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Competition among companies leads to a race in order to improve the management system of their production with respect to the delivery time. In the last few decades, the optimization of production scheduling has attracted the interest of numerous researchers. This article deals with the Flexible Jobshop Scheduling Problem (FJSP) as one of the most challenging combinatorial optimization problems. FJSP is an extension of the classical jobshop scheduling problem, in which an operation can be processed by several different machines. This article presents a new decomposition based artificial bee colony algorithm for the multi-objective FJSP. The proposed algorithm is tested and compared to other algorithms from the literature on benchmarks of the multi-objective FJSP and its superior performance and robust results have been proved. [ABSTRACT FROM AUTHOR]

The optimization problems with more than three objectives are Manyobjective Optimization Problems (MaOPs) that exist in various scientific and engineering domains. The existing multi-objective evolutionary algorithms are not found effective in addressing the MaOPs. Its limitations initiated the need to develop an algorithm that efficiently solves MaOPs. The proposed work presents the design of the Many-Objective Hybrid Differential Evolution (MaOHDE) algorithm to address MaOPs. Initially, two multi-objective evolutionary algorithms viz. Non-dominated Sorting based Multi-Objective Differential Evolution (NS-MODE) and Non-dominated Sorting based Multi-Objective Particle Swarm Optimization (NS-MOPSO) algorithms were designed. These algorithms were developed by incorporating the non-dominated sorting approach from Non-dominated Sorting-based Genetic Algorithm II (NSGA-II), the ranking approach, weight vector, and reference points. Tchebycheff-a decomposition-based approach, was applied to decompose the MaOPs. The MaOHDE algorithm was developed by hybridizing the NS-MODE with the NS-MOPSO algorithm. The strength of the presented approach was determined using 20 instances of DTLZ functions, and its effectiveness and efficiency were verified upon its comparison with the recently developed state of algorithms existing in the literature. From the results, it is observed that the MaOHDE responds better than its competitors or is competitive for most of the test instances and the convergence rate is also good. [ABSTRACT FROM AUTHOR]

When the evolutionary algorithm is applied to handle high-dimensional expensive multi-objective optimization problems (MOPs), population evolution is crucial since it controls exploration and exploitation and decides if promising candidate solutions could be generated. However, little attention has been paid to this issue, evolution operators based on Genetic Algorithm (GA) and Differential Evolution (DE) are still the two most common approaches, whose convergence on high-dimensional MOPs with a limited number of fitness evaluations remains challenging. In this paper, we propose a decomposition-based local learning strategy to accelerate convergence in the high-dimensional search space of MOPs. Specifically, an individual is updated by learning from one of the best solutions of its corresponding local area based on the multi-objective decomposition approach. Accordingly, a surrogate-assisted evolutionary algorithm is proposed for better solving expensive high-dimensional MOPs. Experimental studies on MOPs with up to 100 decision variables and with 300 fitness evaluations demonstrate the effectiveness of the proposed method. Furthermore, we use the proposed method to solve a 2-objective shape design problem of the blended wing-body underwater glider (BWBUG) with 39 decision variables, and an impressive solution set is obtained. • Efficacy of evolution operators on high-dimensional EMOPs is analyzed. • Propose a novel evolution strategy based on decomposition for high-dimensional EMOPs. • A surrogate-assisted evolutionary algorithm is accordingly developed. • Comprehensive experimental studies on benchmark problems and an engineering application. [ABSTRACT FROM AUTHOR]

The increasing penetration of wind power (WP) and demand response (DR) programs into modern power systems poses more challenges on transmission expansion planning (TEP). To ensure the economical, secure and reliable operations of power systems, this study presents a risk‐averse TEP framework. Instead of using the deterministic security criterion, an insecurity risk cost (RC) is proposed to provide network planners with the insight into the problem, options and future implications in decision making. Specifically, this RC can quantify the system security degree, considering the probability and the severity of contingencies. Meanwhile, the economic value of DR is modelled and incorporated into the optimal operation solutions. Moreover, to enhance the computational efficiency, an iterative solution algorithm based on the Benders decomposition is developed to solve the formulated TEP problem. The proposed approach is numerically verified on the Garver's 6‐bus, IEEE 24‐bus RTS, and 2383‐bus polish systems. Case study results demonstrate that the proposed approach can effectively investigate the impacts of large‐scale integration of WP and DR on system operations and planning. Moreover, the proposed risk‐averse approach is economically efficient and more robust to stochastic variations. [ABSTRACT FROM AUTHOR]

Resource-constrained project scheduling problem is a classic problem in construction project. Aimed at solving this problem, an effective approach with decomposition on time windows is proposed in this paper. This approach is to select one activity to do decomposition and to partition the feasible space of the original problem into some feasible subspaces, in which solutions are generated by using an extended serial scheduling scheme. Double justification is also performed in the process of searching in subspace. Four strategies for selecting activity to do decomposition, three strategies for decomposition and a strategy on sampling size in various subspaces are designed. The results of experiments on two real construction projects show that the strategy based on degree for selecting activity and the strategy based on initial schedule for decomposition can obtain the best results. When compared with some other exiting algorithms, it is proven that the decomposition-based approach is effective and competitive. [ABSTRACT FROM AUTHOR]

The resource-constrained project scheduling problem (RCPSP) is one of the project scheduling problems which are widely used in construction and many industrial disciplines. The challenge of the problem is to design some appropriate search mechanism for finding solutions in feasible space. An improved genetic algorithm based on time window decomposition is proposed in this paper. Three derivation methods are applied to increase population diversity. The sampling count allocation strategy and the use of destructive lower bounds improve the search efficiency. The computational experiments on PSPLIB show that the proposed approach is more effective than that only using the decomposition mechanism and is competitive in solving two real-life cases. This research illustrates that continuously changing the search subspaces has potential advantages, which may be useful for studying RCPSP using other evolutionary algorithms in future. Some other better results may be obtained by using machine learning methods to flexibly determine the sampling times for each individual. • Propose a genetic algorithm based on time windows decomposition for solving resource-constrained project scheduling problem. • A double-chain encoding scheme consisting of earliest finish time sequence and latest finish time sequence is designed. • The subspaces are continuously changed and a serial schedule generation scheme is used to search the subspace. • The optimal solutions are obtained with high success rate and small sampling size in two real construction cases. [ABSTRACT FROM AUTHOR]