Using real-time information to reschedule jobs in a flowshop with variable processing times

Versión revisada. Embargo 36 mesesIn a time where detailed, instantaneous and accurate information on shop-floor status is becoming available in many manufacturing companies due to Information Technologies initiatives such as Smart Factory or Industry 4.0, a question arises regarding when and how this data can be used to improve scheduling decisions. While it is acknowledged that a continuous rescheduling based on the updated information may be beneficial as it serves to adapt the schedule to unplanned events, this rather general intuition has not been supported by a thorough experimentation, particularly for multi-stage manufacturing systems where such continuous rescheduling may introduce a high degree of nervousness in the system and deteriorates its performance. In order to study this research problem, in this paper we investigate how real-time information on the completion times of the jobs in a flowshop with variable processing times can be used to reschedule the jobs. In an exhaustive computational experience, we show that rescheduling policies pay off as long as the variability of the processing times is not very high, and only if the initially generated schedule is of good quality. Furthermore, we propose several rescheduling policies to improve the performance of continuous rescheduling while greatly reducing the frequency of rescheduling. One of these policies, based on the concept of critical path of a flowshop, outperforms the rest of policies for a wide range of scenarios.Ministerio de Ciencia e Innovación DPI2016-80750-

A robust flexible flow shop problem under processing and release times uncertainty

The aim of this paper is to present a simheuristic approach that obtains robust solutions for a multi-objective hybrid flow shop problem under uncertain processing and release times. This approach minimizes the expected tardiness and standard deviation of tardiness, as a robustness measure for the stated problem. The simheuristic algorithm hybridizes the NSGA-II with a Monte Carlo Simulation process. Initially, the deterministic scenario was tested on 32 different created small size instances and 32 medium and large benchmarked instances. As a result, the proposed algorithm improved quality of solutions by 1.21% against the MILP model and it also performed better than ERD, NEHedd, and ENS2, while consuming a reasonable computational time. Afterwards, one experimental design was carried out using 10 random instances from the same benchmark as a blocking factor, where four factors of interest were considered. The factors and their respective values are number of generations (50, 100), crossover probability (0.8, 0.9), mutation probability (0.1, 0.2), and population size (60, 100). Results show that the factors instance, mutation probability and number of generations, as well as other interactions between them, have a significant effect in the total tardiness for the deterministic scenario, proving the importance of an appropriate selection of parameters when using genetic algorithms to obtain quality solutions. Then, the performance of the proposed NSGA-II was compared against ERD, NEHedd, and ENS2 methods. Results show that our algorithm improves the quality of the solutions for both objective functions, proving the robustness of our solutions for the HFS problem. Finally, two additional generalized experiments were carried out to analyze the effect of number of jobs (10, 20), number of stages (2, 3), shop condition (0.2, 0.6), probability distribution (uniform, lognormal), and CV (0.05, 0.25, 0.4) on both objective functions. The shop condition, probability distribution and CV were proven to be highly influential on the variability of the results, with the only exception being the coefficient of variation having no statistically significant effect on the total tardiness.The aim of this paper is to present a simheuristic approach that obtains robust solutions for a multi-objective hybrid flow shop problem under uncertain processing and release times. This approach minimizes the expected tardiness and standard deviation of tardiness, as a robustness measure for the stated problem. The simheuristic algorithm hybridizes the NSGA-II with a Monte Carlo Simulation process. Initially, the deterministic scenario was tested on 32 different created small size instances and 32 medium and large benchmarked instances. As a result, the proposed algorithm improved quality of solutions by 1.21% against the MILP model and it also performed better than ERD, NEHedd, and ENS2, while consuming a reasonable computational time. Afterwards, one experimental design was carried out using 10 random instances from the same benchmark as a blocking factor, where four factors of interest were considered. The factors and their respective values are number of generations (50, 100), crossover probability (0.8, 0.9), mutation probability (0.1, 0.2), and population size (60, 100). Results show that the factors instance, mutation probability and number of generations, as well as other interactions between them, have a significant effect in the total tardiness for the deterministic scenario, proving the importance of an appropriate selection of parameters when using genetic algorithms to obtain quality solutions. Then, the performance of the proposed NSGA-II was compared against ERD, NEHedd, and ENS2 methods. Results show that our algorithm improves the quality of the solutions for both objective functions, proving the robustness of our solutions for the HFS problem. Finally, two additional generalized experiments were carried out to analyze the effect of number of jobs (10, 20), number of stages (2, 3), shop condition (0.2, 0.6), probability distribution (uniform, lognormal), and CV (0.05, 0.25, 0.4) on both objective functions. The shop condition, probability distribution and CV were proven to be highly influential on the variability of the results, with the only exception being the coefficient of variation having no statistically significant effect on the total tardiness.Ingeniero (a) IndustrialPregrad

