Reverse logistics (RL) is emerging as a significant area of activity for business and industry, motivated by both commercial profitability and wider environmental sustainability factors. However, planning and implementing an appropriate RL network within existing supply chains for product recovery that increases customer satisfaction, decreases overall costs, and provides a competitive advantage over other companies is complex. In the current study, we developed a mixed integer linear programming (MILP) model for a reverse logistics network design (RLND) in a multi-period setting. The RL network consists of collection centres, capacitated inspection and remanufacturing centres and customer zones to serve. Moreover, the model incorporates significant characteristics such as vehicle type selection and carbon emissions (through transportation and operations). Since the network design problems are NP-hard, we first propose a solution approach based on benders decomposition (BD). Then, based on the structure of the problem we propose a three-phase heuristic approach. Finally, to establish the performance and robustness of the proposed solution approach, the results are compared with benchmark results obtained using CPLEX in terms of both solution quality and computational time. From the computational results, we validated that the three-phase heuristic approach performs superior to the BD and Branch &Cut approach.
Robotics and autonomous systems are reshaping the world, changing healthcare, food production and biodiversity management. While they will play a fundamental role in delivering the UN Sustainable Development Goals, associated opportunities and threats are yet to be considered systematically. We report on a horizon scan evaluating robotics and autonomous systems impact on all Sustainable Development Goals, involving 102 experts from around the world. Robotics and autonomous systems are likely to transform how the Sustainable Development Goals are achieved, through replacing and supporting human activities, fostering innovation, enhancing remote access and improving monitoring. Emerging threats relate to reinforcing inequalities, exacerbating environmental change, diverting resources from tried-and-tested solutions and reducing freedom and privacy through inadequate governance. Although predicting future impacts of robotics and autonomous systems on the Sustainable Development Goals is difficult, thoroughly examining technological developments early is essential to prevent unintended detrimental consequences. Additionally, robotics and autonomous systems should be considered explicitly when developing future iterations of the Sustainable Development Goals to avoid reversing progress or exacerbating inequalities.
This research note presents the preliminary findings from a study into the mobilisation of volunteers during the coronavirus pandemic. Data gathered from 49 semi-structured interviews with representatives from local authorities (LAs), voluntary and community sector (VCS) organisations and mutual aid coordinators offer important insights into the state of the sector at this critical juncture, as we find ourselves in a third national lockdown. The role of the VCS in both strategic responses and on-the-ground community action has accorded it renewed respect and credibility. At the same time, the funding landscape for voluntary organisations is uncertain, demand for services is overwhelming, and staff and volunteers are suffering from fatigue. Our findings highlight the imperative of embedding the lessons of the first national lockdown in 2020 by valuing the sector’s contribution to the emergency effort and retaining its seat at the table, ensuring its role in economic and social recovery.
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