Abstract.A GRASP algorithm is presented for solving a sequencing problem in a mixed-model assembly line. The problem is focused on obtaining a manufacturing sequence that completes the greatest possible amount of required work and fulfils the production regularity property. The implemented GRASP algorithm is compared with other resolution procedures by means of instances from a case study linked to the Nissan's engine plant in Barcelona.Keywords: GRASP; Sequencing; Mixed-model assembly line; Production mix preservation.
PreliminaresA mixed-model assembly line is able to manufacture several variants of the same product (e.g. engines for SUVs (Sport Utility Vehicle) and different types of vans) without physical changes at workstations and without significant setup times between consecutive different units. This type of assembly lines presents two categories of problems that are solved traditionally sequentially: (1) balancing problems [1], and (2) product sequencing problems [2]. The first problem type consists of assigning a set of tasks (relating to the product assembly) into a set of workstations arranged in series with the maximum efficiency as possible and fulfilling a set of conditions. Once solved the first problem and given a demand plan and the time to perform the said plan, the second type of problems consists of establishing the manufacturing order of products regarding one or more criteria. The objectives taken into account when the units are sequenced are not necessarily mutually exclusive. Indeed these objectives often respond to several concerns about production [3]. Among them, there are: (o.1) maximise the useless time, completing the maximum number of units and therefore reducing the unnecessary waitings [4]; (o.2) maximise the level of satisfaction of the set of constraints, which are related with spatial components of the products [5]; and (o.3) maintain constant the manufacturing rate of products and the component consumption rate in order to minimise the maximum levels of component stocks [6].
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