The performance of decomposition-based algorithms is sensitive to the Pareto front shapes since their reference vectors preset in advance are not always adaptable to various problem characteristics with no a priori knowledge. For this issue, this article proposes an adaptive reference vector reinforcement learning (RVRL) approach to decomposition-based algorithms for industrial copper burdening optimization. The proposed approach involves two main operations, that is: 1) a reinforcement learning (RL) operation and 2) a reference point sampling operation. Given the fact that the states of reference vectors interact with the landscape environment (quite often), the RL operation treats the reference vector adaption process as an RL task, where each reference vector learns from the environmental feedback and selects optimal actions for gradually fitting the problem characteristics. Accordingly, the reference point sampling operation uses estimation-of-distribution learning models to sample new reference points. Finally, the resultant algorithm is applied to handle the proposed industrial copper burdening problem. For this problem, an adaptive penalty function and a soft constraint-based relaxing approach are used to handle complex constraints. Experimental results on both benchmark problems and real-world instances verify the competitiveness and effectiveness of the proposed algorithm.

In special aluminum alloy production, smelting for aluminum ingots is the first process that affects production efficiency and product quality in subsequent processes directly. There exists two problems that charging plans cannot be made efficiently and furnace-grouping results are not optimal in the smelting process due to product variety and difference of batch size. To solve them, a furnace-grouping optimization model is established. The furnace-grouping problem is formulated with two objectives of minimizing the number of charging plans and the percentage of scrap metal with some constraints such as capacity of melting furnace and ingot-grouping rules in this model. According to the feature of this model, real number coding rule is employed that takes the percentage of order allocation as decision variable. A specialized multi-objective approach combining multi-swarm cooperative artificial bee colony is proposed to solve this optimization model. Decomposition strategy and multi-swarm strategy with information learning is employed to improve optimizing ability of the algorithm. The simulation experiment is designed on the basis of the truthful data of special aluminum alloy production. The numerical results demonstrate that this optimization model meets the requirements of manufacturing enterprises and the proposed algorithm is a powerful search and optimization technique for the furnace-grouping problem of special aluminum ingots.

The flexible job shop scheduling problem (FJSP) with transportation resources such as automated guided vehicles (AGVs) is prevalent in manufacturing enterprises. Multi-type AGVs are widely adopted to transfer jobs and realize the collaboration of different machines, but are often ignored in current research. Therefore, this paper addresses the FJSP with multi-type AGVs (FJSP-MTA). Considering the difficulties caused by the introduction of transportation and the NP-hard nature, the artificial bee colony (ABC) algorithm is adopted as a fundamental solution approach. Accordingly, a Q-learning hybrid multi-objective ABC (Q-HMOABC) algorithm is proposed to deal with the FJSP-MTA. First, to minimize both the makespan and total energy consumption (TEC), this paper proposes a novel mixed-integer linear programming (MILP) model. In Q-HMOABC, a three-layer encoding strategy based on operation sequence, machine assignment, and AGV dispatching with type selection is used. Moreover, during the employed bee phase, Q-learning is employed to update all individuals; during the onlooker bee phase, variable neighborhood search (VNS) is used to update nondominated solutions; and during the scout bee phase, a restart strategy is adopted. Experimental results demonstrate the effectiveness and superiority of Q-HMOABC.

This paper addresses an integrated production–transportation scheduling problem in assembly workshops, encompassing the processes of part machining, material handling via handling resources, and final synchronous assembly. The finite buffer capacities of production resources can cause blocking, thereby reducing efficiency. Material handling resources are subject to different service area restrictions, and some share safety zones with production resources, preventing simultaneous processing. To address this, a mixed-integer programming model is formulated with makespan and total empty travel time as bi-objective optimization targets. Since the mixed-integer linear programming (MILP) model faces difficulties in solving medium- and large-scale instances, an improved memetic NSGA-II algorithm (IMNSGA-II) is proposed. The algorithm adopts a three-segment chromosome encoding and incorporates a VNS-SA local search mechanism within the global evolutionary framework of NSGA-II. Small-scale computational experiments using Gurobi are first used to verify the correctness of the model. Decoupling experiments further demonstrate the necessity of integrated optimization: compared with phased baseline methods, IMNSGA-II reduces makespan and empty travel time by approximately 10.16% and 12.33%, respectively. In ablation and comparative experiments, results based on hypervolume (HV) and inverted generational distance (IGD) show that the proposed method achieves better convergence, diversity, and overall Pareto front quality than multiple baseline algorithms. These experiments confirm the effectiveness of the proposed model and algorithm.