Generating artificial chromosomes with probability control in genetic algorithm for machine scheduling problems

In this paper, a novel genetic algorithm is developed by generating artificial chromosomes with probability control to solve the machine scheduling problems. Generating artificial chromosomes for Genetic Algorithm (ACGA) is closely related to Evolutionary Algorithms Based on Probabilistic Models (EAPM). The artificial chromosomes are generated by a probability model that extracts the gene information from current population. ACGA is considered as a hybrid algorithm because both the conventional genetic operators and a probability model are integrated. The ACGA proposed in this paper, further employs the ``evaporation concept'' applied in Ant Colony Optimization (ACO) to solve the permutation flowshop problem. The ``evaporation concept'' is used to reduce the effect of past experience and to explore new alternative solutions. In this paper, we propose three different methods for the probability of evaporation. This probability of evaporation is applied as soon as a job is assigned to a position in the permutation flowshop problem. Experimental results show that our ACGA with the evaporation concept gives better performance than some algorithms in the literature.

Optimal scheduling of a processor executing a communication protocol stack

We consider the problem of optimally scheduling a processor executing a multilayer protocol in an intelligent Network Interface Controller (NIC). In particular, we assume a typical LAN environment with class 4 transport service, a connectionless network service, and a class 1 link level protocol. We develop a queuing model for the problem. In the most general case this becomes a cyclic queuing network in which some queues have dedicated servers, and the others have a common schedulable server. We use sample path arguments and Markov decision theory to determine optimal service schedules. The optimal throughputs are compared with those obtained with simple policies. The optimal policy yields upto 25% improvement in some cases. In some other cases, the optimal policy does only slightly better than much simpler policies.

Algorithms for Routing and Centralized Scheduling in IEEE 802.16 Mesh Networks

IEEE 802.16 standards for Wireless Metropolitan Area Networks (WMANs) include a mesh mode of operation for improving the coverage and throughput of the network. In this paper, we consider the problem of routing and centralized scheduling for such networks. We first fix the routing, which reduces the network to a tree. We then present a finite horizon dynamic programming framework. Using it we obtain various scheduling algorithms depending upon the cost function. Next we consider simpler suboptimal algorithms and compare their performances.

Efficient and Fair Scheduling of Uplink and Downlink in IEEE 802.16 OFDMA Networks

We develop new scheduling algorithms for the IEEE 802.16d OFDMA/TDD based broadband wireless access system, in which radio resources of both time and frequency slots are dynamically shared by all users. Our objective is to provide a fair and efficient allocation to all the users to satisfy their quality of service.

Heuristic algorithms for scheduling of a batch processor in automobile gear manufacturing

In this paper we address a scheduling problem for minimising total weighted tardiness. The motivation for the paper comes from the automobile gear manufacturing process. We consider the bottleneck operation of heat treatment stage of gear manufacturing. Real life scenarios like unequal release times, incompatible job families, non-identical job sizes and allowance for job splitting have been considered. A mathematical model taking into account dynamic starting conditions has been developed. Due to the NP-hard nature of the problem, a few heuristic algorithms have been proposed. The performance of the proposed heuristic algorithms is evaluated: (a) in comparison with optimal solution for small size problem instances, and (b) in comparison with `estimated optimal solution' for large size problem instances. Extensive computational analyses reveal that the proposed heuristic algorithms are capable of consistently obtaining near-optimal solutions (that is, statistically estimated one) in very reasonable computational time.

Past Queue Length Based Low-Overhead Link Scheduling in Multi-beam Wireless Mesh Networks

Wireless mesh networks with multi-beam capability at each node through the use of multi-antenna beamforming are becoming practical and attracting increased research attention. Increased capacity due to spatial reuse and increased transmission range are potential benefits in using multiple directional beams in each node. In this paper, we are interested in low-complexity scheduling algorithms in such multi-beam wireless networks. In particular, we present a scheduling algorithm based on queue length information of the past slots in multi-beam networks, and prove its stability. We present a distributed implementation of this proposed algorithm. Numerical results show that significant improvement in delay performance is achieved using the proposed multi-beam scheduling compared to omni-beam scheduling. In addition, the proposed algorithm is shown to achieve a significant reduction in the signaling overhead compared to a current slot queue length approach.

Dynamic scheduling in manufacturing systems using brownian approximations

Recently, Brownian networks have emerged as an effective stochastic model to approximate multiclass queueing networks with dynamic scheduling capability, under conditions of balanced heavy loading. This paper is a tutorial introduction to dynamic scheduling in manufacturing systems using Brownian networks. The article starts with motivational examples. It then provides a review of relevant weak convergence concepts, followed by a description of the limiting behaviour of queueing systems under heavy traffic. The Brownian approximation procedure is discussed in detail and generic case studies are provided to illustrate the procedure and demonstrate its effectiveness. This paper places emphasis only on the results and aspires to provide the reader with an up-to-date understanding of dynamic scheduling based on Brownian approximations.

