Earliness–tardiness minimization on scheduling a batch processing machine with non-identical job sizes

A new scheduling model in which both two-agent and a batch processing machine with non-identical job sizes exist is considered in this paper. Two agents compete to process their respective job sets on a common single batch processing machine. The objectives of the two agents are both to minimize the makespan. It is proved in the literature[7] that the complexity of minimizing makespan of one agent on a single batch processing machine with non-identical job sizes is NP-hard in the strong sense. We developed an improved ant colony optimal algorithm to search for the Pareto optimal solutions. The experimental results showed that the proposed algorithm could get better non-dominated solutions compared with the non-dominated sorting genetic algorithm (NSGA-II) which was widely used in solving the multi-objective optimization problem.

An effective hybrid multi-objective genetic algorithm for bi-criteria scheduling on a single batch processing machine with non-identical job sizes

This paper considers online scheduling with incompatible job families on single batch processing machine and parallel batchprocessing machines. Online algorithm is developed and the performance ratio of the algorithm is analyzed for the problem under consideration, respectively.

This paper aims at minimizing the total completion time for a single batch processing machine with non-identical job sizes. For this problem, each job has a corresponding processing time and size. The machine can process the jobs in batches as long as the total size of all the jobs in a batch does not exceed the machine capacity. The processing time of a batch is equal to the longest processing time among all the jobs in that batch. This problem is NP-hard and hence a chaotic ant colony optimization algorithm based on batch sequence (BCACO) is proposed. Random instances were used to test the effectiveness of the proposed approach. Computational results show that BCACO significantly outperforms other algorithms addressed in the literature.

A novel algorithm called Free Search (FS) is applied to solve the scheduling problems on a single batch processing machine with non-identical job sizes (NSBM) in this paper. The concept of sensibility was introduced in FS, with which the algorithm had norestriction of the probability to zero for the parts of the search space and thus it could avoid premature convergence. In order to make it feasible to NSBM problem, we modify the parameters settings and also provide coding by vector method which makes FS proper to discrete problem. Then the algorithm of batching based FS (BFS) is proposed. In the experiment, all levels of instances are simulated and the results show that BFS has better performance on global search and outperforms other algorithm in the literature.

In network function virtualization, network services are implemented as service function chains (SFCs). An extensive body of work exists on SFC orchestration, although a vast majority of them consider reactive algorithms that reprovision resources in response to service demand fluctuations. As such, they result in unpredictable and often significant delays that negatively affect the performance of delay-sensitive SFCs. In this paper, we consider proactive SFC orchestration and develop exact and approximate algorithms that perform well under general service demands without requiring frequent resource reprovisioning. Specifically, we first formulate SFC orchestration with deadline as a mixed integer non-linear program and show that it can be reduced to a second-order cone program, which can be solved using standard optimization software, albeit for small problem instances. We then design an approximate algorithm for large problem instances by applying iterative rounding and variable fixing techniques to the exact problem formulation. We present extensive model-driven simulation results to study the behavior of our algorithms in small and large problem instances and demonstrate their ability to achieve any desired provisioning-reprovisioning trade-off. We further compare the performance of our approximate algorithm against two recently proposed algorithms called FFCA and MaxZ.

We consider the problem of minimizing the makespan on a batch processing machine, in which jobs are not all compatible. Only compatible jobs can be included into the same batch. This relation of compatibility is represented by a split graph. Jobs have release dates. The capacity of the batch processing machine is finite or infinite. The processing time of a batch is given by the processing time of the longest job in the batch. We establish the NP-hardness of the general problem and present polynomial algorithms for several special cases. Relating scheduling theory and graph theory appears to be an interesting and important concept.

This paper considers the problem of minimizing makespan on a single batch-processing machine considering preventive maintenance. A mathematical model for integrating flexible preventive maintenance into the batch-processing machine scheduling is proposed. A two-stage method is proposed to solve the problem. A batching rule considering maintenance is proposed to group jobs into batches firstly. And then we improve existing heuristics to schedule production and maintenance. Computational results indicate that the two-stage method based on proposed batching rule and improved heuristics is effective.

