Abstract
With the emergence of new Industry 4.0 technologies, real-time order acceptance and scheduling is a key problem in a make-to-order (MTO) production system where customers place orders in real-time and the decision maker has to make acceptance or rejection decisions based on the available resources at that point in time. This paper focuses on simultaneously accepting orders and scheduling decisions in real-time, as is required for the operation of an MTO flow shop production system, a topic that has received little attention in academia due to the complexity of the problem. This paper presents a hybrid genetic algorithm and particle swarm optimization algorithm (GA-PSO) to solve the considered problem. A detailed computational study based on realistic problem instances has been conducted. In this study, the hybrid GA- and PSO-based approach performed better than other state-of-the-art approaches reported in the literature.
Highlights
The permutation flow shop scheduling problem (PFSP) is one of the most challenging scheduling problems that arises in manufacturing industries such as pharmaceuticals, food processing, steel, automobiles, and semiconductors [1], [2]
This paper proposes a hybrid approach by integrating a genetic algorithm (GA) and particle swarm optimization (PSO), referred to hereafter as a genetic algorithm and particle swarm optimization algorithm (GA-PSO)-based real-time strategy for solving real-time multiple order PFSPs
EXPERIMENTAL RESULTS AND ANALYSIS experimental results conducted for a multiple order PFSP with the proposed approaches are presented and analyzed
Summary
The permutation flow shop scheduling problem (PFSP) is one of the most challenging scheduling problems that arises in manufacturing industries such as pharmaceuticals, food processing, steel, automobiles, and semiconductors [1], [2]. In a traditional PFSP, a set of n jobs is scheduled in a set of m machines, where each job has to follow the same processing order in all machines. For solving PFSPs, makespan minimization is a common measure of performance. In flow shop manufacturing systems, the production system can be either make-to-order (MTO) or make-to-stock (MTS) production systems [1]. A huge volume of research has been reported on PFSPs that considers static situations where an order (consisting of a set of jobs) is processed by the set of machines and scheduling is performed only once. In static single and multiple order PFSPs, the production managers receive a single order (for single order PFSPs) or a pool of orders (for multiple orders) at the beginning of
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