Balancing mixed-model assembly lines with adjacent task duplication

Abstract

When a mixed-model assembly line (MAL) is balanced, it is generally assumed that the variants of a task over different models should be assigned to an identical station. In this study, this restriction is relaxed and the variants of a task over different models can be duplicated on two adjacent stations (referred to as adjacent task duplication) to improve the MAL's efficiency. The adjacent task duplication incurs few additional training and tool duplication costs as each task is duplicated on at most two stations. Moreover, for each task, the assembly part storage is not duplicated as it can be shared by the two adjacent stations. The mathematical model of this problem is formulated and some important properties are characterised. A branch, bound and remember algorithm is then developed to solve the problem. The performance of the proposed algorithm is tested on 900 representative instances, of which 889 instances are optimally solved. The experimental results show that the use of the adjacent task duplication policy effectively reduces the number of stations, especially when the WEST ratios are small. IntroductionAssembly lines are flow-oriented production systems that are typical in the industrial production of high-quantity standardised commodities (Becker and Scholl 2006). The labour productivity of assembly lines is largely driven by how the tasks (indivisible elements of work) are assigned to stations subject to technological constraints, commonly referred to as assembly line balancing (ALB) (Battaïa and Dolgui 2013). The simple assembly line balancing problem (SALBP) aims to minimise the number of stations given the cycle time (type I) or the cycle time given the number of stations (type II) (Baybars 1986).The mixed-model assembly line (MAL) is a more complex environment in which several product models are assembled simultaneously in intermixed sequences (Thomopoulos 1967). MALs are widely used in many industries such as automobiles, white goods and consumer electronics, where demand is characterised by high variability and relatively small volume for each model (Becker and Scholl 2006). A MAL generally assembles different versions of one basic product rather than completely different items. Hence, many studies assume that a single precedence diagram can be drawn to resume the precedence relationships among different tasks over each model (Merengo, Nava, and Pozzetti 1999). However, the processing time of a task may differ over the various models, and each variant of the task over these models should be regarded as a separate work element (Boysen, Fliedner, and Scholl 2008). The MAL balancing problem (MALBP) involves assigning all the task variants to stations.A straightforward approach for modelling and solving MALBPs is to restrict all the variants of a task over different models to an identical station. This restriction is reasonable, considering that these task variants may require the same skills, tools or assembly parts (Thomopoulos 1967). With this restriction, MALBPs are reduced to some version of...

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