In the logistics arena, green warehousing has been achieving increasing attention from both practitioners and academia. On the one hand, practitioners have started to search for solutions to decrease the environmental impact of their logistics facilities and incorporate practices towards greener warehousing processes. On the academic side, a rising – though limited – number of papers have been found addressing the impact of the green warehousing practices in place, together with the related effects on warehouse consumption and environmental performance. In this context, conditioned warehouses represent a key challenge due to their temperature constraints and the ever-demanding logistics performances, and related studies are still lacking. This paper aims to address this research gap by proposing a simulation-based approach where multiple scenarios of a real conditioned logistics facility are discussed, grounded on a conceptual framework of green warehousing practices selection process. Three different scenarios are proposed, and the related performances are examined in terms of energy consumption and CO2eq emissions. Implications of the results are discussed and streams for future investigation are identified.

Manual picker-to-parts order picking systems remain predominant in retail warehousing and have been identified as one of the comparatively most labor-intensive processes. While previous studies have delved into the effects of work intensity and worker experience on performance, they have typically examined each construct separately while neglecting workload-related experience. Given that the interaction remains under-explored, we here investigate how workload-related experience could possibly mitigate the negative performance effects of work intensity. We obtain a unique longitudinal real-world retail warehouse data set including 1,739,352 storage location visits performed by 74 order pickers from January to April 2023. We apply a mixed-effects model allowing for random intercepts for each order picker and utilize order picking task performance time as our dependent variable. We find that work intensity increases task performance time at increasing rates and that workload-related experience can mitigate this effect. Our research informs operations managers under which conditions they can capitalize on the positive effects of workload-related experience while mitigating the negative consequences of work intensity.

Despite the evident connections between Demand Forecasting and Inventory Control, both, researchers and practitioners tend to perform and analyze those tasks separately. Yet, the application of appropriate Demand Forecast-ing Methods promises meeting inventory-related target values while reducing Inventory Costs. A significant difficulty consists in identifying the appropri-ate Demand Forecasting Method. Thus, practitioners require a framework that supports the decision process of selecting said method. Depending on the chosen Forecasting Method, different configurations of the Inventory Control Policy might be suitable. The aim of this work is to facilitate the complex task of connecting the Forecasting Method Selection and Inventory Control Policy Configuration for a group of numerous and heterogeneous products. Thus, a simple framework that generates recommendations regard-ing the appropriate Forecasting Method and Inventory Policy will be devised and empirically tested. However, due to capacity restrictions, only two of the three suggested Forecast Methods will be investigated further. The applica-tion of the framework regarding sales data of a biotech company shows that it enables a significant reduction of stockouts which translates to higher ser-vice levels. The proposed methods therefore contribute to efficient and eco-nomically sustainable warehouse operations and inventory control manage-ment concepts.

With increasing concerns surrounding environmental impact in the renewable energy sector, this study delves into the analysis of carbon dioxide (CO2) emissions generated by maritime logistics operations within Offshore Wind Farms.

Automatic Identification System (AIS) data is collected from Crew Transfer Vessels (CTVs). The primary objective is to quantify the environmental impact of these operations and, consequently, contribute to the development of sustainable solutions that can be alternatively used. Using data science and software tools, utilizing AIS data and Python, NumPy, Pandas, and the nautical-calculations library, the authors calculated the total CO2 emissions produced by the sample Crew Transfer Vessel. This CTV sailed 60,000 nautical miles over 5 years.

Additionally, the authors will explore the feasibility of integrating hybrid or electric CTVs to curtail the overall CO2 emissions associated with wind farm operations. These findings collectively offer a pathway towards a greener and more sustainable offshore wind farm operations.

By incorporating machine learning models in the future, the framework can enhance the efficiency of routes used by the CTV, leading to reduced fuel consumption and further minimizing environmental impact.

