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The purpose of this paper is to empirically explore the potential of outsourcing of humanitarian logistics activities to commercial logistics service providers (LSPs) throughout the different disaster phases. The authors identify incentives for initiating outsourcing of humanitarian logistics activities to commercial logistics providers, humanitarian logistics activities to be outsourced and selection criteria for partners.

This study is based on empirical data collected by interviewing 12 practitioners from commercial LSPs and 12 practitioners from humanitarian organizations (HOs). A review of related literature guided this research.

This research shows that incentives for initiating outsourcing engagements, partner selection criteria and activities to be outsourced are changing throughout the different disaster phases. A number of research propositions are presented.

This research constitutes a first step towards the goal of a comprehensive analysis of humanitarian logistics outsourcing throughout the different disaster phases. The authors collected data from practitioners and large organizations based mainly in Europe and the USA. Hence, insights from national and local organizations of other parts of the world are missing.

This research provides a deeper understanding of outsourcing of humanitarian logistics activities. As the main implication for practice, the research suggests a strategic use of outsourcing during the three disaster phases. The authors acknowledge that business objectives, risks, stakeholder agendas and requirements, as well as costs play a vital and changing role for outsourcing decision-making during the three disaster stages. The managerial implications arising from the research can provide support to commercial LSPs and HOs that initiate or develop strategic outsourcing relationships.

This study covers the gap in the humanitarian literature related to context-specific factors of outsourcing in humanitarian logistics by empirically investigating the phenomenon. This is one of the first studies that empirically investigate the potential of outsourcing of humanitarian logistics activities throughout the disaster phases.

This paper aims to analyze the benefits of the blockchain to the circular economy (CE), which is composed of both closed-loop supply chain (CLSC) systems and reverse omnichannel solutions. By ensuring transparency, traceability, visibility and security, the blockchain allows firms to acquire operational capabilities through a CLSC and service capabilities through a reverse omnichannel, which can boost business performance considerably. The related network of relationships can be reinforced by establishing incentives, which entail both smart contracts in the blockchain and active return approaches in CE.

After identifying the boundaries of the theoretical framework, several research hypotheses are developed according to the literature review and emerging gaps. These gaps link to the impact of the blockchain on CE systems (CLSC and reverse omnichannel), as well as the influence on business performance. The hypotheses are then tested using structural equation modeling and adopting a partial least squares-path modeling technique on a dataset composed of 157 firms. Finally, multigroup analysis is used to test the impact of incentives on the research hypotheses.

The blockchain facilitates a more efficient CE system, although reverse omnichannel solutions seldom bring any benefits to performance. The shift from a passive to an active return approach must be carefully evaluated. The CLSC network can benefit from an active return approach by developing appealing incentives for collectors and enhancing the positive effects of the blockchain. In contrast, consumer incentives can have detrimental effects on the blockchain. Various combinations of incentives can only bring a few business performance increases, while collector incentives are vital to reinforce the CE system's operational and service capabilities.

This paper takes a new approach toward the study of CE, which considers a dual circular system composed of a CLSC and a reverse omnichannel. The research explores whether the adoption of blockchain technology enables better return processes by improving the operations in CLSC and services in reverse omnichannel. Finally, this is the first empirical work to evaluate the benefits emerging from incentives, which can activate smart contracts in the blockchain and enable active return approaches in CE.

The purpose of this paper is to investigate empirically how information and communication technologies (ICT) can contribute to reduction of CO₂ emissions in road freight transport and to identify opportunities for further improvements.

This research adopts a multiple case study approach with three leading UK grocery retailers as exemplars of fast-moving consumer goods retailers, conducted using multiple data collection techniques including interviews, system demonstrations, onsite observations and the use of archive information.

ICT solutions have a direct positive impact on CO₂ emissions reduction but opportunities to further reduce CO₂ emissions are perceived as lying beyond retailers’ own distribution networks. These opportunities are not fully utilised due to the complexities of collaborative ICT provisions and retailers’ reluctance to share information with competitors.

A limitation of the study is that it is exploratory and only three cases were examined. Even though these three retailers represent over 60 per cent of the UK grocery retail sector, other retailers may deploy significantly different ICT applications.

The research provides an overarching insight for businesses on how to leverage the existing and emerging information technologies for environmental and economic benefits.

While sustainability issues have received increasing attention recently, the role of ICT in freight transport for CO₂ emissions reduction has not been investigated in depth and its impact is largely unknown. This research advances understanding about how ICT contributes CO₂ emissions reductions and provides a framework for further investigation.

