Computers & Industrial Engineering最新文献

Recycling food waste (FW) into secondary products can transform the waste-to-value. This study proposes reducing FW through IoT controlled preservation and consuming it through a secondary supply chain (SSC). It explores the best policies for SSC management that recycles FW. The food chain comprises of multiple food processors for preparing multiple food products, and a common retailer where IoT based preservation curtails food deterioration. The SSC consumes the FW by recycling into multiple secondary products at multiple recycling plants and retailing at a secondary retailer. A dual channel waste collection viz. from retailers and consumers is maintained. Each product within primary and secondary supply chains (PSSCs) has a different lifetime, rate of deterioration (RoD), and preservation effectiveness. A nonlinear mathematical model is presented that optimizes decision-making for maximizing profitability. Outcomes of computational experiments demonstrate that the SSC removes 100% of the FW, conserves 89% of material resources, reduces 16% of the total cost, minimizes preservation efforts by 50%, improves replenishment cycle, and increases profitability by 39%. Some managerial insights are provided for making vital supply chain decisions.

With the advancement and wide adoption of Artificial Intelligence (AI) technology, various industries have recognized its immense potential and significance, especially in the e-commerce sector. This study considers an E-commerce Platform Supply Chain (EPSC) consisting of a manufacturer and a platform that may provide AI service. The primary purpose of this research is to explore the strategic interaction between different customer service strategies (i.e., Manual or AI service) and different selling modes (i.e., Agency or reselling mode). The research results show that if the manual service sensitivity is relatively high, the manufacturer is more willing to set a higher wholesale price. When the service efficiency attenuation effect of the manual customer towards demand is stronger, AI service would bring about more demand. Additionally, when the service efficiency attenuation effect is stronger, or it is weaker but the AI service efficiency is higher, the EPSC could utilize AI service to obtain more profit under reselling mode. Under agency mode, AI service can benefit the EPSC more when the service effect attenuation coefficient is relatively larger. Last but not least, we find that when the AI service cost coefficient is relatively small, the reselling mode can benefit the EPSC more. Most importantly, compared with manual service, AI service provides the EPSC with a new opportunity to embrace the reselling mode more.

The digital transformation, propelled by Industry 4.0, is essential for businesses to adapt to evolving market demands and sustainability pressures. This study addresses the need for improved plant optimisation by enhancing machinery and workstations through advanced digital modelling. Using the Tecnomatix Plant Simulation environment, we developed a simulation platform to assess the feasibility of a flexible production system. The model, based on a validated case study, demonstrated a 15% improvement in system performance by incorporating varying levels of machine flexibility. This improvement highlights the importance of designing production systems that can adapt to changing requirements efficiently. The study shows how increased flexibility in manufacturing environments can enhance operational efficiency and machine utilisation across different scenarios.

The Liner Shipping Network (LSN) is a crucial component of the maritime supply chain (MSC) but remains susceptible to disruptions caused by natural disasters or emergencies, given its intricate structure and high level of interdependence. In this study, we present a novel cascading failure model that accounts for the heterogeneity of distances and transportation times between nodes, coupled with actual shipping lines. This comprehensive approach aims to thoroughly evaluate the resilience of the LSN. The study’s results reveal noteworthy variations in the cascading effects across diverse load distribution distances and transportation times. Furthermore, it delves into load distribution strategies to improve the resilience of the LSN and investigates the impact of capacity parameters on cascading effects. The results indicate that the distribution strategy proposed in this paper, integrating transportation time and redundancy levels, effectively suppresses the spread of cascading faults and improves network resilience. The outcomes of this research contribute to a deeper understanding of strategies for enhancing resilience and managing risks in shipping networks, providing valuable insights for management and decision-making in maritime operations.

The rapidly developing high-speed railway system in China has posed increasing challenges to train and station scheduling. During rush hours, several switches and tracks are often congested due to the lack of synchronized train platforming and service scheduling. Therefore, we investigate a train platforming and shunting with service scheduling (TPSSS) problem of high-speed railway hub at the tactical level. This joint optimization approach can reduce conflicts, decrease the shunting work, and promote balanced resource utilization at stations and depots. By constructing a two-layer time–space network based on the hub layout, a multi-commodity flow model and a heuristic algorithm that utilizes the alternating direction method of multipliers (ADMM) are developed. Applying an iterative framework that includes inner and outer loops, as well as a pre-assignment strategy, the algorithm can produce a near-optimal (gap < 5 %) TPSSS plan within a computation time of 1 h in cases where there are no cancelled trains. Furthermore, a comparison between conservative and aggressive strategies indicates that the aggressive strategy can achieve an overall better solution.