Moreover, the integration of hybrid or electric CTVs presents an opportunity to not only reduce CO2 emissions but also decrease reliance on fossil fuels in offshore wind farm operations. Ultimately, implementing these findings can contribute to a more environmentally friendly and sustainable use of CTV in the Offshore Wind industry.

Ferry traffic is particularly dominant in inland seas such as the Baltic Sea, where it can exploit its advantage of high departure frequency and short journey times, thus enabling fast-moving traffic throughout Europe. Roll on/Roll off (RoRo) and Roll on/Roll off Passenger (RoPax) ports, however, are confronted with increasing competition for port expansion areas from various developments such as the rezoning to urban areas. Therefore, maintaining adequate access to RoRo/RoPax ports is becoming increasingly challenging and can only be achieved through the interaction of different stakeholders such as port authorities, ferry companies or city planners. In urban areas in particular, traffic situations increasingly occur that make it difficult for trucks or other vehicles to reach the port reliably and thus place a heavy load on terminal and shipping company resources.

After analysing the literature on simulation approaches with respect to truck arrival management in the RoRo/RoPax and the container terminal segment, the application of a pre-gate concept to the RoPax port Turku (Finland) in combination with a call-off structure was analysed by a simulation.

In the paper three different scenarios were compared regarding the positioning of pre-gate parking spaces according to the parameters: travel time, vehicle arrival time at the terminal and queue length at a prominent intersection.

The approach adopted offers a controllability that can be actively used by the terminal operators and stevedoring to make terminal operations and vehicle handling more efficient.

Transporting goods via inland waterways offers significant advantages over transport via road or rail. The inland waterway vessel is more environmentally friendly, reliable, and quieter than other transport modalities. This paper presents a framework based on this motivation to develop so-called MicroPorts to strengthen inland waterway transport and increase its attractiveness. MicroPorts are new small-scale transshipment facilities for inland waterways based on the conversion of existing infrastructure. Through this, the network of transshipment points on inland waterways can be expanded while keeping construction costs and impact on nature low and the transported goods closer to their destination, shortening the last few kilometers by road or rail. The MicroPorts framework was developed through several workshops with an inland shipping owner, pro-cess analysis at an inland waterway port and literature analyses. It comprises five elements: characteristics, operational requirements, technical requirements, loca-tion, and assessment parameters. Based on the framework, a method was derived to develop MicroPorts. The method contains four steps: 1. Identify potential loca-tions, 2. Selection of possible operational concepts, 3. Selection of possible tech-nical implementations, and 4. Evaluation of feasibility. The method can be used for the identification of new transshipment locations and planning of new Mi-croPorts. This paper also presents first developed MicroPorts concepts alongside an exemplary route.

In robotic mobile fulfillment systems, which are warehousing technologies that follow the parts-to-picker concept, the order picking process involves two decisions of how to schedule the processing of orders and of how to sequence the racks that are lifted and transported by robots to the picking station in order to supply the requested items. We propose a heuristic solution approach for solving the order-scheduling and rack-sequencing problem at a single picking station. Our approach utilizes column generation to partition the set of orders into batches. The goal is to minimize the number of rack assignments to these batches, which minimizes the rack-visits. The generated batches possess a specific property that allows for the straightforward derivation of an order-processing schedule and rack sequence. To further improve the solution, we refine the heuristic approach by rearranging the processing of batches and their assigned racks. We conducted a comprehensive and comparative computational. The method outperforms several other heuristics in terms of both solution quality and runtime on the majority of instances. Additionally, our heuristic yields satisfactory results when embedded into a framework designed to solve the problem across multiple picking stations, particularly for small-case data.

In order to provide fast access times to the stored items in warehouses, different types of storage systems exist. A number of factors, including the physical size and weight of the items to be stored, the frequency of use, and the resources (such as space) available, will determine the type of system that is suitable for a particular warehouse. In this talk, we will examine a new type of storage system that has received little attention from the scientific community so far, the compact storage systems. Compact storage systems aim at achieving the highest possible space utilization rate given limited storage space.

