This paper considers the problem of scheduling hybrid flowshops with machine availability constraints (MAC) to minimize makespan. The paper deals with a specific case of MAC caused by preventive maintenance (PM) operations. Contrary to previous papers considering fixed or/and conservative policies, we explore a case in which PM activities might be postponed or expedited while necessary. Regarding this flexibility in PM activities, we expect to obtain more efficient schedule. A simple technique is employed to schedule production jobs along with the flexible MACs caused by PM. To solve the problem, we present a high performing metaheuristic based on Memetic algorithm incorporating some advanced features. To evaluate the proposed algorithm, the paper compares the proposed algorithm with several wellknown algorithms taken from the literature. Finally, we conclude that the proposed algorithm outperforms other algorithms.

In this paper we address the VRPCD, in which a set of homogeneous vehicles are used to transport products from the suppliers to customers via a cross-dock. The products can be consolidated at the cross-dock but cannot be stored for very long as the cross-dock does not have long-term inventory-holding capabilities. The objective of the VRPCD is to minimize the total traveled distance while respecting time window constraints of suppliers and customers and a time horizon for the whole transportation operation. Following the literature on vehicle routing problems with cross-docking, it seems that few studies consider that customer will receive its requests from several suppliers; therefore, the present study is an attempt to investigate this case. A heuristic and a Memetic algorithm are used to solve the problem. The proposed algorithms are implemented and tested on data sets involving up to 200 nodes (customers and suppliers). The first results show that the Memetic algorithm can produce high quality solutions. It is able to find the optimal solution for small instances, for the large ones, it is very powerful comparing with the best insertion heuristic, the gap achieved 30%.

Automated Guided Vehicle System (AGVS)provides the flexibility and automation demanded byFlexible Manufacturing System (FMS). However, with thegrowing concern on responsible management of resourceuse, it is crucial to manage these vehicles in an efficientway in order reduces travel time and controls conflicts andcongestions. This paper presents the development processof a new Memetic algorithm (MA) for optimizing partitioningproblem of tandem AGVS. MAs employ a Geneticalgorithm (GA), as a global search, and apply a localsearch to bring the solutions to a local optimum point. Anew Tabu Search (TS) has been developed and combinedwith a GA to refine the newly generated individuals by GA. The aim of the proposed algorithm is to minimize themaximum workload of the system. After all, the performanceof the proposed algorithm is evaluated using Matlab. This study also compared the objective function of theproposed MA with GA. The results showed that the TS, asa local search, significantly improves the objective functionof the GA for different system sizes with large and smallnumbers of zone by 1. 26 in average.