Artificial intelligence and data analytics capabilities have enabled the introduction of automation, such as robotics and Automated Guided Vehicles (AGVs), across different sectors of the production spectrum which successively has profound implications for operational efficiency and productivity. However, the environmental sustainability implications of such innovations have not been yet extensively addressed in the extant literature. This study evaluates the use of AGVs in container terminals by investigating the environmental sustainability gains that arise from the adoption of artificial intelligence and automation for shoreside operations at freight ports. Through a comprehensive literature review, we reveal this research gap across the use of artificial intelligence and decision support systems, as well as optimisation models. A real-world container terminal is used, as a case study in a simulation environment, on Europe’s fastest-growing container port (Piraeus), to quantify the environmental benefits related to routing scenarios via different types of AGVs. Our study contributes to the cross-section of operations management and artificial intelligence literature by articulating design principles to inform effective digital technology interventions at non-automated port terminals, both at operational and management levels.

Purpose: Population growth, urbanisation and the increased use of online shopping are some of the key challenges affecting the traditional logistics model. The purpose of this paper is to focus on the distribution of grocery products ordered online and the subsequent home delivery and click and collect services offered by online retailers to fulfil these orders. These services are unsustainable due to increased operational costs, carbon emissions, traffic and noise. The main objective of the research is to propose sustainable logistics models to reduce economic, environmental and social costs whilst maintaining service levels. Design/methodology/approach: The authors have a mixed methodology based on simulation and mathematical modelling to evaluate the proposed shared logistics model using: primary data from a major UK retailer, secondary data from online retailers and primary data from a consumer survey on preferences for receiving groceries purchased online. Integration of these three data sets serves as input to vehicle routing models that reveal the benefits from collaboration by solving individual distribution problems of two retailers first, followed by the joint distribution problem under single decision maker assumption. Findings: The benefits from collaboration could be more than 10 per cent in the distance travelled and 16 per cent in the time required to deliver the orders when two online grocery retailers collaborate in distribution activities. Originality/value: The collaborative model developed for the online grocery market incentivises retailers to switch from current unsustainable logistics models to the proposed collaborative models.

The grocery sector has transitioned into an omnichannel operating mode, allowing consumers to buy online and have their order delivered to their chosen address. The last mile delivery service leads to avoidable inefficiencies such as low asset utilisation and repeated trips to nearby neighbourhoods, increasing vehicle emissions, traffic, and operational costs. Combining historical order and delivery data of an online grocery retailer with secondary data publicly available on other retailers, we employ Monte Carlo simulation to estimate grocery home delivery demand per 1-hour time windows. We use the simulation output as an input to daily vehicle routing problem instances under independent and collaborative last mile delivery operation to estimate the impact of collaboration. Our analyses show distance savings of around 17% and route reduction of around 22%. These results can support policies incentivising vehicle and infrastructure sharing settings and decoupling the last mile delivery from the core grocery retail services.