We want to shed light on the specifics of such storage systems and analyze how to optimize the storage and retrieval of items within them (e.g., by order picking and batching, the sequencing of the retrievals, and control, navigation, and relocation of storage units). In this talk, we formulate combinatorial optimization problems addressing the retrieval of items, discuss algorithmic approaches and their computational complexity.

Delivery of parcels in urban environments can be conducted in various ways. Next to traditional van delivery, cargo bikes are in use already today. In the future, also robots or drones may be used for this purpose. Each of those systems comes with certain (dis-)advantages regarding capacity, speed, environmental friendliness, and compatibility with customer preferences. The availability of these alternative transportation systems therefore raises the question, which of them to apply in a particular city environment and how to combine complementary systems in order to benefit from their mutual advantages. In order to investigate this, we propose a mathematical optimization model for the operations management of two-tier urban parcel logistics, which can handle alternative modes of transportation in isolation but also in combination. We propose an Adaptive Large Neighborhood Search for solving this optimization model and present computational results that shed light on the performance of the heuristic as well as the suitability of combined fleets of vans, bikes, robots and/or drones in urban environments. Through this, we can draw recommendations on which technologies to consider when implementing innovative parcel delivery systems for urban environments.

Green hydrogen production, distribution and use is seen as a central element of a carbon-neutral economy. Specifically, the establishment of offshore green hydrogen production facilities amidst wind energy parks is seen as a promising concept for European countries like Germany. Yet, such green hydrogen manufacturing and distribution concepts are not evaluated in a comprehensive sustainability perspective. In order to avoid unintended sus-tainability effects, an ex-ante evaluation regarding the three triple bottom line perspectives of environmental, economic and social sustainability is advisa-ble. As especially offshore green hydrogen production and transportation concepts are completely new, even the evaluation concept to be used for such a required comprehensive sustainability check is largely missing. Alt-hough dedicated evaluation and decision support methods in the fields of LCA and SLCA are available for sustainability evaluation issues, the ques-tion of selecting matching method frameworks for a future offshore-based green hydrogen supply chain is yet to be answered. This contribution is pro-vided by this paper in a conceptual approach based on existing method sets and analytical results for neighboring application fields like solar or biogas green hydrogen production and distribution.

Green hydrogen, produced mainly by electrolysis, is a promising energy carrier to de-fossilise different economy sectors, from heavy industry to logistics. A fully transformed economy would use hydrogen as a process gas and a fuel for heat generation and vehicles. However, since the technology to produce green hydrogen has yet to be available at an industrial scale, there are no projections for forming regional hydrogen hubs. This article contributes to synthesising a holistic framework to specify and optimise hydrogen-based applications in logistics from an ecological and economic perspective. These applications utilise logistics macrostructures, like logistics hubs. Alternatively, they may use industrial supply chains, like direct reduced iron (DRI) based steel plants, which modify their operations and transform their logistic ecosystems. The framework includes a configuration of policies and economic boundary conditions that influence the logistic hubs’ transformation paths. The article describes the synthesis of the framework based on an initial problem analysis and a systematic literature review. The framework helps policymakers and planners evaluate and optimise the composition and design of hydrogen and logistics hubs.

Animal feed supply networks heavily rely on just-in-time deliveries between raw material producers, retailers, manufacturers, and customers. Accordingly, transportation contributes largely to this industry's CO₂e footprint. This article extends an existing simulation model with capabilities to track the CO₂e footprint of individual products across the supply network. It further integrates the capability to simulate the use of electric transport vehicles. This article presents a simulation study to investigate using electric trucks instead of diesel trucks in terms of \coe and kilometers traveled. The results show that the animal feed distribution is particularly suitable for electric vehicles due to the comparably localized area covered by these supply networks and can achieve reductions of up to 70 % CO₂e for a well-utilized fleet.

