852 research outputs found
Multi Objectives Fuzzy Ant Colony Optimization Design of Supply Path Searching
One of problem faced in supply chain management is path searching. The best path depend not only on distance, but also other variables, such as: the quality of involved companies, quality of delivered product, and other value resulted by quality measurement. Commonly, the ant colony optimization could search the best path that has only one objective path. But it would be difficult to be adopted, because in the real case, the supply path has multi path and objectives (especially in palm oil based bioenergy supply). The objective of this paper is to improve the ant colony optimization for solving multi objectives based supply path problem by using fuzzy ant colony optimization. The developed multi objectives fuzzy ant colony optimization design was explained here, that it was used to search the best supply path.
Salah satu masalah yang dihadapi dalam Supply Chain Management adalah pencarian jalur. Jalur terbaik tidak hanya tergantung pada jarak, tetapi juga variabel lain, seperti: kualitas Perusahaan yang terlibat, kualitas produk yang dikirimkan, dan nilai lain yang dipengaruhi oleh pengukuran kualitas. Umumnya, Ant Colony Optimization bisa mencari jalur terbaik yang hanya memiliki satu jalur objektif. Tapi akan sulit untuk diadopsi, karena dalam kasus nyata, jalur supply memiliki banyak jalur dan tujuan (khususnya pasokan minyak kelapa sawit berbasis bioenergi). Tujuan dari penelitian ini adalah untuk meningkatkan Ant Colony Optimization dalam menyelesaikan masalah jalur supply dengan menggunakan Fuzzy Ant Colony Optimization. Tujuan pengembangan Fuzzy Ant Colony Optimization dijelaskan disini, yaitu digunakan untuk mencari jalur supply terbaik
Matheuristics: using mathematics for heuristic design
Matheuristics are heuristic algorithms based on mathematical tools such as the ones provided by mathematical programming, that are structurally general enough to be applied to different problems with little adaptations to their abstract structure. The result can be metaheuristic hybrids having components derived from the mathematical model of the problems of interest, but the mathematical techniques themselves can define general heuristic solution frameworks.
In this paper, we focus our attention on mathematical programming and its contributions to developing effective heuristics. We briefly describe the mathematical tools available and then some matheuristic approaches, reporting some representative examples from the literature. We also take the opportunity to provide some ideas for possible future development
Bio-inspired multi-agent systems for reconfigurable manufacturing systems
The current market’s demand for customization and responsiveness is a major challenge for producing intelligent, adaptive manufacturing systems. The Multi-Agent System (MAS) paradigm offers an
alternative way to design this kind of system based on decentralized control using distributed,
autonomous agents, thus replacing the traditional centralized control approach. The MAS solutions
provide modularity, flexibility and robustness, thus addressing the responsiveness property, but usually
do not consider true adaptation and re-configuration. Understanding how, in nature, complex things
are performed in a simple and effective way allows us to mimic nature’s insights and develop powerful
adaptive systems that able to evolve, thus dealing with the current challenges imposed on manufactur-
ing systems. The paper provides an overview of some of the principles found in nature and biology and
analyses the effectiveness of bio-inspired methods, which are used to enhance multi-agent systems to
solve complex engineering problems, especially in the manufacturing field. An industrial automation
case study is used to illustrate a bio-inspired method based on potential fields to dynamically route
pallets
Development of a hybrid metaheuristic for the efficient solution of strategic supply chain management problems: application to the energy sector
Supply chain management (SCM) addresses the strategic, tactical, and operational
decision making that optimizes the supply chain performance. The
strategic level defines the supply chain configuration: the selection of suppliers,
transportation routes, manufacturing facilities, production levels, technologies.
The tactical level plans and schedules the supply chain to meet
actual demand. The operational level executes plans. Tactical and operational
level decision-making functions are distributed across the supply
chain.
To increase or optimize performance, supply-chain functions must be
perfectly coordinated. But the cycles of the enterprise and the market make
this difficult: raw material does not arrive on time, production facilities
fail, workers are ill, customers change or cancel orders, therefore, causing
deviations from the plan. In some cases, these situations may be dealt
with locally. In other cases, the problem cannot be ”locally contained” and
modifications across many functions are required. Consequently, the supply
chain management system must coordinate the revision of plans or schedules.
The ability to better understand an algorithm is important to focus on the
following variables: tactical and operational levels of the supply chain so that
the timely dissemination of information, accurate coordination of decisions,
and management of actions among people and systems is achieved ultimately determines the efficient, coordinated achievement of enterprise goal
A survey on metaheuristics for stochastic combinatorial optimization
Metaheuristics are general algorithmic frameworks, often nature-inspired, designed to solve complex optimization problems, and they are a growing research area since a few decades. In recent years, metaheuristics are emerging as successful alternatives to more classical approaches also for solving optimization problems that include in their mathematical formulation uncertain, stochastic, and dynamic information. In this paper metaheuristics such as Ant Colony Optimization, Evolutionary Computation, Simulated Annealing, Tabu Search and others are introduced, and their applications to the class of Stochastic Combinatorial Optimization Problems (SCOPs) is thoroughly reviewed. Issues common to all metaheuristics, open problems, and possible directions of research are proposed and discussed. In this survey, the reader familiar to metaheuristics finds also pointers to classical algorithmic approaches to optimization under uncertainty, and useful informations to start working on this problem domain, while the reader new to metaheuristics should find a good tutorial in those metaheuristics that are currently being applied to optimization under uncertainty, and motivations for interest in this fiel
Survey on Additive Manufacturing, Cloud 3D Printing and Services
Cloud Manufacturing (CM) is the concept of using manufacturing resources in a
service oriented way over the Internet. Recent developments in Additive
Manufacturing (AM) are making it possible to utilise resources ad-hoc as
replacement for traditional manufacturing resources in case of spontaneous
problems in the established manufacturing processes. In order to be of use in
these scenarios the AM resources must adhere to a strict principle of
transparency and service composition in adherence to the Cloud Computing (CC)
paradigm. With this review we provide an overview over CM, AM and relevant
domains as well as present the historical development of scientific research in
these fields, starting from 2002. Part of this work is also a meta-review on
the domain to further detail its development and structure
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Optimized cross-organizational business process monitoring: Design and enactment
Organizations can implement the agility required to survive in the rapidly evolving business landscape by focusing on their core business and engaging in collaborations with other partners. This entails the need for organizations to monitor the behavior of the partners with which they collaborate. The design and enactment of monitoring, in this scenario, must become flexible and adapt as the collaboration evolves. We propose an approach to flexibly design and enact cross-organizational business process monitoring based on Product-Based Workflow Design. Our approach allows organizations to capture monitoring requirements, optimize such requirements, e.g. choosing the monitoring process with lowest cost or highest availability, and enacting the optimal monitoring process through a service-oriented approach. Optimization, in particular, is made efficient by adopting an Ant-colony optimization heuristic. The paper also describes a prototypical implementation of our approach in the ProM framework
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