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A single machine that includes loading/unloading areas for each job processed, loading and unloading could be performed while the machine is running causing minimized jobs completion time with lowest machine idle time. This design requires special kind of scheduling technique to ensure the accomplishment of those objectives if jobs’ processing, loading and unloading times are varying. The machine is modelled as a flow shop with blocking constraint. This research focuses on finding a solution to schedule this special case of flow shop of more than two machines with objective of minimizing jobs maximum completion time (makespan). The proposed solution in this research includes using a newly developed meta-heuristic known as Meta-heuristic for Randomized Priority Search (Meta-RaPS). Meta-RaPS construction phase is applied with the use of NEH flow shop scheduling algorithm and would provide very good schedules. The suggested technique is evaluated in comparison to top performing current meta-heuristics and construction heuristics on the famous benchmark flow shop data set of Taillard 20 . The results would suggest that applying Meta-RaPS for this flow shop problem is a great choice for constructing solutions and would provide high quality solutions with opportunity of more improvement in further research. [ABSTRACT FROM AUTHOR]

Though metaheuristics have been frequently employed to improve the performance of data mining algorithms, the opposite is not true. This paper discusses the process of employing a data mining algorithm to improve the performance of a metaheuristic algorithm. The targeted algorithms to be hybridized are the Meta-heuristic for Randomized Priority Search (Meta-RaPS) and an algorithm used to create an Inductive Decision Tree. This hybridization focuses on using a decision tree to perform on-line tuning of the parameters in Meta-RaPS. The process makes use of the information collected during the iterative construction and improvement phases Meta-RaPS performs. The data mining algorithm is used to find a favorable parameter using the knowledge gained from previous Meta-RaPS iterations. This knowledge is then used in future Meta-RaPS iterations. The proposed concept is applied to benchmark instances of the Vehicle Routing Problem. [ABSTRACT FROM AUTHOR]

Abstract: In their search for satisfactory solutions to complex combinatorial problems, metaheuristics methods are expected to intelligently explore the solution space. Various forms of memory have been used to achieve this goal and improve the performance of metaheuristics, which warranted the development of the Adaptive Memory Programming (AMP) framework [1]. This paper follows this framework by integrating Machine Learning (ML) concepts into metaheuristics as a way to guide metaheuristics while searching for solutions. The target metaheuristic method is Meta-heuristic for Randomized Priority Search (Meta-RaPS). Similar to most metaheuristics, Meta-RaPS consists of construction and improvement phases. Randomness coupled with a greedy heuristic are typically employed in the construction phase. While a local search heuristic is used in the improvement phase. This paper proposes adding a new learning phase, in which a ML method will be integrated. An Inductive Decision Tree (IDT) will be incorporated into the learning phase in an effort to learn from the information collected during the construction and improvement phases. The proposed approach will be demonstrated using instances for the Capacitated Vehicle Routing Problem (CVRP). [Copyright &y& Elsevier]