The collaborative organization of production scheduling and machine maintenance is crucial for achieving effective manufacturing. However, since these two activities are typically managed by different self-interested managers with distinct optimization needs in practice, their collaboration faces the challenge of simultaneous consideration and balanced optimization of both parties’ interests. To this end, this study investigates a novel game theory-based production-maintenance collaborative scheduling problem, wherein the typical objectives individually concerned by two activities are explicitly considered and the decision interactions between activities are modeled as a non-cooperative stochastic game. Nash equilibrium within the game model provides the ideal solution for balancing the interests of both parties. To solve this problem, an imitation-enhanced alternating-training reinforcement learning method is presented. In this method, an imitation learning-based initialization mechanism is designed to accelerate the training of two game agents, and an alternating training mechanism are developed to facilitate two agents efficiently learning the optimal responses to each other’s behaviors and ultimately achieving the decisions that converge to Nash equilibrium. The superiority of the proposed method is verified through comprehensive experiments.

Driven by the rapid progress of digital technology, manufacturing cells of the smart shop floor have achieved the ability to collect data, compute, reason, and make autonomous decisions. By utilizing such cell intelligence, production schedules can be flexibly adjusted and production performance can be improved. Against this backdrop, this study proposes a novel distributed scheduling method based on data-augmented QMIX for smart shop floors to fully exploit the potential of cell intelligence. In the proposed method, QMIX is adopted to construct the scheduling agents for manufacturing cells and collaboratively optimize the decisions of agents. Furthermore, a data augmentation mechanism based on denoising diffusion probabilistic model is developed to improve the training efficiency of the QMIX algorithm. Finally, experiments conducted on the semiconductor production shop floor MiniFab verified the performance of the proposed method.

This paper addresses the multi-objective distributed permutation flowshop scheduling problem with sequence-dependent setup times (MODPFSP_SDST), whose optimization objectives are the makespan, the total energy consumption and the noise emission. An effective hybrid variable neighborhood search (HVNS) algorithm is proposed for solving this problem. HVNS utilizes a probabilistic matrix to store structural information of the promising solution, and then employs matrix-based search operators to execute variable neighborhood search. Additionally, the objective-oriented local intensification search is integrated to further enhance solution quality. Simulations and comparisons demonstrate the effectiveness of HVNS in solving the MODPFSP_SDST.

Production scheduling and logistics scheduling are vital means for organizing manufacturing activities in flexible job shops. Given the intricate coupling relationship between them, corresponding collaborative scheduling becomes urgent need and challenging. Meanwhile, the actual manufacturing process is inevitably affected by disturbances, necessitating the consideration of dynamic environments. To this end, this study investigates a new production-logistics collaborative scheduling problem in dynamic flexible job shops (PLCSP-DFJS). The high-frequency disturbance of new job arrivals is incorporated into the PLCSP-DFJS, and two objectives, namely makespan and total logistics cost, are optimized. A multi-objective deep reinforcement learning (MODRL) method is presented to solve PLCSP-DFJS. In MODRL, a weight-decomposition and neighborhood-inheritance training mechanism is devised to obtain the near-optimal Pareto front, and a dual-level channel-driven framework capable of achieving decentralized decision-making of production and logistics is designed. The performance of MODRL is verified through experiments conducted in an aviation component production shop.

This work is motivated by the need to adapt an optimal production schedule for dynamic and stochastic environments in semiconductor manufacturing systems. When unexpected events occur, such as machine breakdown and due date changes, manufacturers need to react quickly and revise the schedule accordingly. This paper presents a novel partial repair rescheduling solution that consists of a criterion and a scheduler. The former decides the segment of the original schedule to be repaired by detecting a match-up point. The impact of disturbances caused by unexpected events can be limited between the disturbing time and this match-up point. The scheduler generates a new schedule segment to replace the original and impacted one with single machine-oriented or machine-group-oriented match-up rescheduling algorithms. On practical demand, the proposed solution can be further updated due to the changing consideration of unexpected events and/or expanded performance criteria. The simulation results show that the proposed rescheduling methods have higher stability and efficiency than some well-known existing rescheduling methods.

Sintering is recognized as one of the most energy-intensive process in the iron and steel enterprise. Energy-aware optimization introduces energy indicators into the sintering proportioning process, so that a reduction in energy consumption could be achieved without affecting normal production on the sintering shop floor. This paper proposes a novel cascade multi-objective optimization model (CMOM) that dynamically produces optimal dosing schemes. Firstly, an improved back-propagation neural network (IBPNN) with appending momentum and adaptive variable learning rate is proposed to better mimic solid energy consumption (SEC) and other indictors of sinter. Then, a cost based proportioning model (CBPM) is established with the consideration of chemical and physical characteristics of the resulting sinter. An improved immune-simulated annealing algorithm (ISAA) is designed to fast converge toward optimal solutions. Subsequently, by achieving feasible dosing scheme with initial chemical and physical requirements, energy-aware indicator is introduced into the cascade optimization framework. The CBPM would be repeatedly optimized by adjusting the expected performance until the predicted SEC and other indicators would have been in accord with the expected intervals. A case study of an actual sintering process in steel enterprise demonstrates the effectiveness of the framework and models. Meanwhile, results show that energy consumption is reduced by 5.28% on an optimal scenario.

In recent years? the study of social network is raising more and more attentions of researchers, locating the key nodes in social network is a hot research point. Lots of papers about how to discover the key nodes in social network such as mail network, micro log network was published. However, few people study on key nodes discovery in logistics network. In addition, most of methods of key nodes discovery only take relationship strength between nodes into account, few take the weight of node into account. In this paper, a node activity degree[1] based on users behavior features was defined, As a result, the logistics networks can be considered as a double-weighted networks by taking relationship strength as edge's weight and node activity as node weight. Based on Page Rank algorithm, an improved algorithms was proposed in this paper. The nodes weights were used as damping coefficient, and weight of the edges was used to compute importance of nodes during iterative process. At last? we implemented the improved Page Rank algorithm[2] using MapReduce. One dataset from a logistics company were selected and comprehensive experiments were conducted. The experimental results show that proposed algorithms can effectively and efficiently discover key nodes in real logistics network.

As one of the most complicated manufacturing processes, semiconductor manufacturing consists of four steps, wafer sort, wafer fabrication, assembly, and testing. Among them, wafer fabrication is the most costly, complex, and time consuming step. Its operation management and optimization are challenging modeling and scheduling researchers. To address its modeling issue, a hierarchical colored timed Petri net (HCTPN) is proposed, which can be used to describe various states, behavior and substructures of a wafer fabrication system. To address its scheduling issue, intelligent algorithms are introduced to the proposed HCTPN. An extended genetic algorithm (EGA) embedded scheduling strategy over HCTPN is studied to optimize the combination of scheduling policies. The combined approach can conduct more efficient search with better scheduling performance. At last, a real case is presented to illustrate the results. Based on comparing simulation results of different scheduling strategies, the HCTPN and EGA combined scheduling is proved to be valid and efficient.