35466192
Scheduling on parallel identical machines to minimize total tardiness
This paper focuses on the problem of scheduling n independent jobs on m identical parallel machines for the objective of minimizing total tardiness of the jobs. We develop dominance properties and lower bounds, and develop a branch and bound algorithm using these properties and lower bounds as well as upper bounds obtained from a heuristic algorithm. Computational experiments are performed on randomly generated test problems and results show that the algorithm solves problems with moderate sizes in a reasonable amount of computation time. � 2005 Elsevier B.V. All rights reserved.
205099619
Simulated annealing and genetic algorithms for scheduling products with multi-level product structure
We consider a short-term production scheduling problem in a manufacturing system producing products with multi-level product structure, and their components and subassemblies. The problem is to schedule production of components, subassemblies, and final products with the objective of minimizing the weighted sum of tardiness and earliness of the items. We consider due dates of final products, precedence relationships among items, and processing capacity of the manufacturing system. The scheduling problem is solved with simulated annealing and genetic algorithms, which are search heuristics often used for global optimization in a complex search space. To compare the performance of these algorithms with the finite loading method, which is often used in practice, computational experiments are carried out using randomly generated test problems and results are reported.
21112002
A backward approach in list scheduling algorithms for multi-machine tardiness problems
Abstract List scheduling algorithms are commonly used for multi-machine scheduling problems, especially for those with precedence relationships among operations. In most list scheduling algorithms, operations are scheduled forward. In this paper, we suggest a backward approach in which the operations are scheduled backward. We discuss some details that must be considered when applying the backward approach to problems with due dates and suggest several methods of defining parameters needed in the backward approach. Performance of this approach is tested on job shop problems and identical machine problems with several types of precedence constaints.
15690106
Genetic Search For Fixed Channel Assignment Problem With Limited Bandwidth
We propose a hybrid genetic algorithm for the fixed channel assignment problem with limited bandwidth. A local optimization algorithm was devised to enhance the genetic algorithm's fine-tunning. In a matrix representation, the local optimization algorithm improves on a chromosome by horizontal and vertical moves of channels. The crossover is directly performed on two-dimensional matrices. Experimental results showed dramatic improvement against previous works.
1907049
A spatial mapping algorithm for heterogeneous coarse-grained reconfigurable architectures
In this work, we investigate the problem of automatically mapping applications onto a coarse-grained reconfigurable architecture and propose an efficient algorithm to solve the problem. We formalize the mapping problem and show that it is NP-complete. To solve the problem within a reasonable amount of time, we divide it into three subproblems: covering, partitioning and layout. Our empirical results demonstrate that our technique produces nearly as good performance as hand-optimized outputs for many kernels
40677929
Use of multi-attribute decision analysis for designing operations system framework in telecommunications management network
Abstract This paper is concerned with the design of an Operations System (OS) framework for implementing a proper telecommunications network in Korea Telecom. Since several types of OS frameworks are under consideration, the telecommunication network manager should now choose the best alternative for use later in establishing the infrastructure of operations systems. The decision making is done in this paper by means of a Multi-Attribute Decision-Making (MADM) analysis. A point to be noted in the present case is that the decision relevant information such as attribute weights and values of alternatives are not known exactly. Rather, these are to be taken in the form of rankings and/or bounds. To deal with such incomplete information, we employ a mathematical programming model-based MADM approach to the choice of an optimal OS framework design. This approach enables us to check dominance of alternatives and to obtain a set of non-dominated alternatives from evaluating five alternatives in reference to Telecommunications Management Network (TMN) standards. Scope and purpose Telecommunications Management Network (TMN) can be stated as a globalized and unified skeleton to meet the advanced operational environments of telecommunications networks and to handle heterogeneous and complex network elements. Although there are various subjects that should be addressed to accomplish TMN, we confine our attention to dealing with one important subject, design of an Operations System (OS) framework. This is necessary to manage telecommunications resources including networks in effective manner and for use as guidance in developing individual operations systems and in constructing a network management center. Several types of OS frameworks have been suggested by TMN standards. It is hence needed to evaluate each of these OS frameworks and then to choose an appropriate one in Korean TMN situation. Since this evaluation involves multiple criteria (or attributes), it is realized in this paper by a Multi-Attribute Decision-Making (MADM) analysis approach. The decision relevant information such as trade-off weights and individual criterion values are elicited from expert opinions which, however, is rather incomplete than exact numerical values. The decision making process with the incompletely identified information is presented in this paper.
15089682
Lagrange Multiplier Method for Multi-campaign Assignment Problem
It is crucial to maximize marketing efficiency and customer satisfaction in personalized marketing. In this paper, we raise the multiple recommendation problem which occurs when performing several personalized campaigns simultaneously. We formulate the multi-campaign assignment problem to solve this issue and propose methods for solving the problem. The most notable element is the Lagrange multiplier method. Roughly speaking, Lagrange multiplier reduces problem complexity with a minor impact on optimality. However, it is not easy to find Lagrange multipliers in exact accord with problem constraints. We use a genetic algorithm for finding optimal Lagrange multipliers. Through the experiments, we verify the effectiveness of the problem formulation and our genetic approach.
18915158
Revisiting random key pre-distribution schemes for wireless sensor networks
Key management is one of the fundamental building blocks of security services. In a network with resource constrained nodes like sensor networks, traditional key management techniques, such as public key cryptography or key distribution center (e.g., Kerberos), are often not effective. To solve this problem, several key pre-distribution schemes have been proposed for sensor networks based on random graph theory. In these schemes, a set of randomly chosen keys or secret information is pre-distributed to each sensor node and a network is securely formed based on this information. Most of the schemes assumed that the underlying physical network is dense enough, that is, the degree of each node is hig.
 In this paper, we revisit the random graph theory and use giant component theory by Erdos and Renyi to show that even if the node degree is small, most of the nodes in the network can be connected. Further, we use this fact to analyze the Eschenhauer et. al's, Du et. al's, and Chan et. al's key pre-distribution schemes and evaluate the relation between connectivity, memory size, and security. We show that we can reduce the amount of memory required or improve security by trading-off a very small number of isolated nodes. Our simulation results show that the communication overhead does not increase significantly even after reducing the node degree. In addition, we present an approach by which nodes can dynamically adjust their transmission power to establish secure links with the targeted networked neighbors. Finally, we propose an effcient path-key identification algorithm and compare it with the existing scheme.