Simheuristic and learnheuristic algorithms for the temporary-facility location and queuing problem during population treatment or testing events

Epidemic outbreaks, such as the one generated by the coronavirus disease, have raised the need for more efficient healthcare logistics. One of the challenges that many governments have to face in such scenarios is the deployment of temporary medical facilities across a region with the purpose of providing medical services to their citizens. This work tackles this temporary-facility location and queuing problem with the goals of minimizing costs, the expected completion time, population travel and waiting times. The completion time for a facility depends on the numbers assigned to those facilities as well as stochastic arrival times. This work proposes a learnheuristic algorithm to solve the facility location and population assignment problem. Firstly a machine learning algorithm is trained using data from a queuing model (simulation module). The learnheuristic then constructs solutions using the machine learning algorithm to rapidly evaluate decisions in terms of facility completion and population waiting times. The efficiency and quality of the algorithm is demonstrated by comparison with exact and simulation-only (simheuristic) methodologies. A series of experiments are performed which explore the trade offs between solution cost, completion time, population travel and waiting times.Peer ReviewedPostprint (author's final draft

Assessment in Bologna context from the teaching perspective, similarities and differences between disciplines

In higher education, assessment is especially significant due to the high level of autonomy and self-regulation that is assumed in the students at this stage. While there is a bulk of research on which assessment evidences are used in higher education, research on how university professors design these evidences is lacking. Using a mixed method technique, we analyzed the assessment methodologies used in three different degrees (Mathematics, Medicine and Sport Sciences), and the design process followed by the teachers in each degree. We found important differences in the assessment methodologies used and the approaches to the assessment design in the degrees. This study shows the way in which teachers of different degrees modified their assessment methods during the Bologna process, as well as the factors that influenced them throughout the process

A simheuristic algorithm for solving an integrated resource allocation and scheduling problem

Modern companies have to face challenging configuration issues in their manufacturing chains. One of these challenges is related to the integrated allocation and scheduling of resources such as machines, workers, energy, etc. These integrated optimization problems are difficult to solve, but they can be even more challenging when real-life uncertainty is considered. In this paper, we study an integrated allocation and scheduling optimization problem with stochastic processing times. A simheuristic algorithm is proposed in order to effectively solve this integrated and stochastic problem. Our approach relies on the hybridization of simulation with a metaheuristic to deal with the stochastic version of the allocation-scheduling problem. A series of numerical experiments contribute to illustrate the efficiency of our methodology as well as their potential applications in real-life enterprise settings

Paisaje y turismo. El corredor bético de Alcaraz (Albacete)

It is our intention to raise some considerations concerning the function played by cultural tourism and the emerging role of landscape as a resource. A relevant case of this process in Castilla-La Mancha is the territory organized by the Guadalmena River, in the province of Albacete, and its immediate surroundings. The most relevant aspect of this valley is its relief, which creates its characteristic sharp lines. This relief contains several landscape units. The biogeographic characters of this climatologic crossroad result in a new singular element in the territory. Today, an interesting landscape tourist route, which starts at the historic town of Alcaraz, can be created by implementing the inventory of natural and cultural resources in the Valley of Gualdamena River.Se plantea una reflexión introductoria acerca de la función del turismo cultural y del papel emergente del paisaje como recurso territorial. En Castilla-La Mancha un caso relevante de este proceso lo constituye el territorio que ocupa el valle del río Guadalmena, en la provincia de Albacete, y su entorno inmediato. El elemento más destacado y por el que el valle adquiere sus caracteres mejor definidos es el relieve, que comprende varias unidades de paisaje. Los caracteres biogeográficos propios de una encrucijada climatológica añaden un nuevo elemento de singularidad a este territorio. La integración de los recursos naturales y culturales del valle del río Guadalmena facilita el desarrollo de una interesante ruta turística paisajística que tiene a la histórica ciudad de Alcaraz como punto de partida

A variable neighborhood search simheuristic for project portfolio selection under uncertainty

With limited nancial resources, decision-makers in rms and governments face the task of selecting the best portfolio of projects to invest in. As the pool of project proposals increases and more realistic constraints are considered, the problem becomes NP-hard. Thus, metaheuristics have been employed for solving large instances of the project portfolio selection problem (PPSP). However, most of the existing works do not account for uncertainty. This paper contributes to close this gap by analyzing a stochastic version of the PPSP: the goal is to maximize the expected net present value of the inversion, while considering random cash ows and discount rates in future periods, as well as a rich set of constraints including the maximum risk allowed. To solve this stochastic PPSP, a simulation-optimization algorithm is introduced. Our approach integrates a variable neighborhood search metaheuristic with Monte Carlo simulation. A series of computational experiments contribute to validate our approach and illustrate how the solutions vary as the level of uncertainty increases

