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The scheduling of manufacturing systems using Artificial Intelligence (AI) techniques in order to find optimal/near-optimal solutions.
This thesis aims to review and analyze the scheduling problem in general and Job Shop Scheduling Problem (JSSP) in particular and the solution techniques applied to these problems. The JSSP is the most general and popular hard combinational optimization problem in manufacturing systems. For the past sixty years, an enormous amount of research has been carried out to solve these problems. The literature review showed the inherent shortcomings of solutions to scheduling problems. This has directed researchers to develop hybrid approaches, as no single technique for scheduling has yet been successful in providing optimal solutions to these difficult problems, with much potential for improvements in the existing techniques.
The hybrid approach complements and compensates for the limitations of each individual solution technique for better performance and improves results in solving both static and dynamic production scheduling environments. Over the past years, hybrid approaches have generally outperformed simple Genetic Algorithms (GAs). Therefore, two novel priority heuristic rules are developed: Index Based Heuristic and Hybrid Heuristic. These rules are applied to benchmark JSSP and compared with popular traditional rules. The results show that these new heuristic rules have outperformed the traditional heuristic rules over a wide range of benchmark JSSPs. Furthermore, a hybrid GA is developed as an alternate scheduling approach. The hybrid GA uses the novel heuristic rules in its key steps. The hybrid GA is applied to benchmark JSSPs. The hybrid GA is also tested on benchmark flow shop scheduling problems and industrial case studies. The hybrid GA successfully found solutions to JSSPs and is not problem dependent. The hybrid GA performance across the case studies has proved that the developed scheduling model can be applied to any real-world scheduling problem for achieving optimal or near-optimal solutions. This shows the effectiveness of the hybrid GA in real-world scheduling problems.
In conclusion, all the research objectives are achieved. Finaly, the future work for the developed heuristic rules and the hybrid GA are discussed and recommendations are made on the basis of the results.Board of Trustees, Endowment Fund Project, KPK University of Engineering and Technology (UET), Peshawar and Higher Education Commission (HEC), Pakista
Climbing depth-bounded adjacent discrepancy search for solving hybrid flow shop scheduling problems with multiprocessor tasks
This paper considers multiprocessor task scheduling in a multistage hybrid
flow-shop environment. The problem even in its simplest form is NP-hard in the
strong sense. The great deal of interest for this problem, besides its
theoretical complexity, is animated by needs of various manufacturing and
computing systems. We propose a new approach based on limited discrepancy
search to solve the problem. Our method is tested with reference to a proposed
lower bound as well as the best-known solutions in literature. Computational
results show that the developed approach is efficient in particular for
large-size problems
An improved constraint satisfaction adaptive neural network for job-shop scheduling
Copyright @ Springer Science + Business Media, LLC 2009This paper presents an improved constraint satisfaction adaptive neural network for job-shop scheduling problems. The neural network is constructed based on the constraint conditions of a job-shop scheduling problem. Its structure and neuron connections can change adaptively according to the real-time constraint satisfaction situations that arise during the solving process. Several heuristics are also integrated within the neural network to enhance its convergence, accelerate its convergence, and improve the quality of the solutions produced. An experimental study based on a set of benchmark job-shop scheduling problems shows that the improved constraint satisfaction adaptive neural network outperforms the original constraint satisfaction adaptive neural network in terms of computational time and the quality of schedules it produces. The neural network approach is also experimentally validated to outperform three classical heuristic algorithms that are widely used as the basis of many state-of-the-art scheduling systems. Hence, it may also be used to construct advanced job-shop scheduling systems.This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/01 and in part by the National Nature Science Fundation of China under Grant 60821063 and National Basic Research Program of China under Grant 2009CB320601
Dynamic scheduling in a multi-product manufacturing system
