Computers & Industrial Engineering最新文献

Manufacturing service composition (MSC) is an essential issue in cloud manufacturing, which streamlines complex manufacturing tasks into manageable subtasks and integrates distributed services to enhance task completion. Existing studies allocate services for subtasks with maximizing quality of service (QoS) simultaneously, assuming that all subtasks are of equal importance. However, different subtasks hold varied significance and priorities. One rational method is to prioritize the allocation of premium or scarce services to important subtasks. Therefore, this study proposes a two-phase, subtask priority-based approach for the hierarchical allocation of the MSC. The initial phase applies a multi-attribute decision-making method based on complex networks, the Enhanced Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS-EK), to assess subtask importance. The TOPSIS-EK method ascertains subtask importance, delineating the subtasks into Key Manufacturing Subtasks (KMTs) and Ordinary Manufacturing Subtasks (OMTs). The second phase uses bilevel optimization for the hierarchical allocation of the MSC to KMTs and OMTs, respectively. A hybrid Particle Swarm Optimization and Genetic Algorithm with Chaos-sequence and Inheritance (PSOGA-CI) is developed to solve the model. The proposed approach is validated with a case on the production of an airplane engine turbine rotor blade.

Large-scale group decision making (LSGDM) problem is common in real life. With the increase in the number of alternatives and the limited rationality of human beings, consistency problem is inevitable when pairwise comparison method is used. We propose an improved consistency calculation approach to generate consistent distributed preference relations (DPRs), which adopts adjacent score intervals to calculate the score intervals of non-adjacent alternative pairs. By using optimization model, the initial DPR is preserved as much as possible on the premise of order consistency. As for the consensus analysis, the concept of relationship-possibility degree is defined to capture the ignorance and fuzzy uncertainty in assessments. An ordinal consensus measure method considering absolute position difference and relative position dissimilarity with relationship-possibility degree is proposed. Ordinal-cardinal consensus adjustment model based on DPR is then constructed to obtain the minimum consensus adjustment of decision makers or subgroups coalition which are considered as coalition payoff. In addition, to distribute the ordinal-cardinal minimum consensus adjustment reasonably, we construct a two-stage consensus adjustment allocation mechanism adopting the improved multi-weighted Shapley function in the cooperative game. Several optimization models are constructed to obtain the adjusted DPRs of decision makers or subgroups. Finally, an illustrative example is presented to demonstrate the validity of the proposed method in dealing with the decision problems of product development engineering. It is expected to make the LSGDM procedure in a more intelligent way.

This study considers a pharmaceutical supply chain system consisting of a manufacturer, an underfunded retailer, a logistics provider, and a bank. First, in accordance with bank financing (hereafter “BF”), logistics provider financing (hereafter “LF”), and manufacturer financing (hereafter “MF”), we respectively obtain and compare the optimal decisions and profits of the manufacturer, retailer, and logistics provider. This study provides two key conclusions that differ from the existing relevant studies. The logistics provider or manufacturer takes a certain negative interest rate when providing the underfunded retailer with a financing service, which can raise revenues of all enterprises (manufacturer, retailer and logistics provider). To put it differently, for a retailer with relatively high capital, the negative interest financing from the logistics provider or manufacturer is a new incentive mechanism that results in a Pareto improvement of supply chain. Under certain conditions, adopting the MF mode will generate more revenue for the logistics provider, and adopting the LF mode will create greater profit for the manufacturer.

Manufacturing Execution Systems (MES) have been considered the ‘entrance door’ to the Industry 4.0 journey. As part of this journey, this system must operate in real-time and be integrated with several other systems, resulting in modular adaptation and customized implementation of this solution. This increased complexity underscores the importance of the relationship between companies and their technology providers, requiring intensive knowledge-sharing (KS) activities between the parties. In particular, information asymmetry between buyers and MES 4.0 providers may be critical for the successful implementation of the system, but little is known about this issue, which has a high impact on the first stage of the Industry 4.0 journey. Thus, we aim to understand how knowledge sharing affects information asymmetry between buyers and technology providers for MES 4.0 implementation. Our investigation is based on qualitative interviews with 56 key experts from 33 companies, which allowed us first to define MES configurations for Industry 4.0, i.e., MES 4.0 as a differentiation from early versions of MES. Then, we conducted a multiple case study with three buyer-provider dyads in the pre-and post-contract phases of MES purchasing to analyze KS dynamics during the implementation of MES 4.0. We propose a model that explains the relationship between MES 4.0 complexity and KS intensity and a decision framework that describes the steps for MES 4.0 implementation while focusing on reducing information asymmetry during the collaboration.

Forging is an important sector in China’s machinery manufacturing industry. To complete the processing of forgings, it is often necessary to go through multiple processes, which are commonly performed by different workshops. Due to the complexity of cross-workshop production, there are few studies on cross-workshop scheduling in the forging industry. Therefore, in order to realize resource sharing and collaborative production between multiple workshops, and improve the overall production efficiency and resource utilization rate, it is very important to optimize the scheduling of linked cross-workshop production. In this paper, a new cross-workshop partial flexible hammer forging scheduling model (CSPFH-FSM) is established to solve the scheduling problem of linked cross-workshop production with production time and energy consumption serving as the overall optimization goals in the whole partially flexible free forging production line (P3FPL). A single-machine forward-prediction variable genetic operator NGSA-II algorithm (SPVGO-NGSA II) is proposed to solve the multi-objective optimization problem of partially flexible production, in which the variable genetic operator is added to the effective coding, and the search strategy is dynamically adjusted to avoid reaching locally optimal solutions. Due to the interference of maintenance and the insufficient utilization of energy after forging, a fixed maintenance disturbance and a residual temperature utilization strategy are added to the scheduling process. Finally, the optimization obtained using the proposed variable and traditional fixed genetic operators are compared for different orders, and the algorithm proposed in this paper is compared with the typical multi-objective optimization algorithms. The results validate the effectiveness of the proposed algorithm, and provide a basic scheme for the linked scheduling of the whole production line in practical applications.

