This paper briefly introduces management characteristics of interconnection, integration, services, customization, and time-varying in intelligent manufacturing. The challenges these characteristics pose to production and operation management is analyzed, including multi-dimensional integration for digital supply networks, flexible and networked production, self-organizing optimization of manufacturing resources, decentralized autonomous decision-making and collaborative control, learning operation management, powerful self optimization and adaptability, forward-looking decision-making, etc. Subsequently, three aspects are identified as scientific issues that need to be addressed: value creation mechanism, resource organization and reconfiguration mechanism, and production planning and scheduling systems. Additionally, four key research directions are proposed: integration mechanism, resource reconfiguration methods, production planning and scheduling methods, and logistics operation and management methods. Finally, this paper highlights latest development in intelligent manufacturing production and operation management, providing new ideas for production management theories and practice.

The application of industrial engineering theory and methods in healthcare management runs through various aspects, including hospital facility planning, medical process optimization, medical resource management and disease diagnosis, providing strong support for the improvement of medical services and efficiency. With the development of new generation information technologies such as the internet of things, cloud computing, big data and artificial intelligence, the application of industrial engineering theory and methods in healthcare management has undergone significant transformations. Based on four hot topics of literature from 2014 to 2023 in the field of healthcare management, i.e., full life cycle health management, medical resource scheduling and optimization, hospital operation management, and medical logistics and supply chain management, this paper reviews the application of key industrial engineering technologies in these topics. Then, we summarize and analyze the innovation of new generation information technology application in the field of healthcare management, clarifying the technical and management empowerment of industrial engineering theory and methods in this field and the era of data and intelligence. Finally, we envision the application prospects of industrial engineering theory and methods in the informationization, refinement and intelligence of healthcare management in this new era.

Truck platooning organized by the logistics alliance may become a new form of future logistics transportation for reducing operating cost. The key technologies and related research progress on planning and control of connected autonomous truck (CAT) platooning is analyzed in this paper through literature search. From the perspective of commercial application, the challenges of currently implementing CAT platooning are discussed, also the cost allocation issues among relevant stakeholders are analyzed. From the perspective of technologies, the corresponding fundamentals and methods are interpreted considering planning and control of CAT platooning. By reviewing the existing studies of CAT platooning, the future research interests are concluded. The motivation of this paper is to provide a possible reference for researchers to understand the trends on CAT platooning.

To solve the problem of multi-AGV route planning and conflicts in unmanned warehouses, with the objective of minimizing the total travel time, a multi-AGV route planning model is established, and an improved DQN algorithm based on dynamic decision-making is proposed. An empirical knowledge model based on static route planning of a single AGV is designed to guide the learning and exploration direction of AGVs. It avoids conflicts and obstacles for AGVs in advance, and accelerates the convergence of the proposed algorithm. Also, a conflict resolution strategy based on the shortest total travel time is proposed to fundamentally solve the problem of multi-AGV route conflicts and deadlocks. Finally, a grid map of an unmanned warehouse is established for simulation experiments. Results show that, compared with other DQN algorithms, the convergence speed of the proposed model and algorithm is increased by 13.3%, and the average loss value is reduced by 26.3%. This result indicates that the model and algorithm are conducive to avoiding and resolving the conflicts of multi-AGV route planning in unmanned warehouses, reducing the total travel time of multiple AGVs and having important guiding significance to improve the efficiency of unmanned warehouse operations.

To effectively reduce the complexity and solution space of the multi-mode resource-constrained project scheduling model, three mixed-integer linear programming models are established in this paper. The upper bound of time series T is reduced by using the tight upper bound T_TUB, while the upper and lower bounds of the completion time of each activity are reduced by using the critical path method. Thereby, complexity and solution space of the model are reduced. 1106 instances with different scales are selected from the MRCPSP benchmark library to verify the effectiveness of the improved model. Results show that the CPM-based multi-mode resource-constrained project scheduling model has smaller solution space; the number of decision variables is reduced by 3~65 times, and the number of constraints is reduced by 1~4 times; the average computation time is decreased by 53%~112%, resulting in that the performance of the proposed model is significantly better than that of others. Moreover, the results of the 1106 instances also indicate that the closer α is to 1, the lower the complexity and the smaller the solution space of the model, which verifies the performance of parameter α. But the difficulty of exploring feasible solutions increases with the increase of instance scales. Therefore, the value of α should be appropriately relaxed for large-scale instances.

In order to reduce the waste of waiting caused by the complete set of materials in an assembly job shop, and to effectively coordinate the processing schedules of related parts with assembly constraints, this paper designs a control system balance by card-based navigation (COBACABANA). It realizes visual progress collaborative control logic of order release and job priority dispatching based on two types of card loops. The operating mechanism and system control parameters of are introduced in detail. By establishing a generalized simulation model for assembly workshops, the performance changes of each control parameter with different assembly correlation degrees are explored. Experimental results show that the COBACABANA system has good performance, and the selection of appropriate control parameters can effectively improve the schedule collaboration of related parts.