To remain competitive in global marketplace, manufacturing companies need to improve their operational practices. One of the methods to increase competitiveness in manufacturing is by implementing proper scheduling system. This is important to enable job orders to be completed on time, minimize waiting time and maximize utilization of equipment and machineries. The dynamics of real manufacturing system are very complex in nature. Schedules developed based on deterministic algorithms are unable to effectively deal with uncertainties in demand and capacity. Significant differences can be found between planned schedules and actual schedule implementation. This study attempted to develop a scheduling system that is able to react quickly and reliably for accommodating changes in product demand and manufacturing capacity. A case study, 6 by 6 job shop scheduling problem was adapted with uncertainty elements added to the data sets. A simulation model was designed and implemented using ARENA simulation package to generate various job shop scheduling scenarios. Their performances were evaluated using scheduling rules, namely, first-in-first-out (FIFO), earliest due date (EDD), and shortest processing time (SPT). An artificial neural network (ANN) model was developed and trained using various scheduling scenarios generated by ARENA simulation. The experimental results suggest that the ANN scheduling model can provided moderately reliable prediction results for limited scenarios when predicting the number completed jobs, maximum flowtime, average machine utilization, and average length of queue. This study has provided better understanding on the effects of changes in demand and capacity on the job shop schedules. Areas for further study includes: (i) Fine tune the proposed ANN scheduling model (ii) Consider more variety of job shop environment (iii) Incorporate an expert system for interpretation of results. The theoretical framework proposed in this study can be used as a basis for further investigation
The relevance of outsourcing and leagile strategies in performance optimization of an integrated process planning and scheduling
Over the past few years growing global competition has forced the manufacturing industries to upgrade their old production strategies with the modern day approaches. As a result, recent interest has been developed towards finding an appropriate policy that could enable them to compete with others, and facilitate them to emerge as a market winner. Keeping in mind the abovementioned facts, in this paper the authors have proposed an integrated process planning and scheduling model inheriting the salient features of outsourcing, and leagile principles to compete in the existing market scenario. The paper also proposes a model based on leagile principles, where the integrated planning management has been practiced. In the present work a scheduling problem has been considered and overall minimization of makespan has been aimed. The paper shows the relevance of both the strategies in performance enhancement of the industries, in terms of their reduced makespan. The authors have also proposed a new hybrid Enhanced Swift Converging Simulated Annealing (ESCSA) algorithm, to solve the complex real-time scheduling problems. The proposed algorithm inherits the prominent features of the Genetic Algorithm (GA), Simulated Annealing (SA), and the Fuzzy Logic Controller (FLC). The ESCSA algorithm reduces the makespan significantly in less computational time and number of iterations. The efficacy of the proposed algorithm has been shown by comparing the results with GA, SA, Tabu, and hybrid Tabu-SA optimization methods
A Comparative Representation Approach to Modern Heuristic Search Methods in a Job Shop
The job shop problem is among the class of NP- hard combinatorial problems. This Research paper addresses the problem of static job shop scheduling on the job-based representation and the rule based representations. The popular search techniques like the genetic algorithm and simulated annealing are used for the determination of the objectives like minimizations of the makespan time and mean flow time. Various rules like the SPT, LPT, MWKR, and LWKR are used for the objective function to attain the results. The summary of results from this paper gives a conclusion that the genetic algorithm gives better results in the makespan time determination on both the job based representation and the rule based representation and the simulated annealing algorithm gives the better results in the mean flow time in both the representations
ADAPTIVE, MULTI-OBJECTIVE JOB SHOP SCHEDULING USING GENETIC ALGORITHMS
This research proposes a method to solve the adaptive, multi-objective job shop scheduling problem. Adaptive scheduling is necessary to deal with internal and external disruptions faced in real life manufacturing environments. Minimizing the mean tardiness for jobs to effectively meet customer due date requirements and minimizing mean flow time to reduce the lead time jobs spend in the system are optimized simultaneously. An asexual reproduction genetic algorithm with multiple mutation strategies is developed to solve the multi-objective optimization problem. The model is tested for single day and multi-day adaptive scheduling. Results are compared with those available in the literature for standard problems and using priority dispatching rules. The findings indicate that the genetic algorithm model can find good solutions within short computational time
A decomposition heuristics based on multi-bottleneck machines for large-scale job shop scheduling problems
Purpose: A decomposition heuristics based on multi-bottleneck machines for large-scale job
shop scheduling problems (JSP) is proposed.