Industry 4.0 represents a novel paradigm centered around digital factories, capa-ble of integrating information technologies and machines with intelligent prod-ucts. In this context, this article addresses the added monetary value resulting from adopting Industry 4.0 technologies in the development of a scraped surface heat exchanger equipment. This research aims to estimate the added value of a technology-based redesign of a food processing machine, considering the will-ingness to pay. The methodology employed to evaluate the integration of these technologies into the product is based on the Stated Preference (SP). The findings reveal a hierarchy among the enhancement opportunities that Industry 4.0 tech-nologies bring to the product. Consequently, in this case, incorporating features from Industry 4.0 that encompass the maintenance aspects contributes significant-ly to the product's value.

Digital twins play an essential role in manufacturing companies to adopt Industry 4.0. However, their uptake has been lagging, especially in European manufacturing firms. This can be attributed to the absence of automated methods for digitizing physical manufacturing resources and creating digital representations accessible and processable by both humans and computers. Our research addresses this challenge by automating the digitization of manufacturing resources captured on the shop floor. We employ object detection techniques on a set of images and align the results with an ontology that standardizes the semantic description of digital representations. This research aims to accelerate digital transformation for manufacturing companies, providing digital representations to their physical resources. The ontology-based digital representation fosters interoperability among diverse equipment and machines from various vendors. It enables the automated deployment of digital twins, improving the efficiency of planning and control of manufacturing systems.

The convergence of Value Stream Management with cutting-edge technologies represents a dynamic area of research, as underscored by recent studies. These studies reveal a growing emphasis on digitalization and share a common goal: proposing data-driven techniques to enhance and optimize conventional Value Stream Management struggling to adapt in rapidly changing environments. By the present paper, a digital Value Stream Map according to the Digital Twin (DT) concept is investigated. This Digital Value Stream Twin (DVST) is based on the orchestration of multiple DT, representing core elements of Value Stream Management such as material flowing through the value stream and related resources. Overcoming the fixed structure of the automation pyramid, business application systems and machine signals are merged as data sources into one model, verified by a business scenario, mainly carried out in an SAP S4/HANA (ERP - enterprise resource planning) test environment. In this context, the present study is built upon a validation using a digital value stream model according to the Digital Shadow (DS) approach. Conceptually, the expansion of the DS into a DT is described. From this, potentials regarding the value stream method are derived and investigated.

Transport times in container terminals have been widely studied, often with a focus on autonomous vehicles, while manual operations remain prevalent in many ports. Predicting travel times for straddle carriers in container terminals is a challenging problem, impacting the efficiency and productivity of manual container handling.

In our work, we propose a machine learning-based method for predicting travel times, leveraging a unique dataset obtained from a digital twin. This dataset incorporates positional and operational data gathered from IoT devices. The dataset is sourced from the Port of Hamburg, which is one of the 20 largest container ports in the world.

Our method demonstrates better accuracy and performance compared to conventional methods, tailored to the needs of manual container handling. The conducted research provides insights for optimizing container handling in container terminals. It also has implications for the container terminal simulation and an underlying digital twin.

The Capacitated Vehicle Routing Problem with Time Windows (CVRPTW) is a well-known combinatorial optimization problem that extends the classical Vehicle Routing Problem to account for additional real-world constraints such as truck capacity and customer time windows. Recent studies have explored the use of deep graph convolutional networks (GCNs) to predict the arcs that are part of the optimal tour for the Travelling Salesman Problem and related routing problems.

