A multiobjective model and evolutionary algorithms for robust time and space assembly line balancing under uncertain demand

Abstract

Changes in demand when manufacturing different products require an optimization model that includes robustness in its definition and methods to deal with it. In this work we propose the r-TSALBP, a multiobjective model for assembly line balancing to search for the most robust line configurations when demand changes. The robust model definition considers a set of demand scenarios and presents temporal and spatial overloads of the stations in the assembly line of the products to be assembled. We present two multiobjective evolutionary algorithms to deal with one of the r-TSALBP variants. The first algorithm uses an additional objective to evaluate the robustness of the solutions. The second algorithm employs a novel adaptive method to evolve separate populations of robust and non-robust solutions during the search. Results show the improvements of using robustness information during the search and the outstanding behavior of the adaptive evolutionary algorithm for solving the problem. Finally, we analyze the managerial impacts of considering the r-TSALBP model for the different organization departments by exploiting the values of the robustness metrics.

Benefits of robust multiobjective optimization for flexible automotive assembly line balancing

“…Regarding unbalanced assembly lines some authors have used simulation to investigate them, for example taking into account their operation time means, coefficients of variation and/or buffer sizes (Shaaban and Hudson 2012). In the particular case of the TSALBP, uncertainty may appear in different parts of the optimization process such as the uncertain demand of the products (Chica et al 2013(Chica et al , 2016. Several particular scenarios can be generally stated in order to test the robustness of solutions.…”

“…The assembly of these different products is based on similar processing tasks with common features but require, for each product type, different components, specific work, and tools. But within this industrial context, even small variations in the demand of the products' type could lead to unstable assembly line balancing and therefore, a need of constant re-balancing operations (Chica et al 2016). An a posteriori adaptation to the latter variations corresponds to the reaction of an already existing manufacturing system to changes in the product (ElMaraghy and AlGeddawy 2012).…”

moGrams: A Network-Based Methodology for Visualizing the Set of Nondominated Solutions in Multiobjective Optimization

“…Then, the similarity metric Sim(bq 1 , bq 2 ) is normalized and subtracted from 1 in order to reflect similarity instead of the distance as presented in Equation 6.…”

“…A good visualization enables her/him to obtain more insights of the problem and the different solutions to identify differences and similarities before coming to the final decision [5]. In particular, the flexibility (i.e., the ease to change one solution by another in the decision space) is an important property to respond to frequent environmental changes in many managerial and operation research problems in which the information is uncertain [6]. Additional information about the flexibility of the non-dominated solutions will be really worthy for the DM.…”

A case study at the Nissan Barcelona factory to minimize the ergonomic risk and its standard deviation in a mixed-model assembly line

“…The set of workstations on the line, which can be finite or infinite 3. The set of sequencing constraints, such as the precedence relationships between tasks, incompatibility between tasks, and restrictions that may affect the workstations with respect to their assignable time, their available area, and their admissible risk Like the SALBP (Baybars, 1986;Scholl and Becker, 2006) and TSALBP families (Bautista and Pereira, 2007;Chica et al, 2010Chica et al, , 2013Chica et al, , 2016Chica et al, , 2018, the TSALBP_erg family focuses on assigning all tasks to workstations in order to achieve maximum efficiency regarding some of the considered attributes, while all constraints imposed are fulfilled. Accordingly, this family of problems also comprises a set of problem types in accordance with the optimization criteria.…”