Soft-computing approaches for rescheduling problems in a manufacturing industry

Abstract

<jats:p>This research addresses a novel Flexible Manufacturing System focused on rescheduling production which combines the techniques of Preti Nets(PetN), discrete simulation and memetic algorithms such as genetic algorithms(AG), simulated annealing (SA) and local search (LS). Reactive scheduling is caused by a failure of the machines. This hybrid algorithm is called as "PetNMA" and consider the performance measures such as makespan and Total Weighted Tardiness (TWT). The reactive program considers the operations which have not yet been processed at the time of the failure and the operation is processed by the machine at that moment. The PetNMA algorithm consists of three sub-algorithms: the first for the initial scheduling; the second to simulate the execution of the initial program set until the machine failure occurs; and the third to build reactive scheduling through the application of a Memetic Algorithm. We consider the problems that represent an FMS those are contrasted with bottleneck heuristics and dispatch rules obtaining good results in the proposed objectives. In the initial schedule instances, the proposed genetic algorithm improves in 50%, 70% and 70%, in the makespan, total weighted tardiness and combination respectively. The 58% of the results in rescheduling have a variation between 0% and 2% relative to the initial scheduling. The 95%, 85% and 85% of the rescheduling results show a variation between 0% and 2% with respect to the dispatch rules for the Makespan, the total weighted tardiness and its combination respectively.</jats:p>

Mapping and visualizing flexible manufacturing system in business and management: a systematic review and future agenda

“…Their results demonstrate how metaheuristics outshine mathematical models. Acevedo-Chedid et al (2021) have also addressed the problem of scheduling and rescheduling. For this purpose, they have integrated Petri nets, discrete simulation and memetic algorithms.…”

“…The results show increased flexibility and a reduction in the setup time, thus increasing the system’s robustness. Acevedo-Chedid et al (2021) have used a memetic algorithm to solve the rescheduling problem. Their soft-computing approach shows performance improvement up to 70% during scheduling and rescheduling.…”

“…The FMS philosophy instills three levels of adaptability in a manufacturing organization (Browne et al, 1984). These are in layers, and their associated segments are explained below: (1) Basic flexibility Machine flexibilitythe ability of the system to make a new variety of products and to alter the sequence of operations. Material handling flexibilityhow easily the subparts can be moved and positioned correctly on a shop floor.…”

Carbon Emission Reduction and Coordination Strategies for New Energy Vehicle Closed‐Loop Supply Chain under the Carbon Trading Policy

“…To alleviate the energy crisis and improve the environment, China promotes energy vehicles by incentives, such as price regulation and fiscal subsidies. However, as the sales of NEVs continue to increase, the green technology for alternative resources like solar, water, and wind energy, along with efficient equipment and machinery, is costly for setup the manufacturing system [17]. us, large-scale fiscal and tax subsidies are difficult to sustain.…”

A hyper-heuristic for distributed parallel machine scheduling with machine-dependent processing and sequence-dependent setup times

“…In this environment, adding new constraints, such as sequencedependent setup times, makes the problem more complicated (Sana et al 2019). Therefore, our considered problem, distributed scheduling with the unrelated parallel machines, is also NPhard, and a heuristic/meta-heuristic algorithm must be used to solve it in a reasonable time, especially in large-size instances (Acevedo-Chedid et al 2021). In this regard, the present study uses a hyper-heuristic algorithm to approach the optimal solution.…”