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Shuttle-based compact systems are new automated multideep unit-load storage systems with lifts that can potentially achieve both low operational cost and large volume flexibility. In this paper, we develop novel queuing network models to estimate the performance of both single-tier and multitier shuttle-based compact systems. Each tier is modeled as a multiclass semi-open queuing network, whereas the vertical transfer is modeled using an open queue. For a multitier system, the models corresponding to tiers and vertical transfer are linked together using the first and second moment information of the queue departure processes. The models can handle both specialized and generic shuttles and both continuous and discrete lifts. The accuracy of the models is validated through both simulation and a real case. Errors are acceptable for conceptualizing initial designs. Numerical studies provide new design insights. Results show that the best way to minimize expected throughput time in single-tier systems is to have a depth/width ratio around 1.25. Moreover, specialized shuttles are recommended for multitier systems because the higher cost of generic shuttles is not balanced by savings in reduced throughput time and equipment needs.

Warehouses occupy much space and land, which has become increasingly scarce in many parts of Europe, Asia, and the United States, particularly close to areas where demand is generated, such as large cities. This paper studies live-cube compact storage systems that may solve this space shortage problem as they do not require travel aisles. Each stored unit load is accessible individually and can be moved in x and y directions by a shuttle as long as an empty location is available, comparable to the well-known 15-puzzle in which 15 numbered tiles slide within a 4 × 4 grid. When multiple empty locations are available on a level, the shuttles can cooperate to create a virtual aisle for fast retrieval of a desired unit load. A lift moves the unit loads across different levels in z direction. Such storage systems are increasingly used in different service sectors like car parking, warehousing, and container handling, but so far they have hardly been studied. For live-cube systems, many research questions still have to be answered, including cycle time calculations, cost comparisons, and energy requirements. In this paper, we first derive simple to use closed-form formulas for expected retrieval time of an arbitrary unit load and validate the quality of these formulas by comparing them with a real application. Second, we propose and solve a mixed-integer nonlinear model to optimize system dimensions by minimizing the retrieval time. We obtain closed-form expressions for minimum retrieval time that are simple to apply in practice. Third, we compare the investment, operational costs, and energy consumption of live-cube systems with traditional systems based on a real application.

Given the low space usage rate of the traditional automated storage/retrieval system and the long aisle, it is easy for a stacker to take a long time to enter/leave the warehouse. Thus, a new type of double-ended compact storage system is proposed. This paper addresses the scheduling problem for the stacker to execute the single and dual commands mixed tasks in the system where the I/O ports are located at both ends of the aisle, and the power conveyor devices on the rack can meet the requirement of multi-depth storage and generate displacement. An improved shuffled frog leaping algorithm (ISFLA) is developed for the scheduling problem. In order to eliminate the disadvantages of local optimum and slow convergence in the standard shuffled frog leaping algorithm, a set of hybrid perturbation update methods are designed based on a role model learning strategy, and the feasibility of the improved algorithm is verified by a numerical simulation. The experimental results show that the solution quality and the convergence ability of the ISFLA are significantly improved, and it can effectively solve the stacker-scheduling problem in the double-ended compact storage system.

The autonomous shuttle and stacker crane (AC/SC) warehousing system, as a new automated deep-lane unit load storage/retrieval system, has been becoming more popular, especially for batch order fulfilment because of its high flexibility, low operational cost and improved storage capacity. This system consists of a shuttle sub-system that controls motion along the x-axis and a stacker crane sub-system that controls motion along the y-axis and z-axis. The combination of shuttles and a stacker crane performs storage and retrieval tasks. Modelling the parallel motion is an important design tool that can be used to calculate the optimal number of shuttles for a given configuration of the warehousing system. In this study, shuttle movements from one lane to another are inserted into the stock-keeping unit (SKU) task queue, and convert such that they are consistent with the retrieval tasks. The tasks are then grouped according to their starting lane, and converted to an assembly-line parallel job problem by analysing the operating mode with the objectives of minimising the total working time of the stacker crane and the wasted shuttle time. A time sequence mathematical model based on the motion of the shuttles and stacker crane is proposed, and an improved Pareto-optimal elitist non-dominated sorting genetic algorithm is used to solve this multi-objective optimization problem. The model is validated via a simulation study, and via a real-world warehousing case study. We go on to describe guidelines for the layout and configuration of AS/SC warehousing systems, including the optimal number of shuttles and number of x-axis storage cells of lanes, which can improve efficiency and minimise both capital investment and operating costs.

