Alfa Company is a medium-sized Iraqi distributor of laboratory equipment and supplies; in this study, we analyze their experience with implementing an automated inventory management system. This ...research explains the potential benefits and implementation challenges of such a system for small and medium-sized businesses. Improved inventory tracking, less storage fees, and happier consumers resulted from adopting this system. However, implementation was complex and time-consuming, requiring the expertise of outside IT consultants as well as the training of internal staff. This case study shows how beneficial it can be for small and medium-sized enterprises to work with external IT consultants and vendors to help them evaluate the costs and benefits of installing an automated inventory management system. In sum, the case study is instructive for other SMEs thinking about investing in automated inventory management systems to boost efficiency and cut costs.
Automatic detection and recognition of traffic signs plays a crucial role in management of the traffic-sign inventory. It provides an accurate and timely way to manage traffic-sign inventory with a ...minimal human effort. In the computer vision community, the recognition and detection of traffic signs are a well-researched problem. A vast majority of existing approaches perform well on traffic signs needed for advanced driver-assistance and autonomous systems. However, this represents a relatively small number of all traffic signs (around 50 categories out of several hundred) and performance on the remaining set of traffic signs, which are required to eliminate the manual labor in traffic-sign inventory management, remains an open question. In this paper, we address the issue of detecting and recognizing a large number of traffic-sign categories suitable for automating traffic-sign inventory management. We adopt a convolutional neural network (CNN) approach, the mask R-CNN, to address the full pipeline of detection and recognition with automatic end-to-end learning. We propose several improvements that are evaluated on the detection of traffic signs and result in an improved overall performance. This approach is applied to detection of 200 traffic-sign categories represented in our novel dataset. The results are reported on highly challenging traffic-sign categories that have not yet been considered in previous works. We provide comprehensive analysis of the deep learning method for the detection of traffic signs with a large intra-category appearance variation and show below 3% error rates with the proposed approach, which is sufficient for deployment in practical applications of the traffic-sign inventory management.
The popularity of bike-sharing systems has constantly increased throughout the recent years. Most of such success can be attributed to their multiple benefits, such as user convenience, low usage ...costs, health benefits and their contribution to environmental relief. However, satisfying all user demands remains a challenge, given that the inventories of bike-sharing stations tend to be unbalanced over time. Bike-sharing system operators must therefore intervene to rebalance station inventories to provide both available bikes and empty docks to the commuters. Due to limited rebalancing resources, the number of stations to be rebalanced often exceeds the system’s rebalancing capacity, especially close to peak hours. As a consequence, operators are forced to manually select a subset of stations that should be prioritized for rebalancing. While most of the literature has concentrated either on predicting optimal station inventories or on the rebalancing itself, the identification of critical stations that should be prioritized for rebalancing has received little attention. Given the importance of this step in current operating practices, we propose three strategies to select the stations that should be prioritized for rebalancing, using features such as the predicted trip demand and the inventory levels at the stations themselves. Two sets of computational experiments aim at evaluating the performance of the proposed prioritization strategies on real-world data from Montreal’s bike-sharing system operator. The first set of experiments focuses on both the 2019 and 2020 seasons, each of which exhibits distinct travel patterns given the restrictive measures implemented in 2020 to prevent the spread of COVID-19. One of these strategies significantly improves by reducing the estimated lost demand by up to 65%, while another strategy reduces the estimated number of required rebalancing operations by up to 33% when compared to the prioritization scheme currently in use at the considered bike-sharing system. The second set of experiments evaluates the performance of the proposed strategies when rebalancing decisions are optimized in a rolling horizon planning. The results highlight various benefits of the proposed strategies, which are efficiently solved as transportation problems and improve lost demand over two intuitive baselines.
•We proposed data-driven strategies to prioritize unbalanced stations for rebalancing.•Proposed prioritization strategies adapt well to demand changes (e.g. COVID-19 pandemic).•Numerical experiments embed prioritization in single-period rebalancing planning.•Results show our strategies outperforming baselines in lost demand and rebalancing.
Serials librarians are well aware of the fact that print serials require unique methods for proper management. From first check-in to shelving, maintaining, and potential weeding, there are a variety ...of tasks to engage in along the way to ensure essential management. This paper outlines two combined presentations on print serials management. The first section showcased how a newly appointed serials librarian “discovered” and managed her print collection over the course of a year and highlights her plans for future developments. The second part of the session provided a method for carrying out a multipart approach to a print serials inventory project that includes training, inventory assignments, quality control measures, and progress tracking. The inventory process aimed to enhance the library’s catalog and OCLC local holdings records; check the health of the print collection; and record an accurate total number of bound serial units.
Abstract
This article studies a perishable inventory system with a production unit. The production process is governed by (
s, S
) policy and it is exponentially distributed. The primary arrival ...follows Markovian arrival process(MAP) and the service time is phase-type distributed random variable. The inventoried items are subject to decay exponentially with a linear rate. A newly arriving customer realizes that system is running out of stock or server busy either moves to infinite waiting space with a pre-assigned probability or exit system with complementary probability. Customers in the waiting space make retrials to access the free server at a linear rate. If the system is running out of stock or the server is busy upon retrial, customers go back to orbit with different pre-defined probabilities according to the level of inventory or exit the system with corresponding complementary probabilities. The system is analysed using Matrix Analytic Method(MAM) and the findings are numerically illustrated.
•Developed RL policies perform better than the other algorithms.•RL learn better when the age of the products is used in state representation.•Age and demand variance are important for perishable ...inventory management.•The value of the age becomes critical when the lifetime of the product decreases.
In this study, we deal with the inventory management system of perishable products under the random demand and deterministic lead time in order to minimize the total cost of a retailer. We investigate two different ordering policies to emphasize the importance of the age information in the perishable inventory systems using Reinforcement Learning (RL). Stock-based policy replenishes stocks according to the stock quantities, and Age-based policy considers both inventory level and the age of the items in stock. The problem considered in this article has been modeled using Reinforcement Learning and the policies are optimized using Q-learning and Sarsa algorithms. The performance of the proposed policies compared with similar policies from the literature. The experiments demonstrate that the ordering policy which takes into account the age information appears to be an acceptable policy and learning with RL provides better results when demand has high variance and products has short lifetimes.
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