Abstract
Algorithmic systems based on artificial intelligence (AI) increasingly play a role in decision-making processes, both in government and industry. These systems are used in areas such as retail, finances, and manufacturing. In the latter domain, the main priority is that the solutions are interpretable, as this characteristic correlates to the adoption rate of users (e.g., schedulers). However, more recently, these systems have been applied in areas of public interest, such as education, health, public administration, and criminal justice. The adoption of these systems in this domain, in particular the data-driven decision models, has raised questions about the risks associated with this technology, from which ethical problems may emerge. We analyse two important characteristics, interpretability and trustability, of AI-based systems in the industrial and public domains, respectively.

Abstract
The integration of the outbound and the inbound logistics of a company leads to a large transportation network, allowing to detect backhauling opportunities to increase the efficiency of the transportation. In collaborative networks, backhauling is used to find profitable services in the return trip to the depot and to reduce empty running of vehicles. This work investigates the vertical collaboration between a shipper and a carrier for the planning of integrated inbound and outbound transportation. Based on the hierarchical nature of the relation between the shipper and the carrier and their different goals, the problem is formulated as a bilevel Vehicle Routing Problem with Selective Backhauls (VRPSB). At the upper level, the shipper decides the minimum cost delivery routes and the set of incentives offered to the carrier to perform integrated routes. At the lower level, the carrier decides which incentives are accepted and on which routes the backhaul customers are visited. We devise a mathematical programming formulation for the bilevel VRPSB, where the routing and the pricing problems are optimized simultaneously, and propose an equivalent reformulation to reduce the problem to a single-level VRPSB. The impact of collaboration is evaluated against non-collaborative approaches and two different side payment schemes. The results suggest that our bilevel approach leads to solutions with higher synergy values than the approaches with side payments.

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
The backroom of retail stores has structural differences when compared with other warehouses and distribution centres, which are more traditionally studied in the literature. This paper presents a mathematical optimisation approach for an unequal area facility layout problem, applied in designing the backroom layout in grocery retail. A set of rectangular facilities (backroom departments) with given area requirements has to be placed, without overlapping, on a limited floor space (backroom area), which can have a regular or an irregular shape. The objective is to find the location and format of the storage departments, such that the walking distances in the store by store employees are minimised. The proposed approach is tested in a European grocery retailer. In the computational experiments, several real store layouts are compared with the ones suggested by the proposed model. The decrease in the walking distances is, on average, 30 percent. In order to understand what the current designers' strategy is, a set of scenarios was created and compared with the real layouts. Each scenario ignores a characteristic of the problem. The goal is to understand what aspect designers are currently discarding. The findings indicate that, currently, designers neglect the different replenishment frequencies of storage departments.

Abstract
Firms have available many forms of collaboration, including cooperatives or joint ventures, in this way leveraging their market power. Customers, however, are atomic agents with few mechanisms for collaborating, leading to an unbalanced buyer-supplier relationship and economic surpluses that shift to producers. Some group buying websites helped alleviate the problem by offering bulk discounts, but more advancements can be made with the emergence of technologies, such as the blockchain. In this article, we propose a customer-push e-marketplace built on top of Ethereum, where customers can aggregate their proposals, and suppliers try to outcompete each other in reverse auction bids to fulfil the order. Furthermore, smart contracts make it possible to automate many operational activities, such as payment escrows/release upon delivery confirmation, increasing the efficiency along the supply chain. The implementation of this network is expected to improve market efficiency by reducing transaction costs, time delays, and information asymmetry. Furthermore, concepts such as increased bargaining power and economies of scale, and their effects in buyer-supplier relationships, are also explored.

Abstract
Omnichannel retailers are increasingly introducing subscription-based delivery services. By subscribing to this service and paying fees upfront, customers are entitled to have orders delivered to their home for a given period without paying any extra delivery charge. We analyze the resulting changes in customer behavior from two perspectives:(i) ordering behavior and (ii) delivery preferences. The model is estimated from the online transactional data of a grocery retailer and combines matching and difference in-differences approaches. We confirm that subscription customers spend more per month and purchase more frequently online than customers without subscriptions. However, this outcome is compromised by shifts towards narrower time slots in the mornings and at night, where slots are requested with less advance notice. When weighing the increased revenue and higher operational costs, we show that subscriptions have a negative impact on a retailer's incremental profit. This remains valid for a wide range of assumptions about (i) the cannibalisation of sales from the retailer's offline business, (ii) picking cost and (iii) delivery cost. To mitigate the impact of subscriptions on retailer profits, we develop a data-driven algorithm that predicts whether certain customers should receive promotions for the subscription plan, rather than it being advertised to all customers. As an extension, we also study whether the addition of a minimum order threshold to subscription plans changes consumer behaviour. We find that this introduction encourages customers to seek more variety and increase their basket size, but does not reduce their order frequency, a phenomena which may be ascribed to cross-selling.