558 research outputs found
Flexible flow shop scheduling with stochastic processing times: A decomposition-based approach
Flexible flow shop scheduling problems are NP-hard and tend to become more complex when stochastic uncertainties are taken into consideration. Although some methods have been developed to address such problems, they remain inherently difficult to solve by any single approach. This paper presents a novel decomposition-based approach (DBA), which combines both the shortest processing time (SPT) and the genetic algorithm (GA), to minimizing the makespan of a flexible flow shop (FFS) with stochastic processing times. In the proposed DBA, a neighbouring K-means clustering algorithm is developed to firstly group the machines of an FFS into an appropriate number of machine clusters, based on their stochastic nature. Two optimal back propagation networks (BPN), corresponding to the scenarios of simultaneous and non-simultaneous job arrivals, are then selectively adopted to assign either SPT or GA to each machine cluster for sub-schedule generation. Finally, an overall schedule is generated by integrating the sub-schedules of machine clusters. Computation results show that the DBA outperforms SPT and GA alone for FFS scheduling with stochastic processing times. © 2012 Elsevier Ltd. All rights reserved.postprin
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An intelligent manufacturing system for heat treatment scheduling
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This research is focused on the integration problem of process planning and scheduling in steel heat treatment operations environment using artificial intelligent techniques that are capable of dealing with such problems.
This work addresses the issues involved in developing a suitable methodology for scheduling heat treatment operations of steel. Several intelligent algorithms have been developed for these propose namely, Genetic Algorithm (GA), Sexual Genetic Algorithm (SGA), Genetic Algorithm with Chromosome differentiation (GACD), Age Genetic Algorithm (AGA), and Mimetic Genetic Algorithm (MGA). These algorithms have been employed to develop an efficient intelligent algorithm using Algorithm Portfolio methodology. After that all the algorithms have been tested on two types of scheduling benchmarks.
To apply these algorithms on heat treatment scheduling, a furnace model is developed for optimisation proposes. Furthermore, a system that is capable of selecting the optimal heat treatment regime is developed so the required metal properties can be achieved with the least energy consumption and the shortest time using Neuro-Fuzzy (NF) and Particle Swarm Optimisation (PSO) methodologies. Based on this system, PSO is used to optimise the heat treatment process by selecting different heat treatment conditions. The selected conditions are evaluated so the best selection can be identified. This work addresses the issues involved in developing a suitable methodology for developing an NF system and PSO for mechanical properties of the steel.
Using the optimisers, furnace model and heat treatment system model, the intelligent system model is developed and implemented successfully. The results of this system were exciting and the optimisers were working correctly
Multi crteria decision making and its applications : a literature review
This paper presents current techniques used in Multi Criteria Decision Making (MCDM) and their applications. Two basic approaches for MCDM, namely Artificial Intelligence MCDM (AIMCDM) and Classical MCDM (CMCDM) are discussed and investigated. Recent articles from international journals related to MCDM are collected and analyzed to find which approach is more common than the other in MCDM. Also, which area these techniques are applied to. Those articles are appearing in journals for the year 2008 only. This paper provides evidence that currently, both AIMCDM and CMCDM are equally common in MCDM
Effects of spent garnet on the compressive and flexural strengths of concrete
Sand is the non-renewable resource which has been over-exploited from
rivers in sync with the rapid development of construction industries to produce
concrete. This affected the morphology of rivers and interrupted the functionality of
riverine ecosystems by pollution. Meanwhile, the unrecyclable spent garnets were
disposed of on a large scale and led to waste pollution. Therefore, this study aimed to
determine the compressive and flexural strengths of concrete consisting of spent
garnet as sand replacement. The specimens were prepared with consisting of spent
garnet as sand replacement by weight in 0%, 10%, 20%, 30% and 40%. They were
tested under compressive strength test at the age of 7 and 28 days while flexural
strength test was conducted on the 28days. The findings revealed that the workability
of fresh concrete was enhanced by an incremental amount of spent garnet. However,
the compressive and flexural strengths of concrete consisting of spent garnet were
discerned to be lower than control samples at all levels of replacement. Overall, the
replacement with 20% spent garnet showed the optimum compressive and flexural
strengths. It is concluded that the usage of spent garnet is considered as a promising
resource for reducing consumption of sand and thus, improving the environmental
problems
A particle swarm optimisation for the no-wait flow shop problem with due date constraints.
