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

Hybrid multiobjective genetic algorithm for integrated dynamic scheduling and routing of jobs and automated guided vehicles in flexible manufacturing systems

The dynamic continues trend of adoption and improvement inventive automated technologies is one of the main competing strategies of many manufacturing industries. Effective integrated operations management of Automated Guided Vehicle (AGV) system in Flexible Manufacturing System (FMS) environment results in the overall system performance. Routing AGVs was proved to be NP-Complete and scheduling of jobs was also proved to be NP hard problems. The running time of any deterministic algorithms solving these types of problems increases very rapidly with the size of the problem, which can be many years with any computational resources available presently. Solving AGVs conflict free routing, dispatching and simultaneous scheduling of the jobs and AGVs in FMS in an integrated manner is identified as the only means of safeguarding the feasibility of the solution to each sub-problem. Genetic algorithm has recorded of huge success in solving NP-Complete optimization problems with similar nature to this problem. The objectives of this research are to develop an algorithm for integrated scheduling and conflict-free routing of jobs and AGVs in FMS environment using a hybrid genetic algorithm, ensure the algorithm validity and improvement on the performance of the developed algorithm. The algorithm generates an integrated scheduling and detail paths route while optimizing makespan, AGV travel time, mean flow time and penalty cost due to jobs tardiness and delay as a result of conflict avoidance. The integrated algorithms use two genetic representations for the individual solution entire sub-chromosomes. The first three sub-chromosomes use random keys to represent jobs sequencing, operations allocation on machines and AGV dispatching, while the remaining sub-chromosomes are representing particular routing paths to be used by each dispatched AGV. The multiobjective fitness function use adaptive weight approach to assign weights to each objective for every generation based on objective improvement performance. Fuzzy expert system is used to control genetic operators using the overall population performance history. The algorithm used weight mapping crossover (WMX) and Insertion Mutation (IM) as genetic operators for sub-chromosomes represented with priority-based representation. Parameterized uniform crossover (PUX) and migration are used as genetic operators for sub-chromosomes represented using random-key based encoding. Computational experiments were conducted on the developed algorithm coded in Matlab to test the effectiveness of the algorithm. First scenario uses static consideration, the second scenario uses dynamic consideration with machine failure recovery. Sensitivity analysis and convergence analysis was also conducted. The results show the effectiveness of the proposed algorithm in generating the integrated scheduling, AGVs dispatching and conflict-free routing. The comparison of the result of the developed integrated algorithm using two benchmark FMS scheduling algorithms datasets is conducted. The comparison shows the improvement of 1.1% and 16% in makespan of the first and the second benchmark production dataset respectively. The major novelty of the algorithm is an integrated approach to the individual sub-problems which ensures the legality, and feasibility of all solutions generated for various sub-problems which in the literature are considered separately

Inteligentno stereo-vizuelno upravljanje mobilnih robota i optimalno terminiranje tehnoloških procesa - pregled rezultata istraživanja u okviru projekta MISSION4.0/Intelligent stereo-visual mobile robot control and optimal process planning and scheduling – overview of research results within the project MISSION4.0

Projekat MISSION4.0 podrazumevao je, u okviru nekoliko radnih paketa, razvoj inteligentnog stereo-vizuelnog upravljanja mobilnih robota, kao i optimalno planiranje i terminiranje tehnoloških procesa, i to baziranim na tehnikama veštačke inteligencije, posebno na konvolucionim veštačkim neuronskim mrežama i biološki inspirisanim algoritmima optimizacije. Tokom dvogodišnjih intenzivnih naučnih istraživanja razvijena je nova metodologija za autonomnu navigaciju i inteligentno upravljanje mobilnih robota sopstvenog razvoja, nazvanih RAICO i DOMINO. Generisanje optimalnog plana terminiranja tehnoloških procesa, u okviru koga se izvršava i inteligentni unutrašnji transport korišćenjem mobilnih robota, takođe je bio jedan od važnih ciljeva ovih naprednih istraživanja. U ovom radu, dat je pregled nekih od ključnih rezultata projekta MISSION4.0, poput publikovanih u vodećim međunarodnim i nacionalnim naučnim časopisima, objavljenih poglavlja u naučnim monografijama, saopštenih i odštampanih naučnih radova u zbornicima prestižnih konferencija održanih u inostranstvu i regionu, zatim u okviru verifikovanih tehničkih rešenja, kao i preko skupova podataka sa otvorenim pristupom

Inteligentno stereo-vizuelno upravljanje mobilnih robota i optimalno terminiranje tehnoloških procesa - pregled rezultata istraživanja u okviru projekta MISSION4.0/Intelligent stereo-visual mobile robot control and optimal process planning and scheduling – overview of research results within the project MISSION4.0

