In real manufacturing environments, the number of automatic guided vehicles (AGV) is limited. Therefore, the scheduling problem that considers a limited number of AGVs is much nearer to real production and very important. In this paper, we studied the flexible job shop scheduling problem with a limited number of AGVs (FJSP-AGV) and propose an improved genetic algorithm (IGA) to minimize makespan. Compared with the classical genetic algorithm, a population diversity check method was specifically designed in IGA. To evaluate the effectiveness and efficiency of IGA, it was compared with the state-of-the-art algorithms for solving five sets of benchmark instances. Experimental results show that the proposed IGA outperforms the state-of-the-art algorithms. More importantly, the current best solutions of 34 benchmark instances of four data sets were updated.

In recent years, we have observed a clear trend in the rapid rise of autonomous vehicles and robotics. One of the core technologies enabling these applications, Simultaneous Localization And Mapping (SLAM), imposes two main challenges: first, these workloads are computationally intensive and they often have real-time requirements; second, these workloads run on battery-powered mobile devices with limited energy budget. Hence, performance should be improved while simultaneously reducing energy consumption, two rather contradicting goals by conventional wisdom. Previous attempts to optimize SLAM performance and energy efficiency usually involve optimizing one function and fail to approach the problem systematically. In this paper, we first study the characteristics of visual inertial SLAM workloads on existing heterogeneous SoCs. Then based on the initial findings, we propose π-SoC, a heterogeneous SoC design that systematically optimize the IO interface, the memory hierarchy, as well as the the hardware accelerator. We implemented this system on a Xilinx Zynq UltraScale MPSoC and was able to deliver over 60 FPS performance with average power less than 5 W.

In today’s customer-centric economy, the demand for personalized products has compelled corporations to develop manufacturing processes that are more flexible, efficient, and cost-effective. Flexible job shops offer organizations the agility and cost-efficiency that traditional manufacturing processes lack. However, the dynamics of modern manufacturing, including machine breakdown and new order arrivals, introduce unpredictability and complexity. This study investigates the multiplicity dynamic flexible job shop scheduling problem (MDFJSP) with new order arrivals. To address this problem, we incorporate the fluid model to propose a fluid randomized adaptive search (FRAS) algorithm, comprising a construction phase and a local search phase. Firstly, in the construction phase, a fluid construction heuristic with an online fluid dynamic tracking policy generates high-quality initial solutions. Secondly, in the local search phase, we employ an improved tabu search procedure to enhance search efficiency in the solution space, incorporating symmetry considerations. The results of the numerical experiments demonstrate the superior effectiveness of the FRAS algorithm in solving the MDFJSP when compared to other algorithms. Specifically, the proposed algorithm demonstrates a superior quality of solution relative to existing algorithms, with an average improvement of 29.90%; and exhibits an acceleration in solution speed, with an average increase of 1.95%.

To schedule material supply intelligently and meet the production demand, studies concerning the material logistics planning problem are essential. In this paper, we consider the problem based on the scenario that more than one vehicle may visit each station in batches. The primary objective is to satisfy the demands in the time windows, followed by logistics planning with the minimum vehicles and travel time as the optimization objective. We construct a multi-objective mixed-integer programming model for the scenario of discrete material supply in workshops. First, we propose a hybrid heuristic algorithm combining NSGA-II and variable neighborhood search. This proposed algorithm combines the global search capability of NSGA-II and the strong local search capability, which can balance intensification and diversification well. Second, to maintain the diversity of population, we design the population diversity strategy and various neighborhood operators. We verify the effectiveness of the hybrid algorithm by comparing with other algorithms. To test the validity of the proposed problem, we have carried out research and application in a construction machinery enterprise.

Market-oriented production systems generally have the characteristics of multi-product and small-batch production. Dynamic virtual cellular manufacturing systems create virtual manufacturing cells periodically in a planning horizon to respond to changing demands flexibly and quickly, and thus are suitable for production planning problems of market-oriented production systems. In the current research, we propose a dynamic virtual cell reconfiguration framework under a dynamic environment with unstable demands and multiple planning cycles. In this framework, we formulate a two-phase dynamic virtual cell formation (DVCF) model. In the first phase, the proposed model aims to maximize processing similarity and balance the workload in the system. In the second phase, we consider the objective of reconfiguration stability based on the first phase model. To address the proposed model, we design a hybrid metaheuristic named Lévy-NSGA-Ⅱ, and perform various computational experiments to examine the performance of the proposed algorithm. Results of experiments indicate that the proposed Lévy-NSGA-Ⅱ based algorithm outperforms multi-objective cuckoo search (MOCS) and NSGA-Ⅱ in solution quality and optimal solution size.

Mass customization makes it necessary to upgrade production planning systems to improve the flexibility and resilience of production planning in response to volatile demand. The ongoing development of digital twin technologies supports the upgrade of the production planning system. In this paper, we propose a data-driven methodology for Hierarchical Production Planning (HPP) that addresses the upgrade requests in the production management system of a fuel tank manufacturing workshop. The proposed methodology first introduces a novel hybrid neural network framework with symmetry that integrates a Long Short-Term Memory network and a Q-network (denoted as LSTM-Q network) for real-time iterative demand forecast. The symmetric framework balances the forward and backward flow of information, ensuring continuous extraction of historical order sequence information. Then, we develop two relax-and-fix (R&F) algorithms to solve the mathematical model for medium- and long-term planning. Finally, we use simulation and dispatching rules to realize real-time dynamic adjustment for short-term planning. The case study and numerical experiments demonstrate that the proposed methodology effectively achieves systematic optimization of production planning.