In our talk, we propose a novel context complemented graph convolutional network (CCGCN) which is integrated into a Monte Carlo Tree Search (MCTS) to solve the CVRPTW sequentially. The CCGCN consists of a deep convolutional part that builds efficient CVRPTW graph representations and a context part. The context part processes information of partially built tours to output probabilities on which vertex to add next to the tour, which is used during the expansion of the search tree. For simulating the final solution value based on a given partial solution in the Monte Carlo search tree, we apply a beam search that uses the context complemented graph convolutional network in an autoregressive form to build valid complete solutions for evaluating a given node in the MCTS. Moreover, a variation of the Upper Confidence Bound applied to trees is used in combination with the prediction probabilities given by the network to manage the tradeoff between exploration and exploitation. Evaluations of the proposed heuristic are performed on benchmark instances of the CVRPTW with up to 100 customers; these show promising results.

Online tour planning in logistics involves dynamically incorporating new transportation requests into precomputed tour plans, without foresight into future demands. Prominent examples include medical logistics involving the timely delivery of medical equipment, pharmaceuticals, or even patients. Online decision-making is quite well understood from an algorithmic and worst-case perspective and tight performance bounds are known.

However, the assumption of not having any prior knowledge about future requests seems overly pessimistic. Given the success of machine-learning methods and the available data in many tour planning problems, one may expect to have access to predictions about future requests. However, simply trusting them might lead to very poor solutions as these predictions come with no quality guarantee. In this talk we present recent developments in the young line of research that integrates such error-prone predictions into algorithm design to break through worst case barriers. We discuss algorithmic challenges with a focus on online routing present algorithms with error-dependent performance guarantees and we shortly discuss the choice of error metrics.

This is an overview talk that builds mainly on joint work with G. Bernardini, A. Marchetti-Spaccamela, L. Stougie, and M. Sweering, published at NeurIPS 2022.

Amidst rising stakeholder expectations and recent disruptive events, manufacturing firms are re-evaluating strategies, focusing on sustainability and resilience. Operations managers face a resource allocation challenge balancing these priorities.

This conceptual paper delves into sustainable resilience, exploring the relationships between congruent operations, network capabilities, and sustainable firm performance, considering uncertainty and sequential capability-building. Through a conceptual literature review, this paper presents a conceptual model and associated hypotheses, laying the groundwork for an extensive empirical study.

Building on the cumulative capability theory, we provide a nuanced perspective on the traditional Sand Cone model, emphasising sequence testing of operations and network capabilities. This approach paves the way for a multi-dimensional understanding of sustainable resilience. Addressing paradoxical tensions from trade-offs, our model outlines a path for subsequent research, aiming to guide firms through the journey of multiple priorities in today's volatile environment.

Secondary trading of clothing can support the transition to the circular economy by prolonging the lifespan of products through reuse. From a consumer point of view, second-hand consumption promotes a reduction in first-hand purchasing. From a company point of view, resale allows to take care of end-of-life products, according to the increasing environmental responsibilities requested to manufacturers, but also to monetize with left-over products, overstock, and returned goods. Indeed, resale models offer the chance to take control over a product’s lifecycle and to dispose of slow-moving inventory, unsold products at the end of the season, and returns with sustainability-focused solutions.

With the growing interest in re-commerce, firms have increasingly entered this market over the last decade, anyway, it proved to be both expensive and logistically challenging for brands used to sell only new items. The objective of this exploratory study is to analyze the end-of-life product management of luxury companies. Specifically, through multiple case studies based on semi-structured interviews of businesses operating in the luxury second-hand market, we explore the strategic partnership developed by companies and second-hand trading platforms to manage used products or returns and embrace circular economy.

The results will show how resale can be both an environmental opportunity to embrace circularity and an economical one. Managerial insights of this research will guide managers towards practices to promote efficient logistic coordination and achieve cost-minimizing and profit-maximizing, which are the key factors used in determining processing options for returned and unsold goods.

