7,509 research outputs found
Intelligent systems in manufacturing: current developments and future prospects
Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS
Multi-objective discrete particle swarm optimisation algorithm for integrated assembly sequence planning and assembly line balancing
In assembly optimisation, assembly sequence planning and assembly line balancing have been extensively studied because both activities are directly linked with assembly efficiency that influences the final assembly costs. Both activities are categorised as NP-hard and usually performed separately. Assembly sequence planning and assembly line balancing optimisation presents a good opportunity to be integrated, considering the benefits such as larger search space that leads to better solution quality, reduces error rate in planning and speeds up time-to-market for a product. In order to optimise an integrated assembly sequence planning and assembly line balancing, this work proposes a multi-objective discrete particle swarm optimisation algorithm that used discrete procedures to update its position and velocity in finding Pareto optimal solution. A computational experiment with 51 test problems at different difficulty levels was used to test the multi-objective discrete particle swarm optimisation performance compared with the existing algorithms. A statistical test of the algorithm performance indicates that the proposed multi-objective discrete particle swarm optimisation algorithm presents significant improvement in terms of the quality of the solution set towards the Pareto optimal set
An investigation into minimising total energy consumption and total completion time in a flexible job shop for recycling carbon fiber reinforced polymer
The increased use of carbon fiber reinforced polymer (CFRP) in industry coupled with European Union restrictions on landfill disposal has
resulted in a need to develop relevant recycling technologies. Several methods, such as mechanical grinding, thermolysis and solvolysis, have
been tried to recover the carbon fibers. Optimisation techniques for reducing energy consumed by above processes have also been developed.
However, the energy efficiency of recycling CFRP at the workshop level has never been considered before. An approach to incorporate energy
reduction into consideration while making the scheduling plans for a CFRP recycling workshop is presented in this paper. This research sets in
a flexible job shop circumstance, model for the bi-objective problem that minimise total processing energy consumption and makespan is developed.
A modified Genetic Algorithm for solving the raw material lot splitting problem is developed. A case study of the lot sizing problem
in the flexible job shop for recycling CFRP is presented to show how scheduling plans affect energy consumption, and to prove the feasibility
of the model and the developed algorithm
Global supply chains of high value low volume products
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A GA-based technique for the scheduling of storage tanks
YesThis paper proposes the application of a
genetic algorithm based methodology for the scheduling
of storage tanks. The proposed approach is an
integration of GA and heuristic rule-based techniques,
which decomposes the complex mixed integer
optimisation problem into integer and real number subproblems.
The GA string considers the integer problem,
and the heuristic approach solves the real number
problems within the GA framework. The algorithm is
demonstrated for a test problem related to a water
treatment facility at a port, and has been found to give a
significantly better schedule than those generated using a
heuristic-based approach
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