29,126 research outputs found
Process Planning Optimization In Reconfigurable Manufacturing Systems
Trends and perspectives in dynamic environments point towards a need for optimal
operating levels in reconfigurable manufacturing activities. Central to the goal of
meeting this need is the issue of appropriate techniques for manufacturing process
planning optimization in reconfigurable manufacturing, i.e. (i) what decision making
models and (ii) what computational techniques, provide an optimal manufacturing
process planning solution in a multidimensional decision variables space? Conventional
optimization techniques are not robust, hence; they are not suitable for handling
multidimensional search spaces. On the other hand, process planning optimization for
reconfigurable manufacturing is not amenable to classical modeling approaches due to
the presence of complex system dynamics. Therefore, this study explores how to model
reconfigurable manufacturing activities in an optimization perspective and how to
develop and select appropriate non-conventional optimization techniques for
reconfigurable process planning.In this study, a new approach to modeling Manufacturing Process Planning
Optimization (MPPO) was developed by extending the concept of manufacturing
optimization through a decoupled optimization method. The uniqueness of this
approach lies in embedding an integrated scheduling function into a partially integrated
process planning function in order to exploit the strategic potentials of flexibility and
reconfigurability in manufacturing systems. Alternative MPPO models were constructed
and variances associated with their utilization analyzed. Five (5) Alternative Algorithm
Design Techniques (AADTs) were developed and investigated for suitability in
providing process planning solutions suitable for reconfigurable manufacturing. The
five (5) AADTs include; a variant of the simulated annealing algorithm that implements
heuristic knowledge at critical decision points, two (2) cooperative search schemes
based on a âloose hybridizationâ of the Boltzmann Machine algorithm with (i)
simulated annealing, and (ii) genetic algorithm search techniques, and two (2) modified
genetic algorithms.
The comparative performances of the developed AADTs when tasked to solve an
instance of a MPPO problem were analyzed and evaluated. In particular, the relative
performances of the novel variant of simulated annealing in comparison to: (a) (i) a
simulated annealing search, and (ii) a genetic search in the Boltzmann Machine
Architecture, and (b) (i) a modified genetic algorithm and (ii) a genetic algorithm with a
customized threshold operator that implements an innovative extension of the diversity
control mechanism to gene and genome levels; were pursued in this thesis.Results show that all five (5) AADTs are capable of stable and asymptotic convergence
to near optimal solutions in real time. Analysis indicates that the performances of the
implemented variant of simulated annealing are comparable to those of other
optimization techniques developed in this thesis. However, a computational study
shows that; in comparison to the simulated annealing technique, significant
improvements in optimization control performance and quality of computed solutions
can be realized through implementing intelligent techniques. As evidenced by the
relative performances of the implemented cooperative schemes, a genetic search is
better than a simulated annealing search in the Boltzmann Machine Architecture. In
addition, little performance gain can be realized through parallelism in the Boltzmann
Machine Architecture. On the other hand, the superior performance of the genetic
algorithm that implements an extended diversity control mechanism demonstrates that
more competent genetic algorithms can be designed through customized operators.