Task and personnel scheduling problems are critical to production management. In the condition of time-of-use electricity tariffs, it is difficult to strike a balance between manufacturing and labor costs: electricity prices are lower at night but the salary for working-at-night is higher, while hourly wages are lower during the day but electricity prices are higher. If the two problems are jointly modeled, the resulting large-scale model is not easy to solve. In applications, it is usually addressed in the way that the task scheduling is firstly solved, and then the personnel scheduling is determined afterwards; however, it is difficult to guarantee a low-cost solution. To resolve this issue, this paper proposes a surrogate model method, which uses: 1) multiple sets of relatively optimal feasible solutions of the two sub-problems generated by genetic algorithm, as training samples; and 2) BP neural network, deep learning and broad learning systems to respectively fit the surrogate model for the combination problem. Then, BFGS algorithm is used to search for optima. The proposed adaptive sampling algorithm can effectively simplify the problem in terms of dimensions as the number of jobs and processes increases. Results show that the new method can obtain significantly better results than those obtained by genetic algorithm. In addition, it can save up to 11.9 % of the total cost of electricity and manpower for enterprises.

The current research on energy-efficient flexible job-shop scheduling problems (EFJSPs) cannot make full use of historical production data, and is insufficiently adaptable to the complex, dynamic and changeable job-shop production environment. In view of this, deep reinforcement learning is introduced to solve EFJSPs, where a representative method named deep Q-network (DQN) is utilized. First, EFJSP is transformed into a Markov decision process corresponding to reinforcement learning. Moreover, the state values characterizing the job-shop production states are extracted as inputs of a neural network. By fitting the state value function through the neural network, compound scheduling action rules are output to achieve the selection of workpieces and processing machines. Furthermore, scheduling action rules and reward functions are utilized to jointly optimize the total production energy consumption. Finally, solutions of the proposed method are compared with those using typical intelligent optimization algorithms, such as non-dominated sorting genetic algorithm, hyper-heuristic genetic algorithm and multi-objective wolf pack algorithm, in three cases with different scales. Results demonstrate the powerful search capability of DQN algorithm, and the distribution of optimal solutions is consistent with the optimization objective obtained by the proposed EJFSP model. These verify the effectiveness of the utilized DQN method.

The Manufacturing Purchasing Managers Index (PMI) is an important indicator in the manufacturing industry reflecting the performance of a country’s economy. However, traditional forecasting models have low accuracy for predicting such time series data. Focusing on the characteristics of non-linearity, volatility and limited data volume of PMI index in the manufacturing industry, a combined model based on one-dimensional discrete wavelet transform for data preprocessing is proposed. After the wavelet transform of time-series data, steady-state low-frequency data are processed by an auto regressive moving average – generalized autoregressive conditional heteroscedasticity model (ARMA-GARCH), while the gated recurrent unit (GRU) handles high-frequency data with strong volatility. The prediction results of each frequency band are combined to get final prediction result. In order to verify the effectiveness of the model, a certain amount of data with PMI indices is selected for experiments. Results show that, compared with other common models, the combined model established in this paper has better prediction accuracy and performance, where the mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE) reach 0.00 329, 0.004 162, 0.65%.

Most existing studies of stochastic discrete event systems (SDESs) are based on the assumption that the observability of events is invariable. However, the observability of events in many practical systems is usually related to the states of systems. Therefore, this paper proposes a safety diagnosability method of SDESs under dynamic observations. First, a dynamic observation is introduced, in which each state has its independent observable events. Then, we formalize the notion of the safety diagnosticability of SDESs under dynamic observations. Finally, the sufficient and necessary conditions for the safety diagnosability of SDESs under dynamic observation are deduced based on a safety diagnoser automaton, achieving the safety diagnosis of SDESs under dynamic observations.

Dynamic traffic assignment is a hot topic and difficult issue in the transportation research field, where the application of dynamic traffic assignment methods to large-scale networks often results in high computation costs. By studying the quasi-dynamic traffic assignment problem, the time continuity of dynamic traffic assignment and the complexity of corresponding models can be reduced. A new calculation method of residual traffic demand is defined based on link travel time. Then, considering the propagation of residual traffic demand between time periods and the differences in the familiarity of travelers with the road network, a Logit-based quasi-dynamic traffic assignment model with stochastic user equilibrium is established. An algorithm is designed to solve the model based on the successive average method. Finally, the application of the model is conducted on a Braess network and a nine-node network. The sensitivity analysis of model parameters is carried out to verify the effectiveness of the model and the algorithm. Results enrich the basic theory of the transportation research field and can provide references for transportation policymakers.

