Solution algorithms and heuristics for an energy efficient hybrid flowshop problem

The files contain psuedocodes of the main algorithm, the hyperheuristics, metaheuristics, and the modified exact algorithm for the scheduling problem. The main algorithm first decides on how the choice of machines at the stations with parallel processors would be made, before it decides the job orders followed in every stage. The hyperheuristic consists of six low level heuristics (LLH) which are combined with the metaheuristc by Nawaz, Enscore and Ham (NEH) and GA to form the Improved Hyper heuristic NEH (IHNEH) algorithm, and the Improved Hyper heuristic GA (IHGA) algorithm. Each of these two algorithms operate in three stages and are improved using the local search heurisitc, and share the first step, which is where the hyper heuristic is used to select a low-level heuristic for implementation. The second step makes use of the selected LLH and processing time vector derived thereby to create long replication cycles for the problem instances created, consisting of the combination of each sequencing rule (IHNEH and IHGA) and job size, followed by the neighbourhood search algorithms. The final step of the algorithm tests the effectiveness of the two solutions against the exact algorithm called the Branch and Bound (B&B) algorithm in terms of the value of makespan returned, the energy consumption level, and the running time taken to obtain the solutions. The first part of the datasets contains figures and tables of the average makespan, energy consumptions, and running times of the algorithms. The last part contains figures of the convergence of the genetic algorithm and the energy threshold reduction factors to validate. the choice of the particular parameter values over others in developing the algorithms

本数据集文件包含针对调度问题的主算法、超启发式算法（hyperheuristic）、元启发式算法（metaheuristic）以及改进精确算法的伪代码（pseudocode）。主算法首先确定具备并行处理器的工位处的机器选择规则，随后再确定各阶段的作业调度顺序。该超启发式算法包含6种低级启发式算法（LLH），将其与Nawaz、Enscore与Ham提出的元启发式算法（NEH）以及遗传算法（Genetic Algorithm, GA）相结合，分别构建得到改进超启发式NEH（IHNEH）算法与改进超启发式GA（IHGA）算法。上述两种算法均包含三个执行阶段，且均通过局部搜索启发式算法进行优化，二者共享第一阶段流程：即利用超启发式算法选取一款低级启发式算法执行。第二阶段借助选定的低级启发式算法与由此得到的加工时间向量，为由各调度规则IHNEH、IHGA与作业规模组合而成的问题实例生成长重复实验周期，并辅以邻域搜索算法。算法的最终阶段以分支定界（Branch and Bound, B&B）精确算法作为参照算法，从返回的最大完工时间（makespan）、能耗水平与求解所需运行时间三个维度，验证两种算法所得解的有效性。本数据集的第一部分包含各算法的平均最大完工时间、平均能耗与平均运行时间的图表与表格。数据集的最后一部分则包含遗传算法收敛性与能耗阈值缩减因子的相关图表，用于验证算法开发过程中所选用的特定参数值相较于其他参数的合理性。