5311612
A genetic algorithm for job sequencing problems with distinct due dates and general early-tardy penalty weights
Abstract A job scheduling problem with distinct due dates in a single machine is considered. General penalty weights which are not necessarily proportional to the processing times are applied to jobs either early or tardy. An optimal timing algorithm is presented which decides the optimal starting time of each job in a given job sequence. Idle times are inserted between blocks of jobs such that the cost function of each newly created job block is minimized. Prominent near optimal sequences are generated via a genetic algorithm N best reproduction without duplicates, uniform order-based crossover and intra-block mutation operators are employed. The proposed genetic algorithm is proved to outperform existing heuristic methods. On average, 12–33% cost reduction effect is obtained by the evolution algorithm compared to the solution by an excellent heuristic procedure. The near optimality of the solutions by the GA is also illustrated by the comparison with an exact algorithm.
13953434
A design of multi‐layered location registration areas in microcellular systems
Multi‐layered location registration areas are considered to minimize the control signals at boundary cells in the microcellular systems. The decision of multi‐layered location registration (LR) areas is formulated as an integer programming problem by examining the regional subscriber densities, traffic flows and call arrival rates. By relaxing the constraints of forward and reverse control channels the problem is decomposed into a number of subproblems each of which corresponds to a layer. Each subproblem is solved via a modified graph partitioning algorithm. Feasible LR areas are obtained by considering the two relaxed constraints. Computational experiments are performed in a system with 80 microcells to investigate the performance of the proposed multi‐layer scheme. The simulation results indicate that the two‐layer scheme generally outperforms the one‐layer and three‐layer methods in view of control signals.
29820357
Service specific call admission control in WCDMA system
When a new call arrives, the system has to determine whether to accept the call or not. These admission decisions are performed by call admission control (CAC). CAC belongs to the resource management category and is widely being investigated in many other researches as well. However, researches on CAC have mostly focused on channel allocation in the traditional FDMA and TDMA systems. We focus on a WCDMA system, which has no fixed channel capacity. In this study, a new CAC scheme for WCDMA systems is proposed. It also addresses CDMA capacity evaluation and code assignment problems. The proposed scheme consists of handoff and new CACs. For each service class, handoff and new CACs are considered with different priorities. The handoff CAC process precedes that of the new CAC process. Finally, we evaluate the performance of the proposed scheme by using Markov analysis. The performance measures that we focus on are the blocking probabilities of handoff and new calls and the dropping probabilities of on-going calls.
8596907
Multiple multicast tree allocation in IP network
The multicasting is defined as the distribution of the same information stream from one to many nodes concurrently. There has been an intensive research effort to design protocols and construct multicast routing graphs for a single multicast group. In this paper, the multiple multicast tree allocation problem is discussed and algorithms are proposed to solve the congestion problem in the IP network. As the congestion measure the minimum residual capacity is considered. Two phase algorithm MMTA is investigated for multiple multicast tree allocation both for identical and different bandwidth requirement by the multicast groups. The central and distributed implementation of the multiple multicast tree is discussed for the deployment in the real IP network. The performance of the proposed MMTA is compared with other procedures. Computational results show that the two-phase MMTA outperforms other procedures. Approximately 3-7% improvement in the residual capacity is obtained by the MMTA. The solution gap from the upper bound by the well-known branch and bound is within 2-11% depending on the problem size.
14489935
Dynamic resource allocation for CDMA-TDD indoor wireless systems
Future wireless communication systems are expected to provide a broad range of multimedia services that have a significant traffic asymmetry between uplink and downlink. The code division multiple access-time division duplex (CDMA-TDD) system is a promising solution to cope with the problem of traffic asymmetry. However, the TDD system is subject to inter-cell interference compared to frequency division duplex (FDD) system. Since both uplink and downlink share the same frequency in TDD, uplink and downlink may interfere each other especially when neighboring cells require different rates of traffic load. Thus, the resource allocation among cells is an important issue in TDD. In this paper, the resource allocation in the CDMA-TDD is formulated as a mixed integer programming (MIP) problem. A dynamic resource allocation algorithm (DRAA) is provided that effectively solves the traffic asymmetry problem. The MIP problem is also solved by a well-known branch and bound procedure. Both the crossed slot and non-crossed slot allocation are examined and compared to the DRAA. Computational result shows that proposed DRAA gives a good performance as the traffic asymmetry increases between the uplink and downlink. Copyright Â© 2003 John Wiley & Sons, Ltd.