The requirements for modern storage systems are steadily increasing due to limited space, cost, time, and personnel. Robotic compact storage and retrieval systems (RCS/RS), where containers are stacked and arranged in a block layout with robots operating from above, offer a promising solution. Some systems benefit from a self-sorting effect, where robots relocate previously moved containers after accessing non-directly accessible ones, resulting in demand-based sorted stacks. Despite various analytical models for automated storage systems, RCS/RS remain under-researched. Apart from two distinct papers on performance evaluation, there are no general, fast, and easy-to-use tools to assess system throughput under demand-based access patterns. Additionally, the performance benefits of self-sorting have not yet been studied. This paper presents an analytical approach to predict RCS/RS performance using a class-based access structure. A discrete event simulation validates the model, and an optimization example demonstrates the model's broad applicability and ease of use.

Biomedical ontologies have gained particular relevance in the life science domain due to its prominent role in representing knowledge in this domain. However, the existing biomedical ontologies could define the same biomedical concept in different ways, which yields the biomedical ontology heterogeneous problem. To implement the inter-operability among the biomedical ontologies, it is critical to establish the semantic links between heterogenous biomedical concepts, so-called biomedical ontology matching. Since modeling the ontology matching problem is a complex and time-consuming task, swarm intelligent algorithm (SIA) becomes a state-of-the-art methodology for solving this problem. However, when addressing the biomedical ontology matching problem, the existing SIA-based matchers tend to be inefficient due to biomedical ontology’s large-scale concepts and complex semantic relationships. In this work, we propose a compact firefly algorithm (CFA), where the explicit representation of the population is replaced by a probability distribution and two compact movement operators are presented to save the memory consumption and runtime of the population-based SIAs. We exploit the anatomy track, disease and phenotype track and biodiversity and ecology track from the ontology alignment evaluation initiative (OAEI) to test CFA-based matcher’s performance. The experimental results show that CFA can improve the FA-based matcher’s memory consumption and runtime by, respectively, 68.92% and 38.97% on average, and its results significantly outperform other SIA-based matchers and OAEI’s participants.

This study performs a Life Cycle Assessment on the production of a commercial PCM building application developed by RUBITHERM to quantify its environmental impacts and identify environmental hotspots across manufacturing, unlocking climate change mitigation potential. This research adds to the consideration of embodied energy demands and emissions when developing building efficiency solutions, especially for innovative material applications where knowledge is limited. The building application under examination is a compact storage module, consisting of an aluminium case filled with salt hydrate PCM, with a targeted performance of 94 Wh heat storage capacity. The LCA of the manufacturing stage resulted in a climate change impact of 5.81 kg CO2 eq. The research showed that the aluminium of the case to be filled in with PCM is the main contributor to almost all impact categories addressed, including climate change, while the sensitivity analysis revealed that the total climate change of the final product is highly dependent on the recycled aluminium content, which could be decreased by 46% by increasing the new scrap and post-consumer scrap aluminium streams. Finally, the study provides detailed Life Cycle Inventory data, based on real data shared by RUBITHERM, and methodology transparency to facilitate built-up research in the field.