Current Trends in Simheuristics: from smart transportation to agent-based simheuristics

Simheuristics extend metaheuristics by adding a simulation layer that allows the optimization component to deal efficiently with scenarios under uncertainty. This presentation reviews both initial as well as recent applications of simheuristics, mainly in the area of logistics and transportation. We also discuss a novel agent-based simheuristic (ABSH) approach that combines simheuristic and multi-agent systems to efficiently solve stochastic combinatorial optimization problems. The presentation is based on papers [1], [2], and [3], which have been already accepted in the prestigious Winter Simulation Conference.Peer ReviewedPostprint (published version

A simheuristic approach for the two-dimensional vehicle routing problem with stochastic travel times

Altres ajuts: this work has been partially supported by the Erasmus+ programme (2018-1-ES01-KA103-04976).The two-dimensional vehicle routing problem (2L-VRP) is a realistic extension of the classical vehicle routing problem in which customers' demands are composed by sets of non-stackable items. Examples can be found in real-life applications such as the transportation of furniture or industrial machinery. Often, it is necessary to consider stochastic travel times due to traffic conditions or customers availability. However, there is a lack of works discussing stochastic versions of the 2L-VRP. This paper offers a model of the 2L-VRP with stochastic travel times that also includes penalty costs generated by overtime. To solve this stochastic and non-smooth version of the 2L-VRP, a hybrid simheuristic algorithm is proposed. Our approach combines Monte Carlo simulation, an iterated local search framework, and biased-randomised routing and packing heuristics. Our algorithm is tested on an extensive benchmark, which extends the deterministic one for the 2L-VRP with unrestricted and non-oriented loading

Biased-randomized iterated local search for a multiperiod vehicle routing problem with price discounts for delivery flexibility

Altres ajuts: this work has been partially supported by the Spanish Ministry of Education, Culture, and Sports via a Jose Castillejo grant (CAS16/00201).The multiperiod vehicle routing problem (MPVRP) is an extension of the vehicle routing problem in which customer demands have to be delivered in one of several consecutive time periods, for example, the days of a week. We introduce and explore a variant of the MPVRP in which the carrier offers a price discount in exchange for delivery flexibility. The carrier's goal is to minimize total costs, which consist of the distribution costs and the discounts paid. A biased-randomized iterated local search algorithm is proposed for its solution. The two-stage algorithm first quickly generates a number of promising customer-to-period assignments, and then intensively explores a subset of these assignments. An extensive computational study demonstrates the efficacy of the proposed algorithm and highlights the benefit of pricing for delivery flexibility in different settings

A strategic oscillation simheuristic for the Time Capacitated Arc Routing Problem with stochastic demands

The Time Capacitated Arc Routing Problem (TCARP) extends the classical Capacitated Arc Routing Problem by considering time-based capacities instead of traditional loading capacities. In the TCARP, the costs associated with traversing and servicing arcs, as well as the vehicle's capacity, are measured in time units. The increasing use of electric vehicles and unmanned aerial vehicles, which use batteries of limited duration, illustrates the importance of time-capacitated routing problems. In this paper, we consider the TCARP with stochastic demands, i.e.: the actual demands on each edge are random variables which specific values are only revealed once the vehicle traverses the arc. This variability affects the service times, which also become random variables. The main goal then is to find a routing plan that minimizes the expected total time required to service all customers. Since a maximum time capacity applies on each route, a penalty time-based cost arises whenever a route cannot be completed within that limit. In this paper, a strategic oscillation simheuristic algorithm is proposed to solve this stochastic problem. The performance of our algorithm is tested in a series of numerical experiments that extend the classical deterministic instances into stochastic ones

The non-smooth and bi-objective team orienteering problem with soft constraints

In the classical team orienteering problem (TOP), a fixed fleet of vehicles is employed, each of them with a limited driving range. The manager has to decide about the subset of customers to visit, as well as the visiting order (routes). Each customer offers a different reward, which is gathered the first time that it is visited. The goal is then to maximize the total reward collected without exceeding the driving range constraint. This paper analyzes a more realistic version of the TOP in which the driving range limitation is considered as a soft constraint: every time that this range is exceeded, a penalty cost is triggered. This cost is modeled as a piece-wise function, which depends on factors such as the distance of the vehicle to the destination depot. As a result, the traditional reward-maximization objective becomes a non-smooth function. In addition, a second objective, regarding the design of balanced routing plans, is considered as well. A mathematical model for this non-smooth and bi-objective TOP is provided, and a biased-randomized algorithm is proposed as a solving approach. © 2020 by the authors.This work has been partially supported by the Spanish Ministry of Economy and Competitiveness & FEDER (SEV-2015-0563), the Spanish Ministry of Science (PID2019-111100RB-C21, RED2018-102642-T), and the Erasmus+ Program (2019-I-ES01-KA103-062602)