‘Everyone seems to be agreeing at the minute that face-to-face is the way forward’: practitioners’ perspectives on post-pandemic mathematics and statistics support

Mathematics and statistics support (MSS) is now firmly embedded in the learning and teaching infra-structure of most UK universities and in many universities worldwide. In common with other higher education activities, in response to restrictions put in place to reduce the spread of Covid-19, MSS transitioned rapidly to online delivery in spring 2020. This paper reports on thinking within the practitioner community about good practice in the delivery of online MSS. A two-phase approach was used to collect this shared wisdom: an initial questionnaire in May 2020 (just after provision had moved online) and interviews with practitioners in January/February 2021 after colleagues had some experience in online provision and had had the opportunity to reflect on and modify the measures hastily put in place in spring 2020. The focus of the study is not only on what is currently being provided but also on what MSS will look like once all pandemic related restrictions have been ended. The overall feeling of the participants is that face-to-face MSS will return as the dominant form of delivery but that the benefits of online provision are such that a significant minority of provision will remain in this form.

A two-phase approach for solving the multi-skill resource-constrained multi-project scheduling problem: a case study in construction industry

PurposeThe purpose of this research is to study the Multi-Skill Resource-Constrained Multi-Project Scheduling Problem (MSRCMPSP), where (1) durations of activities depend on the familiarity levels of assigned workers, (2) more efficient workers demand higher per-day salaries, (3) projects have different due dates and (4) the budget of each period varies over time. The proposed model is bi-objective, and its objectives are minimization of completion times and costs of all projects, simultaneously.Design/methodology/approachThis paper proposes a two-phase approach based on the Statistical Process Control (SPC) to solve this problem. This approach aims to develop a control chart so as to monitor the performance of an optimizer during the optimization process. In the first phase, a multi-objective statistical model has been used to obtain control limits of this chart. To solve this model, a Multi-Objective Greedy Randomized Adaptive Search Procedure (MOGRASP) has been hired. In the second phase, the MSRCMPSP is solved via a New Version of the Multi-Objective Variable Neighborhood Search Algorithm (NV-MOVNS). In each iteration, the developed control chart monitors the performance of the NV-MOVNS to obtain proper solutions. When the control chart warns about an out-of control state, a new procedure based on the Conway’s Game of Life, which is a cellular automaton, is used to bring the algorithm back to the in-control state.FindingsThe proposed two-phase approach has been used in solving several standard test problems available in the literature. The results are compared with the outputs of some other methods to assess the efficiency of this approach. Comparisons imply the high efficiency of the proposed approach in solving test problems with different sizes.Practical implicationsThe proposed model and approach have been used to schedule multiple projects of a construction company in Iran. The outputs show that both the model and the NV-MOVNS can be used in real-world multi-project scheduling problems.Originality/valueDue to the numerous numbers of studies reviewed in this research, the authors discovered that there are few researches on the multi-skill resource-constrained multi-project scheduling problem (MSRCMPSP) with the aforementioned characteristics. Moreover, none of the previous researches proposed an SPC-based solution approach for meta-heuristics in order to solve the MSRCMPSP.