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.

Minimizing deviations of car sequencing in a JIT production painting line

Purpose The first in first out (FIFO) rule is an indispensable rule in the automotive industry. However, during production, the order of coming to welding operation and exiting through the painting operation is broken. Consequently, the rule of FIFO also gets broken. The purpose of this paper is to eliminate the deviations of the automotive painting process in a just in time (JIT) environment. Design/methodology/approach The paper has been solved using observations and expert’s opinions and recorded data in the real problem area. Bypass technique and express line have been used as a real problem for the minimization of these deviations. The current process and the bypass process have been compared with simulation optimization. Findings This paper provides analytical insights about how to prevent delays during the JIT production painting process. Delays have been prevented by using the bypass technique and express line together. The number of delays reduced with an improvement of 129. The ratio of effect (for four months) has been founded to be 0.12. The output rate has been increased from 80% to 89%. Research limitations/implications This paper focused on the number of vehicles in the cooling area of the painting process in the study; there may occur bottleneck processes or products. It does not contain station-based bottleneck processes. Practical implications From the real-case problem, one will see that the bypass technique and the express line can be applied and generalized to many similar problems and help companies conduct their assembly lines more efficiently. Originality/value This paper is the only example that has been used to bypass technique and express line together on the paint/cooling process in a JIT production system.

Integrating Tier-1 module suppliers in car sequencing problem

<p class="Abstract"><span lang="EN-GB">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.</span></p>

Estudio de factibilidad para la aplicación de algoritmos de estimación de distribución al problema de secuenciación de vehículos

The main objective of an automobile production plant is to deliver on time and form the orders that are received daily. These orders are not homogeneous since they involve large quantities of cars that generally belong to different models and must be painted in different colors. The car sequencing problem that takes these characteristics into account was proposed by the Renault Company in 2005 as part of the ROADEF Challenge. This problem is NP-Hard and various techniques have been proposed to solve it, from exact methods to different heuristic algorithms. This work presents a feasibility study to apply two Distribution Estimation Algorithms (EDAs) to solve this problem. In addition, three important aspects are presented: the adaptation process of the algorithms, a technique for the execution of the algorithms called the "Stepped Approach with Discard" and a methodology that involves tolerance in the substitution of the individuals. The results obtained by the algorithms are also shown. The analysis of the results shows the algorithms adaptation process and the adjustments that can be made to improve their competence with the state of art.