32212919
Balancing loads on sonet rings with integer demand splitting
SONET Self Healing ring is one of the most intriguing schemes which provide survivability for telecommunication networks. To design a cost effective SONET ring it is necessary to consider load balancing problems by which the link capacity is determined. In this paper, we consider the load balancing problem in SONET ring when demand splitting is allowed only by integer. A bounded approximation to the optimal solution is presented and proved by employing the arc-demand matrix which represents the amount of demand routed on each arc. A very efficient algorithm is provided which always satisfies the optimality at termination. The algorithm iteratively decreases the maximum load by considering counter-clockwise routing of a demand which passes through the arc with maximum load. Justification of the algorithm is also provided.
30064492
Genetic reinforcement learning approach to the machine scheduling problem
This paper focuses on the development of a learning-based heuristic for the machine scheduling problem, which automatically captures the search control knowledge or the common features of good schedules while generating a number of schedules. Defining states and actions of the machine shop, the machine scheduling problem is transformed into a problem of reinforcement learning (RL) in which a learner or a scheduler will learn to select the right action at each state of the machine shop, using the reward from a schedule evaluator for executing the action. Implementing the proposed reinforcement learning with a genetic algorithm results in the genetic reinforcement learning (GRL) approach to the machine scheduling problem. Although the learning-based heuristic has the overhead of acquiring knowledge on the problem, it can be easily adapted for a wide variety of machine scheduling problems due to the weak dependence on the problem structures and objectives. A GRL-based scheduler, called EVIS (Evolutionary Intracell Scheduler), has been developed and applied to various classes of machine scheduling problems, such as the job-shop scheduling, the flow-shop scheduling and the open-shop scheduling problems, and even the processor scheduling problem, the performance evaluation of EVIS with a number of different problem instances has shown that the learning-based heuristic is robust and its performance is comparable with that of other problem-specific heuristics or search-oriented heuristics in the quality of solutions.
20301861
Ontology-based intelligent healthcare agent and its application to respiratory waveform recognition
In recent years, the population has been aging gradually, and the number of patients with chronic respiratory disease has grown increasingly; therefore the respiratory healthcare plays an important role in the clinical care. This paper presents an ontology-based intelligent healthcare agent for the respiratory waveform recognition to assist the medical staff in judging the meaning of the graph reading from ventilators. The intelligent healthcare agent contains three modules, including the respiratory waveform ontology, ontology construction mechanism, and fuzzy recognition agent, to classify the respiratory waveform. The respiratory waveform ontology represents the respiratory domain knowledge, which will be utilized to classify and recognize the respiratory waveform by the intelligent healthcare agent. The ontology construction mechanism will infer the fuzzy numbers of each respiratory waveform from the patient or respiratory waveform repository. Next, the fuzzy recognition agent will classify and recognize the respiratory waveform into different types of respiratory waveforms. Finally, after the confirmation of medical experts, the classified and recognized results are stored in the classified waveform repository. The experimental results show that our approach can classify and recognize the respiratory waveform effectively.
33340433
An evolutionary approach to the job-shop scheduling problem
This paper focuses on the heuristic hybridization and the genetic search as a methodology to develop a computationally efficient heuristic for the job-shop scheduling problem (JSP). In order to adapt the JSP to a genetic algorithm (GA), the ASGPL (Active-Schedule Generation with a Priority-List) algorithm with a hopping scheme was proposed, and using a GA, an iterative schedule improvement procedure called EVIS (Evolutionary Intracell Scheduler) was designed. The genetic search in EVIS was parallelized with a model of subpopulations and migration. Without implementing any problem-tailored heuristic for the job-shop scheduling problem, EVIS was able to find optimal solutions to a number of different problem instances in reasonable computation time.<<ETX>>
29270156
A Fuzzy Decision Agent for Meeting Scheduling Supported System
20686264
Finite-Horizon Scheduling of Radar Dwells with Online Template Construction
Timing constraints for radar tasks are usually specified in terms of the minimum and maximum temporal distance between successive radar dwells. We utilize the idea of feasible intervals for dealing with the temporal distance constraints. In order to increase the freedom that the scheduler can offer a high-level resource manager, we introduce a technique for nesting and interleaving dwells online while accounting for the energy constraint that radar systems need to satisfy. Further, in radar systems, the task set changes frequently and we advocate the use of finite horizon scheduling in order to avoid the pessimism that is inherent in schedulers that assume a task executes forever. We also develop the notion of modular schedule update which allows portions of a schedule to be altered without affecting the entire schedule, thereby simplifying the scheduler. Through extensive simulations, we validate our claims of providing greater scheduling flexibility without compromising on performance when compared with earlier work based on templates constructed offline.