Perbandingan Teknik Pengkodean Langsung dan Tidak Langsung pada Kasus Penjadwalan Jobshop

Development of technology help human life in problem solving. Scheduling is a one of the problem which could be solved with it. In scheduling research, jobshop case is frequently used to test the scheduling problem solving algorithm. This study provide the comparison between direct encoding and indirect encoding approach. These approach are choices in jobshop secheduling problem research. The apparent differences of these approach are in used technique. Genetic algorithm is used as the testing algorithm. The Cases which will be used are the common cases from OR-Lib. The testing is done by looking the makespan, processing time, and objective value transformation. Testing result shows the direct encoding approach found more small makespan. Whereas indirect encoding approach can found optimal makespan in running with large number of generation

The Three-Objective Optimization Model of Flexible Workshop Scheduling Problem for Minimizing Work Completion Time, Work Delay Time, and Energy Consumption

In recent years, the optimal design of the workshop schedule has received much attention with the increased competition in the business environment. As a strategic issue, designing a workshop schedule affects other decisions in the production chain. The purpose of this thesis is to design a three-objective mathematical model, with the objectives of minimizing work completion time, work delay time and energy consumption, considering the importance of businesses attention to reduce energy consumption in recent years. The developed model has been solved using exact solution methods of Weighted Sum (WS) and Epsilon Constraint (Ɛ) in small dimensions using GAMS software. These problems were also solved in large-scale problems with NSGA-II and SFLA meta-heuristic algorithms using MATLAB software in single-objective and multi-objective mode due to the NP-Hard nature of this group of large and real dimensional problems. The standard BRdata set of problems were used to investigate the algorithms performance in solving these problems so that it is possible to compare the algorithms performance of this research with the results of the algorithms used by other researchers. The obtained results show the relatively appropriate performance of these algorithms in solving these problems and also the much better and more optimal performance of the NSGA-II algorithm compared to the performance of the SFLA algorithm

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

Modeling a Remanufacturing Reverse Logistics Planning Problem: Some Insights into Disruptive Technology Adoption

Remanufacturing is the process to restore the functionality of high-value end-of-life (EOL) products, which is considered a substantial link in reverse logistics systems for value recovery. However, due to the uncertainty of the reverse material fow, the planning of a remanufacturing reverse logistics system is complex. Furthermore, the increasing adoption of disruptive technologies in Industry 4.0/5.0, e.g., the Internet of things (IoT), smart robots, cloud-based digital twins, and additive manufacturing, has shown great potential for a smart paradigm transition of remanufacturing reverse logistics operations. In this paper, a new mixed-integer program is modeled for supporting several tactical decisions in remanufacturing reverse logistics, i.e., remanufacturing setups, production planning and inventory levels, core acquisition and transportation, and remanufacturing line balancing and utilization. The model is further extended by incorporating utilization-dependent nonlinear idle time cost constraints and stochastic takt time to accommodate diferent real-world scenarios. Through a set of numerical experiments, the infuences of diferent demand patterns and idle time constraints are revealed. The potential impacts of disruptive technology adoption in remanufacturing reverse logistics are also discussed from managerial perspectives, which may help remanufacturing companies with a smart and smooth transition in the Industry 4.0/5.0 era

Bi-level dynamic scheduling architecture based on service unit digital twin agents

Pure reactive scheduling is one of the core technologies to solve the complex dynamic disturbance factors in real-time. The emergence of CPS, digital twin, cloud computing, big data and other new technologies based on the industrial Internet enables information acquisition and pure reactive scheduling more practical to some extent. However, how to build a new architecture to solve the problems which traditional dynamic scheduling methods cannot solve becomes a new research challenge. Therefore, this paper designs a new bi-level distributed dynamic workshop scheduling architecture, which is based on the workshop digital twin scheduling agent and multiple service unit digital twin scheduling agents. Within this architecture, scheduling a physical workshop is decomposed to the whole workshop scheduling in the first level and its service unit scheduling in the second level. On the first level, the whole workshop scheduling is executed by its virtual workshop coordination (scheduling) agent embedded with the workshop digital twin consisting of multi-service unit digital twins. On the second level, each service unit scheduling coordinated by the first level scheduling is executed in a distributed way by the corresponding service unit scheduling agent associated with its service unit digital twin. The benefits of the new architecture include (1) if a dynamic scheduling only requires a single service unit scheduling, it will then be performed in the corresponding service unit scheduling without involving other service units, which will make the scheduling locally, simply and robustly. (2) when a dynamic scheduling requires changes in multiple service units in a coordinated way, the first level scheduling will be executed and then coordinate the second level service unit scheduling accordingly. This divide-and-then-conquer strategy will make the scheduling easier and practical. The proposed architecture has been tested to illustrate its feasibility and practicality

Hybrid Cat Swarm Optimization and Simulated Annealing for Dynamic Task Scheduling on Cloud Computing Environment

