Progress in Material Handling Research: 2016

Table of contents

Optimization of storage and picking systems in warehouses

La croissance du commerce électronique exige une hausse des performances des systèmes d'entreposage, qui sont maintenant repensés pour faire face à un volume massif de demandes à être satisfait le plus rapidement possible. Le système manuel et le système à robots mobile (SRM) sont parmi les plus utilisés pour ces activités. Le premier est un système centré sur l'humain pour réaliser des opérations complexes que les robots actuels ne peuvent pas effectuer. Cependant, les nouvelles générations de robots autonomes mènent à un remplacement progressif par le dernier pour augmenter la productivité. Quel que soit le système utilisé, plusieurs problèmes interdépendants doivent être résolus pour avoir des processus de stockage et de prélèvement efficaces. Les problèmes de stockage concernent les décisions d'où stocker les produits dans l'entrepôt. Les problèmes de prélèvement incluent le regroupement des commandes à exécuter ensemble et les itinéraires que les cueilleurs et les robots doivent suivre pour récupérer les produits demandés. Dans le système manuel, ces problèmes sont traditionnellement résolus à l'aide de politiques simples que les préparateurs peuvent facilement suivre. Malgré l'utilisation de robots, la même stratégie de solution est répliquée aux problèmes équivalents trouvés dans le SRM. Dans cette recherche, nous étudions les problèmes de stockage et de prélèvement rencontrés lors de la conception du système manuel et du SRM. Nous développons des outils d'optimisation pour aider à la prise de décision pour mettre en place leurs processus, en améliorant les mesures de performance typiques de ces systèmes. Certains problèmes traditionnels sont résolus avec des techniques améliorées, tandis que d'autres sont intégrés pour être résolus ensemble au lieu d'optimiser chaque sous-système de manière indépendante. Nous considérons d'abord un système manuel avec un ensemble connu de commandes et intégrons les décisions de stockage et de routage. Le problème intégré et certaines variantes tenant compte des politiques de routage communes sont modélisés mathématiquement. Une métaheuristique générale de recherche de voisinage variable est présentée pour traiter des instances de taille réelle. Des expériences attestent de l'efficience de la métaheuristique proposée par rapport aux modèles exacts et aux politiques de stockage communes. Lorsque les demandes futures sont incertaines, il est courant d'utiliser une stratégie de zonage qui divise la zone de stockage en zones et attribue les produits les plus demandés aux meilleures zones. Les tailles des zones sont à déterminer. Généralement, des dimensions arbitraires sont choisies, mais elles ignorent les caractéristiques de l'entrepôt et des demandes. Nous abordons le problème de dimensionnement des zones pour déterminer quels facteurs sont pertinents pour choisir de meilleures tailles de zone. Les données générées à partir de simulations exhaustives sont utilisées pour trainer quatre modèles de régression d'apprentissage automatique - moindres carrés ordinaire, arbre de régression, forêt aléatoire et perceptron multicouche - afin de prédire les dimensions optimales des zones en fonction de l'ensemble de facteurs pertinents identifiés. Nous montrons que tous les modèles entraînés suggèrent des dimensions sur mesure des zones qui performent meilleur que les dimensions arbitraires couramment utilisées. Une autre approche pour résoudre les problèmes de stockage pour le système manuel et pour le SRM considère les corrélations entre les produits. L'idée est que les produits régulièrement demandés ensemble doivent être stockés près pour réduire les coûts de routage. Cette politique de stockage peut être modélisée comme une variante du problème d'affectation quadratique (PAQ). Le PAQ est un problème combinatoire traditionnel et l'un des plus difficiles à résoudre. Nous examinons les variantes les plus connues du PAQ et développons une puissante métaheuristique itérative de recherche tabou mémétique en parallèle capable de les résoudre. La métaheuristique proposée s'avère être parmi les plus performantes pour le PAQ et surpasse considérablement l'état de l'art pour ses variantes. Les SRM permettent de repositionner facilement les pods d'inventaire pendant les opérations, ce qui peut conduire à un processus de prélèvement plus économe en énergie. Nous intégrons les décisions de repositionnement des pods à l'attribution des commandes et à la sélection des pods à l'aide d'une stratégie de prélèvement par vague. Les pods sont réorganisés en tenant compte du moment et de l'endroit où ils devraient être demandés au futur. Nous résolvons ce problème en utilisant la programmation stochastique en tenant compte de l'incertitude sur les demandes futures et suggérons une matheuristique de recherche locale pour résoudre des instances de taille réelle. Nous montrons que notre schéma d'approximation moyenne de l'échantillon est efficace pour simuler les demandes futures puisque nos méthodes améliorent les solutions trouvées lorsque les vagues sont planifiées sans tenir