The conventional portfolio optimization problem aims to maximize returns and minimize risks, which is of importance in financial areas. In this work, we focus on a special kind of multi-objective portfolio optimization problem that considers the profit of assets (MOPOPA). MOPOPA differs from conventional portfolio optimization problems in that it optimizes the profit of assets and the risk of the portfolio simultaneously. Given the features of real-life investment management, we propose the MOPOPA model for the first time. To solve the problem, a Pareto evolutionary algorithm based on the Markov chain (PEAMC) is proposed. In PEAMC, we first present an offline-collected method to generate a high-quality initial population. Then, a learning approach based on the Markov chain and the Gaussian distribution model is proposed to learn pertinent information. Thereby, we predict a population based on the learned information, while producing another population through the nondominated sorting method. Afterward, a simple yet effective cooperation-based improvement mechanism is proposed to integrate the two populations and generate the offspring solutions of PEAMC. Results of experiments on 50 instances with up to 200 assets show that the proposed PEAMC performs better than state-of-the-art methods. Moreover, we make the 50 instances publicly available to facilitate future research on MOPOPA.

Nowadays, industrial intelligence has become a strong driving force for the progress of smart manufacturing. In this paper, a multi-Agent reinforcement learning framework based on Multi-Head Attention as Mixture-of-Head Attention Network (MOHN) is proposed to effectively solve the classical problem in flexible manufacturing, the flexible job shop scheduling problem (FJSP). To begin with, an innovative approach is introduced for formulating FJSP as a Markov decision process (MDP). This methodology integrates both process selection and machine allocation into a unified action space. Additionally, a dynamic decision timing mechanism is ingeniously incorporated, significantly enhancing the effectiveness of the MDP's action space. Second, in order to efficiently extract more comprehensive and potential state feature information from the action space, this paper proposes a MOHA-based state extraction method to capture the complex linkages between processes and machines in the manufacturing process, so as to improve the decision-making efficiency of the agent. Experiments on a large number of instances at different scales show that the method outperforms the traditional PDRs algorithm with strong generalization ability.

Cloud computing is an emerging computational infrastructure for cost-efficient workflow execution that provides flexible and dynamically scalable computing resources at pay-as-you-go pricing. Workflow scheduling, as a typical NP-Complete problem, is one of the major issues in cloud computing. However, in the cloud scenario with unlimited resources, how to generate an efficient and economical workflow scheduling scheme under the deadline constraint is still an extraordinary challenge. In this paper, we propose a hybrid heuristic algorithm called enhanced task type first algorithm (ET2FA) to solve deadline-constrained workflow scheduling in cloud with new features such as hibernation and per-second billing. The objectives to be minimized include the total cost and total idle rate. ET2FA involves three phases: 1) Task type first algorithm, which schedules tasks based on topological level and task types, and utilizes a compact-scheduling-condition based VM selection method to assign each task. 2) Delay operation based on block structure, which further optimizes total cost and total idle rate based on block structure properties. 3) Instance hibernate scheduling heuristic, which sets an instance to hibernate if idle for a duration. Extensive simulation experiments based on seven well-known real-world workflow applications show that ET2FA delivers better performance in comparison to the state-of-the-art algorithms.

The no-wait flow-shop scheduling problem (NFSSP) with sequence-dependent setup times (SDSTs) and release times (RTs) is applicable in many areas, such as steel production, food processing, and chemical processing. Estimation of the distribution algorithm (EDA) has recently been recognized as a prominent metaheuristic methodology in the field of evolutionary computation due to its excellent performance of global exploration. In this article, an innovative matrix-cube-based (i.e., 3-D) EDA (MCEDA) is first proposed to minimize the total earliness and tardiness (TET) of the NFSSP with SDSTs and RTs. This problem is NP-hard in the strong sense. First, a 3-D matrix cube is devised to learn the valuable information from promising solutions or excellent individuals. Second, an EDA model or probabilistic model based on the matrix cube and a special sampling method is presented to perform effective exploration in solution space and find promising regions. Third, based on a series of newly defined subneighborhoods, a new local search with both a speed-up scanning method and one search strategy is developed to execute exploitation from promising regions. Fourth, a speed-up evaluation method based on the problem’s property is designed to reduce the computational complexity for calculating criterion and accelerate the search process. Owing to the reasonable hybridization of exploration and exploitation, MCEDA can perform very efficient search in solution space. Extensive test results on instances of such a just-in-time problem first show that MCEDA can achieve better solution than state-of-the-art algorithms in obviously less computation time. Additional experiments on instances of various NFSSPs further confirm the efficiency and robustness of MCEDA.

The flow shop scheduling problem (FSSP) with limited buffers constraint is a typical NP-hard combinatorial optimization problem and represents an important area in production scheduling. In this paper, a class of differential evolution (DE) method with the optimal computing budget allocation (OCBA) technique and hypothesis test (HT), namely OHTDE, is proposed for the stochastic flow shop scheduling with limited buffers between consecutive machines to minimize the maximum completion time (i.e., makespan). In the OHTDE, the population-based search mechanism of DE and a special crossover are applied for well exploration and exploitation, and the OCBA technique is used to allocate limited sampling budgets to provide reliable evaluation and identification for good individuals. Meanwhile, HT is also applied to perform a statistical comparison to avoid some repeated search to some extent. The results and comparisons demonstrate the superiority of OHTDE in terms of effectiveness and robustness.