Free and regular mixed-model sequences by a linear program-assisted hybrid algorithm GRASP-LP

A linear program-assisted hybrid algorithm (GRASP-LP) is presented to solve a mixed-model sequencing problem in an assembly line. The issue of the problem is to obtain manufacturing sequences of product models with the minimum work overload, allowing the free interruption of operations at workstations and preserving the production mix. The implemented GRASP-LP is compared with other procedures through a case study linked with the Nissan’ Engine Plant from Barcelona.Peer ReviewedPostprint (author's final draft

Solving the mixed model sequencing problem with workload minimization with product mix preservation

This work is supported by the Spanish Ministerio de Educación y Ciencia under Project DPI2010-16759 (PROTHIUS-III) including EDRF fundings.Postprint (published version

Minimisation des retards dans le séquencement des véhicules sur une ligne d'assemblage multi modèles

National audienceDans cet article, nous considérons le problème du séquencement sur une ligne d'assemblage à transport continu de véhicules industriels. Pour équilibrer au mieux la charge dynamique, nous proposons de minimiser les retards. Nous proposons une formalisation par un modèle de type programmation linéaire. Un modèle monoposte est présenté puis une généralisation sur le cas multipostes est proposée. Le modèle est testé sur des instances du cas d'étude de l'usine de montage de Renault Trucks à Bourg en Bresse et une étude expérimentale des facteurs de complexité est développée

Balancing and Sequencing of Mixed Model Assembly Lines

Assembly lines are cost efficient production systems that mass produce identical products. Due to customer demand, manufacturers use mixed model assembly lines to produce customized products that are not identical. To stay efficient, management decisions for the line such as number of workers and assembly task assignment to stations need to be optimized to increase throughput and decrease cost. In each station, the work to be done depends on the exact product configuration, and is not consistent across all products. In this dissertation, a mixed model line balancing integer program (IP) that considers parallel workers, zoning, task assignment, and ergonomic constraints with the objective of minimizing the number of workers is proposed. Upon observing the limitation of the IP, a Constraint Programming (CP) model that is based on CPLEX CP Optimizer is developed to solve larger assembly line balancing problems. Data from an automotive OEM are used to assess the performance of both the MIP and CP models. Using the OEM data, we show that the CP model outperforms the IP model for bigger problems. A sensitivity analysis is done to assess the cost of enforcing some of the constraint on the computation complexity and the amount of violations to these constraints once they are disabled. Results show that some of the constraints are helpful in reducing the computation time. Specifically, the assignment constraints in which decision variables are fixed or bounded result in a smaller search space. Finally, since the line balance for mixed model is based on task duration averages, we propose a mixed model sequencing model that minimize the number of overload situation that might occur due to variability in tasks times by providing an optimal production sequence. We consider the skip-policy to manage overload situations and allow interactions between stations via workers swimming. An IP model formulation is proposed and a GRASP solution heuristic is developed to solve the problem. Data from the literature are used to assess the performance of the developed heuristic and to show the benefit of swimming in reducing work overload situations

Mathematical model and agent based solution approach for the simultaneous balancing and sequencing of mixed-model parallel two-sided assembly lines

Copyright © 2014 Elsevier. NOTICE: this is the author’s version of a work that was accepted for publication in International Journal of Production Economics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in International Journal of Production Economics, DOI: 10.1016/j.ijpe.2014.08.010One of the key factors of a successfully implemented mixed-model line system is considering model sequencing problem as well as the line balancing problem. In the literature, there are many studies, which consider these two tightly interrelated problems individually. However, we integrate the model sequencing problem in the line balancing procedure to obtain a more efficient solution for the problem of Simultaneous Balancing and Sequencing of Mixed-Model Parallel Two-Sided Assembly Lines. A mathematical model is developed to present the problem and a novel agent based ant colony optimisation approach is proposed as the solution method. Different agents interact with each other to find a near optimal solution for the problem. Each ant selects a random behaviour from a predefined list of heuristics and builds a solution using this behaviour as a local search rule along with the pheromone value. Different cycle times are allowed for different two-sided lines located in parallel to each other and this yields a complex problem where different production cycles need to be considered to build a feasible solution. The performance of the proposed approach is tested through a set of test cases. Experimental results indicate that considering model sequencing problem with the line balancing problem together helps minimise line length and total number of required workstations. Also, it is found that the proposed approach outperforms other three heuristics tested

Mixed integer linear programming models for Flow Shop Scheduling with a demand plan of job types

This is a post-peer-review, pre-copyedit version of an article published in Central european journal of operations research. The final authenticated version is available online at: http://www.doi.org/10.1007/s10100-018-0553-8This paper presents two mixed integer linear programming (MILP) models that extend two basic Flow Shop Scheduling problems: Fm/prmu/Cmax and Fm/block/Cmax. This extension incorporates the concept of an overall demand plan for types of jobs or products. After using an example to illustrate the new problems under study, we evaluated the new models and analyzed their behaviors when applied to instances found in the literature and industrial instances of a case study from Nissan’s plant in Barcelona. CPLEX solver was used as a solution tool and obtained acceptable results, allowing us to conclude that MILP can be used as a method for solving Flow Shop Scheduling problems with an overall demand plan.Peer ReviewedPostprint (published version

GRASP para secuenciar modelos mixtos en una línea con sobrecarga, tiempo inerte y regularidad en la producción

Se presenta un algoritmo GRASP para resolver un problema de secuenciación de productos en una línea de montaje de modelos mixtos. El objetivo del problema es obtener una secuencia de fabricación de productos con máximo trabajo total completado y cumpliendo la propiedad de regularidad en la producción. El algoritmo GRASP implementado se compara con otros procedimientos de resolución, empleando para ello las instancias de un caso de estudio asociado a la planta de fabricación de motores de Nissan en Barcelona.Postprint (author's final draft