Juan G. Villegas

A GRASP with evolutionary path relinking for the truck and trailer routing problem

In the truck and trailer routing problem (TTRP) a heterogeneous fleet composed of trucks and trailers has to serve a set of customers, some only accessible by truck and others accessible with a truck pulling a trailer. This problem is solved using a route-first, cluster-second procedure embedded within a hybrid metaheuristic based on a greedy randomized adaptive search procedure (GRASP), a variable neighborhood search (VNS) and a path relinking (PR). We test PR as a post-optimization procedure, as an intensification mechanism, and within evolutionary path relinking (EvPR). Numerical experiments show that all the variants of the proposed GRASP with path relinking outperform all previously published methods. Remarkably, GRASP with EvPR obtains average gaps to best-known solutions of less than 1% and provides several new best solutions.

GRASP/VND and multi-start evolutionary local search for the single truck and trailer routing problem with satellite depots

In the single truck and trailer routing problem with satellite depots (STTRPSD) a vehicle composed of a truck with a detachable trailer serves the demand of a set of customers reachable only by the truck without the trailer. This accessibility constraint implies the selection of locations to park the trailer before performing the trips to the customers. We propose two metaheuristics based on greedy randomized adaptive search procedures (GRASP), variable neighborhood descent (VND) and evolutionary local search (ELS) to solve this problem. To evaluate these metaheuristics we test them on a set of 32 randomly generated problems. The computational experiment shows that a multi-start evolutionary local search outperforms a GRASP/VND. Moreover, it obtains competitive results when applied to the multi-depot vehicle routing problem (MDVRP), that can be seen as a special case of the STTRPSD.

A matheuristic for the truck and trailer routing problem

In the truck and trailer routing problems (TTRPs) a fleet of trucks and trailers serves a set of customers. Some customers with accessibility constraints must be served just by truck, while others can be served either by truck or by a complete vehicle (a truck pulling a trailer). We propose a simple, yet effective, two-phase matheuristic that uses the routes of the local optima of a hybrid GRASP×ILS as columns in a set-partitioning formulation of the TTRP. Using this matheuristic we solved both the classical TTRP with fixed fleet and the new variant with unlimited fleet. This matheuristic outperforms state-of-the-art methods both in terms of solution quality and computing time. While the best variant of the matheuristic found new best-known solutions for several test instances from the literature, the fastest variant of the matheuristic achieved results of comparable quality to those of all previous method from the literature with an average speed-up of at least 2.5.

A hybrid metaheuristic for the vehicle routing problem with stochastic demand and duration constraints

The vehicle routing problem with stochastic demands (VRPSD) consists in designing optimal routes to serve a set of customers with random demands following known probability distributions. Because of demand uncertainty, a vehicle may arrive at a customer without enough capacity to satisfy its demand and may need to apply a recourse to recover the route’s feasibility. Although travel times are assumed to be deterministic, because of eventual recourses the total duration of a route is a random variable. We present two strategies to deal with route-duration constraints in the VRPSD. In the first, the duration constraints are handled as chance constraints, meaning that for each route, the probability of exceeding the maximum duration must be lower than a given threshold. In the second, violations to the duration constraint are penalized in the objective function. To solve the resulting problem, we propose a greedy randomized adaptive search procedure (GRASP) enhanced with heuristic concentration (HC). The GRASP component uses a set of randomized route-first, cluster-second heuristics to generate starting solutions and a variable-neighborhood descent procedure for the local search phase. The HC component assembles the final solution from the set of all routes found in the local optima reached by the GRASP. For each strategy, we discuss extensive computational experiments that analyze the impact of route-duration constraints on the VRPSD. In addition, we report state-of-the-art solutions for a established set of benchmarks for the classical VRPSD.

Hybrid biobjective evolutionary algorithms for the design of a hospital waste management network

Colombian environmental authorities are exploring new alternatives for improving the disposal of hospital waste generated in the Department of Boyacá (Colombia). To design this hospital waste management network we propose a biobjective obnoxious facility location problem (BOOFLP) that deals with the existing tradeoff between a low-cost operating network and the negative effect on the population living near the waste management facilities. To solve the BOOFLP we propose a hybrid approach that combines a multiobjective evolutionary algorithm (NSGA II) with a mixed-integer program. The algorithms are compared against the Noninferior Set Estimation (NISE) method and tested on data from Boyacá’s hospital waste management network and publicly available instances.