Marine transportation has been faced with an increasing demand for containerized cargo during the past decade. Marine container terminals (MCTs), as the facilities for connecting seaborne and inland transportation, are expected to handle the increasing amount of containers, delivered by vessels. Berth scheduling plays an important role for the total throughput of MCTs as well as the overall effectiveness of the MCT operations. This study aims to propose a novel island-based metaheuristic algorithm to solve the berth scheduling problem and minimize the total cost of serving the arriving vessels at the MCT.

A universal island-based metaheuristic algorithm (UIMA) was proposed in this study, aiming to solve the spatially constrained berth scheduling problem. The UIMA population was divided into four sub-populations (i.e. islands). Unlike the canonical island-based algorithms that execute the same metaheuristic on each island, four different population-based metaheuristics are adopted within the developed algorithm to search the islands, including the following: evolutionary algorithm (EA), particle swarm optimization (PSO), estimation of distribution algorithm (EDA) and differential evolution (DE). The adopted population-based metaheuristic algorithms rely on different operators, which facilitate the search process for superior solutions on the UIMA islands.

The conducted numerical experiments demonstrated that the developed UIMA algorithm returned near-optimal solutions for the small-size problem instances. As for the large-size problem instances, UIMA was found to be superior to the EA, PSO, EDA and DE algorithms, which were executed in isolation, in terms of the obtained objective function values at termination. Furthermore, the developed UIMA algorithm outperformed various single-solution-based metaheuristic algorithms (including variable neighborhood search, tabu search and simulated annealing) in terms of the solution quality. The maximum UIMA computational time did not exceed 306 s.

Some of the previous berth scheduling studies modeled uncertain vessel arrival times and/or handling times, while this study assumed the vessel arrival and handling times to be deterministic.

The developed UIMA algorithm can be used by the MCT operators as an efficient decision support tool and assist with a cost-effective design of berth schedules within an acceptable computational time.

A novel island-based metaheuristic algorithm is designed to solve the spatially constrained berth scheduling problem. The proposed island-based algorithm adopts several types of metaheuristic algorithms to cover different areas of the search space. The considered metaheuristic algorithms rely on different operators. Such feature is expected to facilitate the search process for superior solutions.

This study aims to explore and empirically test variables influencing material delivery schedule inaccuracies?

A mixed-method case approach is applied. Explanatory variables are identified from the literature and explored in a qualitative analysis at an automotive original equipment manufacturer. Using logistic regression and random forest classification models, quantitative data (historical schedule transactions and internal data) enables the testing of the predictive difference of variables under various planning horizons and inaccuracy levels.

The effects on delivery schedule inaccuracies are contingent on a decoupling point, and a variable may have a combined amplifying (complexity generating) and stabilizing (complexity absorbing) moderating effect. Product complexity variables are significant regardless of the time horizon, and the item’s order life cycle is a significant variable with predictive differences that vary. Decoupling management is identified as a mechanism for generating complexity absorption capabilities contributing to delivery schedule accuracy.

The findings provide guidelines for exploring and finding patterns in specific variables to improve material delivery schedule inaccuracies and input into predictive forecasting models.

The findings contribute to explaining material delivery schedule variations, identifying potential root causes and moderators, empirically testing and validating effects and conceptualizing features that cause and moderate inaccuracies in relation to decoupling management and complexity theory literature?

The purpose of this research is to identify the current reverse logistics practices adopted by soft drink companies and the prominent factors which can decide the efficiency and effectiveness of the entire process of the reverse logistics channel. The paper employs Pareto analysis and the Analytical Hierarchy Process (AHP) method on data collected from logistics professionals involved in the software industry in Sri Lanka using two questionnaires. As the prominent factors, transportation, accidents, packaging, a method of storage, the cleaning process and sorting process was identified and the first four prominent factors have a higher influence on both measures of efficiency and effectiveness. They can also identify the external factors which can emerge inefficiencies due to outsourced dealers. Lack of previous literature on the subject matter and the difficulty to access the filed data were the main limitations of this study. The identified factors will help to identify the correct root causes for the inefficiencies of the current reverse logistics practices and concentrating on these factors will give an opportunity for the soft drink industry players to successfully implement a sustainable green supply chain which reduces waste at each stage of its forwards and reverse logistics process. Transportation, Accidents, Packaging, and Storage have been previously identified as considerations in reverse logistics processes and the current study showed that they have a higher impact on both efficiency and effectiveness on reverse logistics and these factors should be given specific consideration while in the operations.