compte de l'avenir. Cette thèse est structurée comme suit. Après un chapitre d'introduction, nous présentons une revue de la littérature sur le système manuel et le SRM, et les décisions communes prises pour mettre en place leurs processus de stockage et de prélèvement. Les quatre chapitres suivants détaillent les études pour le problème de stockage et de routage intégré, le problème de dimensionnement des zones, le PAQ et le problème de repositionnement de pod. Nos conclusions sont résumées dans le dernier chapitre.The rising of e-commerce is demanding an increase in the performance of warehousing systems, which are being redesigned to deal with a mass volume of demands to be fulfilled as fast as possible. The manual system and the robotic mobile fulfillment system (RMFS) are among the most commonly used for these activities. The former is a human-centered system that handles complex operations that current robots cannot perform. However, newer generations of autonomous robots are leading to a gradual replacement by the latter to increase productivity. Regardless of the system used, several interdependent problems have to be solved to have efficient storage and picking processes. Storage problems concern decisions on where to store products within the warehouse. Picking problems include the batching of orders to be fulfilled together and the routes the pickers and robots should follow to retrieve the products demanded. In the manual system, these problems are traditionally solved using simple policies that pickers can easily follow. Despite using robots, the same solution strategy is being replicated to the equivalent problems found in the RMFS. In this research, we investigate storage and picking problems faced when designing manual and RMFS warehouses. We develop optimization tools to help in the decision-making process to set up their processes and improve typical performance measures considered in these systems. Some classic problems are solved with improved techniques, while others are integrated to be solved together instead of optimizing each subsystem sequentially. We first consider a manual system with a known set of orders and integrate storage and routing decisions. The integrated problem and some variants considering common routing policies are modeled mathematically. A general variable neighborhood search metaheuristic is presented to deal with real-size instances. Computational experiments attest to the effectiveness of the metaheuristic proposed compared to the exact models and common storage policies. When future demands are uncertain, it is common to use a zoning strategy to divide the storage area into zones and assign the most-demanded products to the best zones. Zone sizes are to be determined. Commonly, arbitrary sizes are chosen, which ignore the characteristics of the warehouse and the demands. We approach the zone sizing problem to determine which factors are relevant to choosing better zone sizes. Data generated from exhaustive simulations are used to train four machine learning regression models - ordinary least squares, regression tree, random forest, and multilayer perceptron - to predict the optimal zone sizes given the set of relevant factors identified. We show that all trained models suggest tailor-made zone sizes with better picking performance than the arbitrary ones commonly used. Another approach to solving storage problems, both in the manual and RMFS, considers the correlations between products. The idea is that products constantly demanded together should be stored closer to reduce routing costs. This storage policy can be modeled as a quadratic assignment problem (QAP) variant. The QAP is a traditional combinatorial problem and one of the hardest to solve. We survey the most traditional QAP variants and develop a powerful parallel memetic iterated tabu search metaheuristic capable of solving them. The proposed metaheuristic is shown to be among the best performing ones for the QAP and significantly outperforms the state-of-the-art for its variants. The RMFS allows easy repositioning of inventory pods during operations that can lead to a more energy-efficient picking process. We integrate pod repositioning decisions with order assignment and pod selection using a wave picking strategy such that pods are parked after being requested considering when and where they are expected to be requested next. We solve this integrated problem using stochastic programming considering the uncertainty about future demands and suggest a local search matheuristic to solve real-size instances. We show that our sample average approximation scheme is effective to simulate future demands since our methods improve solutions found when waves are planned without considering the future demands. This thesis is structured as follows. After an introductory chapter, we present a literature review on the manual and RMFS, and common decisions made to set up their storage and picking processes. The next four chapters detail the studies for the integrated storage and routing problem, the zone sizing problem, the QAP, and the pod repositioning problem. Our findings are summarized in the last chapter