Analysis of transportation networks subject to natural hazards – Insights from a Colombian case

This study provides an applied framework to derive the connectivity reliability and vulnerability of inter-urban transportation systems under network disruptions. The proposed model integrates statistical reliability analysis to find the reliability and vulnerability of transportation networks. Most of the modern research in this field has focused on urban networks where the primary concerns are guaranteeing predefined standards of capacity and travel time. However, at a regional and national level, especially in developing countries, the connectivity of remote populations in the case of disaster is of utmost importance. The applicability of the framework is demonstrated with a case study in the state of Antioquia, Colombia, using historical records from the 2010 to 2011 rainy season, an aspect that stands out and gives additional support compared to previous studies that considers simulated data from assumed distributions. The results provide significant insights to practitioners and researchers for the design and management of transportation systems and route planning strategies under this type of disruptions.

A branch-and-cut algorithm for the single truck and trailer routing problem with satellite depots

In the single truck and trailer routing problem with satellite depots (STTRPSD), a truck with a detachable trailer based at a main depot must serve the demand of a set of customers accessible only by truck. Therefore, before serving the customers, it is necessary to detach the trailer in an appropriate parking place (called either a satellite depot or a trailer point) and transfer goods between the truck and the trailer. This problem has applications in milk collection for farms that cannot be reached using large vehicles. In this work we present an integer programming formulation of the STTRPSD. This formulation is tightened with several families of valid inequalities for which we have developed different (exact and heuristic) separation procedures. Using these elements, we have implemented a branch-and-cut algorithm for the solution of the STTRPSD. A computational experiment with published instances shows that the proposed branch-and-cut algorithm consistently solves problems with up to 50 customers and 10 satellite depots, and it has also been able to solve instances with up to 20 satellite depots and 100 clustered customers.

Districting Decisions in Home Health Care Services: Modeling and Case Study

Home health care (HHC) services are a growing segment in the global health care industry in which patients receive coordinated medical care at their homes. When designing the service, HHC providers face a set of logistics decisions that include the districting configuration of the coverage area. In HHC, the districting problem seeks to group small geographic basic units-BUs (i.e., city quarters) into districts with balanced workloads. In this work, we present a modeling approach for the problem that includes a mixed integer linear programming (MILP) formulation and a greedy randomized adaptive search procedure (GRASP). The MILP formulation solves instances up to 44 BUs, while the GRASP allows to solve instances up to 484 BUs in less than 2.52 min. Computational experiments performed with a set of real instances from a Colombian HHC provider, show that the GRASP can reduce workload imbalances in a 57%.

A Two-Phase Heuristic for the Collection of Waste Animal Tissue in a Colombian Rendering Company

This work addresses the planning of the collection of waste animal tissue in a Colombian rendering company. Over a week, the rendering company visits more than 800 slaughterhouses, butchers, and supermarkets in the Aburra’s Valley, the metropolitan area of Medellin (Colombia) to supply their plant (located in the outskirts of the city) with raw material that are transformed into value-added products. The underlying vehicle routing problem have several distinguishing features: periodicity, consistency, clustered customers and heterogeneous fleet. To solve this rich VRP we present a two-phase heuristic. The first phase of the heuristic groups the collection points using a capacitated concentrator location problem (CCLP). Then, in the second phase a mixed integer program schedules the visits of the collection points in each cluster to balance the number of visits performed daily based on the capacities of the available vehicles. These two phases aim at getting consistent and evenly spread visits during the week. Preliminary results with the data of the current operation reveal a savings potential of 5 out of 15 vehicles, and a better spread of the visits over the planning horizon.

An evolutionary strategy for multiobjective reinsurance optimization

AbstractIn this work we tackle a multiobjective reinsurance optimization problem (MOROP) from the point of view of an insurance company. The MOROP seeks to find a reinsurance program that optimizes two conflicting objectives: the maximization of the expected value of the profit of the company and the minimization of the risk of the insurance losses retained by the company. To calculate these two objectives we built a probabilistic model of the portfolio of risks of the company. This model is embedded within an evolutionary strategy (ES) that approximates the efficient frontier of the MOROP using a combination of four classical reinsurance structures: surplus, quota share, excess-of-loss and stop-loss. Computational experiments with the risks of a specific line of business of a large Colombian general insurance company show that the proposed evolutionary strategy outperforms the classical non-dominated sorting genetic algorithm. Moreover, the analysis of the solutions in the efficient frontier obtained with our ES gave several insights to the company in terms of the structure and properties of the solutions for different risk-return trade-offs.