Robust and stable flexible job shop scheduling with random machine breakdowns using a hybrid genetic algorithm

TLDR: A two-stage Hybrid Genetic Algorithm is proposed to generate the predictive schedule, which optimizes the primary objective, minimizing makespan in this work, where all the data is considered to be deterministic with no expected disruptions.

About: This article is published in International Journal of Production Economics.The article was published on 2011-08-01 and is currently open access. It has received 213 citations till now. The article focuses on the topics: Job shop scheduling & Flow shop scheduling.

Figures

Figure 2.3: POX example

Figure 1.1: An example of Gantt-chart.

Table 3.5: One-way ANOVA results concerning AMSRI and ASTBI of the proposed method

Figure 2.6: Diagram of Gantt-chart for the production scheduling of Ex5.

Table 3.3: Computational results of instances subjected to breakdown type BD1 and BD2

Table 4.2: Computational results – deviation of schedules when subjected to random uniform processing time variations

Frequently asked questions

Q1. What contributions have the authors mentioned in the paper "Optimizing the flexible job-shop scheduling problem using hybridized genetic algorithms" ?

In this paper, the authors defined six different bi-objective performance robustness and stability measures and investigated their effectiveness in producing robust and stability. 

Q2. What is the role of performance modeling in the planning, design, and operation of an FMS?

During the operation phase of an FMS, performance modeling can help in making decisions related to finding the best routes in the event of breakdowns, predicting the effect of adding or withdrawing resources and parts, obtaining optimal schedules in the event of machine failures, uncertainty of processing times of operations or sudden changes in part mix or demands, and in avoiding unusable situation, such as deadlocks (Wang, 1998). 

Q3. What are the main approaches to solve the complex FJSP?

used approaches to solve the complex FJSP can be categorized into two main basic approaches: concurrent approaches and hierarchical approaches. 

Q4. What is the main advantage of using GA in scheduling problems?

The main advantage of using GA in scheduling problems is its ability to find optimal or near-optimal scheduling solutions in a relatively short period of time. 

Q5. What is the main difficulty of the mathematical scheduling method?

One difficulty that most mathematical scheduling method encountered is the high computational complexity involved in finding an optimum solution, especially in systems with high processing flexibility. 

Q6. What are the three main approaches to scheduling a project?

General classifications of these approaches are analytical, simulation, heuristic, and meta-heuristic approaches.approachesSeveral mathematical techniques are reported for scheduling project activities. 

Q7. What is the probability of a chromosome being infeasible?

when applying genetic operators; crossover and/or mutation; there is a high chance of forming infeasible chromosomes by, for example, violating the precedence constraints among operations of the same job. 

Q8. What is the definition of a task scheduling problem?

The tasks scheduling consist of defining a schedule that can meet all timing and logical constraints of the jobs’ operations, and in general, it has been classified as an NP-hard problem. 

Q9. What are some of the meta-heuristics that have been proposed in the past few?

A number of meta-heuristics were proposed in literature for the past few decades to deal with FJSP such as simulated annealing (SA), tabu search (TS) and genetic algorithm (GA). 

Q10. What is the procedure for forming the receivers mating sub-pool?

Phase 2 procedure starts by33selecting n different random number of chromosomes from the population and moves them to the receivers’ mating-sub-pool. 

Q11. What is the way to solve the dynamic online scheduling problem?

In a relevant work, Wu et al. (1999) concluded12that dynamic online scheduling performs best when levels of disturbances and uncertainties are high.