Peer ReviewedThis paper considers the no-wait flow shop scheduling problem with due date constraints. In the no-wait flow shop problem, waiting time is not allowed between successive operations of jobs. Moreover, a due date is associated with the completion of each job. The considered objective function is makespan. This problem is proved to be strongly NP-Hard. In this paper, a particle swarm optimisation (PSO) is developed to deal with the problem. Moreover, the effect of some dispatching rules for generating initial solutions are studied. A Taguchi-based design of experience approach has been followed to determine the effect of the different values of the parameters on the performance of the algorithm. To evaluate the performance of the proposed PSO, a large number of benchmark problems are selected from the literature and solved with different due date and penalty settings. Computational results confirm that the proposed PSO is efficient and competitive; the developed framework is able to improve many of the best-known solutions of the test problems available in the literature
Evolutionary methods for the design of dispatching rules for complex and dynamic scheduling problems
Three methods, based on Evolutionary Algorithms (EAs), to support and automate the design
of dispatching rules for complex and dynamic scheduling problems are proposed in this thesis.
The first method employs an EA to search for problem instances on which a given dispatching
rule performs badly. These instances can then be analysed to reveal weaknesses of the
tested rule, thereby providing guidelines for the design of a better rule. The other two methods
are hyper-heuristics, which employ an EA directly to generate effective dispatching rules. In
particular, one hyper-heuristic is based on a specific type of EA, called Genetic Programming
(GP), and generates a single rule from basic job and machine attributes, while the other generates
a set of work centre-specific rules by selecting a (potentially) different rule for each
work centre from a number of existing rules. Each of the three methods is applied to some
complex and dynamic scheduling problem(s), and the resulting dispatching rules are tested
against benchmark rules from the literature. In each case, the benchmark rules are shown to be
outperformed by a rule (set) that results from the application of the respective method, which
demonstrates the effectiveness of the proposed methods
Algorithms for Scheduling Problems
This edited book presents new results in the area of algorithm development for different types of scheduling problems. In eleven chapters, algorithms for single machine problems, flow-shop and job-shop scheduling problems (including their hybrid (flexible) variants), the resource-constrained project scheduling problem, scheduling problems in complex manufacturing systems and supply chains, and workflow scheduling problems are given. The chapters address such subjects as insertion heuristics for energy-efficient scheduling, the re-scheduling of train traffic in real time, control algorithms for short-term scheduling in manufacturing systems, bi-objective optimization of tortilla production, scheduling problems with uncertain (interval) processing times, workflow scheduling for digital signal processor (DSP) clusters, and many more
Simulation in Automated Guided Vehicle System Design
The intense global competition that manufacturing companies face today results in an
increase of product variety and shorter product life cycles. One response to this threat is
agile manufacturing concepts. This requires materials handling systems that are agile
and capable of reconfiguration. As competition in the world marketplace becomes
increasingly customer-driven, manufacturing environments must be highly
reconfigurable and responsive to accommodate product and process changes, with rigid,
static automation systems giving way to more flexible types.
Automated Guided Vehicle Systems (AGVS) have such capabilities and AGV
functionality has been developed to improve flexibility and diminish the traditional
disadvantages of AGV-systems. The AGV-system design is however a multi-faceted
problem with a large number of design factors of which many are correlating and
interdependent. Available methods and techniques exhibit problems in supporting the
whole design process. A research review of the work reported on AGVS development in
combination with simulation revealed that of 39 papers only four were industrially
related. Most work was on the conceptual design phase, but little has been reported on
the detailed simulation of AGVS.
Semi-autonomous vehicles (SA V) are an innovative concept to overcome the problems
of inflexible -systems and to improve materials handling functionality. The SA V
concept introduces a higher degree of autonomy in industrial AGV -systems with the
man-in-the-Ioop. The introduction of autonomy in industrial applications is approached
by explicitly controlling the level of autonomy at different occasions. The SA V s are
easy to program and easily reconfigurable regarding navigation systems and material
handling equipment. Novel approaches to materials handling like the SA V -concept
place new requirements on the AGVS development and the use of simulation as a part
of the process. Traditional AGV -system simulation approaches do not fully meet these
requirements and the improved functionality of AGVs is not used to its full power.
There is a considerflble potential in shortening the AGV -system design-cycle, and thus
the manufacturing system design-cycle, and still achieve more accurate solutions well
suited for MRS tasks.
Recent developments in simulation tools for manufacturing have improved production
engineering development and the tools are being adopted more widely in industry. For
the development of AGV -systems this has not fully been exploited. Previous research
has focused on the conceptual part of the design process and many simulation
approaches to AGV -system design lack in validity. In this thesis a methodology is
proposed for the structured development of AGV -systems using simulation. Elements of
this methodology address the development of novel functionality.
The objective of the first research case of this research study was to identify factors for
industrial AGV -system simulation. The second research case focuses on simulation in
the design of Semi-autonomous vehicles, and the third case evaluates a simulation based
design framework. This research study has advanced development by offering a
framework for developing testing and evaluating AGV -systems, based on concurrent
development using a virtual environment. The ability to exploit unique or novel features
of AGVs based on a virtual environment improves the potential of AGV-systems
considerably.University of Skovde. European Commission for funding the INCO/COPERNICUS Projec
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