In this article, we propose a hybrid artificial bee colony (ABC) algorithm to solve a parallel batching distributed flow-shop problem (DFSP) with deteriorating jobs. In the considered problem, there are two stages as follows: 1) in the first stage, a DFSP is studied and 2) after the first stage has been completed, each job is transferred and assembled in the second stage, where the parallel batching constraint is investigated. In the two stages, the deteriorating job constraint is considered. In the proposed algorithm, first, two types of problem-specific heuristics are proposed, namely, the batch assignment and the right-shifting heuristics, which can substantially improve the makespan. Next, the encoding and decoding approaches are developed according to the problem constraints and objectives. Five types of local search operators are designed for the distributed flow shop and parallel batching stages. In addition, a novel scout bee heuristic that considers the useful information that is collected by the global and local best solutions is investigated, which can enhance searching performance. Finally, based on several well-known benchmarks and realistic industrial instances and via comprehensive computational comparison and statistical analysis, the highly effective performance of the proposed algorithm is favorably compared against several algorithms in terms of both solution quality and population diversity.

With the global energy shortage, climate anomalies, environmental pollution becoming increasingly prominent, energy saving scheduling has attracted more and more concern than before. This paper studies the energy-efficient distributed hybrid flow-shop scheduling problem (DHFSP) with blocking constraints. Our aim is to find the job sequence with low energy consumption as much as possible in a limited time. In this paper, we formulate a mathematical model of the DHFSP with blocking constraints and propose an improved iterative greedy (IG) algorithm to optimize the energy consumption of job sequence. In the proposed algorithm, first, a problem-specific strategy is presented, namely, the global search strategy, which can assign appropriate jobs to the factory and minimize the energy consumption of each processing factory. Next, a new selection mechanism inspired by Q-learning is proposed to provide strategic guidance for factory scheduling. This selection mechanism provides historical experience for different factories. Finally, five types of local search strategies are designed for blocking constraints of machines and sequence to be scheduled. These proposed strategies can further improve the local search ability of the QIG algorithm and reduce the energy consumption caused by blocking. Simulation results and statistical analysis on 90 test problems show that the proposed algorithm is superior to several high-performance algorithms on convergence rate and quality of solution.

Automated guided vehicles (AGVs) have become indispensable transportation tools in intelligent production workshops. The current AVG scheduling system has almost no processing capacity for temporary special cases and mostly depends on the path planning part to solve them, which can only reduce the cost waste caused to a certain extent. In this article, a dynamic AGV scheduling model is proposed, including an aperiodic departure method and a real-time task list update method. Compared with the static AGV scheduling model, the new model can reassign the AGVs for new tasks and special cases. A discrete invasive weed optimization (DIWO) algorithm with parameter adaptation and computing time adaptation is used to prove the effectiveness of the new model. The proposed model is verified by the cases from actual production workshops, which proves the effectiveness of the proposed dynamic AGV scheduling model for the special cases.

Flexible job shop scheduling problem (FJSP) is one of the challenging issues in industrial systems. In this study, we propose a bi-population balancing multi-objective evolutionary algorithm, to solve the distributed FJSPs from a steelmaking system, with considering the fuzzy processing time and crane transportation processes. Two objectives are considered simultaneously, including minimization of the maximum fuzzy completion time and the energy consumption during machine processing and crane transportation. Firstly, the mathematical model is formulated for the considered problem. Then, an efficient problem-specific initialization heuristic is developed. To balance the convergence and diversity abilities, a novel crossover operator and two cooperative population environmental selection mechanisms are developed. In addition, an efficient population size adaptive adjustment mechanism is designed. Then, an enhanced local search heuristic is developed to further improve the searching abilities. Finally, a set of randomly generated instances based on realistic industrial processes are tested, and through comprehensive computational comparison and statistical analysis, the highly effective performance of the proposed algorithm is favorably compared against several presented algorithms. Note to Practitioners —In practical manufacturing processes, the processing times for each job should not be considered as deterministic values because of the disruption events, such as machine breakdown, resource limitation, and machine maintenance. Therefore, the fuzzy scheduling should be considered in many industrial procedures. This study considered multi-objective optimization flexible job shop with energy and robotic transportations, where the fuzzy makespan and energy consumptions are minimized simultaneously. Two populations balancing the convergence and diversity abilities are developed. Efficient problem-specific heuristics are designed to enhance the searching performance. The proposed methods can be generalized and applied to many applications considering both the realistic constraints and objectives.

Multimodal multiobjective optimization problems (MMOPs) are commonly seen in real-world applications. Many evolutionary algorithms have been proposed to solve continuous MMOPs. However, little effort has been made to solve combinatorial (or discrete) MMOPs. Searching for equivalent Pareto optimal solutions in the discrete decision space is challenging. Moreover, the true Pareto optimal solutions of a combinatorial MMOP are usually difficult to know, which has limited the development of its optimizer. In this paper, we first propose a test problem generator for multimodal multiobjective traveling salesman problems (MMTSPs). It can readily generate MMTSPs with known Pareto optimal solutions. Then we propose a novel evolutionary algorithm to solve MMTSPs. In our proposed algorithm, we develop two new edge assembly crossover operators, which are specialized in searching for superior solutions to MMTSPs. Moreover, the proposed algorithm uses a new environmental selection operator to maintain a good balance between the objective space diversity and decision space diversity. We compare our algorithm with five state-of-the-art designs. Experimental results convincingly show that our algorithm is powerful in solving MMTSPs.