The traditional single intelligent algorithm has slow convergence speed in the frequency assignment, and the effect cannot meet the increasing frequency equipment in the naval battlefield. Based on the single intelligent frequency assignment algorithm and the frequency conflict analysis model between systems, this paper proposes three heuristic frequency assignment algorithms based on hybrid genetic algorithms, namely, Genetic Algorithm and Tabu Search (GATS), Hybrid Genetic Simulated Annealing Algorithm (HGSAA) and Genetic Algorithm-ant Colony Algorithm (GAA). The simulation results show that the hybrid algorithm can quickly converge to a better allocation result than the single intelligent algorithm in the frequency assignment problem, and the optimal value is better than the single intelligent algorithm. Among them, the convergence speed and optimal value of the GAA are the best of the three algorithms. Therefore, this algorithm can be applied to frequency assignment with more frequency-using equipment within a fixed range.

A hybrid genetic algorithm with decomposition phases for the Unequal Area Facility Layout Problem

A hybrid genetic local search algorithm for the permutation flowshop scheduling problem

Large scale batch scheduling problems with complex constraints are difficult and time-consuming to solve. Therefore, this paper addresses the green batch scheduling problem on non-identical parallel machines with time-of-use electricity prices. The objective of the problem is to minimise total electricity costs (TEC) in production. Two kinds of algorithms—single-population genetic algorithms (SPGA) and multi-population genetic algorithm (MPGA)—are proposed to solve the problem. In the algorithms, the products are allocated into batches and are then allocated to machines randomly. A greedy strategy is designed to arrange the production sequence and the starting time of the batches. Furthermore, a self-adaptive parameter adjustment strategy is proposed to enhance the adaptability of the algorithm. Computational experiments with CPLEX solver have been conducted to evaluate the performance of the algorithms. On small instances, both SPGA and MPGA can achieve approximate results compared with those obtained by CPLEX, and can also achieve smaller TEC on large instances with less computing time. In addition, the proposed MPGA implemented by parallel computing outperforms SPGA in getting better results with nearly the same computing time.

In this paper, we consider the problem of scheduling semiconductor burn-in operation, where the burn-in oven is modelled as a batch processing machine. In this study, both oven capacity and the number of boards available are taken as constraints. The objective measure of the problem is minimising the total completion time of all jobs. The computational difficulty in getting optimal solution is shown empirically. Since the problem under study is NP-hard, two variants of Tabu Search method are proposed. A series of computational experiments show that the proposed variants of Tabu Search method outperforms the existing heuristic for most system configurations in very meagre CPU times on P4 machine.

This paper proposed and implemented an genetic and weed invasion hybrid algorithm in Sparse Array Synthesis. The classical weed invasion algorithm has high requirements on the convergence parameters in the process of generating weeds from seeds. Inappropriate parameters can easily lead to local convergence of evolution. The mutation and crossover operations similar to the genetic algorithm were introduced into the classical weed invasion algorithm. The method improves convergence speed, avoids local convergence, and enhances the stability of the algorithm. The algorithm is used in practical application in the synthesis of linear sparsely distributed array pattern, and the predetermined index is obtained through calculation. At the same time, compared with the genetic algorithm and the classical weed invasion algorithm, the results show that the hybrid algorithm has certain advantages.

The paper addresses the problem scheduling a batch processing machine with multiple incompatible job families, non-identical job dimensions, non-identical job sizes and non-agreeable release dates to minimize makespan. The research problem is solved by proposing a mixed integer programming model that appropriately takes into account the parameters considered in the problem. The proposed is validated using a numerical example. The experiment conducted show that the model can pose significant difficulties in solving the large scale instances. The paper concludes by giving the scope for future work and some alternative approaches one can use for solving these class of problems.