In 2015 and 2016, the Chinese government implemented the National Big Data Comprehensive Pilot Zone (NBDCPZ) policy in eight regions of eastern, central, western, and northeastern China. The main goal of the NBDCPZ policy is to promote industrial transformation and upgrading in the region, and promote the application and development of the big data industry. Based on Chinese city data from to 2009–2019, this study’s estimates show that urban carbon emissions (CE) decreased by 11.8% after the implementation of the NBDCPZ policy. To ensure the robustness of our results, various tests were conducted, all of which confirmed the negative causal effect of NBDCPZ on CE. Heterogeneity studies have shown that cities with higher initial carbon endowments have greater reductions in carbon emissions owing to the introduction of NBDCPZ policies. In addition, the impact of the policy on urban energy conservation and emission reduction in eastern China is particularly significant. The mechanism analysis shows that NBDCPZ policy achieves emission reduction through activating the CE reduction effect of green technology and encouraging low-carbon virtual life.

Maintenance optimization for balanced systems has received increasing attention for its significance in engineering practice. Most existing maintenance models for balanced systems consider a single failure mode, which is inconsistent with many practical situations involving multiple failure modes. This paper investigates the maintenance optimization problem for a balanced system that consists of two balanced components and a core component. The balanced components deteriorate during operation and a soft failure occurs if one of their deterioration levels exceeds a critical level. The core component is subject to hard failure and the failure rate depends on its age and the balance condition quantified by the deterioration difference between balanced components. Corrective replacement is performed for soft or hard failure, whichever occurs first. If no failure occurs before a decision epoch, one in three actions, including do-nothing, preventive repair, and preventive replacement, should be chosen based on the information of age and deterioration The objective is to determine the optimal maintenance policy that minimizes the long-run average cost rate. The optimization problem is formulated in the semi-Markov decision process (SMDP) framework. A recursive method is employed to assess conditional reliability. A policy-iteration algorithm is developed to obtain the optimal policy. Results from a numerical example confirm the effectiveness of the proposed approach.

Accelerated life-tests (ALTs) are applied for inferring lifetime characteristics of highly reliable products. In some cases, due to cost or product nature constraints, continuous monitoring of devices is infeasible and so the units are inspected at particular inspection time points, resulting in interval-censored responses. Furthermore, when a test unit fails, there is often more than one competing risk. In this paper, we assume that all competing risks are independent and follow an exponential distribution depending on the stress level. Under this setup, we present a family of robust estimators based on the density power divergence (DPD), including the classical maximum likelihood estimator as a particular case. We then derive asymptotic and robustness properties of the minimum DPD estimators (MDPDEs). Based on these MDPDEs, estimates of some lifetime characteristics of the product as well as estimates of some cause-specific lifetime characteristics are developed. Direct, transformed and bootstrap confidence intervals are proposed, and their performance is empirically compared through Monte Carlo simulations. The methods of inference discussed in this work are finally illustrated with a real-data example regarding electronic devices.

The knowledge of the variables that impact energy consumption allows for a better understanding of how to mitigate energy consumption and emissions production for public passenger transport. The current operation of public passenger transport in a large city with unregulated types of public passenger transport, as seen in developing countries, causes an increase in energy consumption and emissions generation. Therefore, the objective of this paper is to propose a pilot simulation that investigates the direct impact of public passenger transport operations on energy consumption. This will enable the incorporation of multiple variables, such as rim radius, tire characteristics, vehicle weight, road gradient, air density, and drag coefficient, as well as developing countries’ public transport characteristics. The study reveals that frequent stops every 100 m lead to the highest energy consumption due to the continuous acceleration and deceleration that this entails. Conversely, wider stop spacings of 250, 350, 500, and 1,000 m result in progressively reduced energy usage. However, it is crucial to balance efficiency gains with passenger needs and service convenience, emphasizing the importance of tailored planning and optimization strategies in urban transport management. The observed differences in energy consumption across various stop spacings emphasize the need to consider this factor when proposing improvement measures for public transport systems. The current energy consumption estimates account for initial and final speeds only, suggesting that further reductions might be achieved by incorporating additional variables. This comprehensive approach is essential for developing feasible solutions aimed at minimizing energy consumption in public passenger transport. Additionally, future simulations will integrate emission estimation scripts and measures such as lane delineation, the promotion of efficient driving behaviors, and the implementation of robust maintenance plans, which will also be integral to optimizing energy efficiency.

In many industrial processes, cost or time constraints make some input variables harder to change or control than others. An appropriate experimental design method is restricted randomization, which results in split-plot experiments. Empirical models that connect multiple quality characteristics with input variables play a crucial role in robust parameter design for split-plot experiments. At present, many modeling methods typically adopt the single response model for analyzing industrial processes in the split-plot experiments without considering correlation among multiple responses, correlation among whole plots, and uncertainty of model parameters. However, ignoring these issues can lead to poor product or process design. To solve these issues, this paper suggests a novel Bayesian modeling and optimization approach. We first construct a Bayesian multi-response linear mixed-effects model and obtain the posterior distribution for model parameters by employing Bayesian theorem. Then, the Gibbs sampling procedure is employed for the estimation of model parameters. Finally, the overall weighted desirability optimization function meeting the specification is developed to avoid acquiring ideal input settings with outliers. A simulation and an engineering case study demonstrate the validity of the proposed method. In comparison to existing methods, the optimization results given the proposed method are more robust and reliable.