Dynamic Flexible Flow Shop Research Articles

GPU based parallel genetic algorithm for solving an energy efficient dynamic flexible flow shop scheduling problem

Due to new government legislation, customers’ environmental concerns and continuously rising cost of energy, energy efficiency is becoming an essential parameter of industrial manufacturing processes in recent years. Most efforts considering energy issues in scheduling problems have focused on static scheduling. But in fact, scheduling problems are dynamic in the real world with uncertain new arrival jobs after the execution time. This paper proposes an energy efficient dynamic flexible flow shop scheduling model using the peak power value with consideration of new arrival jobs. As the problem is strongly NP-hard, a priority based hybrid parallel Genetic Algorithm with a predictive reactive complete rescheduling strategy is developed. In order to achieve a speedup to meet the short response in the dynamic environment, the proposed method is designed to be highly consistent with the NVIDIA CUDA software model. Finally, numerical experiments are conducted and show that our approach can not only solve the problem flexibly, but also gain competitive results and reduce time requirements dramatically.

A control method to save machine energy in production

Nowadays, energy saving is one of the most talked about issues in our life, it is also increasingly important in the manufacturing industry. This research considers the dynamic flexible flow shop scheduling (DFFS) problem, which is an extended version of the classical flow-shop scheduling problem. A flexible flow shop has multiple stages with multiple machines at each stage for processing multiple products. Previous research on DFFS aimed to achieve just-in-time production, or reducing difference between the actual completion time and the due date of each job. However, little research has been made on energy saving of machines in production. To address such a need, this paper proposes a method that dynamically turns on and off machines so as to reduce energy consumption while achieving JIT production. The proposed method has been tested on different environments, and the results show that it is high performing for both JIT production and energy saving.

A stable reactive approach in dynamic flexible flow shop scheduling with unexpected disruptions: A case study

In industrial environments, scheduling systems often operate under dynamic and stochastic circumstances. In these conditions, it is inevitable to encounter some disruptions which are inherently stochastic or totally unexpected events. These disruptions may cause the initial schedule to become infeasible and non-optimal. So, appropriate revisions and rescheduling methods are needed to overcome the unfavorable subsequent of these disruptions. In this paper, we address a dynamic flexible flow shop (FFS) environment considering unexpected arrival of new jobs into the process as disruptions. A novel reactive model is proposed based on a classical objective function (total weighted tardiness) and two new surrogate measures, stability and resistance to change. In fact the proposed model is presented to generate a stable reschedule against of any possible occurrences of mentioned disruption. Due to the computational complexity, a variable neighborhood search (VNS) algorithm is implemented to solve the problem. To show the performance of the reactive approach, a case study in petrochemical industry is studied. Computational experiments and comparisons of the proposed algorithm with three dispatching rule and an efficient rescheduling approach show the efficiency of the presented reactive approach to reschedule the jobs.

Energy-efficient dynamic scheduling for a flexible flow shop using an improved particle swarm optimization

Due to increasing energy requirements and associated environmental impacts, nowadays manufacturing companies are facing the emergent challenges to meet the demand of sustainable manufacturing. Most existing research on reducing energy consumption in production scheduling problems has focused on static scheduling models. However, there exist many unexpected disruptions like new job arrivals and machine breakdown in a real-world production scheduling. In this paper, it is proposed an approach to address the dynamic scheduling problem reducing energy consumption and makespan for a flexible flow shop scheduling. Since the problem is strongly NP-hard, a novel algorithm based on an improved particle swarm optimization is adopted to search for the Pareto optimal solution in dynamic flexible flow shop scheduling problems. Finally, numerical experiments are carried out to evaluate the performance and efficiency of the proposed approach.

Dispatching Rule-based Algorithms for a Dynamic Flexible Flow Shop Scheduling Problem with Time-dependent Process Defect Rate and Quality Feedback

We consider a three-stage dynamic flexible flow shop scheduling problem in which jobs of multiple types arrive dynamically over time, a quality feedback mechanism is present, and the setup timing and the process defect rate are closely related. At each machine in the second stage, a sequence-independent setup operation is necessary to changeover job types. Once a setup is done for a job type at a machine, the defect rate for the job type at the machine is reset to a low and stable phase which will be maintained for a predetermined time periods. However, after the phase, it turns to a relatively high and unstable phase. At the final inspection stage, jobs are inspected and the quality feedback will be given to the previous stage when the accumulative defect rate of each job type exceeds a certain tolerance level. To cope with the dynamic nature of the flexible flow shop scheduling problem, we propose two dispatching rule-based scheduling algorithms which consider the quality feedback as well as the real time shop information for the objectives of maximizing the quality rate and the mean tardiness of the finished jobs. The results of a series of simulation experiments will be given to evaluate the performance of the suggested algorithms. Since there have been few research on the shop floor scheduling problems with quality feedback, we expect that this research will make a contribution to the development of a practical real time scheduling methodology in multi-stage production systems with the consideration of the imperfect process quality.

Dynamic flexible flow shop problems—Scheduling heuristics vs. autonomous control

Flexible flow shop problems are well known and common tasks. Two practice-oriented extensions are unrelated parallel machines on the one hand and dynamic aspects in terms of distributed release times on the other hand. Triggered by the growing complexity of logistics systems, the paradigm of central planning is being shifted to decentralized autonomous control. This work focuses on two different autonomous control methods applied to flexible flow shop problems with both of the extensions mentioned above. In order to evaluate these methods, various common scheduling heuristics and one genetic algorithm are taken as references. Problem instances are defined and solved by autonomous control and the reference algorithms. The results of this evaluation are shown and discussed.

New dispatching rules to minimize rejection and tardiness costs in a dynamic flexible flow shop

This paper studies a flexible flow shop system considering dynamic arrival of jobs and the ability of acceptance and rejection of new jobs. The problem objective is to determine a schedule that minimizes sum of the tardiness and rejection costs of jobs. A 0–1 mixed integer model of the problem is formulated. Since this problem class is NP-hard, four dispatching rules have been developed to solve the problem approximately. Moreover, a discrete event simulation model of the flexible flow shop system is developed for the purpose of experimentation. Four dispatching rules from the literature and four new dispatching rules proposed in this paper are incorporated in the simulation model. Simulation experiments have been conducted under various experimental conditions characterized by factors such as shop utilization level, due date tightness and number of stages in flexible flow shop. The results indicate that proposed dispatching rules provide better performance under problem assumptions.