A hybrid algorithm for large-scale non-separable nonlinear multicommodity flow problems

We propose an approach for large-scale non-separable nonlinear multicommodity flow problems by solving a sequence of subproblems which can be addressed by commercial solvers. Using a combination of solution methods such as modified gradient projection, shortest path algorithm and golden section search, the approach can handle general problem instances, including those with (i) non-separable cost, (ii) objective function not available analytically as polynomial but are evaluated using black-boxes, and (iii) additional side constraints not of network flow types. Implemented as a toolbox in commercial solvers, it allows researchers and practitioners, currently conversant with linear instances, to easily manage large-scale convex instances as well. In this article, we compared the proposed algorithm with alternative approaches in the literature, covering both theory and large test cases. New test cases with non-separable convex costs and non-network flow side constraints are also presented and evaluated. The toolbox is available free for academic use upon request

ICT for Sustainable Last-Mile Logistics:Data, People and Parcels

In this paper we present a vision of how ICT can be leveraged to help combat the impact on pollution, congestion and carbon emissions contributed by the parcel delivery sector. This is timely given annual growth in parcel deliveries, especially same-day deliveries, and the need to inform initiatives to clean up our cities such as the sales ban on new petrol and diesel vehicles in the UK by 2040. Our insights are informed by research on parcel logistics in Central London, leveraging a data set of parcel manifests spanning 6 months. To understand the impact of growing e-commerce trends on parcel deliveries we provide a mixed methods case study leveraging data-driven analysis and qualitative fieldwork to demonstrate how ICT can uncover the impact of parcel deliveries on delivery drivers and their delivery rounds during seasonal deliveries (or “the silly season”). We finish by discussing key opportunities for intervention and further research in ICT4S and co-created Smart Cities, connecting our findings with existing research and data as a call to the ICT4S community to help tackle the growth in carbon emissions, pollution and congestion linked to parcel deliveries

The scope for pavement porters: addressing the challenges of last-mile parcel delivery in London

The UK parcel sector generated almost £9 billion in revenue in 2015, with growth expected to increase by 15.6% in 2019 and is characterized by many independent players competing in an ‘everyone-delivers-everywhere’ culture leading to much replication of vehicle activity. With road space in urban centers being increasingly reallocated to pavement widening, bus and cycle lanes, there is growing interest in alternative solutions to the last-mile delivery problem. We make three contributions in this paper: firstly, through empirical analysis using carrier operational datasets, we quantify the characteristics of last-mile parcel operations and demonstrate the reliance placed on walking which can make up over 60% of the round time; secondly we introduce the concept of ‘portering’ where vans rendezvous with porters who operate within specific ‘patches’ to service consignees on-foot, potentially saving 86% in driving distance on some rounds; finally, we highlight the wider practical issues and optimization challenges associated with operating driving and portering rounds in inner urban areas

A data analytics model for improving process control in flexible manufacturing cells

With the need of more responsive and resilient manufacturing processes for high value, customised products, Flexible Manufacturing Systems (FMS) remain a very relevant manufacturing approach. Due to their complexity, quality monitoring in these types of systems can be very difficult, particularly in those scenarios where the monitoring cannot be fully automated due to functional, safety and legal characteristics. In these scenarios, quality practitioners concentrate on monitoring the most critical processes and leaving out the inspection of those that are still meeting quality requirements but showing signs of future failure. In this paper we introduce a methodology based on data analytics that simplifies the monitoring process for the operator, allowing the practitioner to concentrate on the relevant issues, anticipate out of control processes and take action. By identifying a reference model or best performing machine, and the occurring patterns in the quality data, the presented approach identifies the adjustable processes that are still in control, allowing the practitioner to decide if any changes in the machine’s settings are needed (tool replacement, repositioning the axis, etc.). An initial deployment of the tool at BMW Plant Hams Hall to monitor a focussed set of part types and features has shown a reduction in scrap of 97% throughout 2020 in relation to the monitored features compared to the previous year. This in the long run will reduce reaction time in following quality control procedure, reduce significant scrap costs and ultimately reduce the need for measurements and enable more output in terms of volume capacity.Engineering and Physical Sciences Research Council (EPSRC): EP/R511730/1 and EP/R032777/