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 simheuristic algorithm for solving an integrated resource allocation and scheduling problem

Modern companies have to face challenging configuration issues in their manufacturing chains. One of these challenges is related to the integrated allocation and scheduling of resources such as machines, workers, energy, etc. These integrated optimization problems are difficult to solve, but they can be even more challenging when real-life uncertainty is considered. In this paper, we study an integrated allocation and scheduling optimization problem with stochastic processing times. A simheuristic algorithm is proposed in order to effectively solve this integrated and stochastic problem. Our approach relies on the hybridization of simulation with a metaheuristic to deal with the stochastic version of the allocation-scheduling problem. A series of numerical experiments contribute to illustrate the efficiency of our methodology as well as their potential applications in real-life enterprise settings

A simulation-based approach for solving the flowshop problem

A simulation-based algorithm for the Permutation Flowshop Sequencing Problem (PFSP) is presented. The algorithm uses Monte Carlo Simulation and a discrete version of the triangular distribution to incorporate a randomness criterion in the classical Nawaz, Enscore, and Ham (NEH) heuristic and starts an iterative process in order to obtain a set of alternative solutions to the PFSP. Thus, a random but biased lo We can then consider several properties per solution other than the makespan, such as balanced idle times among machines, number of completed jobs at a given target time, etc. This allows the decision-maker to consider multiple solution characteristics apart from those defined by the aprioristic objective function. Therefore, our methodology provides flexibility during the sequence selection process, which may help to improve the scheduling process. Several tests have been performed to discuss the effectiveness of this approach. The results obtained so far are promising enough to encourage further developments and improvements on the algorithm and its applications in real-life scenarios. In particular, Multi-Agent Simulation is proposed as a promising technique to be explored in future works.Postprint (published version

Permutation Flow Shop via Simulated Annealing and NEH

Permutation Flow Shop Scheduling refers to the process of allocating operations of jobs to machines such that an operation starts to process on machine j only after the processing completes in j-1machine. At a time a machine can process only one operation and similarly a job can have only one operation processed at a time. Finding a schedule that minimizes the overall completion times for Permutation Flow Shop problems is NP-Hard if the number of machines is greater than 2. Sowe concentrates on approaches with approximate solutions that are good enough for the problems. Heuristics is one way to find the approximate solutions for a problem. For our thesis, we have used two heuristics - NEH and Simulated Annealing, both individually and in a combined form, to find the solutions for Permutation Flow Shop problems. We have compared NEH and Simulated Annealing algorithm based on result and execution time and also compared the combined algorithm with existing ones. Standard benchmarks are used to evaluate the performances of the implemented algorithm

HYBRID GENETIC AND PENGUIN SEARCH OPTIMIZATION ALGORITHM (GA-PSEOA) FOR EFFICIENT FLOW SHOP SCHEDULING SOLUTIONS

This paper presents a novel hybrid approach, fusing genetic algorithms (GA) and penguin search optimization (PSeOA), to address the flow shop scheduling problem (FSSP). GA utilizes selection, crossover, and mutation inspired by natural selection, while PSeOA emulates penguin foraging behavior for efficient exploration. The approach integrates GA's genetic diversity and solution space exploration with PSeOA's rapid convergence, further improved with FSSP-specific modifications. Extensive experiments validate its efficacy, outperforming pure GA, PSeOA, and other metaheuristics

Scheduling strategies for the furniture industry

Technological developments and more demanding production standards have constantly been pushing the envelope, changing the perception of what is possible and desired in manufacturing processes. Such improvements are often made at marginal cost, yet have the potential to significantly benefit performance, enabling a strong competitive advantage. In this case study, a factory in the furniture industry is considered, where there are vast improvement opportunities and an increase in flexibility is needed. Furthermore, this problem can be best approximated by the flow shop model and the most critical characteristic is sequence-dependent setup times. To address this problem, an iterated greedy with local search meta-heuristic is implemented, which will be responsible for scheduling production orders in the way that best suits makespan and, consequently, productivity. Additionally, OptQuest, the optimiser functionally built into the Flexsim simulating software was also tested against the meta-heuristic and, still through simulation, a local rule was implemented, which allowed each workstation to define its own sequence of jobs, to minimise setup times. Lastly, the best performing of the previous methods was also compared to the original heuristic that had previously been specifically created for this problem. Through testing, it was found that the iterated greedy with local search meta-heuristic was able to generate solutions that had a much better makespan value than the ones produced by OptQuest, while the local rule was not able to provide significant improvement. Then, the meta-heuristic was compared to the original heuristic and, although the newly implemented algorithm did not consider all characteristics of the problem, productivity far outperformed that of the original technique