Therefore, this study has revealed that extending manufacturing optimization concepts
through a decoupled optimization method is an effective modeling approach that is
capable of handling complex decision scenarios in reconfigurable manufacturing
activities. The approach provides a powerful decision framework for process planning
optimization activities of a multidimensional nature. Such an approach can be
implemented more efficiently through intelligent techniques. Hence; intelligent
techniques can be utilized in manufacturing process planning optimization strategies
that aim to improve operating levels in reconfigurable manufacturing with the resultant
benefits of improved performance levels
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
AI and OR in management of operations: history and trends
The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested
Multiobjective scheduling for semiconductor manufacturing plants
Scheduling of semiconductor wafer manufacturing system is identified as a complex problem, involving multiple and conflicting objectives (minimization of facility average utilization, minimization of waiting time and storage, for instance) to simultaneously satisfy. In this study, we propose an efficient approach based on an artificial neural network technique embedded into a multiobjective genetic algorithm for multi-decision scheduling problems in a semiconductor wafer fabrication environment
Energy efficiency in discrete-manufacturing systems: insights, trends, and control strategies
Since the depletion of fossil energy sources, rising energy prices, and governmental regulation restrictions, the current manufacturing industry is shifting towards more efficient and sustainable systems. This transformation has promoted the identification of energy saving opportunities and the development of new technologies and strategies oriented to improve the energy efficiency of such systems. This paper outlines and discusses most of the research reported during the last decade regarding energy efficiency in manufacturing systems, the current technologies and strategies to improve that efficiency, identifying and remarking those related to the design of management/control strategies. Based on this fact, this paper aims to provide a review of strategies for reducing energy consumption and optimizing the use of resources within a plant into the context of discrete manufacturing. The review performed concerning the current context of manufacturing systems, control systems implemented, and their transformation towards Industry 4.0 might be useful in both the academic and industrial dimension to identify trends and critical points and suggest further research lines.Peer ReviewedPreprin
Heuristic Solutions for Loading in Flexible Manufacturing Systems
Production planning in flexible manufacturing system deals with the efficient organization of the production resources in order to meet a given production schedule. It is a complex problem and typically leads to several hierarchical subproblems that need to be solved sequentially or simultaneously. Loading is one of the planning subproblems that has to addressed. It involves assigning the necessary operations and tools among the various machines in some optimal fashion to achieve the production of all selected part types. In this paper, we first formulate the loading problem as a 0-1 mixed integer program and then propose heuristic procedures based on Lagrangian relaxation and tabu search to solve the problem. Computational results are presented for all the algorithms and finally, conclusions drawn based on the results are discussed
Performance optimization of a leagility inspired supply chain model: a CFGTSA algorithm based approach
Lean and agile principles have attracted considerable interest in the past few decades. Industrial sectors throughout the world are upgrading to these principles to enhance their performance, since they have been proven to be efficient in handling supply chains. However, the present market trend demands a more robust strategy incorporating the salient features of both lean and agile principles. Inspired by these, the leagility principle has emerged, encapsulating both lean and agile features. The present work proposes a leagile supply chain based model for manufacturing industries. The paper emphasizes the various aspects of leagile supply chain modeling and implementation and proposes a new Hybrid Chaos-based Fast Genetic Tabu Simulated Annealing (CFGTSA) algorithm to solve the complex scheduling problem prevailing in the leagile environment. The proposed CFGTSA algorithm is compared with the GA, SA, TS and Hybrid Tabu SA algorithms to demonstrate its efficacy in handling complex scheduling problems
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Decision support for build-to-order supply chain management through multiobjective optimization
This paper aims to identify the gaps in decision-making support based on
multiobjective optimization for build-to-order supply chain management (BTOSCM).
To this end, it reviews the literature available on modelling build-to-order
supply chains (BTO-SC) with the focus on adopting multiobjective optimization
(MOO) techniques as a decision support tool. The literature has been classified based
on the nature of the decisions in different part of the supply chain, and the key
decision areas across a typical BTO-SC are discussed in detail. Available software
packages suitable for supporting decision making in BTO supply chains are also
identified and their related solutions are outlined. The gap between the modelling and
optimization techniques developed in the literature and the decision support needed in
practice are highlighted and future research directions to better exploit the decision
support capabilities of MOO are proposed
A simheuristic algorithm for solving an integrated resource allocation and scheduling problem
Modern companies have to face challenging configuration issues in their manufacturing chains. One of these challenges is related to the integrated allocation and scheduling of resources such as machines, workers, energy, etc. These integrated optimization problems are difficult to solve, but they can be even more challenging when real-life uncertainty is considered. In this paper, we study an integrated allocation and scheduling optimization problem with stochastic processing times. A simheuristic algorithm is proposed in order to effectively solve this integrated and stochastic problem. Our approach relies on the hybridization of simulation with a metaheuristic to deal with the stochastic version of the allocation-scheduling problem. A series of numerical experiments contribute to illustrate the efficiency of our methodology as well as their potential applications in real-life enterprise settings
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