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

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Random job arrivals that happen frequently in manufacturing practice may create a need for dynamic scheduling. This paper considers an issue of how to reschedule the randomly arrived new jobs to pursue both performance and stability in a job shop. Firstly, a mixed integer programming model is established to minimize three objectives, including the discontinuity rate of new jobs during the processing, the makespan deviation of initial schedule, and the sequence deviation on machines. Secondly, four match-up strategies from references are modified to determine the rescheduling horizon. Once new jobs arrive, the rescheduling process is immediately triggered with ongoing operations remain. The ongoing operations are treated as machine unavailable constraints (MUC) in the rescheduling horizon. Then, a particle swarm optimization (PSO) algorithm with improvements is proposed to solve the dynamic job shop scheduling problem. Improvement strategies consist of a modified decoding scheme considering MUC, a population initialization approach by designing a new transformation mechanism, and a novel particle movement method by introducing position changes and a random inertia weight. Lastly, extensive experiments are conducted on several instances. The experiments results show that the modified rescheduling strategies are statistically and significantly better than the compared strategies. Moreover, comparative studies with five variants of PSO algorithm and three state-of-the-art meta-heuristics demonstrate the high performance of the improved PSO algorithm

A research survey: review of flexible job shop scheduling techniques

In the last 25 years, extensive research has been carried out addressing the flexible job shop scheduling (JSS) problem. A variety of techniques ranging from exact methods to hybrid techniques have been used in this research. The paper aims at presenting the development of flexible JSS and a consolidated survey of various techniques that have been employed since 1990 for problem resolution. The paper comprises evaluation of publications and research methods used in various research papers. Finally, conclusions are drawn based on performed survey results. A total of 404 distinct publications were found addressing the FJSSP. Some of the research papers presented more than one technique/algorithm to solve the problem that is categorized into 410 different applications. Selected time period of these research papers is between 1990 and February 2014. Articles were searched mainly on major databases such as SpringerLink, Science Direct, IEEE Xplore, Scopus, EBSCO, etc. and other web sources. All databases were searched for “flexible job shop” and “scheduling” in the title an

Integrated process planning and scheduling in dynamic environment: The state-of-the-art

U ovom radu je dat detaljan pregled stanja u oblasti istraživanja jedne od funkcija inteligentnih tehnoloških sistema (ITS) - integrisano planiranje i terminiranje tehnoloških procesa u dinamičkim uslovima (DIPPS). U tom smislu, datje opis DIPPSproblema, razmatrani su kriterijumi na osnovu kojih se vrši odabir optimalanog plana terminiranja, definisane su usvojene pretpostavke i predstavljen je matematički model ovog problema. Takođe, detaljno su razmatrani i sledeći poremećajni faktori koji se mogu javiti u okviru tehnoloških sistema: (i) prestanak rada mašine alatke, (ii) dolazak novog dela u sistem i (iii) otkaz obrade dela. Analizirani su pristupi za rešavanje DIPPS problema bazirani na multiagentnim sistemima, kao i pristupi bazirani na algoritmima. Kada su u pitanju pristupi bazirani na algoritmima, fokus u ovom radu je na biološki inspirisanim algoritmima optimizacije i to: evolucionim algoritmima, algoritmima baziranim na inteligenciji roja, kao i hibridnim pristupima. Kritičkom analizom stanja u ovoj oblasti istraživanja može se zaključiti da biološki inspirisane tehnike veštačke inteligencije imaju veliki potencijal u optimizaciji pomenute funkcije ITS-a.This paper gives a detailed state-of-the art in the research area o f the important function o f Intelligent Manufacturing Systems (IMS) - integrated process planning and scheduling o f manufacturing systems in dynamic environment (DIPPS). Referring to this, description o f the DIPPS problem is given, the criteria on the basis o f which the optimal rescheduling plan are formulated and considered, the adopted assumptions are defined and the mathematical model o f this problem is presented. Furthermore, the disturbances that occur in manufacturing systems are considered in detail: (i) machine breakdown, (ii) arrival of a new job and (iii) job cancellation. Approaches for solving DIPPS problems based on multiagent systems as well as approaches based on algorithms are analyzed. When it comes to approaches based on algorithms, the focus of this paper is on biologically inspired optimization algorithms: evolutionary algorithms, swarm intelligence based algorithms as well as hybrid approaches. The critical analysis within this research area is shown in order to conclude that biologically inspired artificial intelligence techniques have great potential in optimizing the considered IMS function

Integrated process planning and scheduling in dynamic environment: The state-of-the-art

