memetic algorithm

In the classic models of scheduling problems, researchers mostly concentrate on the objectives considering jobs completion time. Due to the relation among economy, energy and environmental concerns, attention to the energy use of machines have been considered by researchers in the field of scheduling in recent years. Also, In the literature of scheduling problems, it is mostly assumed that one agent try to optimize the problem. But, occasionally there are several agents that each has their own jobs and they must use a series of common resources to process them. In this study, a two-agent heterogeneous parallel-machines scheduling problem is studied in which the process speed of each job on each machine is adjustable. Since there is a direct link between the energy used in machines and process speed, the used energy costs affect on scheduling problem. In this study, the first agent is tried to minimize total tardiness penalty as well as energy costs of production machines and the second agent is tried to minimize total tardiness and earliness. The suitable schedule should be considered to allocate and sequence jobs of agents to the common resources to optimize appropriately the agent’s objective functions. Since the proposed problem is Np-hard, in order to solve it in large scale problems, a Memetic algorithm is developed and to verify the performance of this algorithm, we take into comparison the results of Memetic algorithm with the results of GAMS software and of another meta-heuristic algorithm.

Design of reverse supply chain network (RSCN) to efficiently manage the flow of returned products is one of the most important issues in supply chain management. Determining an acquisition price of returned products which could be affected by different factors such as product quality levels has a significant effect on strategic design of RSCN. In this paper¡ a comprehensive mathematical model for designing a multi-level RSCN with a dynamic pricing approach for returned products which is affected by product quality levels. In order to manage the uncertainty of number of returned products¡ a robust optimization based on the uncertainty budget approach is considered. The aim of the proposed model is to design the RSCN to maximize the RSCN total profit and responding to customer demands simultaneously. Due to the NP-hard nature of the network design problems¡ a memetic algorithm based on the non-dominated sorting genetic algorithm II and parallel adaptive variable neighborhood search is proposed to find the optimal Pareto solutions. The performance of the proposed memetic algorithm is compared with multiple similar algorithms. The computational results indicate a significant impact of the dynamic pricing approach on the performance of the reverse logistic network. In addition¡ using the robust optimization based on the uncertainty budget approach can efficiently handle various conservatism levels of decision makers under uncertainty of the business environment. Finally¡ the obtained results show the significant superiority of the proposed hybrid meta-heuristic algorithm to solve a multi-objective RSCN design model via considering the dynamic pricing approach under uncertainty.

Hub location problems are among the extensions of classical location problems that have numerous applications in designing transportation, postal, and telecommunication networks. Despite the evident attention of researchers to these problems, there are few studies on designing hub networks under uncertainty and disruption. In this study, the capacitated single allocation hub location problem under demand uncertainty and disruption is investigated. First, a mathematical model based on budget of uncertainty concept is proposed, then a hybrid solution method based on genetic algorithm and variable neighborhood search is proposed. Computational experiments demonstrate the accuracy of the proposed solution method in solving the testinstances. In addition, the results obtained from conducting Monte-Carlo simulation experiments to analyze the effects of uncertainty on the performance of different hub network configurations show the necessity of considering demand uncertainty and disruption while designing hub networks.

Flexible job shop scheduling problem)FJSP(is an extension of the classical job shop scheduling problem which allows an operation to be processed by any machine from a given set. FJSP is NP-hard and mainly presents two difficulties. The first one is to assign each operation to a machine out of a set of capable machines, and the second one deals with sequencing the assigned operations on the machines. However, it is quite difficult to achieve an optimal solution to this problem in medium and large size problems with traditional optimization approaches. In this paper a memetic algorithm (MA) or flexible job shop scheduling with overlapping in operation is proposed that solves the FJSP to minimize makespan time and obtain the optimum solution for small problem and best solution for medium and large scale problems. In this paper we also used preemption to improve the results of memetic algorithm and reduce the makespan.