With the proliferation of sensors, semantic web technologies are becoming closely related to sensor network. The linking of elements from semantic web technologies with sensor networks is called semantic sensor web whose main feature is the use of sensor ontologies. However, due to the subjectivity of different sensor ontology designer, different sensor ontologies may define the same entities with different names or in different ways, raising so-called sensor ontology heterogeneity problem. There are many application scenarios where solving the problem of semantic heterogeneity may have a big impact, and it is urgent to provide techniques to enable the processing, interpretation and sharing of data from sensor web whose information is organized into different ontological schemes. Although sensor ontology heterogeneity problem can be effectively solved by Evolutionary Algorithm (EA)-based ontology meta-matching technologies, the drawbacks of traditional EA, such as premature convergence and long runtime, seriously hamper them from being applied in the practical dynamic applications. To solve this problem, we propose a novel Compact Co-Evolutionary Algorithm (CCEA) to improve the ontology alignment’s quality and reduce the runtime consumption. In particular, CCEA works with one better probability vector (PV) PVbetter and one worse PV PVworse, where PVbetter mainly focuses on the exploitation which dedicates to increase the speed of the convergence and PVworse pays more attention to the exploration which aims at preventing the premature convergence. In the experiment, we use Ontology Alignment Evaluation Initiative (OAEI) test cases and two pairs of real sensor ontologies to test the performance of our approach. The experimental results show that CCEA-based ontology matching approach is both effective and efficient when matching ontologies with various scales and under different heterogeneous situations, and compared with the state-of-the-art sensor ontology matching systems, CCEA-based ontology matching approach can significantly improve the ontology alignment’s quality.

Wireless sensor networks (WSNs) play a critical role in environmental sensing and data transmission. However, their performances are often hindered by challenges like localization accuracy and storage capacity. Existing variants of DV-Hop algorithm suffer from issues like high memory usage, low localization accuracy, and limited applicability in realistic three-dimensional (3D) environments. To overcome these challenges and solve the localization problem for DV-Hop based WSN nodes in 3D space, this research proposes a novel hybrid optimizer called PCWCO (Parallel Compact Willow Catkin Optimization Algorithm). The PCWCO algorithm incorporates compact technique and a new parallel strategy into the Willow Catkin Optimization (WCO) framework, aiming to reduce memory usage while enhancing solution quality. Rigorous numerical validations are conducted using benchmark functions from the CEC2017 to assess the performance of the proposed PCWCO optimizer. The results demonstrate that PCWCO exhibits competitive performance compared to classical intelligent optimization algorithms. Moreover, we synergistically integrate the PCWCO algorithm with DV-Hop to form a hybrid approach called PCWCO-3D-DV-Hop to facilitate the localization efficiency of WSN nodes in 3D space.

The swift expansion of cities and the surging proliferation of vehicles have precipitated a severe scarcity of parking spaces all over the world. This predicament is aggravated by the spatial limitations of densely populated urban areas. Therefore, this study seeks to address the pressing need for efficient space utilization by introducing a compact automated parking solution. Given the glaring inadequacy of parking spaces relative to the soaring number of cars, this paper meticulously details the design and development of a Compact Automated Parking System (CAPS). The system utilizes a vertical‐rotary mechanism to maximize spatial efficiency while maintaining structural integrity. A fully functional prototype has been seamlessly incorporated with Internet of Things (IoT) technology. IoT enables real‐time monitoring of parking availability. The integration of the Blynk platform ensures an intuitive user interface. The proposed CAPS operates via an Arduino‐based control system, synchronized with sensors and actuators to execute precise motor movements and platform alignment. Although it is a basic control setup, it effectively demonstrates automation and synchronization in a compact prototype. System performance was assessed through time efficiency and user experience metrics, yielding favorable outcomes. Comparative analysis with conventional parking solutions highlights its superior efficiency. A scalable solution is also offered to consider this prototype for real‐world implementation. The fusion of automated vertical‐rotary mechanics and IoT distinguishes this system from existing alternatives. Ultimately, this innovation heralds a transformative shift in urban parking paradigms. This study presents a Compact Automated Parking System (CAPS) using a vertical‐rotary mechanism integrated with IoT, offering a sustainable solution to urban parking by efficiently storing 16–20 cars in the space for 2, all the while enhancing security, comfort, and operational efficiency with a user‐friendly interface and precise control system.