Optimizing the Performance of Robotic Mobile Fulfillment Systems

A robotic mobile fulfillment system is a novel type of automated part-to-picker material handling system. In this type of system, robots transport mobile shelves, called pods, containing items between the storage area and the workstations. It is well suited to e-commerce, due to its modularity and it's ability to adapt to changing orders patterns. Robots can nearly instantaneously switch between inbound and outbound tasks, pods can be continually repositioned to allow for automatic sorting of the inventory, pods can contain many different types of items, and unloaded robots can drive underneath pods, allowing them to use completely different routes than loaded robots. This thesis studies the performance of robotic mobile fulfillment systems by solving decision problems related to warehouse design, inventory and resource allocation, and real-time operations. For warehouse design, a new queueing network is developed that incorporates realistic robot movement, storage zones, and multi-line orders. For inventory allocation, we develop a new type of queueing network, the cross-class matching multi-class semi-open queueing network, which can be applied to other systems as well. Resource (re)allocation is modeled by combining queueing networks with Markov decision processes while including time-varying demand. This model compares benchmark policies from practice wit

SIMULATING CONSUMABLE ORDER FULFILLMENT VIA ADDITIVE MANUFACTURING TECHNOLOGIES

Operational availability of naval aircraft through material readiness is critical to ensuring combat power. Supportability of aircraft is a crucial aspect of readiness, influenced by several factors including access to 9B Cognizance Code (COG) aviation consumable repair parts at various supply echelons. Rapidly evolving additive manufacturing (AM) technologies are transforming supply chain dynamics and the traditional aircraft supportability construct. As of June 2022, there are 595 AM assets within the Navy’s inventory—all for research and development purposes. This report simulates 9B COG aviation consumable fulfillment strategies within the U.S. Indo-Pacific sustainment network for a three-year span, inclusive of traditional supply support avenues and a developed set of user-variable capability inputs. Simulated probabilistic demand configurations are modeled from historical trends that exploit a heuristic methodology to assign a “printability” score to each 9B COG requirement, accounting for uncertainty, machine failure rates, and other continuous characteristics of the simulated orders. The results measure simulated lead time across diverse planning horizons in both current and varied operationalized AM sustainment network configurations. This research indicates a measurable lead time reduction of approximately 10% across all 9B order lead times when AM is employed as an order fulfillment source for only 0.5% of orders.NPS Naval Research ProgramThis project was funded in part by the NPS Naval Research Program.Lieutenant Commander, United States NavyApproved for public release. Distribution is unlimited

INTER-ENG 2020

These proceedings contain research papers that were accepted for presentation at the 14th International Conference Inter-Eng 2020 ,Interdisciplinarity in Engineering, which was held on 8–9 October 2020, in Târgu Mureș, Romania. It is a leading international professional and scientific forum for engineers and scientists to present research works, contributions, and recent developments, as well as current practices in engineering, which is falling into a tradition of important scientific events occurring at Faculty of Engineering and Information Technology in the George Emil Palade University of Medicine, Pharmacy Science, and Technology of Târgu Mures, Romania. The Inter-Eng conference started from the observation that in the 21st century, the era of high technology, without new approaches in research, we cannot speak of a harmonious society. The theme of the conference, proposing a new approach related to Industry 4.0, was the development of a new generation of smart factories based on the manufacturing and assembly process digitalization, related to advanced manufacturing technology, lean manufacturing, sustainable manufacturing, additive manufacturing, and manufacturing tools and equipment. The conference slogan was “Europe’s future is digital: a broad vision of the Industry 4.0 concept beyond direct manufacturing in the company”

Student experiences with instructional videos in online learning environments

Drawing upon qualitative methods of semi-structured interviews and observational talk-through interviews, this qualitative dissertation investigates the ways in which graduate students in an online course context experience online instructional videos. A conceptual framework of user experience and multimodality, as well as the framework of sense-making developed by McCarthy and Wright (2004) guided this study and data analysis. The findings of this dissertation have implications for how students are participating in, interacting with, and making sense of online learning environments. Some of the findings of this research include: (a) students do not necessarily experience course videos as discrete elements (or differentiate them with other aspects of the course); (b) the times and contexts in which students view instructional videos shifts (e.g., between home and commuting); (c) student motivations and expectations shape how they approach and orient themselves towards watching online course videos; and (d) multimodal design elements influence students’ meaning-making of online instructional videos. These data findings are all in support of the overarching conclusion of this dissertation, which is that students have significant agency in these online environments, and their meaning-making of online videos may not align with designers’ intentions. This conclusion argues against deterministic views of design. The emerging findings have design implications related to the creation of learning environments in online spaces, such as: (a) fully integrating videos within the broader instructional design of a course; (b) foregrounding the embedded context of instructional videos; and (c) accounting for the shifting times, places, and contexts in which viewers watch instructional videos. This dissertation is situated in the growing field of online education, in particular higher education, where significant money and resources are increasingly dedicated towards the development of online spaces while still much is unknown in relation to the design, experiences, and impact of these online learning environments

A Neoclassical Realist’s Analysis Of Sino-U.S. Space Policy

During the Cold War, the United States focused its collective policy acumen on forming a competitive, actor-specific strategy to gain advantage over the Soviet Union. The fragmentation of the Soviet Union resulted in a multi-polar geopolitical environment lacking a near-peer rival for the United States. Overwhelming soft and hard power advantages allowed American policy makers to peruse a general, non-actor specific strategy to maintain its hegemonic position. However, the meteoric rise of China as a near-peer competitor in East Asia has challenged this paradigm. In order to maintain its competitive advantage, or at the very least ensure the safety of its geopolitical objectives through encouraging benign competition, U.S. strategy needs to evolve in both focus and complexity. It is essential for Spacepower, as a key element of national power, to be included in this evolution. In order to do so, this analysis will examine Sino-U.S. space relations using neoclassical realism as a baseline methodology. First, structural elements of the Sino-U.S. relationship will be modeled in a semi-quantitative game theoretical framework, using relative economic and military capabilities as primary independent variables. Second, key assumptions will be tested to ensure that this model accurately represents the current geopolitical environment. Third, the decision making apparatuses of the United States and China will be examined as intervening variables. This will account for imperfect rationality and how it modifies the game theoretical framework. Fourth, this framework will be used to present actionable space policy recommendations for the United States so that space can be incorporated into a competitive strategy for East Asia