We consider the Tramp Ship Routing and Scheduling Problem (TSRSP) in which we plan routes for a fleet of tramp shipping vessels operating on a combined contract and spot market. Earlier research has been fragmented due to variations in the side constraints studied. Hence we present the first unified model that can handle speed optimization, chartering costs, bunker planning, and hull cleaning. The model is solved by column generation, where the columns represent the possible routes of a vessel, while the master problem keeps track of the binding constraints. The pricing problem is solved efficiently using a time–space graph and several dominance rules. Real-life instances with up to 40 vessels, 35 geographic regions, and four months planning horizon can be solved to optimality in less than half an hour. The optimized routes increase earnings by 7% compared to historical schedules. Furthermore, policy-makers can use the model as a simulation of a rational agent behavior.
Manufacturing companies who ship goods globally often rely on external Logistics Service Providers (LSPs) to manage the containerization and transportation of their freight. Those LSPs are usually required to follow rules when deciding how to mix the goods in the containers, which complicates the planning task. In this paper, we study such a freight containerization problem with a specific type of cargo mixing requirements recurrently faced by an international LSP. We show that this problem can be formulated as a Multi-Class Constrained Variable Size Bin Packing Problem: given a set of items that all have a size and a fixed number of classes for which they can take certain values, the objective is to pack the items in a minimum-cost set of bins while ensuring that the size capacity and maximum number of distinct values per class are not exceeded in any of the bins. We propose two adapted and one novel greedy heuristics, as well as an Adaptive Large Neighborhood Search (ALNS) metaheuristic, to find feasible solutions to the problem. We also provide a pattern-based formulation that is used to obtain lower bounds using a Column Generation approach. Using three extensive datasets, including a novel one with up to 1000 items and 5 classes reflecting real industrial cases, we show that the novel greedy heuristic outperforms the adaptations of the existing ones and that our ALNS yields significantly better solutions than a commercial solver within a mandatory 5-minute time limit. Practical insights are given about the solutions for the industrial benchmark.
Purpose This study aims to explore how operational resilience can be achieved within supply ecosystems in the delicate yet harsh natural environments of the Arctic. Design/methodology/approach An in-depth, multiple qualitative case study of offshore supply operations in Arctic oil and gas field projects is conducted. Data from semi-structured interviews, personal observations and archival materials are analyzed through institutional work and logics approaches. Findings The findings suggest that achieving social-ecological resilience depends on the interaction between social and natural (irreversible) systems, which are shaped and influenced by various institutional dynamics. Different resilience solutions were detected. Research limitations/implications This study develops a comprehensive understanding of how social-ecological resilience emerges in supply ecosystems through institutional dynamics. The study's empirical basis is limited to offshore oil and gas projects in the Arctic. However, due to anticipated future growth of Arctic economic activities, other types of supply ecosystems may benefit from the study's results.Originality/value This research contributes with empirical knowledge about how social-ecological resilience is created through institutional interaction within supply ecosystems to prevent disruptions of both social and ecological ecosystems under the harsh natural conditions of the Arctic.
The interest in sustainability in the maritime industry has been on the rise. Attention has shifted from how to develop and comply with environmental regulation and labour standards to a more integrated view on sustainable maritime transport that aims at incorporating sustainability in maritime firm strategies. The liner shipping industry, which has been at the forefront, plays a crucial role in global supply chains, with its commitment to sustainable maritime container transport gaining recognition. In particular, procurement relationships stand out as an area where sustainability can exert the most significant impact. Ocean transport is among the most widely outsourced services globally both by shippers and by freight forwarders. Unlike bulk transport, container ocean transport is always outsourced, as shippers do not use their own vessels. Yet, the selection criteria that logistics firms use regarding sustainability when choosing ocean transport service providers and the role of sustainability in value creation among shippers/freight forwarders and ocean transport providers have been scarcely explored.
This article delves into value creation via quality improvement and sustainability practices in ocean freight transport. Employing a case study of an ocean carrier, alongside interviews and survey data, it explores how liner shipping companies can leverage high-quality and sustainable operations to enhance service for their clients and create logistics value. A novel aspect of this study is the application of sustainable supplier management concepts to maritime logistics, highlighting how shippers’ sustainability requirements in sourcing ocean freight services shape procurement relationships and how shipping companies can employ sustainable procurement strategies for value creation.
This chapter argues that state-owned Chinese integrated maritime logistics enterprises are about to change the power balance vis-à-vis the hitherto dominant, privately owned enterprises based in Europe. This shift, which has been actively supported as part of China’s ambitious Belt and Road Initiative, will directly affect the European Union’s common transport and competition policy. Within the larger Belt and Road Initiative, the Maritime Silk Road project can be seen as the umbrella concept for the comprehensive management of the entire supply chain between China and Europe. We discuss possible policy implications for both China and the European Union when it comes to managing the subtle balance between geopolitical considerations and efficient operations of trade and transport controlled by a few dominant actors. As part of our theoretical framework, we use two extensions of the classical obsolescing bargaining model: the one-tier bargaining model and a bargaining model of reciprocation. By combining the two models, we aspire to explain the changing nature of bargaining relations between, on the one hand, the Chinese government and its state-owned enterprises and, on the other, the private-owned European companies as a function of the goals, resources and constraints of the involved parties.
