Optimization Approaches for a Home Healthcare Routing and Scheduling Problem

Welfare community projects, mainly related to health care, are essential for the development of societies. For this reason, the optimization of its resources through methodologies that support decision making becomes of interest for all stakeholders in order to reach the target users. In Colombia, particularly in the city of Medellin, several social projects are being developed seeking to provide health and other social services to vulnerable populations. The purpose of this chapter is to deal with a real application of the home health care routing and scheduling problem (HHCRSP), in which a set of health professionals grouped by teams should visit a set of users geographically scatter over the city. Here, it is proposed a mixed integer linear model and a heuristic solution approach. The mathematical model is based on vehicle routing problem with pickups and deliveries (VRPPD) with additional features related with the specific application and geographical conditions.

A Review on Optimization Modeling of Hybrid Energy Systems

Hybrid energy systems (HESs) are an excellent solution for electrification of remote rural areas where the grid extension is difficult or not economical. Usually, HES generally integrate one or several renewable energy sources such as solar, wind, hydropower, and geothermal with fossil fuel powered diesel/petrol generator to provide electric power where the electricity is either fed directly into the grid or to batteries for energy storage. This chapter presents a review on the solution approaches for determining the HES systems based on various objective functions (e.g. economic, social, technical, environmental and health impact). In order to take account of environmental and health impacts from energy systems, several energy optimization model was developed for minimizing pollution and maximizing the production of renewable energy.

A Multi-Objective Model for the Simultaneous Planning Problems

This chapter examines two of the most important operational problems in seaport terminals, first, the berth allocation problem (BAP) which finds an optimal assignment of ships to the berths that minimise the total waiting time of all ships. Then we consider the ships containers to storage areas assignment problem (SSAP) which finds an allocation of ship containers to storage area that minimises the travelling time and containers dispersion. In the first step, a mixed integer linear program model is designed to address the BA problem with the aim of minimising the ships stay time in the port (known as the scheduling theory by the flow time). In a second step, the output of the first model is used in another mixed integer linear program model to solve the SSA problem with a view at reducing both travelling time and containers dispersion while satisfying storage capacities for the case where the containers of one ship can be partitioned into two different and consecutive storage area when needed. The experimental part is conducted on a real case, namely the Tunisian port of Radès.

An Application for Routing Ambulance via ACO in Home Healthcare

In this study, rovers of ambulances were identified and determined quickly and practically via ant colony optimization. Non-intuitive methods can also be used to determine the routing, but when the number of nodes is large, and the number of operations is very large, the heuristic methods are more practical. The purpose of this work is to use ant colony optimization via C# for ambulance routing. The patients were served as soon as possible thanks to ambulance routing. In this the effectiveness of the ambulance has been increased. In this study, 12 nodes were selected as an application. The nodes were used to determine the route of the ambulance in-home health care.

A Variable Neighborhood Search Algorithm to Solve the Flow Shop-Scheduling Problem Through Blocking

In this chapter, we deal with the flow shop-scheduling problem through blocking known to be difficult, where there is no space and a task remains blocked on a machine until the next machine is available. For this reason, we propose the heuristic approach to minimize the delay (tardiness) as an optimization criterion. This chapter proposes a VNS-based heuristic the solutions of which are compared to those of the metaheuristic Greedy Randomized Adaptive Search Procedure (GRASP). We have developed a heuristic-based VNS to get better solutions in a reasonable time. Finally, comparisons with optimal solutions for small problems have shown that all versions can give good results; it would be interesting to extend the ideas presented in this document to the blocking flow shop at minimizing the total delay.

Improving Interhospital Medical Patient Transportation in Morocco

Healthcare facilities are nowadays facing several challenges in terms of quality of care, costs, and performance. Collaboration with stakeholders is a promising way to overcome these challenges. In Morocco, healthcare access and continuity of care remain difficult due, among others, to the various stakeholders involved and the lack of ambulances for extra-hospital and interhospital medical patient transportation (MPT). In this chapter, the aim was to explore collaboration in healthcare supply chain to improve the availability of ambulances for interhospital MPT (transfers). For this purpose, an overview of the MPT system in Morocco was presented while highlighting its main issues. Then, a case study of three hospitals in Casablanca City was analyzed employing a collaborative approach. It consisted in forecasting transfer requests for next periods based on past data, and redistributing the ambulances of the three hospitals according to the forecasts. Findings attest to the variability in demand in the three hospitals and therefore the need for a dynamic allocation of ambulances.

Solving Nurse Scheduling Problem via Genetic Algorithm in Home Healthcare

The nurse scheduling problem (NSP) is the problem involving allocating the monthly shifts (day and night shifts, holidays, and so on) for nurses under various constraints. Generally, the NSP has a lot of constraints. As a result, it needs a lot of knowledge and experience to make the scheduling table with its constraints, and it has been made by the head nurse or the authority in the hospitals. This allocation of the shifts gives a lot of burden (time and efforts) to them, and it has been growing the demand for the automatic nurse scheduling system. This chapter aims to develop a genetic algorithm application for the Nurse Scheduling Problem (NSP). The application will be developed using Microsoft Visual Studio in C# programming language.

Improvement of the Level Service on a Hospital Warehouse Using Forecast Techniques

This article presents a study case focused on the establishment and improvement of the service level in a central warehouse of a hospital organization in Colombia which provide the products of medical use, clothing, anesthetics, and supplies, to nineteen medical dependencies. The warehouse is managed by a person, developing planning processes, purchases, reception, and administration of products or inventories who depends on the administrative and financial sub-direction of the institution. Through the use of interviews and surveys conducted at different dependencies. As well as the collection of information in the field, there were problems with the availability of products due to problems related to planning purchases which are done empirically without data analysis. Taking into account the problematic previously raised, we took different models of predictions as well as the use of the Mean Squared Error (MSE) and the accuracy of predictions to determine the best model according to with the product analyzed.