An Adaptive Scheduling Algorithm for Dynamic Jobs for Dealing with the Flexible Job Shop Scheduling Problem

Abstract

Modern manufacturing systems build on an effective scheduling scheme that makes full use of the system resource to increase the production, in which an important aspect is how to minimize the makespan for a certain production task (i.e., the time that elapses from the start of work to the end) in order to achieve the economic profit. This can be a difficult problem, especially when the production flow is complicated and production tasks may suddenly change. As a consequence, exact approaches are not able to schedule the production in a short time. In this paper, an adaptive scheduling algorithm is proposed to address the makespan minimization in the dynamic job shop scheduling problem. Instead of a linear order, the directed acyclic graph is used to represent the complex precedence constraints among operations in jobs. Inspired by the heterogeneous earliest finish time (HEFT) algorithm, the adaptive scheduling algorithm can make some fast adaptations on the fly to accommodate new jobs which continuously arrive in a manufacturing system. The performance of the proposed adaptive HEFT algorithm is compared with other state-of-the-art algorithms and further heuristic methods for minimizing the makespan. Extensive experimental results demonstrate the high efficiency of the proposed approach.

A New Perspective for Solving Manufacturing Scheduling Based Problems Respecting New Data Considerations

“…Additionally, it is worth mentioning that the number of individuals resulting from the crossover process varied depending on the way the evolution based algorithm implements the mating procedure. [39], (b) chromosome is a machine coded of job-operation number as a tuple, (c) chromosome cell is a triple tuple of job-operation-machine coding [40], and (d) three chromosomes of a single cell information represents job as a feature string followed by operation and machine strategy indexation.…”

“…Figure 3. Examples of chromosome coding: (a) chromosome is a bi-part coding multiple routes of a job indexation and the corresponding operation sequence[39], (b) chromosome is a machine coded of job-operation number as a tuple, (c) chromosome cell is a triple tuple of job-operation-machine coding[40], and (d) three chromosomes of a single cell information represents job as a feature string followed by operation and machine strategy indexation.…”

An improved particle swarm optimization algorithm for dynamic job shop scheduling problems with random job arrivals

“…The parameters for five PSO variants are set to the same as the proposed PSO algorithm. Besides, it is compared with the optimal solution obtained by the CPLEX optimizer (version 12.9.0) as well as three state-of-the-art meta-heuristic algorithms in the literature, including hybrid artificial bee colony algorithm (HABC) [31], parallel bat algorithm (PBA) [32], and an adaptive scheduling algorithm (A-HEFT) [57]. The CPU time limits for each run on each problem instance is 60 s in CPLEX.…”

Discrete Dynamic Modeling Analysis of Engineering Management and Quality Optimization Innovation Mode Based on Big Data Intelligent Algorithm

“…e basic big data technology can collect, process, and store most of the information generated in the project activities and finally realize the effective control of information and data. Taking building construction as an example, we use big data technology and intelligent algorithm to make the application data in the process of building use more accurate (Qin) [3]. It can greatly improve the management efficiency of construction projects and reduce the capital investment and labor cost in the process of project management.…”