Abschlussbericht zu dem IGF-Forschungsvorhaben "Intelligente Beleuchtungssysteme in der Lagerwirtschaft"
Abstract
Die Zielsetzung des Projekts bestand in der Untersuchung von Nutzenpotenzialen intelligenter
Beleuchtungssysteme in der Lagerhaltung. Intelligente Beleuchtungssysteme sind in der Lage, Gänge
des Lagers nutzungsabhängig auszuleuchten, sodass die Beleuchtungsintensität und damit auch der
Energieverbrauch an die tatsächliche Nutzung des Lagers angepasst werden kann. Da die Nutzungsintensität der Gänge des Lagers wiederum von der Steuerung des Lagers durch das Unternehmen abhängt und damit aktiv beeinflusst werden kann, entstehen zwischen den operativen Betriebsstrategien für das Lager und den Nutzenpotenzialen intelligenter Beleuchtungssysteme zahlreiche Wechselwirkungen. Diese Wechselwirkungen wurden im vorliegenden Projekt, insbesondere durch die Entwicklung integrierter
athematischer Optimierungsmodelle für die Kommissionierung sowie durch die Entwicklung eines Simulationsmodells, explizit untersucht. Die Bearbeitung der Themenstellung ist in enger Zusammenarbeit mit dem projektbegleitenden Ausschuss erfolgt.
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Warehouses are major contributors to the rise of greenhouse gas emissions in supply chains. Thus, it is not surprising that the attention of academic research to green and sustainable warehousing has been growing in recent years. This attention has led to an increasing number of publications in this field, which is why a systematic literature review on the topic of green warehousing is proposed in the paper at hand. This work provides a comprehensive overview and classification of the existing research on green warehousing, summarizes and synthesizes the available knowledge on this topic, and identifies key trends. Based on the evaluation of the literature, promising ideas for future research are proposed. Citation and network analyses are carried out to evaluate the relationships among the topics covered. The results show an increasing interest in sustainability topics within the warehousing literature, where energy saving has been the most frequently studied objective, followed by environmental impact of warehouse buildings, and green warehouse management in general. The green warehousing literature, however, lacks case studies and empirical data. The main contribution of this paper is an exhaustive summary of the state of knowledge on green warehousing in terms of the macro-themes addressed, the specific topics investigated and the methodological approaches, including a comprehensive and systematic classification of the relevant literature. An outline of managerial guidelines about green warehouse management and the propositions of future research ideas contribute to the further development of this emerging research field.
Occupancy-based lighting control strategies have been proven to be effective in diminishing offices’ energy consumption. These strategies have typically worked by controlling lighting at the room level but, recently, lighting systems have begun to be equipped with sensors on a more fine-grained level, enabling lighting control at the desk level. For some office cases, however, the savings gained using this strategy may not outweigh the costs and design efforts compared to room control. This is because, in some offices, individual occupancy patterns are similar, hence the difference in savings between desk and room control would be minimal. This study examined the influence of occupancy pattern variance within an office space on the relative energy savings of control strategies with different control zone sizes. We applied stochastic modeling to estimate the occupancy patterns, as this method can account for uncertainty. To validate our model, simulation results were compared to earlier studies and real measurements, which demonstrated that our simulations provided realistic occupancy patterns. Next, office cases varying in both job-function type distribution and office policy were investigated on energy savings potential to determine the influence of occupancy pattern variance. The relative energy savings potential of the different control strategies differed minimally for the test cases, suggesting that variations in individual occupancy patterns negligibly influence energy savings. In all cases, lighting control at the desk level showed a significantly higher energy savings potential than strategies with lower control zone granularity, suggesting that it is useful to implement occupancy-based lighting at the desk level in all office cases. This strategy should, thus, receive more attention from both researchers and lighting designers.