U ovom radu je dat detaljan pregled stanja u oblasti istraživanja jedne od funkcija inteligentnih tehnoloških sistema (ITS) - integrisano planiranje i terminiranje tehnoloških procesa u dinamičkim uslovima (DIPPS). U tom smislu, datje opis DIPPSproblema, razmatrani su kriterijumi na osnovu kojih se vrši odabir optimalanog plana terminiranja, definisane su usvojene pretpostavke i predstavljen je matematički model ovog problema. Takođe, detaljno su razmatrani i sledeći poremećajni faktori koji se mogu javiti u okviru tehnoloških sistema: (i) prestanak rada mašine alatke, (ii) dolazak novog dela u sistem i (iii) otkaz obrade dela. Analizirani su pristupi za rešavanje DIPPS problema bazirani na multiagentnim sistemima, kao i pristupi bazirani na algoritmima. Kada su u pitanju pristupi bazirani na algoritmima, fokus u ovom radu je na biološki inspirisanim algoritmima optimizacije i to: evolucionim algoritmima, algoritmima baziranim na inteligenciji roja, kao i hibridnim pristupima. Kritičkom analizom stanja u ovoj oblasti istraživanja može se zaključiti da biološki inspirisane tehnike veštačke inteligencije imaju veliki potencijal u optimizaciji pomenute funkcije ITS-a.This paper gives a detailed state-of-the art in the research area o f the important function o f Intelligent Manufacturing Systems (IMS) - integrated process planning and scheduling o f manufacturing systems in dynamic environment (DIPPS). Referring to this, description o f the DIPPS problem is given, the criteria on the basis o f which the optimal rescheduling plan are formulated and considered, the adopted assumptions are defined and the mathematical model o f this problem is presented. Furthermore, the disturbances that occur in manufacturing systems are considered in detail: (i) machine breakdown, (ii) arrival of a new job and (iii) job cancellation. Approaches for solving DIPPS problems based on multiagent systems as well as approaches based on algorithms are analyzed. When it comes to approaches based on algorithms, the focus of this paper is on biologically inspired optimization algorithms: evolutionary algorithms, swarm intelligence based algorithms as well as hybrid approaches. The critical analysis within this research area is shown in order to conclude that biologically inspired artificial intelligence techniques have great potential in optimizing the considered IMS function

Improving just-in-time delivery performance of IoT-enabled flexible manufacturing systems with AGV based material transportation

Autonomous guided vehicles (AGVs) are driverless material handling systems used for transportation of pallets and line side supply of materials to provide flexibility and agility in shop-floor logistics. Scheduling of shop-floor logistics in such systems is a challenging task due to their complex nature associated with the multiple part types and alternate material transfer routings. This paper presents a decision support system capable of supporting shop-floor decision-making activities during the event of manufacturing disruptions by automatically adjusting both AGV and machine schedules in Flexible Manufacturing Systems (FMSs). The proposed system uses discrete event simulation (DES) models enhanced by the Internet-of-Things (IoT) enabled digital integration and employs a nonlinear mixed integer programming Genetic Algorithm (GA) to find near-optimal production schedules prioritising the just-in-time (JIT) material delivery performance and energy efficiency of the material transportation. The performance of the proposed system is tested on the Integrated Manufacturing and Logistics (IML) demonstrator at WMG, University of Warwick. The results showed that the developed system can find the near-optimal solutions for production schedules subjected to production anomalies in a negligible time, thereby supporting shop-floor decision-making activities effectively and rapidly

Improving just-in-time delivery performance of IoT-enabled flexible manufacturing systems with AGV based material transportation

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. Autonomous guided vehicles (AGVs) are driverless material handling systems used for transportation of pallets and line side supply of materials to provide flexibility and agility in shop-floor logistics. Scheduling of shop-floor logistics in such systems is a challenging task due to their complex nature associated with the multiple part types and alternate material transfer routings. This paper presents a decision support system capable of supporting shop-floor decision-making activities during the event of manufacturing disruptions by automatically adjusting both AGV and machine schedules in Flexible Manufacturing Systems (FMSs). The proposed system uses discrete event simulation (DES) models enhanced by the Internet-of-Things (IoT) enabled digital integration and employs a nonlinear mixed integer programming Genetic Algorithm (GA) to find near-optimal production schedules prioritising the just-in-time (JIT) material delivery performance and energy efficiency of the material transportation. The performance of the proposed system is tested on the Integrated Manufacturing and Logistics (IML) demonstrator at WMG, University of Warwick. The results showed that the developed system can find the near-optimal solutions for production schedules subjected to production anomalies in a negligible time, thereby supporting shop-floor decision-making activities effectively and rapidly