The unpredictable number of tasks arriving at cloud datacenter and the rescaling of virtual processing elements can affect the provisioning of better Quality of Service expectations during task scheduling in cloud computing. Existing researchers have contributed several task scheduling algorithms to provide better QoS expectations but are characterized with entrapment at the local search and high dimensional breakdown due to slow convergence speed and imbalance between global and local search, resulting from lack of scalability. Dynamic task scheduling algorithms that can adjust to long-time changes and continue facilitating the provisioning of better QoS are necessary for cloud computing environment. In this study, a Cloud Scalable Multi-Objective Cat Swarm Optimization-based Simulated Annealing algorithm is proposed. In the proposed method, the orthogonal Taguchi approach is applied to enhance the SA which is incorporated into the local search of the proposed CSMCSOSA algorithm for scalability performance. A multi-objective QoS model based on execution time and execution cost criteria is presented to evaluate the efficiency of the proposed algorithm on CloudSim tool with two different datasets. Quantitative analysis of the algorithm is carried out with metrics of execution time, execution cost, QoS and performance improvement rate percentage. Meanwhile, the scalability analysis of the proposed algorithm using Isospeed-efficiency scalability metric is also reported. The results of the experiment show that the proposed CSM-CSOSA has outperformed Multi-Objective Genetic Algorithm, Multi-Objective Ant Colony and Multi-Objective Particle Swarm Optimization by returning minimum execution time and execution cost as well as better scalability acceptance rate of 0.48110.8990 respectively. The proposed solution when implemented in real cloud computing environment could possibly meet customers QoS expectations as well as that of the service providers

Robotix-Academy Conference for Industrial Robotics (RACIR) 2019

Robotix-Academy Conference for Industrial Robotics (RACIR) is held in University of Liège, Belgium, during June 05, 2019. The topics concerned by RACIR are: robot design, robot kinematics/dynamics/control, system integration, sensor/ actuator networks, distributed and cloud robotics, bio-inspired systems, service robots, robotics in automation, biomedical applications, autonomous vehicles (land, sea and air), robot perception, manipulation with multi-finger hands, micro/nano systems, sensor information, robot vision, multimodal interface and human-robot interaction.

Flexible Job-shop Scheduling Problem with Sequencing Flexibility: Mathematical Models and Solution Algorithms

Marketing strategists usually advocate increased product variety to attend better market demand. Furthermore, companies increasingly acquire more advanced manufacturing systems to take care of the increased product mix. Manufacturing resources with different capabilities give a competitive advantage to the industry. Proper management of the current productions resources is crucial for a thriving industry. Flexible job shop scheduling problem (FJSP) is an extension of the classical Job-shop scheduling problem (JSP) where operations can be performed by a set of candidate capable machines. An extended version of the FJSP, entitled FJSP with sequencing flexibility (FJSPS), is studied in this work. The extension considers precedence between the operations in the form of a directed acyclic graph instead of sequential order. In this work, a mixed integer programming (MILP) formulation is presented. A single objective formulation to minimize the weighted tardiness for the FJSP with sequencing flexibility is proposed. A different objective to minimize makespan is also considered. Due to the NP-hardness of the problem, a novel hybrid bacterial foraging optimization algorithm (HBFOA) is developed to tackle the FJSP with sequencing flexibility. It is inspired by the behaviour of the E. coli bacteria. It mimics the process to seek for food. The HBFOA is enhanced with simulated annealing (SA). The HBFOA has been packaged in the form of a decision support system (DSS). A case study of a small and medium-sized enterprise (SME) manufacturing industry is presented to validate the proposed HBFOA and MILP. Additional numerical experiments with instances provided by the literature are considered. The results demonstrate that the HBFOA outperformed the classical dispatching rules and the best integer solution of MILP when minimizing the weighted tardiness and offered comparable results for the makespan instances. In this dissertation, another critical aspect has been studied. In the industry, skilled workers usually are able to operate a specific set of machines. Hence, managers need to decide the best operation assignments to machines and workers. However, they need also to balance the workload between workers while accomplishing the due dates. In this research, a multi-objective mathematical model that minimizes makespan, maximal worker workload and weighted tardiness is developed. This model is entitled dual-resource FJSP with sequencing flexibility (DRFJSPS). It covers both the machine assignment and also the worker selection. Due to the intractability of the DRFJSPS, an elitist non-dominated sorting genetic algorithm (NSGA-II) is developed to solve this problem efficiently. The algorithm provides a set of Pareto-optimal solutions that the decision makers can use to evaluate the trade-offs of the conflicting objectives. New instances are introduced to demonstrate the applicability of the model and algorithm. A multi-random-start local search algorithm has been developed to assess the effectiveness of the adapted NSGA-II. The comparison of the solutions demonstrates that the modified NSGA-II provides a non-dominated efficient set in a reasonable time. Finally, a situation where there are multiple process plans available for a specific job is considered. This scenario is useful to be able to react to the current status of the shop where unpredictable circumstances (machine breakdown, current product mix, due dates, demand, etc.) can be accurately tackled. The determination of the process plan also depends on its cost. For that, a balance between cost, and the accomplishment of due dates is required. A multi-objective mathematical model that minimizes makespan, total processing cost and weighted tardiness are proposed to determine the sequence and the process plan to be used. This model is entitled flexible job-shop scheduling problem with sequencing and process plan flexibility (FJSP-2F). New instances are generated to show the applicability of the model