A variant of the hub location routing problem studied in this work, which is the problem of locating a set of hub nodes, is establishing the hub-level network and allocating the spoke nodes to the hub nodes. As a particular property of this problem, each cluster of spoke nodes allocated to a hub constitutes a directed route that starts from the hub, visits all the spokes in the same cluster, and terminates to the same hub. We propose a hybrid of hyper-heuristic and a relax-and-cut solution method, which includes cooperation among several low-level heuristics governed and controlled by a learning mechanism. This hybridization provides a mechanism in which the obtained dual information through the Lagrangian relaxation (bundle) method being utilized to guide the local searches for constructing/improving feasible solutions. Several classes of valid inequalities as well as efficient separation routings are also proposed for being used within the relax-and-cut approach. Our extensive computational experiments confirm the efficiency of this solution method in terms of quality as well as computational time.

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— In March 2020, the World Health Organization (WHO) announced the COIVD-19 as a global pandemic that caused thousands of deaths and brought the world to a standstill with a huge economic burden [1]. Health is an essential factor for sustaining a better life in a better world. Today, for different reasons, several districts in our countries would be deprived from the needed health support and thus, in such cases, we need to deliver health care to those regions. Despite its considerable cost, the mobile clinic remains one of the good solutions to deliver health care to critical areas in our countries. A recognized problem in this domain is minimizing the cost of mobile clinics route in a way that the number of served patients is maximized. This problem is known as the mobile clinics routing problem (MCRP). The purpose of this paper is to present a novel approach that, within the given limited resources, it minimizes the cost and the traveling distance of mobile clinics while maximizing the number of served patients as per priorities assigned according to the patients’ medical status. This paper implements and tests an intelligent variable neighbourhood search algorithm for MCRP

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Telemedicine, which is the use of technologies in order to provide clinical health care at a distance, can be insufficient in some cases where there is a need for medical instruments. This implies the need for supplementary services that would provide to overcome some of the deficiencies. Thus, many techniques were proposed, one of them is to dispatch the so-called Mobile Hospital (MH). This study centers around dispatching and scheduling of dispatched MHs. We aim at allocation and scheduling of MHs to serve patients at their locations given that every patient has its priority in receiving services and the provided service for each patient needs an a priori known time slot. In this study, we propose a hyper-heuristic solution method, which includes several low-level heuristics categorized as constructive, improvement, perturbation etc. coordinated via an artificial intelligent method. Our computational results confirm the efficiency of our solution in terms of solution quality and time.

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