MILP model for the planning of a computerized numerical control lathes machining plant

This work introduces the formulation and application of a MILP model to solve the problem of planning the weekly production of a machining plant using numerical control lathes to manufacture spare parts for agricultural machines. The machining plant works under a Flexible Job Shop system and it has reduced workforce with different skills to operate the various high-complexity lathes and to carry out setup operations in each machine. The developed model is based on a basic formulation for the classic problem and we introduce some flexible adjustment for the various situations that may arise from different scheduling problems. The model is applied to various scenarios; and we include a discussion of the improvements brought about by the analysis.Fil: Kañevsky, Federico. Universidad Tecnológica Nacional. Facultad Regional Santa Fe; ArgentinaFil: Franco, Maria Agustina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; ArgentinaFil: Galli, Maria Rosa. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentin

An improved genetic algorithm for multi-AGV dispatching problem with unloading setup time in a matrix manufacturing workshop

This paper investigates a novel problem concerning material delivery in a matrix manufacturing workshop, specifically the multi-automated guided vehicle (AGV) dispatching problem with unloading setup time (MAGVDUST). The objective of the problem is to minimize transportation costs, including travel costs, time penalty costs, AGV costs, and unloading setup time costs. To solve the MAGVDUST, this paper builds a mixed-integer linear programming model and proposes an improved genetic algorithm (IGA). In the IGA, an improved nearest-neighbor-based heuristic is proposed to generate a high-quality initial solution. Several advanced technologies are developed to balance local exploitation and global exploration of the algorithm, including an optimal solution preservation strategy in the selection process, two well-designed crossovers in the crossover process, and a mutation based on Partially Mapped Crossover strategy in the mutation process. In conclusion, the proposed algorithm has been thoroughly evaluated on 110 instances from an actual electronic factory and has demonstrated its superior performance compared to state-of-the-art algorithms in the existing literature

Job shop estocástico con minimización del valor esperado del maximum lateness

The drawbacks that programming in job -shop environment imply, refer to a notorious difficulty for its resolution due to its NP-hard nature. However, the research has grown in the late years because of its constant use in manufacturing industries. According to studies, most of the research has approached the job shop scheduling through a deterministic approach. Nevertheless, real industrial environments are subject to random events as: machinery faults, maintenance duration, processing duration, enlistment times, availability times, among many others. In this project, a stochastic job shop that minimizes the expected maximum lateness is addressed. The problem consider sequence dependent setup times, and the stochastic events are machine breakdowns. To solve the problem a simheuristic approach is proposed. The simheuristic Hybridizes a tabu search algorithm with a Monte Carlo simulation. The problem was solved in three phases: Firstly, a mixed integer linear programming model was designed for the deterministic counterpart of the JSSP studied. Secondly, the meta-heuristic tabu search was designed to solving large instances of the deterministic problem. Thirdly, the simheuristic was designed and implemented to minimize the expected maximum lateness value, considering stochastic machine breakdowns. For the simheuristic designing, stochastic variables were generated: times between failures and repair times, following exponential and log-normal distributions. To generate their respective parameters [expected value (μ) and standard deviation (σ)], the mean time to repair was found (MTTR Mean Time to Repair), out of the total mean time between breakdowns. Four different variation coefficient values were proposed (0%, 5%, 10% and 15%), them being: 0% for the deterministic case and 5%, 10% and 15% for stochastic events, to calculate the (σ) in log-normal distribution. On the other hand, a simulation was performed to calculate the expected objective function. The simheuristic was firstly parametrized through an experimental design considering different tabu list sizes and number of iterations without improvement. With the generated parametrization, another computational experiment was executed for a total of 554 instances of different sizes. First, the performance of the simheuristic, for small instances, was evaluated in comparison with the simulation of optimal solutions obtained with the mathematical model. Results show that the simheuristic improves the results of simulations of the model in a 37% for 4x4 instances and in an 11% for 6x6 instances, demonstrating that the simheuristic is better than a deterministic mathematical model simulated. Additionally, the simheuristic performance was evaluated, for large instances, in comparison with the simulation of EDD dispatching rule sequences. Results show that the average improvement is 28% in log-normal distribution and 10% for exponential distribution.Ingeniero (a) IndustrialAdministrador (a) de EmpresasPregrad

Dynamic allocation of operators in a hybrid human-machine 4.0 context

La transformation numérique et le mouvement « industrie 4.0 » reposent sur des concepts tels que l'intégration et l'interconnexion des systèmes utilisant des données en temps réel. Dans le secteur manufacturier, un nouveau paradigme d'allocation dynamique des ressources humaines devient alors possible. Plutôt qu'une allocation statique des opérateurs aux machines, nous proposons d'affecter directement les opérateurs aux différentes tâches qui nécessitent encore une intervention humaine dans une usine majoritairement automatisée. Nous montrons les avantages de ce nouveau paradigme avec des expériences réalisées à l'aide d'un modèle de simulation à événements discrets. Un modèle d'optimisation qui utilise des données industrielles en temps réel et produit une allocation optimale des tâches est également développé. Nous montrons que l'allocation dynamique des ressources humaines est plus performante qu'une allocation statique. L'allocation dynamique permet une augmentation de 30% de la quantité de pièces produites durant une semaine de production. De plus, le modèle d'optimisation utilisé dans le cadre de l'approche d'allocation dynamique mène à des plans de production horaire qui réduisent les retards de production causés par les opérateurs de 76 % par rapport à l'approche d'allocation statique. Le design d'un système pour l'implantation de ce projet de nature 4.0 utilisant des données en temps réel dans le secteur manufacturier est proposé.The Industry 4.0 movement is based on concepts such as the integration and interconnexion of systems using real-time data. In the manufacturing sector, a new dynamic allocation paradigm of human resources then becomes possible. Instead of a static allocation of operators to machines, we propose to allocate the operators directly to the different tasks that still require human intervention in a mostly automated factory. We show the benefits of this new paradigm with experiments performed on a discrete-event simulation model based on an industrial partner's system. An optimization model that uses real-time industrial data and produces an optimal task allocation plan that can be used in real time is also developed. We show that the dynamic allocation of human resources outperforms a static allocation, even with standard operator training levels. With discrete-event simulation, we show that dynamic allocation leads to a 30% increase in the quantity of parts produced. Additionally, the optimization model used under the dynamic allocation approach produces hourly production plans that decrease production delays caused by human operators by up to 76% compared to the static allocation approach. An implementation system for this 4.0 project using real-time data in the manufacturing sector is furthermore proposed