Design/methodology/approach: In the algorithm, a number of sub-problems are
constructed by iteratively decomposing the large-scale JSP according to the process route of
each job. And then the solution of the large-scale JSP can be obtained by iteratively solving the
sub-problems. In order to improve the sub-problems' solving efficiency and the solution quality,
a detection method for multi-bottleneck machines based on critical path is proposed. Therewith
the unscheduled operations can be decomposed into bottleneck operations and non-bottleneck
operations. According to the principle of âBottleneck leads the performance of the whole
manufacturing systemâ in TOC (Theory Of Constraints), the bottleneck operations are
scheduled by genetic algorithm for high solution quality, and the non-bottleneck operations are
scheduled by dispatching rules for the improvement of the solving efficiency.
Findings: In the process of the subproblems' construction, partial operations in the previous
scheduled sub-problem are divided into the successive sub-problem for re-optimization. This
strategy can improve the solution quality of the algorithm. In the process of solving the sub problems, the strategy that evaluating the chromosome's fitness by predicting the global
scheduling objective value can improve the solution quality.
Research limitations/implications: In this research, there are some assumptions which
reduce the complexity of the large-scale scheduling problem. They are as follows: The
processing route of each job is predetermined, and the processing time of each operation is
fixed. There is no machine breakdown, and no preemption of the operations is allowed. The
assumptions should be considered if the algorithm is used in the actual job shop.
Originality/value: The research provides an efficient scheduling method for the large-scale
job shops, and will be helpful for the discrete manufacturing industry for improving the
production efficiency and effectiveness.Peer Reviewe
Adaptive fuzzy particle swarm optimization for flow-shop scheduling problem
Ovaj rad razmatra novi pristup problemu rasporeÄivanja u protoÄnoj proizvodnji koriĆĄtenjem kombinacije neizrazite logike i optimizacije rojevima Äestica u cilju postizanja sub-optimalnog rjeĆĄenja. PredlaĆŸe se upotreba Tip-1 i Tip-2 modela neizrazite logike u kombinaciji s adaptivnim modelom rojeva Äestica. Razvijeni model je usporeÄen na standardiziranim testnim funkcijama za stohastiÄke algoritme (prvo jednokriterijske, a zatim viĆĄekriterijske postavljene funkcije cilja) kako bi se utvrdila njegova upotrebljivost na opÄe postavljenim problemima. Zatim je testiran na standardiziranim testnim zadacima za probleme protoÄne proizvodnje te konaÄno na dva praktiÄna problema protoÄne proizvodnje (linije montaĆŸe i linije pakiranja). Rezultati ostvareni novim modelom su usporeÄeni s konvencionalnim pravilima prioriteta te je pokazan kvantitativan i kvalitativan napredak primjenom hibrida neizrazite logike i rojeva Äestica.This paper describes the application of a hybrid of fuzzy logic and swarm intelligence in order to achieve sub-optimal solutions for flow-shop scheduling problem. A novel adaptive approach with fuzzy particle swarm optimization is proposed. The developed model is tested with the standardized test functions and compared with selected stochastic algorithms (first with one objective functions and later with multi objective functions) to determine its applicability to general problems. Benchmark examples were utilized to evaluate the approach and determine the optimal number of the algorithm evaluations. Finally, the proposed model is applied on two practical problems of flow production problems (assembly lines and packaging lines). The results achieved were compared with the conventional priority rules and the effectiveness of the application of hybrid fuzzy logic and adaptive particle swarm optimization algorithm was demonstrated
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