Xinli Xu

Parallel machine scheduling with splitting jobs by a hybrid differential evolution algorithm

The problem of parallel machine scheduling for minimizing the makespan is an open scheduling problem with extensive practical relevance. It has been proved to be non-deterministic polynomial hard. Considering a jobs batch size greater than one in the real manufacturing environment, this paper investigates into the parallel machine scheduling with splitting jobs. Differential evolution is employed as a solution approach due to its distinctive feature, and a new crossover method and a new mutation method are brought forward in the global search procedure, according to the job splitting constraint. A specific local search method is further designed to gain a better performance, based on the analytical result from the single product problem. Numerical experiments on the performance of the proposed hybrid DE on parallel machine scheduling problems with splitting jobs covering identical and unrelated machine kinds and a realistic problem are performed, and the results indicate that the algorithm is feasible and efficient.

A hybrid discrete particle swarm optimization for dual-resource constrained job shop scheduling with resource flexibility

In this paper, a novel hybrid discrete particle swarm optimization algorithm is proposed to solve the dual-resource constrained job shop scheduling problem with resource flexibility. Particles are represented based on a three-dimension chromosome coding scheme of operation sequence and resources allocation. Firstly, a mixed population initialization method is used for the particles. Then a discrete particle swarm optimization is designed as the global search process by taking the dual-resources feature into account. Moreover, an improved simulated annealing with variable neighborhoods structure is introduced to improve the local searching ability for the proposed algorithm. Finally, experimental results are given to show the effectiveness of the proposed algorithm.

A Novel Real Number Encoding Method of Particle Swarm Optimization for Vehicle Routing Problem

Vehicle routing problem is a well-known NP problem, many heuristic algorithms, such as genetic algorithm, simulated annealing algorithm is applied in the problem. Particle swarm optimization (PSO) is a new evolutionary computation technique. Although PSO algorithm possesses many attractive properties, the method of encoding in NP problem need further to investigated. In the paper, a novel real number encoding method of particle swarm optimization (PSO) for vehicle routing problem is proposed. The vehicle is mapped into the integer part of the real number; the sequence of the customers in the vehicle is mapped into the decimal fraction of the real number. After decoding, saving algorithm, nearest neighbor algorithm and or-opt optimizes the inner or outer routes and modify the illegal solution. Series of numerical examples were tested and verified, which shows the better performance of the proposed algorithm compare with other particle swarm optimization algorithm and genetic algorithm

A multi-objective particle swarm optimization for dual-resource constrained shop scheduling with resource flexibility

In this paper, a novel multi-objective hybrid particle swarm algorithm is proposed to solve the dual-resource constrained shop scheduling problem with minimizing production period and production cost being the objectives. First, particles are represented and updated directly in the discrete domain. Then simulated annealing with variable neighborhoods structure is introduced to improve the local search ability. Third, an external archive based on Pareto-dominance is applied to store the non-dominated solutions. The computational results are provided and compared with existing methods. It is shown that the proposed algorithm achieves better performance in both convergence and diversity.

An Improved Real Coded Quantum Genetic Algorithm and its Applications

In this paper, an new quantum genetic algorithm (RQGA) is presented to enhance the global optimization capability. Different from previous quantum genetic algorithm, the proposed RQGA uses real-coded replacing binary code, and uses approximation operator replacing rotation gate. RQGA can accelerate the convergence speed, and improve the solution precision. The results of function optimization and 0-1 knapsack problem show that RQGA is an effective algorithm.

Scheduling Batch and Continuous Process Production based on an Improved Differential Evolution Algorithm

Abstract In order to solve the scheduling problems of mixed batch and continuous processes, continuous time was discretized, and an improved differential evolution algorithm was developed. A new chromosome representation was proposed, considering capacity constraints. Also, a new crossover method and a new mutation method were proposed based on the new chromosome representation. The value of the crossover probability CR was obtained by using the logistic chaotic map method, and the selection operator was improved to promote the global search ability. The results of the simulation indicate that the model and the method are feasible.

A batch splitting job shop scheduling problem with bounded batch sizes under multiple-resource constraints using genetic algorithm

Considering alternative resources for operations, requirement of multiple resources to process an operation and a jobpsilas batch size greater than one in the real manufacturing environment, a study is made on the batch splitting scheduling problem with bounded batch sizes under multiple-resource constraints, based on the objective to minimize the maximum completion time. A genetic algorithm which is suitable for this problem is proposed, with a new chromosome representation, which takes into account the batch splitting of the original batches of jobs. And a new crossover method and a new mutation method are proposed based on the new chromosome representation. The results of the simulation indicate that the method is feasible and efficient.

Hybrid Discrete Differential Evolution Algorithm for Lot Splitting with Capacity Constraints in Flexible Job Scheduling

A two-level batch chromosome coding scheme is proposed to solve the lot splitting problem with equipment capacity constraints in flexible job shop scheduling, which includes a lot splitting chromosome and a lot scheduling chromosome. To balance global search and local exploration of the differential evolution algorithm, a hybrid discrete differential evolution algorithm (HDDE) is presented, in which the local strategy with dynamic random searching based on the critical path and a random mutation operator is developed. The performance of HDDE was experimented with 14 benchmark problems and the practical dye vat scheduling problem. The simulation results showed that the proposed algorithm has the strong global search capability and can effectively solve the practical lot splitting problems with equipment capacity constraints.

Improved Particle Swarm Optimization For Traveling Salesman Problem.

To compensate for the shortcomings of existing methods used in TSP (Traveling Salesman Problem), such as the accuracy of solutions and the scale of problems, this paper proposed an improved particle swarm optimization by using a self-organizing construction mechanism and dynamic programming algorithm. Particles are connected in way of scale-free fully informed network topology map. Then dynamic programming algorithm is applied to realize the evolution and information exchange of particles. Simulation results show that the proposed method with good stability can effectively reduce the error rate and improve the solution precision while maintaining a low computational complexity.

Research on hydropower station optimal scheduling considering ecological water demand

Considering that the standard particle swarm optimization has slow convergence speed and is easy to trap into local optimal solution, this paper proposed an improved algorithm with a dynamic neighborhood topology, where the connections between the particles are adjusted with a changing dynamical neighborhood structure of the particle. In the early stage of the algorithm, the impact of the optimal particle is weakened to maintain the diversity of the population and to prevent the algorithm from local optimum, then connections between particles are added to make the algorithm have more rapid convergence in the later stage. Focusing on hydropower optimal scheduling problems, we discussed relevant technologies, built the model of scheduling considering ecological water demand and studied the calculation of river Ecological Water Demand in the ecological operation of hydropower station. We combined ecological operation and generation scheduling taking maximum of power generation as the objective and taking into account constraints like ecological factors of the river, the balance of reservoir water, discharge volume restrictions, output restrictions etc., then we used an improved particle swarm algorithm to solve the optimization problem. The simulation scheduling results show that the algorithm has strong global search ability and rapid convergence speed, which can effectively solve such a multi-constrains, non-linearity problem in hydropower stations scheduling.