21,004 research outputs found
A linear programming-based method for job shop scheduling
We present a decomposition heuristic for a large class of job shop scheduling problems. This heuristic utilizes information from the linear programming formulation of the associated optimal timing problem to solve subproblems, can be used for any objective function whose associated optimal timing problem can be expressed as a linear program (LP), and is particularly effective for objectives that include a component that is a function of individual operation
completion times. Using the proposed heuristic framework, we address job shop scheduling problems with a variety of objectives where intermediate holding costs need to be explicitly considered. In computational testing, we demonstrate the performance of our proposed solution approach
Train Scheduling and Rescheduling in the UK with a Modified Shifting Bottleneck Procedure
This paper introduces a modified shifting bottleneck approach to
solve train scheduling and rescheduling problems. The problem is
formulated as a job shop scheduling model and a mixed integer
linear programming model is also presented. The shifting
bottleneck procedure is a well-established heuristic method for
obtaining solutions to the job shop and other machine scheduling
problems. We modify the classical shifting bottleneck approach to
make it suitable for the types of job shop problem that arises in
train scheduling. The method decomposes the problem into several
single machine problems. Different variations of the method are
considered with regard to solving the single machine problems. We
compare and report the performance of the algorithms for a case
study based on part of the UK railway network
A hybrid framework integrating machine-learning and mathematical programming approaches for sustainable scheduling of flexible job-shop problems
Flexible job shop scheduling has received considerable attention due to its extensive applications in manufacturing. High-quality scheduling solutions are desired but hard to be guaranteed due to the NP-hardness of computational complexity. In this work, a novel energy-efficient hybrid algorithm is proposed to effectively address the scheduling of flexible job shop problems within reasonable time frames. The hybrid framework hybridizes gene expression programming, variable neighborhood search, and simplified mixed integer linear programming approaches to minimize the total energy consumption. It is utilized to address 20 benchmark examples with moderate-or high-complexities. Computational results show that the hybrid algorithm can reach optimality for all considered moderate-size examples within two seconds. The proposed algorithm demonstrates significant competitive advantages relative to the existing mathematical programming approaches and a group-based decomposition method. Specifically, it shortens the computational time over one order of magnitude in some cases and leads to lower total energy consumption with a maximum decrease by 14.5%.info:eu-repo/semantics/publishedVersio
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
A new adaptive neural network and heuristics hybrid approach for job-shop scheduling
Copyright @ 2001 Elsevier Science LtdA new adaptive neural network and heuristics hybrid approach for job-shop scheduling is presented. The neural network has the property of adapting its connection weights and biases of neural units while solving the feasible solution. Two heuristics are presented, which can be combined with the neural network. One heuristic is used to accelerate the solving process of the neural network and guarantee its convergence, the other heuristic is used to obtain non-delay schedules from the feasible solutions gained by the neural network. Computer simulations have shown that the proposed hybrid approach is of high speed and efficiency. The strategy for solving practical job-shop scheduling problems is provided.This work is supported by the National Nature Science Foundation (No. 69684005)
and National High -Tech Program of P. R. China (No. 863-511-9609-003)
Framework for sustainable TVET-Teacher Education Program in Malaysia Public Universities
Studies had stated that less attention was given to the education aspect, such as
teaching and learning in planning for improving the TVET system. Due to the 21st
Century context, the current paradigm of teaching for the TVET educators also has
been reported to be fatal and need to be shifted. All these disadvantages reported
hindering the country from achieving the 5th strategy in the Strategic Plan for
Vocational Education Transformation to transform TVET system as a whole.
Therefore, this study aims to develop a framework for sustainable TVET Teacher
Education program in Malaysia. This study had adopted an Exploratory Sequential
Mix-Method design, which involves a semi-structured interview (phase one) and
survey method (phase two). Nine experts had involved in phase one chosen by using
Purposive Sampling Technique. As in phase two, 118 TVET-TE program lecturers
were selected as the survey sample chosen through random sampling method. After
data analysis in phase one (thematic analysis) and phase two (Principal Component
Analysis), eight domains and 22 elements have been identified for the framework for
sustainable TVET-TE program in Malaysia. This framework was identified to embed
the elements of 21st Century Education, thus filling the gap in this research. The
research findings also indicate that the developed framework was unidimensional and
valid for the development and research regarding TVET-TE program in Malaysia.
Lastly, it is in the hope that this research can be a guide for the nations in producing a
quality TVET teacher in the future
Semi-autonomous Intersection Collision Avoidance through Job-shop Scheduling
In this paper, we design a supervisor to prevent vehicle collisions at
intersections. An intersection is modeled as an area containing multiple
conflict points where vehicle paths cross in the future. At every time step,
the supervisor determines whether there will be more than one vehicle in the
vicinity of a conflict point at the same time. If there is, then an impending
collision is detected, and the supervisor overrides the drivers to avoid
collision. A major challenge in the design of a supervisor as opposed to an
autonomous vehicle controller is to verify whether future collisions will occur
based on the current drivers choices. This verification problem is particularly
hard due to the large number of vehicles often involved in intersection
collision, to the multitude of conflict points, and to the vehicles dynamics.
In order to solve the verification problem, we translate the problem to a
job-shop scheduling problem that yields equivalent answers. The job-shop
scheduling problem can, in turn, be transformed into a mixed-integer linear
program when the vehicle dynamics are first-order dynamics, and can thus be
solved by using a commercial solver.Comment: Submitted to Hybrid Systems: Computation and Control (HSCC) 201
Mixed integer-linear formulations of cumulative scheduling constraints - A comparative study
This paper introduces two MILP models for the cumulative scheduling constraint and associated pre-processing filters. We compare standard solver performance for these models on three sets of problems and for two of them, where tasks have unitary resource consumption, we also compare them with two models based on a geometric placement constraint. In the experiments, the solver performance of one of the cumulative models, is clearly the best and is also shown to scale very well for a large scale industrial transportation scheduling problem
An application of a cocitation-analysis method to find further research possibilities on the area of scheduling problems
In this article we will give firstly a classification scheme of scheduling problems and their solving methods. The main aspects under examination are the following: machine and secondary resources, constraints, objective functions, uncertainty, mathematical models and adapted solution methods. In a second part, based on this scheme, we will examine a corpus of 60 main articles (1015 citation links were recorded in total) in scheduling literature from 1977 to 2009. The main purpose is to discover the underlying themes within the literature and to examine how they have evolved. To identify documents likely to be closely related, we are going to use the cocitation-based method of Greene et al. (2008). Our aim is to build a base of articles in order to extract the much developed research themes and find the less examined ones as well, and then try to discuss the reasons of the poorly investigation of some areas
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