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The stochastic economic lot scheduling problem (SELSP) considers the make-to-stock production of multiple standardized products on a single machine with limited capacity and set-up costs under random demands, random set-up times, and random production times. The SELSP is an NP-hard inventory problem. Current solutions for the SELSP can be classified as analytic or heuristic. In both approaches, however, the computation time needed to obtain an optimal solution is still unsatisfactory. In this paper, the SELSP is first formulated as a fixed-sequence base-stock (FSBS) system with quantity-limited lot-sizing policy. An algorithm combining artificial bee colony (ABC) approach and ordinal optimization (OO) theory, abbreviated as ABCOO, is then proposed to find a good enough base-stock level of the FSBS system using reasonable computation time. The proposed algorithm combines the advantage of multidirectional search in ABC with the advantage of goal softening in OO. Finally, the ABCOO algorithm is used to solve an SELSP involving 12 products and three queuing models. Test results obtained by the ABCOO algorithm are compared with four lot-sizing policies and three meta-heuristic methods. The base-stock level obtained by the ABCOO algorithm is excellent in terms of solution quality and computational efficiency. Furthermore, a time series forecasting technique is used to predict the variant demand rates needed to resolve time-lag problems of the ABCOO algorithm. Tests of the forecasting technique confirm that it considerably improves the performance and enables the proposed algorithm real-time applications. [ABSTRACT FROM PUBLISHER]

This paper proposes a novel optimization scheme by hybridizing an artificial bee colony optimizer (HABC) with a bee life-cycle mechanism, for both stationary and dynamic optimization problems. The main innovation of the proposed HABC is to develop a cooperative and population-varying scheme, in which individuals can dynamically shift their states of birth, foraging, death, and reproduction throughout the artificial bee colony life cycle. That is, the bee colony size can be adjusted dynamically according to the local fitness landscape during algorithm execution. This new characteristic of HABC helps to avoid redundant search and maintain diversity of population in complex environments. A comprehensive experimental analysis is implemented that the proposed algorithm is benchmarked against several state-of-the-art bio-inspired algorithms on both stationary and dynamic benchmarks. Then the proposed HABC is applied to the real-world applications including data clustering and image segmentation problems. Statistical analysis of all these tests highlights the significant performance improvement due to the life-cycle mechanism and shows that the proposed HABC outperforms the reference algorithms. [ABSTRACT FROM PUBLISHER]

Convergence, diversity and feasibility are three important issues when solving constrained multiobjective optimization problems (CMOPs). To deal with the balance among convergence, diversity and feasibility well, this article proposes a cooperative multiobjective evolutionary algorithm with propulsive population (CMOEA-PP) for solving CMOPs. CMOEA-PP has two populations, including propulsive population and normal population, and these two populations work cooperatively. Specifically, propulsive population focuses on convergence. Normal population gives priority to feasibility and is obligated to maintain diversity. To cross through the infeasible region and reach the Pareto front (PF), propulsive population does not consider constraints in the early stage and only considers constraints in the later stage. To further accelerate the speed of convergence, propulsive population only searches for corner solutions and center solutions, while normal population searches for the whole PF. As a result, propulsive population can cross through the infeasible region because of the lack of attention to feasibility. In addition, propulsive population also can guide and accelerate the convergence of the evolutionary process. Comprehensive experiment results on several sets of benchmark problems demonstrate that CMOEA-PP is better than existing state-of-the-art competitors. [ABSTRACT FROM AUTHOR]