Projekat MISSION4.0 podrazumevao je, u okviru nekoliko radnih paketa, razvoj inteligentnog stereo-vizuelnog upravljanja mobilnih robota, kao i optimalno planiranje i terminiranje tehnoloških procesa, i to baziranim na tehnikama veštačke inteligencije, posebno na konvolucionim veštačkim neuronskim mrežama i biološki inspirisanim algoritmima optimizacije. Tokom dvogodišnjih intenzivnih naučnih istraživanja razvijena je nova metodologija za autonomnu navigaciju i inteligentno upravljanje mobilnih robota sopstvenog razvoja, nazvanih RAICO i DOMINO. Generisanje optimalnog plana terminiranja tehnoloških procesa, u okviru koga se izvršava i inteligentni unutrašnji transport korišćenjem mobilnih robota, takođe je bio jedan od važnih ciljeva ovih naprednih istraživanja. U ovom radu, dat je pregled nekih od ključnih rezultata projekta MISSION4.0, poput publikovanih u vodećim međunarodnim i nacionalnim naučnim časopisima, objavljenih poglavlja u naučnim monografijama, saopštenih i odštampanih naučnih radova u zbornicima prestižnih konferencija održanih u inostranstvu i regionu, zatim u okviru verifikovanih tehničkih rešenja, kao i preko skupova podataka sa otvorenim pristupom

Una perspectiva de ingeniería del software para definir sistemas avanzados de planificación de empresas

Los sistemas de planeamiento han recibido un mayor interés desde que se ha ampliado la utilización de sistemas ERP (Enterprise Resource Planning); sin embargo, esto ha incrementado las diferencias que existen entre la teoría y la práctica del planeamiento y scheduling. Conocidos como APS (Advanced Planning Systems), muchos autores han concluido que estos nuevos sistemas no han sido estudiados desde un punto de vista de la Ingeniería del Software. Por lo tanto, el objetivo de este trabajo es describir los APS a través de sus requerimientos funcionales, atributos de calidad y arquitectura del software, definidos siguiendo las pautas establecidas en varios estándares internacionales de Ingeniería del Software, tales como el SEBoK (System Engineering Body of Knowledge), el modelo SQuaRE (Software Product Quality Requirements and Evaluation), entre otros.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Una perspectiva de ingeniería del software para definir sistemas avanzados de planificación de empresas

Los sistemas de planeamiento han recibido un mayor interés desde que se ha ampliado la utilización de sistemas ERP (Enterprise Resource Planning); sin embargo, esto ha incrementado las diferencias que existen entre la teoría y la práctica del planeamiento y scheduling. Conocidos como APS (Advanced Planning Systems), muchos autores han concluido que estos nuevos sistemas no han sido estudiados desde un punto de vista de la Ingeniería del Software. Por lo tanto, el objetivo de este trabajo es describir los APS a través de sus requerimientos funcionales, atributos de calidad y arquitectura del software, definidos siguiendo las pautas establecidas en varios estándares internacionales de Ingeniería del Software, tales como el SEBoK (System Engineering Body of Knowledge), el modelo SQuaRE (Software Product Quality Requirements and Evaluation), entre otros.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Advances in Theoretical and Computational Energy Optimization Processes

The paradigm in the design of all human activity that requires energy for its development must change from the past. We must change the processes of product manufacturing and functional services. This is necessary in order to mitigate the ecological footprint of man on the Earth, which cannot be considered as a resource with infinite capacities. To do this, every single process must be analyzed and modified, with the aim of decarbonising each production sector. This collection of articles has been assembled to provide ideas and new broad-spectrum contributions for these purposes

Addressing real-time control problems in complex environments using dynamic multi-objective evolutionary approaches

The demand for increased automation of industrial processes generates control problems that are dynamic, multi-objective and noisy at the same time. The primary hypothesis underlying this research is that dynamic evolutionary methods could be used to address dynamic control problems where con icting control criteria are necessary. The aim of this research is to develop a framework for on-line optimisation of dynamic problems that is capable of a) representing problems in a quantitative way, b) identifying optimal solutions using multi-objective evolutionary algorithms, and c) automatically selecting an optimal solution among alternatives. A literature review identi es key problems in the area of dynamic multi-objective optimisation, discusses the on-line decision making aspect, analyses existing Multi- Objective Evolutionary Algorithms (MOEA) applications and identi es research gap. Dynamic evolutionary multi-objective search and on-line a posteriori decision maker are integrated into an evolutionary multi-objective controller that uses an internal process model to evaluate the tness of solutions. Using a benchmark multi-objective optimisation problem, the MOEA ability to track the moving optima is examined with di erent parameter values, namely, length of pre-execution, frequency of change, length of prediction interval and static mutation rate. A dynamic MOEA with restricted elitism is suggested for noisy environments.To address the on-line decision making aspect of the dynamic multi-objective optimisation, a novel method for constructing game trees for real-valued multiobjective problems is presented. A novel decision making algorithm based on game trees is proposed along with a baseline random decision maker. The proposed evolutionary multi-objective controller is systematically analysed using an inverted pendulum problem and its performance is compared to Proportional{ Integral{Derivative (PID) and nonlinear Model Predictive Control (MPC) approaches. Finally, the proposed control approach is integrated into a multi-agent framework for coordinated control of multiple entities and validated using a case study of a tra c scheduling problem.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Permanente Optimierung dynamischer Probleme der Fertigungssteuerung unter Einbeziehung von Benutzerinteraktionen

Trotz enormen Forschungsaufwands erhalten die Entscheider in der Fertigungssteuerung nur rudimentäre Rechnerunterstützung. Diese Arbeit schlägt ein umfassendes Konzept für eine permanent laufende algorithmische Feinplanung vor, die basierend auf einer Analyse des Optimierungspotentials und -bedarfs intelligent mit den Entscheidern kollaboriert und zeitnah auf Fertigungsereignisse reagiert. Dynamische Simulationen mit Unternehmensdaten bestätigen die Praxistauglichkeit des Konzepts