A Crew Scheduling Model to Incrementally Optimize Workforce Assignments for Offshore Wind Farm Constructions

In the literature, different authors attribute between 15% to 30% of a wind farm’s costs to logistics during the installation, e.g., for vessels or personnel. Currently, there exist only a few approaches for crew scheduling in the offshore area. However, current approaches only satisfy subsets of the offshore construction area’s specific terms and conditions. This article first presents a literature review to identify different constraints imposed on crew scheduling for offshore installations. Afterward, it presents a new Mixed-Integer Linear Model that satisfies these crew scheduling constraints and couples it with a scheduling approach using a Model Predictive Control scheme to include weather dynamics. The evaluation of this model shows reliable scheduling of persons/teams given weather-dependent operations. Compared to a conventionally assumed full staffing of vessels and the port, the model decreases the required crews by approximately 50%. Moreover, the proposed model shows good runtime behavior, obtaining optimal solutions for realistic scenarios in under an hour.

Finding Installed Capacity For Practical Training Of A Designated Medical Specialty At A University Hospital

Objective: Propose a methodology to determine the number of medical students who can rotate, for the practice of medicine, in a university hospital, so that the quality of training processes and in-patient care are assured. Materials and Methods: A three-step procedure is presented, in order to find the number of students that the institution can accept simultaneously. Results: The method is based on an integer linear model and it was implemented to assess installed capacity of General Surgery service at Hospital Universitario Clínica San Rafael, increasing in two students (33 %) the training capacity. Conclusions: The proposed methodology not only guaranties the quality of training processes and in-patient care, but also generates other intangible results such as having a more agile way of planning, reducing the planification time. The methodology is easily extended to other services within hospitals.

An optimization approach for green tourist trip design

In this paper, the Multi-Objective Multi-Modal Green Tourist Trip Design Problem (MO-MM-GTTDP) as the multi-modal variant of the orienteering problem is investigated. For this problem, a Multi-Objective mixed-integer linear model is formulated, which maximizes the total score of the Trip, minimizes the total cost of the trip as well as the total emission produced in the trip. Various transportation modes are considered for the tourist to choose to move between points of interest (POIs). The tourist choice may be affected by the transportation time and cost. Moreover, choosing the transportation mode will have an impact on the amount of trip pollutants. The cost of visiting POIs, as well as the cost of transportation between POIs, is considered as the total cost of the tour. In addition, a Multi-Objective Variable Neighborhood Search (MOVNS) algorithm is designed to solve instances of this problem. Moreover, a, E - constraint method is implemented in CPLEX and used to evaluate the performance of the presented MOVNS. New instances of the problem are generated based on the existed benchmark OP instances. The conclusion is the high quality of the proposed MOVNS algorithm solutions in practically acceptable computation time (few seconds). Finally, a small case study based on real data on several POIs in the city of Tehran is generated and used to demonstrate the performance of the proposed model and algorithm in practice. For this case study, by using the multi-attribute decision-making method of TOPSIS, the obtained non-dominated solutions are ranked, and the best ones are presented to the tourist.

Production Scheduling in a Flexible Hybrid Flow Shop in the Food Industry Based on the Theory of Constraints

This paper proposes a mathematical model for production scheduling, whose objective is to maximize the profits or Throughput of a company in the food sector through a Flexible Hybrid Flow, based on the theory of constraints. Considering the company's production configuration, which is a two-stage hybrid flow line, a mixed integer linear model programming (MILP) was formulated and programmed to adequately represent the real situation. The mathematical model developed in this study that is an easy and effective tool that helps to control the production process, by optimizing the quantities of each product to be produced, as well as establishing the sequence in which they must be carried out, which becomes an advantage against its competitors and also obtain a timely response to the needs of demand and compliance with the commitments made to its customers. The results obtained with the MILP, with reasonable computational times, allow for maximizing profits, considering the constraints of the problem.

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.