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

Constraint satisfaction adaptive neural network and heuristics combined approaches for generalized job-shop scheduling

Copyright @ 2000 IEEEThis paper presents a constraint satisfaction adaptive neural network, together with several heuristics, to solve the generalized job-shop scheduling problem, one of NP-complete constraint satisfaction problems. The proposed neural network can be easily constructed and can adaptively adjust its weights of connections and biases of units based on the sequence and resource constraints of the job-shop scheduling problem during its processing. Several heuristics that can be combined with the neural network are also presented. In the combined approaches, the neural network is used to obtain feasible solutions, the heuristic algorithms are used to improve the performance of the neural network and the quality of the obtained solutions. Simulations have shown that the proposed neural network and its combined approaches are efficient with respect to the quality of solutions and the solving speed.This work was supported by the Chinese National Natural Science Foundation under Grant 69684005 and the Chinese National High-Tech Program under Grant 863-511-9609-003, the EPSRC under Grant GR/L81468

Job-shop scheduling with an adaptive neural network and local search hybrid approach

This article is posted here with permission from IEEE - Copyright @ 2006 IEEEJob-shop scheduling is one of the most difficult production scheduling problems in industry. This paper proposes an adaptive neural network and local search hybrid approach for the job-shop scheduling problem. The adaptive neural network is constructed based on constraint satisfactions of job-shop scheduling and can adapt its structure and neuron connections during the solving process. The neural network is used to solve feasible schedules for the job-shop scheduling problem while the local search scheme aims to improve the performance by searching the neighbourhood of a given feasible schedule. The experimental study validates the proposed hybrid approach for job-shop scheduling regarding the quality of solutions and the computing speed

An improved adaptive neural network for job-shop scheduling

This article is posted here with permission of IEEE - Copyright @ 2005 IEEEJob-shop scheduling is one of the most difficult production scheduling problems in industry. This paper presents an improved adaptive neural network together with heuristic methods for job-shop scheduling problems. The neural network is based on constraints satisfaction of job-shop scheduling and can adapt its structure and neuron connections during the solving. Several heuristics are also proposed to be combined with the neural network to guarantee its convergence, accelerate its solving process, and improve the quality of solutions. Experimental study shows that the proposed hybrid approach outperforms two classical heuristic algorithms regarding the quality of solution

New Hopfield Neural Network for joint Job Shop Scheduling of production and maintenance.

International audienceJob Shop Scheduling is one of the most difficult problems in industry and it is the main interest of the major researchers in the manufacturing research area. This problem becomes crucial when the production planning and maintenance have to be jointly solved. Several heuristics and intelligent methods have been so far proposed in the literature and applied. This work deals with a Hopfield Neural Network (HNN) method used for solving the JSP taking into account the maintenance tasks. While this method had been already proposed in the literature to solve the JSP alone, our main improvement of this method is to take into account the maintenance periods by extending the Hopfield net to handle the joint problem. Experimental study shows that the proposed HNN algorithm gives efficient results for the resolution of the joint job shop scheduling problem

Human-Machine Collaborative Optimization via Apprenticeship Scheduling

Coordinating agents to complete a set of tasks with intercoupled temporal and resource constraints is computationally challenging, yet human domain experts can solve these difficult scheduling problems using paradigms learned through years of apprenticeship. A process for manually codifying this domain knowledge within a computational framework is necessary to scale beyond the ``single-expert, single-trainee" apprenticeship model. However, human domain experts often have difficulty describing their decision-making processes, causing the codification of this knowledge to become laborious. We propose a new approach for capturing domain-expert heuristics through a pairwise ranking formulation. Our approach is model-free and does not require enumerating or iterating through a large state space. We empirically demonstrate that this approach accurately learns multifaceted heuristics on a synthetic data set incorporating job-shop scheduling and vehicle routing problems, as well as on two real-world data sets consisting of demonstrations of experts solving a weapon-to-target assignment problem and a hospital resource allocation problem. We also demonstrate that policies learned from human scheduling demonstration via apprenticeship learning can substantially improve the efficiency of a branch-and-bound search for an optimal schedule. We employ this human-machine collaborative optimization technique on a variant of the weapon-to-target assignment problem. We demonstrate that this technique generates solutions substantially superior to those produced by human domain experts at a rate up to 9.5 times faster than an optimization approach and can be applied to optimally solve problems twice as complex as those solved by a human demonstrator.Comment: Portions of this paper were published in the Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) in 2016 and in the Proceedings of Robotics: Science and Systems (RSS) in 2016. The paper consists of 50 pages with 11 figures and 4 table

