Shih-Tang Lo

Using novel particle swarm optimization scheme to solve resource-constrained scheduling problem in PSPLIB

This investigation proposes an improved particle swam optimization (PSO) approach to solve the resource-constrained scheduling problem. Two proposed rules named delay local search rule and bidirectional scheduling rule for PSO to solve scheduling problem are proposed and evaluated. These two suggested rules applied in proposed PSO facilitate finding global minimum (minimum makespan). The delay local search enables some activities delayed and altering the decided start processing time, and being capable of escaping from local minimum. The bidirectional scheduling rule which combines forward and backward scheduling to expand the searching area in the solution space for obtaining potential optimal solution. Moreover, to speed up the production of feasible solution, a critical path is adopted in this study. The critical path method is used to generate heuristic value in scheduling process. The simulation results reveal that the proposed approach in this investigation is novel and efficient for resource-constrained class scheduling problem.

Multiprocessor system scheduling with precedence and resource constraints using an enhanced ant colony system

This study presents and evaluates a modified ant colony optimization (ACO) approach for the precedence and resource-constrained multiprocessor scheduling problems. A modified ant colony system is proposed to solve the scheduling problems. A two-dimensional matrix is proposed in this study for assigning jobs on processors, and it has a time-dependency relation structure. The dynamic rule is designed to modify the latest starting time of jobs and hence the heuristic function. In exploration of the search solution space, this investigation proposes a delay solution generation rule to escape the local optimal solution. Simulation results demonstrate that the proposed modified ant colony system algorithm provides an effective and efficient approach for solving multiprocessor system scheduling problems with resource constraints.

An effective ant colony optimization-based algorithm for flow shop scheduling

This article presents a modified scheme named local search ant colony optimization algorithm on the basis of alternative ant colony optimization algorithm for solving flow shop scheduling problems. The flow shop problem (FSP) is confirmed to be an NP-hard sequencing scheduling problem, which has been studied by many researchers and applied to plenty of applications. Restated, the flow shop problem is hard to be solved in a reasonable time, therefore many meta-heuristics schemes proposed to obtain the optima or near optima solution efficiently. The ant colony optimization (ACO) is one of the well-applied meta-heuristics algorithms, nature inspired by the foraging behavior of real ants. Different implementations of state transition rules applied in ACO are studied in this work. Meanwhile, a local search mechanism was introduced to increase the probability of escaping from local optimal. Hence, this work integrates the local search mechanism into ant colony optimization algorithm for solving flow shop scheduling problem to improve the quality of solutions. Simulation results demonstrate that the applied ldquorandom orderrdquo state transition rule used in ACO with local search integrated is an effective scheme for the flow shop scheduling problems.

Combined discrete particle swarm optimization and simulated annealing for grid computing scheduling problem

The grid scheduling problem is concerted with some tasks assigning to a grid distributed system that the relative tasks have to exchange information on different grids. In the original particle swarm optimization (PSO) algorithm, particles search solutions in a continuous solution space. Since the solution space of the grid scheduling problem is discrete. This paper presents a discrete particle swarm optimization (PSO) that combines the simulated annealing (SA) method to solve the grid scheduling problems. The proposed discrete PSO uses a population of particles through a discrete space on the basis of information about each particles local best solution and global best solution of all particles. For generating the next solution of each particle, the SA is adopted into the discrete PSO. The objective is to minimize the maximum cost of the grid, which includes computing cost and communication cost. Simulation results show that the grid scheduling problem can be solved efficiently by the proposed method.

Using particle swarm optimization to solve resource-constrained scheduling problems

This investigation introduced a particle swarm optimization (PSO) approach to solve the multi-processor resource-constrained scheduling problems. There are two new rules are proposed and evaluated, named anti-inertia solution generation rule and bidirectional searching rule of PSO. The anti-inertia solution generation rule enables some jobs with anti-inertia velocity used to decide the start processing time, and escaping from local minimum. The bidirectional searching rule combines forward and backward scheduling to extend the search solution space. These two suggested rules applied in PSO scheme are capable of finding global minimum. The simulation results reveal that the proposed approach in this investigation can successfully solve scheduling problems.

Combining competitive scheme with slack neurons to solve real-time job scheduling problem

Generally, how to satisfy the deadline constraint is the major issue in solving real-time scheduling. Recently, neural network using competitive learning rule provides a highly effective method and deriving a sound solution for scheduling problem with less network complexity. However, due to the availability of resources, the machines may not reach full utilization. To facilitate the problem the extra neuron is introduced to the competitive neural network (CHNN). This study tries to impose slack neuron on CHNN with respect to process time and deadline constraints. Simulation results reveal that the competitive neural network imposed on the proposed energy function with slack neurons integrated ensures an appropriate approach of solving this class of scheduling problems of single or multiple identical machines.

Multi-constraint system scheduling using dynamic and delay ant colony system

This study presents and evaluates a modified ant colony optimization (ACO) approach for the precedence and resource-constrained multiprocessor scheduling problems. A modified ant colony system, with two designed rules, called dynamic and delay ant colony system, is proposed to solve the scheduling problems. The dynamic rule is designed to modify the latest starting time of jobs and hence the heuristic function. A delay solution generation rule in exploration of the search solution space is used to escape the local optimal solution. Simulation results demonstrate that the proposed modified ant colony system algorithm provides an effective and efficient approach for solving multiprocessor system scheduling problems with precedence and resource constraints.

Solving inequality constraints job scheduling problem by slack competitive neural scheme

A competitive neural network provides a highly effective means of attaining a sound solution and of reducing the network complexity. A competitive approach is utilized to deal with fully-utilized scheduling problems. This investigation employs slack competitive Hopfield neural network (SCHNN) to resolve non-fully and fully utilized identical machine scheduling problems with multi-constraint, real time (execution time and deadline constraints) and resource constraints. To facilitate resolving the scheduling problems, extra slack neurons are added on to the neural networks to represent pseudo-jobs. This study presents an energy function corresponding to a neural network containing slack neurons. Simulation results demonstrate that the proposed energy function integrating competitive neural network with slack neurons can solve fully and non-fully utilized real-time scheduling problems.

Solving multiprocessor real-time system scheduling with enhanced competitive scheme

A new method based on Hopfield Neural Networks (HNN) for solving real-time scheduling problem is adopted in this study. Neural network using competitive learning rule provides a highly effective method and deriving a sound solution for scheduling problem. Moreover, competitive scheme reduces network complexity. However, competitive scheme is a 1-out-of-N confine rule and applicable for limited scheduling problems. Restated, the processor may not be full utilization for scheduling problems. To facilitate the non-fully utilized problem, extra neurons are introduced to the Competitive Hopfield Neural Network (CHNN). Slack neurons are imposed on CHNN with respected to pseudo processes. Simulation results reveal that the competitive neural network imposed on the proposed energy function with slack neurons integrated ensures an appropriate approach of solving both full and non-full utilization multiprocessor real-time system scheduling problems.