To reduce unproductive picker walking in traditional picker-to-parts warehousing systems, automated guided vehicles (AGVs) are used to support human order pickers. In an AGV-assisted order-picking system, each human order picker is accompanied by an AGV during the order-picking process. AGVs receive the picked items and, once a picking order is complete, autonomously bring the collected items to the shipping area. Meanwhile, a new AGV is requested to meet the picker at the first storage position of the next picking order. Thus, the picker does not have to return to a central depot and continuously picks order after order. This paper addresses both the routing of an AGV-assisted picker through a single-block, parallel-aisle warehouse and the sequencing of incoming orders. We present an exact polynomial time routing algorithm for the case of a given order sequence, which is an extension of the algorithm of Ratliff and Rosenthal [Ratliff HD, Rosenthal AS ( 1983 ) Order-picking in a rectangular warehouse: A solvable case of the traveling salesman problem. Oper. Res. 1(3):507–521], and a heuristic for the case in which order sequencing is part of the problem. In addition, we investigate the use of highly effective traveling salesman problem (TSP) solvers that can be applied after a transformation of both problem types into a standard TSP. The numerical studies address the performance of these methods and study the impact of AGV usage on picker travel: by using AGVs to avoid returns to the depot and by sequencing in (near-) optimal fashion, picker walking can be reduced by about 20% compared with a traditional setting. Sharing AGVs among the picker workforce enables a pooling effect so that, in larger warehouses, only about 1.5 AGVs per picker are required to avoid picker waiting.
Summary of Contribution: New technologies, such as automatic guided vehicles (AGVs) are currently considered as options to increase the efficiency of the order-picking process in warehouses, which is responsible for a large part of operational warehousing costs. In addition, picker-routing decisions are more and more often based on algorithmic decision support because of their relevance for decreasing unproductive picker walking time. This paper addresses both aspects and investigates routing algorithms for AGV-assisted order picking in parallel-aisle warehouses. We present a dynamic programming routine with polynomial runtime to solve the problem variant in which the sequence of picking orders is fixed. For the variant in which this sequence is a decision, we show that the problem becomes NP-hard, and we propose a greedy heuristic and investigate the use of state-of-the-art exact and heuristic traveling salesman problem solution methods to address the problem. The numerical studies demonstrate the effectiveness of the algorithms and indicate that AGV assistance promises strong improvements in the order-fulfillment process. Because of the practical relevance of AGV-assisted order picking and the presented algorithmic contributions, we believe that the paper is relevant for practitioners and researchers alike.
Order picking has often been described as one of the most labor- and time-consuming internal logistics processes. In manual picker-to-parts order picking systems, order pickers often spend a significant amount of time on travelling through the warehouse to reach storage positions where required items are stored. To reduce the cost of order picking, researchers have developed various optimal and heuristic routing policies in the past. This paper presents the results of a systematic review of research on order picker routing. First, it identifies order picker routing policies in a systematic search of the literature and then develops a conceptual framework for categorizing the various policies. Order picker routing policies identified during the literature search are then descriptively analyzed and discussed in light of the developed framework. The paper also derives insights into the frequencies of usage of the different routing policies available in the literature and applies a citation analysis to identify seminal works that shaped the literature on order picker routing. The paper concludes with an outlook on future research opportunities.
E-commerce retailers face the challenge to assemble large numbers of time-critical picking orders, of which each typically consists of just a few order lines and low order quantities. To efficiently solve this task, many warehouses in this segment are organized according to the mixed-shelves paradigm. Incoming unit loads are isolated into single units, which are randomly spread all over the shelves of the warehouse. In such a setting, the probability that a picker always finds a demanded stock keeping unit (SKU) close-by is high, irrespective of his/her current position in the warehouse. In spite of this organizational adaption, picker routing, i.e., the sequencing of shelf visits when retrieving a set of picking orders, is still an important optimization problem. In a mixed-shelves warehouse, picker routing is much more complex than in traditional environments: Multiple orders are concurrently assembled by each picker, many alternative depots are available, and items of the same SKU are available in multiple shelves. This paper defines the resulting picker-routing problem in mixed-shelves warehouses and provides efficient solution methods. Furthermore, we use the developed methods to explore important managerial aspects. Specifically, we benchmark mixed-shelves storage against traditional storage policies for scenarios with different ratios between small-sized and large-sized orders. In this way, we investigate whether mixed-shelves storage is also a suitable policy if an omni-channel sales strategy is pursued, and large-sized orders of brick-and-mortar stores as well as small-sized online orders are to be jointly processed by the same warehouse.