Social Sustainability (SOS) is crucial to an industry’s success and depends on various factors. This study presents a structural equation model (SEM) that examines how three lean manufacturing tools, focused on continuous improvement, Kaizen, Gemba, and 5Whys, can enhance SOS. Six hypotheses were proposed and validated using information from 411 responses to a survey on the Mexican maquiladora industry, and the partial least squares approach was used to test them. Subsequently, the system dynamics technique was applied to simulate different scenarios and determine the optimum SOS level based on lean manufacturing tools. The findings indicate that only Kaizen can achieve 100 % implementation within less than five years, whereas the other variables require a more extended period. Specifically, the SOS reached 100 % after 10.25 years, and this period is considered too long. Companies should periodically investigate the new needs of employees to improve their SOS. This study is the first to combine SEM and SD to assess the impact of Kaizen, Gemba, and 5Whys as LM Social Sustainability tools, offering a new perspective to achieve sustainable industrial practices. Companies should periodically investigate new employees’ needs to improve their SOS.

Numerous supplementary Shewhart monitoring designs have emerged, customized to data that follows specific non-normal distributions like the Rayleigh distribution (RD). The Rayleigh distribution has a variety of applications in modeling theory of communication, physical sciences, diagnostic imaging, life testing, reliability analysis, applied statistics and clinical studies. The exponential weighted moving average (EWMA) design is frequently advocated in the literature because of its ability to swiftly detect smaller process alterations. However, the common EWMA chart may not perform optimally in detecting all changes in the process parameters. To address this limitation, this study introduces an adaptive EWMA structure for monitoring quality characteristics following the RD, called the adaptive Rayleigh EWMA (AREWMA) chart. To determine the design parameters of the AREWMA chart, a Markov chain model is utilized. Analytical results are then used to assess the performance of the AREWMA chart in comparison to existing competitors. The comparative analysis illustrates the strengths of the proposed AREWMA chart in detecting shifts of various magnitudes during parameter monitoring. Finally, we present a practical application of the proposed AREWMA chart in the manufacturing industry, utilizing real data on the time of failure eld-tracking of devices in a system. Our analysis demonstrates the effectiveness of the AREWMA chart in detecting a range of shifts in the manufacturing process, highlighting its utility for continuous monitoring and quality control.

Equipment will go through multiple degradation stages under complex operating conditions, and the single-stage degradation model cannot accurately describe the degradation process of the equipment at different stages, resulting in inaccurate remaining service life prediction results and reliability analysis. Therefore, this paper establishes a multi-stage Wiener degradation process model that considers measurement errors and includes three different forms of drift functions. First, by calculating the Bayesian information criterion (BIC) values of these three degradation models separately and analysing the variation trends of the BIC values, a method for detecting change-points is proposed to achieve stage division. Next, by comparing the BIC values of the three models, a method for adaptively selecting the optimal model for each stage is proposed. Then, based on the results of stage division and optimal model selection, approximate analytical expressions for the RUL of each stage are derived, and parameter estimation is performed using maximum likelihood estimation (MLE). Finally, the RUL prediction study using the proposed method is carried out through simulation cases and practical cases. The results show that the accuracy of the proposed method is higher than the existing research methods, verifying the effectiveness of the proposed method.

This paper explores modeling approaches for the pre-disaster Evacuation Network Design Problem (ENDP) considering different flow equilibrium conditions. We combine this problem with the modeling idea of Continuous Network Design Problem (CNDP), which we call Continuous Evacuation Network Design Problem (CENDP) in this paper. We develop five CENDP models, which are under the consideration of User Equilibrium (UE), Stochastic User Equilibrium (SUE), Boundedly Rational User Equilibrium (BRUE), and Non-equilibrium (NONE), among which we develop two types of models based on BRUE. The modeling is mainly to consider the single objective of optimizing the total evacuation time, and then to provide reasonable road expansion solutions under certain budget constraints and different equilibrium conditions. Our main motivation for developing models is to introduce various types of equilibrium conditions into models and design algorithms to solve these problems while mining for key insights. We design the corresponding five heuristic algorithms to solve models and verify the applicability of the models and algorithms by two test networks (Nguyen-Dupuis network and Sioux-Falls network). We demonstrate whether evacuation flow equilibrium need or not need to be considered in the CENDP, the applicability of different equilibrium conditions, and the correlation between the total evacuation time, the network investment cost, and the network congestion degree. Additionally, we conduct model and algorithm tests on 40 instance networks, dividing them into medium-sized networks (20 instances) and large-sized networks (20 instances). Not only do we further validate the insights obtained from the test networks, but we also expand upon them. Specifically, the main findings of this study are as follows: (1) We demonstrate that considering evacuation flow equilibrium in CENDP is essential to reduce total evacuation time, construction costs, and mitigate congestion. (2) While increased investment in road construction can meet evacuation time requirements, it is crucial to make informed decisions, as investment alone does not directly reduce total evacuation time and congestion. (3) Optimizing road evacuation time is more effective than merely increasing road capacity for reducing total evacuation time and mitigating congestion. (4) From the perspectives of total evacuation time, investment cost, and network congestion degree, the CENDP model considering user equilibrium performs better in medium-sized networks, while the CENDP model considering stochastic user equilibrium performs better in large-sized networks. Conversely, the CENDP model that does not consider flow equilibrium performs the worst across all above three metrics. Based on this, we also provide recommendations on which model to choose for different metrics. In summary, this study not only reveals the importance of different flow equilibrium conditions in evacuation network design but also provides valua