Considering the carbon cap and trade policies, the influence of carbon futures on production decision-making of risk-averse enterprises remanufacturing construction machines is studied. Firstly, production decision models of risk-averse remanufacturing enterprises considering carbon free futures and carbon futures are established, respectively. Then, the Kuhn-Tucker condition is used to solve the model, while the optimal solutions in two scenarios are compared. Finally, a numerical example is used to analyze the impact of carbon futures and the risk aversion factors on output, enterprise utility, consumer surplus and total carbon emissions. Results show that in the condition of enterprises with high risk aversion, the introduction of carbon futures always increases the output of new products and total output; with the increase of carbon futures, the output of remanufactured products increases with the complete remanufacturing strategy and decreases with the partial remanufacturing strategy; in addition, when the degree of enterprise risk aversion is high, the enterprise utility, consumer surplus and total carbon emissions considering carbon futures are higher than those without considering carbon futures.

To study supply chain decision problems with buyback and restocking strategies when customers are allowed to return goods, a supply chain model composed of a manufacturer, a retailer and customers is built. We analyze the impact of buyback and restocking strategies on the profit of all parties in the supply chain with the objective of maximizing profit under the condition that customer return behavior increases inventory risk. The optimal choices of upstream and downstream members in the supply chain with the two strategies are studied and ordering strategy preferences of different parties are obtained. Finally, a numerical example is given to verify the theoretical results.It shows that only when the return rate is higher than a threshold, the supply chain prefers the restocking strategy, otherwise it prefers the buyback strategy; given the return rate and buyback price, the retailer prefers the restocking strategy when the profit-sharing coefficient exceeds a threshold, otherwise it prefers the buyback strategy; to make the retailer and the manufacturer prefer the same ordering strategy, it is necessary to pay attention to the return rate and give an appropriate profit-sharing coefficient.

In order to study whether brand manufacturers and retailers can achieve exclusive procurement cooperation in the context of imitated product competition, a two-stage Stackelberg game model composed of original brand manufacturers, imitators and retailers is established. The influence of introducing imitated products by retailers on the profits of retailer themself and brand manufacturers is compared, while the boundary conditions for exclusive procurement cooperation between brand manufacturers and retailers in the condition that brand manufacturers give discounts are solved. Results show that: 1) without taking into account the discounts given by brand manufacturers, introducing imitated products by retailers always improves the retailer profits and reduces the brand manufacturer profits, but the demand of brand manufacturers for innovative products increases against the trend; 2) when retailers are game leaders, if brand manufacturers agree to give retailers a wholesale price discount, both brand manufacturers and retailers can realize Pareto improvement in exclusive procurement cooperation, and this discount level threshold is correlated to the quality of imitated products as well as the proportion of loyal consumers of brand manufacturers; 3) when brand manufacturers are game leaders, neither brand manufacturers nor retailers can realize Pareto improvement in exclusive procurement cooperation.

In order to solve the problem of enterprises seeking rent from third-party professional organizations and lacking government supervision in logistics regulation, a tripartite evolutionary game model is established with port logistics enterprises, third-party professional organizations and port administrative departments. The evolutionary equilibrium law in logistics regulation is revealed through model solving and numerical simulation. Results show that the reward and punishment set by port administrative departments must satisfy the condition that the sum of reward and punishment for each party is greater than its respective speculative gain such that the combination of strategies (compliant operation, rejection of rent-seeking, and loose regulation) can be a stable evolutionary strategy; increasing the intensity of both reward and punishment is beneficial to the compliant operation of enterprises and also the implementation of third-party professional organizations to reject rent-seeking strategies, however, as the reward intensity increases, the supervision willingness of port administrative departments decreases, also its effect weakens; the evolution of enterprises toward compliant operation can be facilitated by improving the reputation gain of enterprises, increasing rent-seeking costs and the accountability for government failures.

Under the government's low-carbon regulation, manufacturers and third-party detection institutions may conspire to seek rent, which leads to the frequent occurrence of “pseudo-green products” on the market. This paper builds a tripartite evolutionary game model consisting of government, manufacturers, and third-party detection institutions. On this basis, the combination of stable strategies and implementation conditions for each participant are explored. Finally, through numerical simulation analysis, the impact of multiple factors, including the rent-seeking and speculative cost of manufacturers, the cost difference between producing high and low green products, the rent-seeking cost and benefits of third-party detection institutions, and the incentives and punishments for manufacturers and third-party detection institutions by government, on strategy selections of three participants and the stability of system optimization is discussed, to provide reference for government incentive and regulatory policies with the goal of carbon peaking and carbon neutralization.

In order to solve the problem of QoS evaluation for manufacturing services with heterogeneous decision information and dynamic changes in the number of candidate services in shared manufacturing, this paper proposes a multi-party heterogeneous evaluation method for QoS in manufacturing services that conforms to the characteristics of shared manufacturing based on the tripartite decision information of the platform, service demanders and service providers. First, heterogeneous information is processed through crisp numbers, language variables and Z-number theory. Second, the weight vector of the decision maker is calculated based on consensus maximization, while the attribute weight is determined based on the dispersion maximization model after using the TrFWAA operator to gather multi-party information. On this basis, RIM is used to evaluate and rank each service. Finaly, the proposed method is verified and analyzed through a case study of manufacturing service optimization in the shared manufacturing condition. Results show that the method has good practicality and superiority.