Abstract
We study last-mile delivery with the option of crowd shipping, where a company makes use of occasional drivers to complement its vehicle's fleet in the activity of delivering products to its customers. We model it as a data-driven distributionally robust optimization approach to the capacitated vehicle routing problem. We assume the marginals of the defined uncertainty vector are known, but the joint distribution is difficult to estimate. The presence of customers and available occasional drivers can be random. We adopt a strategic planning perspective, where an optimal a priori solution is calculated before the uncertainty is revealed. Therefore, without the need for online resolution performance, we can experiment with exact solutions. Solving the problem defined above is challenging: not only the first-stage problem is already NP-Hard, but also the uncertainty and potentially the second-stage decisions are binary of high dimension, leading to non-convex optimization formulations that are complex to solve. We propose a branch-price-and-cut algorithm taking into consideration measures that exploit the intrinsic characteristics of our problem and reduce the complexity to solve it.

Abstract
Eco-design is focused on incorporating environmental criteria early in the design process to reduce the environmental impacts of new products. However, while services now represent the largest share of the world & rsquo;s economy, the incorporation of environmental sustainability in the design of new services is very limited. This research proposes the ECO-Service Design (ECO-SD) method that integrates eco-design and service design to conceptualize new environmentally sustainable services. The ECO-SD method bridges environmental criteria from eco-design with the human-centred approach of service design, to foster the environmental sustainability of new services, while offering a desirable user experience. To this end, this method encompasses four stages: service exploration, to understand the service context and how users interact with it; service visualization, to visually identify the barriers to environmental performance and user experience during service provision; service ideation, to conceptualize a new service that overcomes the identified barriers; and service assessment, to understand the changes in environmental sustainability and user experience of the newly designed service. The application of the ECO-SD method to two in-dividual shared transport services shows how it enables integrated identification of opportunities to overcome environmental and user experience barriers in the existing services.

Abstract
The COVID-19 pandemic situation has pushed many higher education institutions into a fast-paced, and mostly unstructured, emergency remote education process. In such an unprecedented context, it is important to understand how technology is mediating the educational process and how teachers and students are experiencing the change brought by the pandemic. This research aims to understand how the learning was mediated by technology during the early stages of the pandemic and how students and teachers experienced this sudden change. Data were collected following a qualitative research design. Thirty in-depth and semi-structured interviews (20 students and 10 teachers) were obtained and analysed following a thematic analysis approach. Results provide evidence on the adoption of remote education technologies due to the pandemic with impacts on the education process, ICT platforms usage and personal adaptation. The emergency remote education context led to mixed outcomes regarding the education process. Simultaneously, ICT platforms usage was mostly a positive experience and personal adaptation was mostly a negative experience. These results bring new insights for higher education organizations on actions they could take, such as curating the learning experience with standard, institutional-wide platforms, appropriate training for students and teachers, and suitable remote evaluation practices.

Abstract
Purpose This article aims to synthesize and integrate current research on customer experience (CX), identifying the intellectual structure of the field, systematizing a conceptual framework and identifying future research opportunities. Design/methodology/approach To analyze 629 articles published in peer-reviewed journals in almost four decades, this study employs both bibliometric co-keyword and thematic literature analysis in a complementary way. Findings This article maps the CX literature by describing its intellectual structure in terms of three research domains (customer, organizational and technological), their corresponding most relevant research themes and topics. Moreover, this study develops a conceptual framework and research propositions to summarize and integrate the CX literature. This work recognizes technology as an important driver for the development of CX research. Lastly, this article provides future research opportunities for moving the field forward, considering an integrative view among domains. Originality/value This paper complements other reviews on CX by using a novel methodological approach (co-keyword and thematic analysis) that enables the identification and visualization of the CX intellectual structure. In addition, the study explores the increasing connection between technology and CX research, by raising evidence that technology, by continuously modifying services and consequently CX, has become a transversal component in the research field. These outcomes may be useful for academics and practitioners.

Abstract
Collaboration between companies in transportation problems seeks to reduce empty running of vehicles and to increase the use of vehicles' capacity. Motivated by a case study in the food supply chain, this paper examines a lateral collaboration between a leading retailer (LR), a third party logistics provider (3 PL) and different producers. Three collaborative strategies may be implemented simultaneously, namely pickup-delivery, collection and cross-docking. The collaborative pickup-delivery allows an entity to serve customers of another in the backhaul trips of the vehicles. The collaborative collection allows loads to be picked up at the producers in the backhauling routes of the LR and the 3 PL, instead of the traditional outsourcing. The collaborative cross-docking allows the producers to cross-dock their cargo at the depot of another entity, which is then consolidated and shipped with other loads, either in linehaul or backhaul routes. The collaborative problem is formulated with three different objective functions: minimizing total operational costs, minimizing total fuel consumption and minimizing operational and CO2 emissions costs. The synergy value of collaborative solutions is assessed in terms of costs and environmental impact. Three proportional allocation methods from the literature are used to distribute the collaborative gains among the entities, and their limitations and capabilities to attend fairness criteria are analyzed. Collaboration is able to reduce the global fuel consumption in 26% and the global operational costs in 28%, independently of the objective function used to model the problem. The collaborative pickup-delivery strategy outperforms the other two in the majority of instances under different objectives and parameter settings. The collaborative collection is favoured when the ordering loads from producers increase. The collaborative cross-docking tends to be implemented when the producers are located close to the depot of the 3 PL.

Abstract
Distribution warehouses are a critical part of supply chains, representing a nonnegligible share of the operating costs. This is especially true for unautomated, labor-intensive warehouses, partially due to time-consuming activities such as picking up items or traveling. Inventory categorization techniques, as well as zone storage assignment policies, may help in improving operations, but may also be short-sighted. This work presents a three-step methodology that uses probabilistic simulation, optimization, and event-based simulation (SOS) to analyze and experiment with layout and storage assignment policies to improve the picking performance. In the first stage, picking performance is estimated under different storage assignment policies and zone configurations using a probabilistic model. In the second stage, a mixed integer optimization model defines the overall warehouse layout by selecting the configuration and storage assignment policy for each zone. Finally, the optimized layout solution is tested under demand uncertainty in the third, final simulation phase, through a discrete-event simulation model. The SOS methodology was validated with three months of operational data from a large retailer's warehouse, successfully illustrating how it may be successfully used for improving the performance of a distribution warehouse.