Heuristic Approaches With Reinforced Parameter Tuning for the Car Sequencing Problems With Block Batches

Motivated by the practical supply chain management of the automobile industry, we study the car sequencing problem (CSP) that minimize the conflicts arising from scheduling cars into an assembly line. The CSP is a well established problem, subject to the paint batching constraints to reduce the the costs for color changes and capacity constraints in the assembly shop to level the usage of the options. However, the existing approaches to this problem do not take into account the block batches, which desires a consecutive production batch of cars requiring a certain option. This requirement often occurs when specialized labor time window is short in the customized car production scenario, and renders additional complexities to the traditional car sequencing problem. In this paper, we propose a novel model to deals with these constraints and simultaneously generate the sequencing and replenishment decisions. In addition to our model formulation, we develop two math-heuristic approaches to solve the propose large-scale car sequencing problem. The selected heuristics are based on relax-and-fix procedures, fix-and-optimize procedures and variable neighborhood search. To solve the large-sized instances (commercial solvers, i.e., Cplex, cannot provide a feasible solution within 1 hour), we design and implement a reinforced parameter tuning mechanism to dynamically select the parameter values, so as to speed up the search process. The proposed models and heuristics are tested on compatible instances from the benchmark in the literature (CSPLib), as well as large-sized instances generated from real-world cases. We report on extensive computational experiments, and provide basic managerial insights into the planning process.