An increasing number of disruptions in ports, plants and warehouses have generated ripple effects over supply networks impacting economic activity. We demonstrate how the spread of the pandemic geographically expands the ripple effect by reducing the workers' participation in production, so undermining the ability of firms and, as a result, the entire cross-border sup- ply chain network to satisfy customers' demands. Our model of the spatio-temporal dynamics of the propagation of Covid-19 infection for supply networks contributes toward ripple effect visualisation and quantification by combining the flow of goods and materials through a typical global supply chain with an epidemiological model. The model enables prospective analyses to be performed in what-if scenarios to simulate the impact on the workforce in each node. The outcome should be helpful tools for managers and scholars. Results from this research will help mitigate the impact and spread of a pandemic in a particular region and the ability of a supply network to overcome the ripple effect. A stylised case study of a cross-border supply chain illustrates the ripple effect by showing how waves with crests at varying dates impact the ability to serve demand showing how a supply chain manager can obtain a forward-looking picture.
Most regulatory tools for low-carbon transition are jurisdiction-specific, respecting the principle of national sovereignty. Although possibly locally successful, they typically capture only scope 1 and scope 2 emissions. Value chains-related (scope 3) emissions remain largely unregulated. This is problematic, as global value chains are commonly organized across multiple jurisdictions with different climate policy ambitions. Products are often produced at different location than where they are consumed, and production-related emissions are transferred with the products. These emissions embedded in imported products amount to large volumes (e.g. in the EU estimated to about 30% of member state’s national emissions). This chapter gathers the scientific evidence on upstream scope 3 emissions and discusses the available regulatory toolbox for reducing those. Both private and public regulatory tools are represented as well as soft and hard regulatory tools, and modifications between those categories. The interactions between the various types of regulation are discussed with the aim to identify possible synergies and conflicts. The chapter takes the EU as its starting point and draws in examples from other jurisdictions where relevant.
Purpose
The purpose of this paper is to investigate the impact of cloud computing (CC) on supply chain management (SCM).
Design/methodology/approach
The paper is conceptual and based on a literature review and conceptual analysis.
Findings
Today, digital technology is the primary enabler of supply chain (SC) competitiveness. CC capabilities support competitive SC challenges through structural flexibility and responsiveness. An Internet platform based on CC and a digital ecosystem can serve as “information cross-docking” between SC stakeholders. In this way, the SC model is transformed from a traditional, linear model to a platform model with the simultaneous cooperation of all partners. Platform-based SCs will be a milestone in the evolution of SCM – here conceptualised as Supply Chain 3.0.
Research limitations/implications
Currently, SCs managed holistically in cyberspace are rare in practice, and therefore empirical evidence on how digital technologies impact SC competitiveness is required in future research.
Practical implications
This research generates insights that can help managers understand and develop the next generation of SCM with the use of CC, a modern and commonly available Information and Communication Technologies (ICT) tool.
Originality/value
The paper presents a conceptual basis of how CC enables structural flexibility of SCs through easy, real-time resource and capacity reconfiguration. CC not only reduces cost and increases flexibility but also offers an effective solution for disruptive new business models with the potential to revolutionise current SCM thinking.
This article examines the theoretical and practical implications of artificial intelligence (AI) integration in supply chain management (SCM). AI has developed dramatically in recent years, embodied by the newest generation of large language models (LLM) that exhibit human-like capabilities in various domains. However, SCM as a discipline seems unprepared for this potential revolution, as existing perspectives do not capture the potential for disruption offered by AI tools. Moreover, AI integration in SCM is not only a technical but also a social process, influenced by human sensemaking and interpretation of AI systems. This article offers a novel theoretical lens called the AI Integration (AII) framework, which considers two key dimensions: the level of AI integration across the supply chain and the role of AI in decision-making. It also incorporates human meaning-making as an overlaying factor that shapes AI integration and disruption dynamics. The article demonstrates that different ways of integrating AI will lead to different kinds of disruptions, both in theory and practice. It also discusses the implications of AI integration for SCM theorizing and practice, highlighting the need for cross-disciplinary collaboration and sociotechnical perspectives.
As a young researcher—admittedly a long time ago—I was struggling with capturing the connections of the many published articles on logistics management. Apparently, many articles dealt with the same topics, yet they were not the same. There seemed to be different viewpoints on what was a scientific contribution and what kinds of methods were allowed. Why did everyone not agree with these questions? Without an understanding of the different research traditions, it was difficult to compare results, validate them and create a meaningful discussion of the various contributions.
Within the Nordic research community, which consists of economists, engineers and management researchers, there were several answers to these questions. In this community, I learnt about systems from engineers. Then, it became clear that there are various philosophies of science that do not have the same understanding of what a theory is and what appropriate methods are. I was searching the philosophy of science literature for a systems perspective and the research implications of such a perspective. To my surprise, I did not find it here, but luckily, I came across a book about methodologies in business research where the systems approach was one of three approaches. I also learnt that, depending on the methodology, the term theory has a different meaning depending on the philosophy of science it originates from. That made a lot of sense to me. Understanding this better, I published my research on this topic in “Schools in Logistics Research” in 2004 (Gammelgaard, 2004). This rather old article is still my daily research guide.