With the increasing popularity of Augmented Reality (AR) applications, especially for mobile devices, the technology supports several construction projects. Here, AR helps to communicate planned construction projects, as its visualization increases the immersion and is better understood than common approaches. However, these use cases are mainly outdoors, which pose special requirements. For most, the (geo-referenced) 3D models of planning projects must be aligned correctly in natural environments, which is a challenge, as many AR devices and standard methods are not working for (large) outdoor environments. For this reason, new research approaches based on different algorithms and sensors arise. This paper defines requirements for developing geo-referenced outdoor AR applications by a structured literature analysis and developing an application with the key requirements: accurate 3D model placement and integration. Creating the mobile outdoor AR application further provides insights for developing such systems. The application considers several outdoor activity requirements and addresses different approaches to geo-referencing with internal and external sensors. This paper also presents two model integration methods: a 2D and a 3D environment scan and algorithmic processing.

Shelf replenishment is a repetitive, manually executed, and time-consuming task in retail. This paper addresses this issue with an intelligent pointer unit that helps staff reduce the orientation time for small and similar products in the shelf replenishment process. The user wears a ring scanner to scan an article or its box, whereon the pointer unit illuminates the target shelf position received from a digital store model.

A comprehensive evaluation extracts the performance of the pointer unit within two user groups. The results show a reduction of the orientation time of \SI{88.5}{\percent} for beginners, respectively \SI{75}{\percent} for experienced staff members. Furthermore, accounting for the times needed for handling and alignment, a reduction of the overall search time of \SI{71.5}{\percent} for beginners, respectively \SI{22.5}{\percent} for experienced staff members, has been achieved.

Global trade heavily relies on maritime transport, with ships carrying most of the world's goods by volume. The effective planning of ports plays a pivotal role in the smooth operation of intermodal hubs required for the timely flow of goods. Ports are dynamic entities, constantly evolving due to technological advancements, economic fluctuations, political changes, and environmental factors, demanding innovative approaches to port planning. Furthermore, the complexity of port planning is compounded by the lengthy concessions that typically span 20 to 30 years.

The project HafenPlanZEN aims at providing a tool to facilitate and improve port planning decisions. The initiative represents a collaborative endeavor involving the Hamburg Port Authority, Hamburg Port Consulting, and the University of Hamburg. HafenPlanZEN harnesses this digital infrastructure, integrating data from various digital twins, offering a comprehensive overview of the port's performance and efficiency.

Central to our project is the development of a simulation and optimization tool aimed at the assessment and enhancement of port performance. HafenPlanZEN adopts a simulation-based approach and is directly fed with data from the port’s sensors and digital twins. Simulations allow port planners to experiment with diverse new development ideas as well as their refinement using optimization techniques. Such techniques can be used to automatically aid with infrastructure-independent decisions such as timing traffic lights and defining minimal parking and handling areas. Moreover, HafenPlanZen's capabilities extend to evaluating the impact of infrastructure changes, such as the construction of a new bridge or tunnel, providing comprehensive insights into port planning and management.

The aim of berth allocation planning is to derive conflict-free vessel assignments to the quay of a container terminal. An important objective of terminal operators in this context is to provide the best possible service quality to the shipping companies, i.e., especially short waiting times. The berthing schedule resulting from solving a dynamic Berth Allocation Problem (BAP) consists of the berthing times and positions of all vessels that are expected to arrive within a certain timeframe; these vessels are scheduled according to their respective arrival and handling times. However, both these times are uncertain due to different influences, e.g., wind and wave or defect handling equipment. Deviations from the planned handling time lead to delayed vessel departures, which cause waiting times for the succeeding vessels and also can ultimately result in conflicts that may impede the schedule’s feasibility. Hence, updating or re-planning of berthing schedules can become necessary, but this is costly and may be impossible when a plan is already in execution.

Therefore, the aim of this work is to derive robust berthing schedules that enhance the schedules’ stability by considering uncertainty already in the planning phase and, thus, are resistant to uncertainties of handling times. With a robust optimization approach which is based on time buffers, uncertainty is proactively considered, resulting in more robust schedules. The results of the new approach are evaluated from an ex post perspective using real ship data from the AIS and actual ship handling times.