An Efficient Simulated Annealing With a Valid Solution Mechanism For TDMA Brodcast Scheduling problem

This paper presents an efficient Simulated Annealing with valid solution mechanism for finding an optimum conflict-free transmission schedule for a broadcast radio network. This is known as a Broadcast Scheduling Problem (BSP) and shown as an NP-complete problem, in earlier studies. Because of this NP-complete nature, earlier studies used genetic algorithms, mean field annealing, neural networks, factor graph and sum product algorithm, and sequential vertex coloring algorithm to obtain the solution. In our study, a valid solution mechanism is included in simulated annealing. Because of this inclusion, we are able to achieve better results even for networks with 100 nodes and 300 links. The results obtained using our methodology is compared with all the other earlier solution methods.

S-nets: A Petri net based model for performance evaluation of real-time scheduling algorithms

A new class of nets, called S-nets, is introduced for the performance analysis of scheduling algorithms used in real-time systems Deterministic timed Petri nets do not adequately model the scheduling of resources encountered in real-time systems, and need to be augmented with resource places and signal places, and a scheduler block, to facilitate the modeling of scheduling algorithms. The tokens are colored, and the transition firing rules are suitably modified. Further, the concept of transition folding is used, to get intuitively simple models of multiframe real-time systems. Two generic performance measures, called �load index� and �balance index,� which characterize the resource utilization and the uniformity of workload distribution, respectively, are defined. The utility of S-nets for evaluating heuristic-based scheduling schemes is illustrated by considering three heuristics for real-time scheduling. S-nets are useful in tuning the hardware configuration and the underlying scheduling policy, so that the system utilization is maximized, and the workload distribution among the computing resources is balanced.

The unusual formation of methyl alpha-(5,6-dimethoxycarbonyl-2,3-dimethoxyazepin-7-ylidene)-alpha-[5-methoxycarbonyl-2,3-dimethoxypyrid-6-yl)acetate during the pyrolysis of ''azido-meta-hemipinate'' - First example of a reaction involving a concomitant ring expansion and ring extrusion

he ortho methoxycarbonyl substituent constitutes a sole exception in the ring closure reactions of ortho substituted aryl azides, as it provides no rate acceleration to this reaction. Pyrolysis of ''azido-meta-hemipinate'', an aryl azide containing such a substituent, led us to the title compound, a new azepinylidenepyridylacetic ester, whose structure has been established unambiguously by a single crystal X-ray diffraction study. This is the first report of a reaction involving both a ring expansion to an azaheptafulvalene and a ring extrusion to a pyridyl ring residue.

Scheduling expression trees with reusable registers on delayed-load architectures

In this paper, we look at the problem of scheduling expression trees with reusable registers on delayed load architectures. Reusable registers come into the picture when the compiler has a data-flow analyzer which is able to estimate the extent of use of the registers. Earlier work considered the same problem without allowing for register variables. Subsequently, Venugopal considered non-reusable registers in the tree. We further extend these efforts to consider a much more general form of the tree. We describe an approximate algorithm for the problem. We formally prove that the code schedule produced by this algorithm will, in the worst case, generate one interlock and use just one more register than that used by the optimal schedule. Spilling is minimized. The approximate algorithm is simple and has linear complexity.

Profile-Based Load Scheduling in Wireless Energy Harvesting Sensors for Data Rate Maximization

In this paper, power management algorithms for energy harvesting sensors (EHS) that operate purely based on energy harvested from the environment are proposed. To maintain energy neutrality, EHS nodes schedule their utilization of the harvested power so as to save/draw energy into/from an inefficient battery during peak/low energy harvesting periods, respectively. Under this constraint, one of the key system design goals is to transmit as much data as possible given the energy harvesting profile. For implementational simplicity, it is assumed that the EHS transmits at a constant data rate with power control, when the channel is sufficiently good. By converting the data rate maximization problem into a convex optimization problem, the optimal load scheduling (power management) algorithm that maximizes the average data rate subject to energy neutrality is derived. Also, the energy storage requirements on the battery for implementing the proposed algorithm are calculated. Further, robust schemes that account for the insufficiency of battery storage capacity, or errors in the prediction of the harvested power are proposed. The superior performance of the proposed algorithms over conventional scheduling schemes are demonstrated through computations using numerical data from solar energy harvesting databases.

Opportunistic splitting for scheduling via stochastic approximation

We consider the problem of scheduling a wireless channel among multiple users. A slot is given to a user with a highest metric (e.g., channel gain) in that slot. The scheduler may not know the channel states of all the users at the beginning of each slot. In this scenario opportunistic splitting is an attractive solution. However this algorithm requires that the metrics of different users form independent, identically distributed (iid) sequences with same distribution and that their distribution and number be known to the scheduler. This limits the usefulness of opportunistic splitting. In this paper we develop a parametric version of this algorithm. The optimal parameters of the algorithm are learnt online through a stochastic approximation scheme. Our algorithm does not require the metrics of different users to have the same distribution. The statistics of these metrics and the number of users can be unknown and also vary with time. We prove the convergence of the algorithm and show its utility by scheduling the channel to maximize its throughput while satisfying some fairness and/or quality of service constraints.