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