Real-Time Order Acceptance and Scheduling Problems in a Flow Shop Environment Using Hybrid GA-PSO Algorithm

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 realtime, 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.INDEX TERMS Order acceptance and scheduling, particle swarm optimization, real-time order arrival, genetic algorithm, flow shop scheduling.

An Immune Genetic Algorithm for Solving NPV-Based Resource Constrained Project Scheduling Problem

“…The VINS [21], [27] is employed to increase the performance of these proposed algorithms as a local search technique. In VINS, with satisfying the activity precedence, each activity in a sequence is inserted from position x th to y th , where x =y.…”

“…However, based on the problem's NP-hard characteristics and the no free lunch theorem, for both a relatively good solution and reasonable computing time, no single heuristic or meta-heuristic algorithm is suitable for solving the RCPSPDC [21]. Moreover, the literature shows that the hybridization of meta-heuristics overcomes the limitations of a single meta-heuristic algorithm [22]- [27].…”

“…The GA is a powerful biological mechanism and natural selection theory-based meta-heuristic algorithm [28], [29]. Among the population-based meta-heuristics, GA is the most widely applied not only in RCPSP [30], [31] and RCPSPDC [10], but also in others sort of optimization domains [27], [32]- [34]. GA generates good quality solutions with a reasonable time when the problem domain is large [10], [33].…”

Intelligent Decision-Making of Scheduling for Dynamic Permutation Flowshop via Deep Reinforcement Learning

“…Given the NP-hard of PFSP, many meta-heuristics, such as genetic algorithm (GA) [ 21 , 22 , 23 , 24 ], particle swarm optimization (PSO) [ 25 , 26 , 27 ], and iterated greedy (IG) algorithm [ 21 , 22 , 23 , 24 ], have been proposed to solve the PFSP. Generally, the PFSP is regarded as a statistic scheduling problem.…”

“…A rescheduling is needed when a new job arrives. This problem is described as a real-time scheduling problem in some literature [ 25 , 38 , 39 , 40 ].…”