E-commerce retailers face the challenge to assemble large numbers of time-critical picking orders each consisting of just a few order lines with low order quantities. Traditional picker-to-parts warehouses are often ill-suited for these prerequisites, so that automated warehousing systems (e.g., automated picking workstations, robots, and AGV-assisted order picking systems) are applied and organizational adaptions (e.g., mixed-shelves storage, dynamic order processing, and batching, zoning and sorting systems) are made in this branch of industry. This paper is dedicated to these warehousing systems especially suited for e-commerce retailers. We discuss suited systems, survey the relevant literature, and define future research needs.
Scattered storage (also denoted as mixed shelves storage) is a warehousing strategy often found in business-to-consumer online retailing. Unit loads are broken down into single items that are spread throughout the warehouse, leading to multiple storage positions per stock keeping unit. This paper investigates the picker routing problem in a rectangular scattered storage warehouse, which differs from classical picker routing problems by being a combined selection and routing problem. A proof of NP-hardness in the strong sense is provided and suited solution procedures are presented. Additionally, the impact of the degree of scatter on the length of picking tours is investigated for differing levels of heterogeneity of the order lines, providing managerial insights on when scattered storage should or should not be applied.
Lighting quality plays an important role in determining the visual performance and visual comfort of the road user, which help keep driver alert. Lighting quality depends on various factors, such as mounting height, luminaire spacing, luminaires, overhang, boom angle, and road surface properties. The light that reflects from the road surface enters the eyes of the road user, and makes the road visible. However, prevailing conditions on the roadway determine the reflection properties of the road surface. This paper presents the analysis of lighting quality in different road surface conditions using the DIALux software. Dry and wet road surfaces are used to study lighting quality following the classifications of the International Commission on Illumination (CIE), where the lighting qualities of high-pressure sodium (HPS) and light-emitting diode (LED) luminaires are compared, based on a roadway without a traffic island. In addition, it presents the design of a roadway lighting system with optimised LED luminaires, by adjusting pole spacing and mounting height, ensuring the best installation in each classification of road surface. Thereafter, energy evaluations of different input powers of LED luminaires are discussed and compared with the HPS luminaire. Results indicate that the HPS luminaire can provide better average illuminance and average luminance values than LED luminaires, resulting in positive visual performance. However, LED luminaires can achieve better overall visual and comfort performances, including energy saving, due to their light distribution efficiency. For lighting quality in different road surface conditions, the wetter the road surface, the higher the lighting quality; because wet road surfaces cause alternating bright and dark areas on the road. This results in increased average luminance of the surface, and decreased longitudinal uniformity. Moreover, road surfaces with high average luminance coefficient, Q0, can accommodate larger pole spacing, reducing energy consumption. Using LED luminaires with lower or higher input power than the optimal power, however, result in higher energy consumption.
The maximization of the system performance in a typical indoor visible light communication system is a major challenge while minimizing the overall resources for the deployment. The intelligent smart lighting systems can be optimized to reduce the requirement of various resources without compromising on the system performance. In this paper, we investigate the optimization of the light emitting diode (LED) resources within an indoor room scenario using a most efficient stochastic optimization technique-Hyper-heuristics evolutionary algorithm (HypEA). The performance of the communication system has been measured in terms of average area outage ratio, computational efficiency and mobility area analysis. The performance of the HypEA has been compared against the most experimented algorithm-Particle swarm optimization (PSO). The detailed investigation and analysis shows that HypEA is computationally more efficient and is able to achieve full mobility with almost 12.5 percent fewer resources as compared to PSO.