Learning Dispatching Rules for Energy-Aware Scheduling of Jobs on a Single Batch Processing Machine

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.

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.

Energy-efficient scheduling of a single batch processing machine with dynamic job arrival times

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

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.

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.

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.

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

In this paper, we study the impact of critical queue time (CQT) constraints in semiconductor wafer fabrication facilities (wafer fabs). Process engineers impose CQT constraints that require wafers to start a subsequent operation within a given time window after a certain operation is completed to prevent native oxidation and contamination effects on the wafer surface. We equip dataset 2 of the SMT2020 testbed with production control logic to avoid CQT constraint violations. Therefore, two different CQT-aware dispatching rules and a combination of a lot stopping strategy with these rules are proposed. The effect of the production control strategies is investigated by means of a simulation study. We show that the number of CQT violations can be reduced without large deteriorations of global performance measures such as cycle time and throughput.

We investigate the performance of different dispatching and scheduling heuristics for batching tools in a semiconductor wafer fabrication facility (wafer fab) by means of discrete event simulation. Because the processing times of lots on batching tools are quite large compared to those of other processes, careful batching decisions may have a great impact on the performance of the entire wafer fab. In a first step, we investigate the performance of certain modifications of the apparent tardiness cost (ATC) dispatching rule that do not take into account future lot arrivals. In a second step, we extend this approach by considering future lot arrivals. In a last step, we combine a genetic algorithm for assignment of the batches to parallel machines with the ATC rule, which takes future lot arrivals into account. We present results of simulation experiments with the different heuristics.

A multi-population genetic algorithm to solve multi-objective scheduling problems for parallel machines

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.

Evolving priority rules for on-line scheduling of jobs on a single machine with variable capacity over time

On-line scheduling is often required in real life situations. This is the case of the one machine scheduling with variable capacity and tardiness minimization problem, denoted \((1,Cap(t)||\sum T_i)\). This problem arose from a charging station where the charging periods for large fleets of electric vehicles (EV) must be scheduled under limited power and other technological constraints. The control system of the charging station requires solving many instances of this problem on-line. The characteristics of these instances being strongly dependent on the load and restrictions of the charging station at a given time. In this paper, the goal is to evolve small ensembles of priority rules such that for any instance of the problem at least one of the rules in the ensemble has high chance to produce a good solution. To do that, we propose a Genetic Algorithm (GA) that evolves ensembles of rules from a large set of rules previously calculated by a Genetic Program (GP). We conducted an experimental study showing that the GA is able to evolve ensembles or rules covering instances with different characteristics and so they outperform ensembles of both classic priority rules and the best rules obtained by GP.

This paper presents the comparative use of Simulated Annealing (SA) and Genetic Algorithm (GA) in a scheduling problem of unrelated parallel machines with set-up times. The problem accounts for allotting batched work parts to the unrelated parallel machines, where each batch is composed of a fixed number of identical jobs. Processing time of each job of a batch is determined according to the machine group as well as the batch group to which the job belongs. Major or minor set-up times are required between two subsequent batches, depending on the batch sequence but yet independent of machines. The objective of the problem is to minimise the Total Weighted Tardiness (TWT) for the unrelated parallel machine scheduling. SA and GA algorithms are proposed to obtain near-optimal solutions of the problem. The performance of proposed heuristics is compared through computational experiments with real data from dicing operations of a compound semiconductor manufacturing facility.