An improved adaptive neural network for job-shop scheduling

Job-shop scheduling is one of the most difficult production scheduling problems in industry. This paper presents an improved adaptive neural network together with heuristic methods for job-shop scheduling problems. The neural network is based on constraints satisfaction of job-shop scheduling and can adapt its structure and neuron connections during the solving. Several heuristics are also proposed to be combined with the neural network to guarantee its convergence, accelerate its solving process, and improve the quality of solutions. Experimental study shows that the proposed hybrid approach outperforms two classical heuristic algorithms regarding the quality of solutions

Design and development of CSP techniques for finding robust solutions in job-shop scheduling problems with Operators

[ES] Se desarrolla una técnica CSP para buscar soluciones robustas en el problema job-shop scheduling. La técnica esta desarrollada en tres pasos. El primer paso resuelve el problema sin tener en cuenta operadores. El segundo paso introduce las restricciones de los operadores y obtiene soluciones teniendo en cuenta el makespan y la robustez. En el tercer paso se mejora la robustez redistribuyendo los buffers. Para probar las robustez de las soluciones obtenidas se aplican incidencias virtuales en las soluciones.[EN] A CSP technique have been developed for finding robust solutions in job-shop scheduling problems with operators. The technique is developed in three steps. The first step solve the problem without operators minimizing the makespan. The second step introduce the operator constraints and give solutions take into account makespan and robustness. The third step improve the robustness redistributing the buffer. Some virtual incidences are created and to check the robustness of the solutions.Escamilla Fuster, J. (2012). Design and development of CSP techniques for finding robust solutions in job-shop scheduling problems with Operators. http://hdl.handle.net/10251/18029Archivo delegad

Production/maintenance cooperative scheduling using multi-agents and fuzzy logic

Within companies, production is directly concerned with the manufacturing schedule, but other services like sales, maintenance, purchasing or workforce management should also have an influence on this schedule. These services often have together a hierarchical relationship, i.e. the leading function (most of the time sales or production) generates constraints defining the framework within which the other functions have to satisfy their own objectives. We show how the multi-agent paradigm, often used in scheduling for its ability to distribute decision-making, can also provide a framework for making several functions cooperate in the schedule performance. Production and maintenance have been chosen as an example: having common resources (the machines), their activities are actually often conflicting. We show how to use a fuzzy logic in order to model the temporal degrees of freedom of the two functions, and show that this approach may allow one to obtain a schedule that provides a better compromise between the satisfaction of the respective objectives of the two functions

Application of Firefly Algorithm and Its Parameter Setting for Job Shop Scheduling

AbstractJob shop scheduling problem (JSSP) is one of the most famous scheduling problems, most of which are categorisedinto Non-deterministic Polynomial (NP) hard problem. The objectives of this paper are to i) present the application of a recent developed metaheuristic called Firefly Algorithm (FA) for solving JSSP; ii) investigate the parameter setting of the proposed algorithm; and iii) compare the FA performance using various parameter settings. The computational experiment was designed and conducted using five benchmarking JSSP datasets from a classical OR-Library. The analysis of the experimental results on the FA performance comparison between with and without using optimised parameter settings was carried out. The FA with appropriate parameters setting that got from the experiment analysis produced the best-so-far schedule better than the FA withoutadopting parameter settings