Six objectives (i.e. many objectives) are optimised simultaneously including minimising the number of colour changes, minimising utility work, minimising total idle time, minimising the total number of ratio constraint violations and minimising total production rate variation. The algorithm namely adaptive multi-objective evolutionary algorithm based on decomposition hybridised with differential evolution algorithm (AMOEA/D-DE) is developed to tackle this problem. The performances in Pareto sense of AMOEA/D-DE are compared with COIN-E, MODE, MODE/D and MOEA/D. The results indicate that AMOEA/D-DE outperforms the others in terms of convergence-related metrics

Heuristic Procedures to Solve Sequencing and Scheduling Problems in Automobile Industry

With the growing trend for greater product variety, mixed-model assembly nowadays is commonly employed in many industries, which can enable just-in-time production for a production system with high variety. Efficient production scheduling and sequencing is important to achieve the overall material supply, production, and distribution efficiency around the mixed-model assembly line. This research addresses production scheduling and sequencing on a mixed-model assembly line for products with multiple product options, considering multiple objectives with regard to material supply, manufacturing, and product distribution. This research also addresses plant assignment for a product with multiple product options as a prior step to scheduling and sequencing for a mixed-model assembly line. This dissertation is organized into three parts based on three papers. Introduction and literature review Part 1. In an automobile assembly plant many product options often need to be considered in sequencing an assembly line with which multiple objectives often need to be considered. A general heuristic procedure is developed for sequencing automobile assembly lines considering multiple options. The procedure uses the construction, swapping, and re-sequencing steps, and a limited search for sequencing automobile assembly lines considering multiple options. Part 2. In a supply chain, production scheduling and finished goods distribution have been increasingly considered in an integrated manner to achieve an overall best efficiency. This research presents a heuristic procedure to achieve an integrated consideration of production scheduling and product distribution with production smoothing for the automobile just-in-time production assembly line. A meta-heuristic procedure is also developed for improving the heuristic solution. Part 3. For a product that can be manufactured in multiple facilities, assigning orders to the facility is a common problem faced by industry considering production, material constraints, and other supply-chain related constraints. This paper addresses products with multiple product options for plant assignment with regard to multiple constraints at individual plants in order to minimize transportation costs and costs of assignment infeasibility. A series of binary- and mixed-integer programming models are presented, and a decision support tool based on optimization models is presented with a case study. Summary and conclusion

Car Sequencing Problem con flotas de vehículos especiales. Presentación

Car Sequencing Problem con demanda parcial incierta. Robustez en una multi-secuencia de vehículos mixtos.Partiendo del Car Sequencing Problem (CSP), introducimos el concepto demanda parcial incierta a través de la incorporación de Flotas de vehículos especiales en un plan de demanda. Tras resaltar las peculiaridades de una Flota y establecer las hipótesis de trabajo, proponemos un modelo de programación lineal entera mixta orientado a satisfacer el máximo número de restricciones CSP. Posteriormente, introducimos el concepto multi-secuencia de producción y proponemos funciones para medir su robustez. La versión robusta del CSP considera un conjunto de escenarios de la demanda para las Flotas y presenta funciones que miden el exceso sobre el requerimiento estándar de las opciones del CSP en planes de demanda, opciones concretas y ciclos de fabricación. Dichas funciones pueden emplearse como función objetivo en problemas de optimización y como métricas ante una muli-secuencia de producción concreta.Preprin