International Journal of Industrial Engineering: Theory, Applications and Practice

This paper aims to simulate non-Gaussian wind speed time series X(t) with a prescribed probability distribution and a given autocorrelation function (ACF). Given a set of historical observations of wind speed time series, the quantile function of X(t) is fitted by the generalized lambda distribution (GLD), the ACF of wind speed time series is fitted by a weighted sum of products of Gaussian function and cosine function. Then, the marginal transformation is applied to map X(t) to a standard normal space, where the Karhunen-Lo`eve (K-L) expansion method is employed to construct a Gaussian stochastic process Z(t) to match the ACF of X(t). The proposed method features the advantage that the spectral decomposition can be performed analytically, analytical formulae can be derived to calculate eigenvalues and eigenfunctions of the ACF of X(t), and Z(t) can be conveniently constructed by K-L expansion. Finally, case studies are performed to check the proposed method, the results indicate that the K-L expansion and GLD can accurately capture the ACF and distribution function of wind speed time series.

In recent years, mechanization and greening have become the inevitable trend of agricultural development. Plant factories have the advantages of high planting area efficiency, stable crop growth and continuous production. Although its demand and scale are increasing, there is still a lack of low-carbon research on the layout of plant factory, which leads to the restriction of actual production capacity and unnecessary waste of resources and environmental burden. Therefore, this paper takes a leafy vegetable processing plant factory as the research object and puts forward a plant layout design based on the improved system layout planning (SLP) method for the design requirements of high yield, high efficiency and low carbon. In this paper, the initial layout plan of a vegetable processing plant is output by combing the plant foundation data (PQRST) needed for layout planning, analyzing and quantifying the logistic, carbon emission, and non-logistic relationships among the operating units. And draw the position correlation map and the area correlation map based on the integrated relationship, and correct them according to the actual constraints and restrictions. For the three alternative layout schemes of the plant factory based on the improved SLP method, the material flow volume and carbon emission factors of the schemes are calculated and analyzed, and the optimal layout scheme 2 is determined according to the results. Combined with the parameter design scheme of the production line, the layout scheme of the vegetable processing plant and the factory simulation software, the detailed design of the layout factory simulation model is established, and the original scheme is compared with the new scheme to verify the design effect.

The key aim of this research is to demonstrate how a discrete-event simulation model and the simulation-based optimization model could help organizers of the mass vaccination distribution site better understand the system dynamics and make effective data-driven decisions. The proposed simulation model was developed based on empirical data collected during the COVID-19 drive-through vaccination events and insights provided by the system experts. Both the simulation and the simulation-based optimization models are found to be effective in helping the COVID-19 drive-through events’ decision-makers by providing information on how their system would respond over time under various scenarios leading to more informed and systematic decisions about the process and resource management. The model could easily be extended and reused for any other drive-through mass vaccination facilities in the future.

Emergency medical services (EMS) are an important part of the modern healthcare system that tries to provide timely medical care and transportation to patients to reduce morbidity and mortality. Performance evaluation of such EMS systems to determine measures such as mean service rates, dispatch probabilities, busy probabilities, and on-scene times is necessary to design effective and efficient systems. In this paper, we consider an urban EMS system that employs three types of emergency vehicles, including advanced life support (ALS), basic life support (BLS) and first responder vehicle (FRV). We consider two types of patients: type A requires ALS to be dispatched, while type B patients are expected to be served by BLS ambulances. We also consider co-located servers so ambulances of different types can be co-located at the same station. The presence of different types of servers (ambulances) and the patients with different dispatch policies, along with co-located servers, makes it applicable to a more realistic system. We first discuss a modification of the hypercube queueing model for the proposed system and then present an approximate approach for application in large EMS systems. These approaches are compared against a simulation-based model by computing server utilization, service times and on-scene time of ambulances.

Improving efficiency in multi-stage flexible flowshop operations is essential for modern industrial systems. Optimizing total completion time while simultaneously considering the unloading and transportation times is vital for enhanced productivity and cost-effectiveness. This study delves into the intricate realm of flexible flow shop scheduling, particularly focusing on scenarios prevalent in modern manufacturing processes such as sand casting. Despite limited exploration in prior literature for particular cases, the significance of this problem underscores the pressing need for efficient algorithms and the exploration of new problem properties. Notably, we unveil the symmetry inherent in the problem, where scheduling from the initial stage to the final one, or vice versa, yields identical optimal solutions, thereby augmenting solution quality. Given the strongly NP-Hard nature of the problem, approximate solutions are preferred, especially for medium to large-scale instances. To address this challenge, we propose a novel two-phase heuristic approach, encompassing both a constructive phase and an improvement phase. Leveraging an existing efficient heuristic tailored for parallel machine scheduling, our method extends to efficiently incorporate considerations for unloading and transportation times. The efficacy of the two-phase heuristic lies in its ability to consistently generate high-quality schedules at each stage. Furthermore, we introduce efficient lower bounds derived from estimating the minimum idle time within each stage, drawing from insights in polynomial parallel machine scheduling with a focus on flow time minimization in preceding stages. These lower bounds serve as critical benchmarks for evaluating the performance of the two-phase heuristic against the relative gap performance measure. Extensive experimentation on benchmark test problems underscores the effectiveness of our proposed algorithms, with results demonstrating an average computation time of 9.48 seconds and a mean relative gap of only 2.42% across varying job and stage quantities up to 200 jobs and 10 stages.