Road freight transportation is already contributing significantly to global warming, and its emissions are predicted to grow dramatically in the following years. Carbon footprint calculation can be used to assess CO_2e emissions to understand how an organization’s activities impact global sustainability. To this end, the main objective of this paper is initially to assess the impact of Green House Gas (GHG) emissions stemming from road freight transportation. Subsequently, we adopt the EN 16258 standard to calculate the carbon footprint of a truck fleet of a freight transport operator in Greece. Based on the obtained results, we assess the performance of the company’s fleet by adopting relevant sustainability indicators. We also evaluate the use of CNG as an alternative fuel and its impact on CO_2e emissions and operational costs. The paper concludes with a list of additional measures toward further reduction and offsetting of CO_2e emissions.

Background: Order picking process is critical for accurate and efficient order fulfilment. Pick-by-vision is a promising technology that may support order picking process, however there is still a limited amount of research concerning the impact of this technology on the performance of order-picking. The purpose of this paper is to investigate certain operational and technical parameters that affect the performance of pick-by-vision technology in item-level order picking via a series of laboratory tests. Methods: A systematic literature review is conducted for the identification of parameters that affect pick-by-vision performance. Subsequently, the analytical hierarchy process is adopted to rank these parameters, concerning their impact on order picking. Then, the design of experiment and NASA task load index methodology are implemented for assessing pick-by-vision efficiency and perceived workload. Results: The results reveal the parameters that significantly affect the performance of the pick-by-vision system, as well as the best configuration of parameters for the implementation of the proposed system in real environments. Conclusions: The results obtained are encouraging, showing how pick-by-vision technology can support order picking efficiency. Furthermore, practical implications are presented that deal with the organizational culture, process re-engineering, staff resistance to change, and motivation for maintaining the new way of executing order-picking processes.

Background: Despite the general impression that digital order-picking supportive technologies can manage a series of emerging challenges, there is still a very limited amount of research concerning the implementation and evaluation of such technologies in manual picker-to-goods order-picking systems. Therefore, this paper aims to evaluate the performance of three alternative picker-to-goods technologies (i.e., Pick-by-Radio Frequency (RF) Scanner, Pick-to-light, and Pick-by-vision) in terms of completion time and perceived workload. Methods: The Design of Experiments (DoE) methodology is adopted to investigate order-picking technologies in terms of completion time. More specifically, a full factorial design has been used (2³ × 3 full factorial design) for the assessment of the aforementioned order-picking technologies via laboratory testing. Furthermore, for the comparative assessment of the reviewed order-picking technologies in terms of workload, the NASA Task Load Index (NASA-TLX) is embraced by system users. Results: The results reveal that the best picker-to-goods technology in terms of order-picking completion time and perceived workload under certain laboratory setup is light picking when combined with few items per order line and many order lines per order. Conclusion: The paper successfully identified the best picker-to-goods technology, however it is important to mention that the adoption of such order-picking technology implies certain managerial implications that include training programs for employees to ensure they are proficient in using such technologies, upfront costs for purchasing and implementing the order picking system, and adjustments to existing workflows.

Regarding climate change and energy resource problems, cargo movement in the urban environment is essential to shift to a more sustainable mode. As cities seek to slash transport-related emissions and tackle traffic congestion, the cargo bike is showing itself to be an attractive and versatile last-mile delivery alternative. To this end, this article presents a series of datasets for the Electric Capacitated Travelling Salesman Problem (EC-TSP). This problem has been built for modeling and solving the e-cargo bike parcel distribution problem in urban environments. For the design of these datasets, real geographical data have been used that are in the city centers of Athens, Thessaloniki, Patra, and Larisa in Greece. These datasets have been used for the assessment of e-cargo bikes versus typical delivery vans in terms of operational efficiency and CO₂e emissions. The entire benchmark is composed of 9 instances comprised of 14-29 nodes each. The datasets are publicly available for use and modification.