Study of event-driven and periodic rescheduling on a single machine with unexpected disruptions

This paper studies the rescheduling problem of a single machine facing unexpected disruptions in order to determine which parameters can help reducing the negative impacts of these disruptions on schedule performance. A Genetic Algorithm (GA) is used to generate the initial schedule and the updated ones according to a reactive strategy. The performance of event-driven rescheduling and periodic rescheduling policies are compared in terms of total tardiness and total cost of rescheduling. Other factors that may affect rescheduling such as disruption time, disruption duration and number of disruptions are investigated. The sensitivity of results to both due date tightness and cost factor variation is tested. The results showed that the timing of the occurrence of disruption as related to scheduling horizon has a major effect on determining the best rescheduling policy. Event-driven policy is superior to other policies for short infrequent disruptions. It was found that the periodic policy is more appropriate for long and frequent disruptions

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

Flexible flow shop scheduling with stochastic processing times: A decomposition-based approach

Flexible flow shop scheduling problems are NP-hard and tend to become more complex when stochastic uncertainties are taken into consideration. Although some methods have been developed to address such problems, they remain inherently difficult to solve by any single approach. This paper presents a novel decomposition-based approach (DBA), which combines both the shortest processing time (SPT) and the genetic algorithm (GA), to minimizing the makespan of a flexible flow shop (FFS) with stochastic processing times. In the proposed DBA, a neighbouring K-means clustering algorithm is developed to firstly group the machines of an FFS into an appropriate number of machine clusters, based on their stochastic nature. Two optimal back propagation networks (BPN), corresponding to the scenarios of simultaneous and non-simultaneous job arrivals, are then selectively adopted to assign either SPT or GA to each machine cluster for sub-schedule generation. Finally, an overall schedule is generated by integrating the sub-schedules of machine clusters. Computation results show that the DBA outperforms SPT and GA alone for FFS scheduling with stochastic processing times. © 2012 Elsevier Ltd. All rights reserved.postprin

A permutation flowshop model with time-lags and waiting time preferences of the patients

The permutation flowshop is a widely applied scheduling model. In many real-world applications of this model, a minimum and maximum time-lag must be considered between consecutive operations. We can apply this model to healthcare systems in which the minimum time-lag could be the transfer times, while the maximum time-lag could refer to the number of hours patients must wait. We have modeled a MILP and a constraint programming model and solved them using CPLEX to find exact solutions. Solution times for both methods are presented. We proposed two metaheuristic algorithms based on genetic algorithm and solved and compared them with each other. A sensitivity of analysis of how a change in minimum and maximum time-lags can impact waiting time and Cmax of the patients is performed. Results suggest that constraint programming is a more efficient method to find exact solutions and changes in the values of minimum and maximum time-lags can impact waiting times of the patients and Cmax significantly

Distribution planning in a weather-dependent scenario with stochastic travel times: a simheuristics approach

In real-life logistics, distribution plans might be affected by weather conditions (rain, snow, and fog), since they might have a significant effect on traveling times and, therefore, on total distribution costs. In this paper, the distribution problem is modeled as a multi-depot vehicle routing problem with stochastic traveling times. These traveling times are not only stochastic in nature but the specific probability distribution used to model them depends on the particular weather conditions on the delivery day. In order to solve the aforementioned problem, a simheuristic approach combining simulation within a biased-randomized heuristic framework is proposed. As the computational experiments will show, our simulation-optimization algorithm is able to provide high-quality solutions to this NP-hard problem in short computing times even for large-scale instances. From a managerial perspective, such a tool can be very useful in practical applications since it helps to increase the efficiency of the logistics and transportation operations.Peer ReviewedPostprint (published version