Abstract
There are extensive studies in the literature about the reorder point/order quantity policies for inventory management, also known as policies. Over time different algorithms have been proposed to calculate the optimal parameters given the demand characteristics and a fixed cost structure, as well as several heuristics and meta-heuristics that calculate approximations with varying accuracy. This work proposes a new meta-heuristic that evolves closed-form expressions for both policy parameters simultaneously - Cooperative Coevolutionary Genetic Programming. The implementation used for the experimental work is verified with published results from the optimal algorithm, and a well-known hybrid heuristic. The evolved expressions are compared to those algorithms, and to the expressions of previous Genetic Programming approaches available in the literature. The results outperform the previous closed-form expressions and demonstrate competitiveness against numerical methods, reaching an optimality gap of less than , while being two orders of magnitude faster. Moreover, the evolved expressions are compact, have good generalisation capabilities, and present an interesting structure resembling previous heuristics.

Abstract
The synchronization of Public Transportation (PT) systems usually considers a simplified network to optimize the flows of passengers at the principal axes of the network. This work aims to identify the most relevant transfer-connections in a PT network. This goal is pursued with the development of a methodology to identify relevant transfer-connections from entry-only Automatic Fare Collection (AFC) data. The methodology has three main steps: the implementation of the Trip-Chaining-Method (TCM) to estimate the alighting stops of each AFC record, the identification of transfers, and finally, the selection of relevant transfer-connections. The adequacy of the methodology was demonstrated with its implementation to the case study of Porto. This methodology can also be applied to PT systems using entry-exit AFC data, and in that case, the TCM would not be required.

Abstract
The negative impacts of urban logistics have fostered the search for new distribution systems in inner city deliveries. In this context, interesting solutions can be developed around two-echelon distribution systems based on mobile depots (2E-MD), where loads arriving from the periphery of the city are directly transferred, at intermediate locations, from larger to smaller vehicles more suited to operate in the city centre. Four types of 2E-MD can be identified, according to the degree of mobility of larger vehicles and their accessibility to customers. In this paper, we propose a generic three-index arc-based mixed integer programming model, for a two-echelon vehicle routing problem, with synchronisation at the satellites and multi-trips at the second echelon. This generic base model is formulated for the most restrictive type of problems, where larger vehicles visit a a single transfer location and do not perform direct deliveries to customers, but it can be easily extended to address the other types of 2E-MD. The paper presents how these extensions account for the characteristics of the different types of 2E-MD. The generic model, its extensions and the impact of a set of valid inequalities are tested using problem instances adapted from the VRP literature. Results show that the proposed extensions do adequately address the specific features of the different types of 2E-MD, including multiple visits to satellites, and direct deliveries to customers. Nevertheless, the resulting models can only tackle rather small instances, even if the formulations can be strengthened by adding the valid inequalities proposed in the paper. © 2020 The Authors. Published by ELSEVIER B.V.

Abstract
Backrooms are an important echelon of the retail supply chain. However, research focus has been mostly targeted to optimise both distribution centres and stores' sales area. In this paper, we propose two mathematical programming formulations to solve the grocery backroom sizing problem. This problem consists of determining the dimension of each storage department in the backroom area to optimise its overall efficiency. The first formulation is a bottom-up approach that aims to reduce the backroom life-cycle costs by determining the optimum floor space and storage height for each department. The second is a top-down approach based on Data Envelopment Analysis (DEA), which determines the efficient level of storage floor space for each backroom department, based on a comparison with the benchmarks observed among existing stores. Each approach has distinct characteristics that turn the models suitable for different retail contexts. We also describe the application of the proposed approaches to a case study of a European retailer. The application of this methodology in the design process demonstrated substantial potential for space savings (6% for the bottom-up model and 16% for the top-down model). This space reduction should either allow higher revenues in the sales area and/or lower backroom-related costs.

Abstract
Notwithstanding its disruptive potential, which has been the object of considerable debate, Industry4.0 (I4.0) operationalisation still needs significant study. Specifically, scheduling is a key process that should be explored from this perspective. The purpose of this study is to shed light on the issues regarding scheduling that need to be considered in the new I4.0 framework. To achieve this, a two-stage cascade literature review is performed. The review begins with an analysis regarding the opportunities and challenges brought by I4.0 to the scheduling field, outputting a set of critical scheduling areas (CSA) in which development is essential. The second-stage literature review is performed to understand which steps have been taken so far by previous research in the scheduling field to address those challenges. Thus, a first contribution of this work is to provide insight on the influence and expected changes brought by I4.0 to scheduling, while showcasing relevant research. Another contribution is to identify the most promising future lines of research in this field, in which relevant challenges such as holistic scheduling, or increased flexibility requirements are highlighted. Concurrently, CSA such as decentralised decision-making, and human-robot collaboration display large gaps between current practice and the required technological level of development.

Abstract
Most vehicle routing approaches disregard the need to refuel fleets. However, planners search for opportunities to refuel at lower prices even if, counter-intuitively, distant fuel stations need to be visited. We propose a novel mathematical formulation and develop branch-and-cut and matheuristic algorithms to efficiently tackle this problem. Results indicate that, to minimize costs, detour distances may increase up to 6 percentage points when fuel stations with lower prices are farther away from the depot. For practice, these insights imply that current policies disregarding station location and/or fuel prices along with "myopic" planning horizons may lead to sub-optimal decisions.