In this paper we address the problem of the sequence dependent setup times no-wait flowshop with learning and forgetting effects to minimize total flowtime. Due to the NP-Hard nature of this problem, several simple metaheuristic methods are presented in this paper. A positionbased learning and forgetting effects model is constructed where the processing times of operations vary according to the positions of the jobs in the schedule. An accelerated neighbourhood construction procedure is presented. Given the the simplicity and excellent performance shown in flowshop scheduling problems, an iterated greedy heuristic is studied. To improve the quality of the solutions, the proposed method employs local search heuristics based on Variable Neighbourhood Descent. The presented procedure is compared with some existing algorithms for similar problems on an exhaustive computational campaign. Comprehensive experimental results show that the proposal obtains the best performance among the compared methods by a wide and statistically significant margin.
In dealing with constrained multi-objective optimization problems (CMOPs), a key issue of multi-objective evolutionary algorithms (MOEAs) is to balance the convergence and diversity of working populations. However, most state-of-the-art MOEAs show poor performance in balancing them, and can easily cause the working populations to concentrate on a part of regions of the Pareto fronts, leading to a serious imbalanced searching between preserving diversity and achieving convergence. This paper proposes a method which combines a multi-objective to multi-objective (M2M) approach with the push and pull search (PPS) framework, namely PPS-M2M. To be more specific, the proposed algorithm decomposes a CMOP into a set of simple CMOPs. Each simple CMOP corresponds to a sub-population and is solved in a collaborative manner. When dealing with constraints, each sub-population follows a procedure of "ignore the constraints in the push stage and consider the constraints in the pull stage", which helps each working sub-population get across infeasible regions. In order to evaluate the performance of the proposed PPS-M2M, it is compared with the other six algorithms, including M2M, MOEA/D-Epsilon, MOEA/D-SR, MOEA/D-CDP, C-MOEA/D and NSGA-II-CDP on a set of benchmark CMOPs. The experimental results show that the PPS-M2M is significantly better than the other six algorithms.
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