Jesica de Armas

Biased randomization of heuristics using skewed probability distributions: A survey and some applications

Abstract Randomized heuristics are widely used to solve large scale combinatorial optimization problems. Among the plethora of randomized heuristics, this paper reviews those that contain biased-randomized procedures (BRPs). A BRP is a procedure to select the next constructive ‘movement’ from a list of candidates in which their elements have different probabilities based on some criteria (e.g., ranking, priority rule, heuristic value, etc.). The main idea behind biased randomization is the introduction of a slight modification in the greedy constructive behavior that provides a certain degree of randomness while maintaining the logic behind the heuristic. BRPs can be categorized into two main groups according to how choice probabilities are computed: (i) BRPs using an empirical bias function; and (ii) BRPs using a skewed theoretical probability distribution. This paper analyzes the second group and illustrates, throughout a series of numerical experiments, how these BRPs can benefit from parallel computing in order to significantly outperform heuristics and even simple metaheuristic approaches, thus providing reasonably good solutions in ‘real time’ to different problems in the areas of transportation, logistics, and scheduling.

Solving the deterministic and stochastic uncapacitated facility location problem: from a heuristic to a simheuristic

Abstract The uncapacitated facility location problem (UFLP) is a popular combinatorial optimization problem with practical applications in different areas, from logistics to telecommunication networks. While most of the existing work in the literature focuses on minimizing total cost for the deterministic version of the problem, some degree of uncertainty (e.g., in the customers’ demands or in the service costs) should be expected in real-life applications. Accordingly, this paper proposes a simheuristic algorithm for solving the stochastic UFLP (SUFLP), where optimization goals other than the minimum expected cost can be considered. The development of this simheuristic is structured in three stages: (i) first, an extremely fast savings-based heuristic is introduced; (ii) next, the heuristic is integrated into a metaheuristic framework, and the resulting algorithm is tested against the optimal values for the UFLP; and (iii) finally, the algorithm is extended by integrating it with simulation techniques, and the resulting simheuristic is employed to solve the SUFLP. Some numerical experiments contribute to illustrate the potential uses of each of these solving methods, depending on the version of the problem (deterministic or stochastic) as well as on whether or not a real-time solution is required.

A multi-start randomized heuristic for real-life crew rostering problems in airlines with work-balancing goals

This paper proposes a multi-start randomized heuristic for solving real-life crew rostering problems in airlines. The paper describes realistic constrains, regulations, and rules that have not been considered in the literature so far. Our algorithm is designed to provide quality solutions satisfying these real-life specifications while, at the same time, it aims at balancing the workload distribution among the different crewmembers. Thus, our approach promotes corporate social responsibility by distributing the workload in a fair way and avoiding that some crewmembers get unnecessarily overstressed. Despite its importance in real-life applications, these aspects have seldom been considered in the crew scheduling literature, where most solving approaches refer to simplified models and are tested on non-realistic benchmarks. The experimental tests show that our algorithm is capable of generating feasible quality solutions to real-life crew rostering problems in just a few seconds. These times are orders of magnitude lower than the times currently employed by some airlines to obtain a single feasible solution, since the ‘optimal’ solutions provided by most commercial software usually require additional adjustments in order to meet all the real-life specifications.

Minimizing Trigger Error in Parametric Earthquake Catastrophe Bonds via Statistical Approaches

The insurance and reinsurance industry, some governments, and private entities employ catastrophe (CAT) bonds to obtain coverage for large losses induced by earthquakes. These financial instruments are designed to transfer catastrophic risks to the capital markets. When an event occurs, a Post-Event Loss Calculation (PELC) process is initiated to determine the losses to the bond and the subsequent recoveries for the bond sponsor. Given certain event parameters such as magnitude of the earthquake and the location of its epicenter, the CAT bond may pay a fixed amount or not pay at all. This paper reviews two statistical techniques for classification of events in order to identify which should trigger bond payments based on a large sample of simulated earthquakes. These statistical techniques are effective, simple to interpret and to implement. A numerical experiment is performed to illustrate their use, and to facilitate a comparison with a previously published evolutionary computation algorithm.

Combining variable neighborhood search with simulation for the inventory routing problem with stochastic demands and stock-outs

Abstract Vendor managed inventory aims at reducing supply chain costs by centralizing inventory management and vehicle routing decisions. This integrated supply chain approach results in a complex combinatorial optimization problem known as the inventory routing problem (IRP). This paper presents a variable neighborhood search metaheuristic hybridized with simulation to solve the IRP under demand uncertainty. Our simheuristic approach is able to solve large sized instances for the single period IRP with stochastic demands and stock-outs in very short computing times. A range of experiments underline the algorithm’s competitiveness compared to previously used heuristic approaches. The results are analyzed in order to provide closer managerial insights.

Solving large-scale time capacitated arc routing problems: from real-time heuristics to metaheuristics

This paper discusses the Time Capacitated Arc Routing Problem (TCARP) and introduces a heuristic and a metaheuristic algorithm for solving large-size instances of it. The TCARP is a realistic extension of the Capacitated Arc Routing Problem in which edge-servicing and edge-traversing costs, as well as vehicle capacities, are all time-based—i.e., given in time units. Accordingly, the TCARP goal is to minimise the total time employed in servicing the required edges, for which other edges might need to be traversed too. According to the numerical experiments carried out, the proposed heuristic is able to provide real-time results of high quality even for the largest instances considered. Likewise, the proposed metaheuristic outperforms other existing approaches, both in quality as well as in computing times.

Using simheuristics to promote horizontal collaboration in stochastic city logistics

This paper analyzes the role of horizontal collaboration (HC) concepts in urban freight transportation under uncertainty scenarios. The paper employs different stochastic variants of the well-known vehicle routing problem (VRP) in order to contrast a non-collaborative scenario with a collaborative one. This comparison allows us to illustrate the benefits of using HC strategies in realistic urban environments characterized by uncertainty in factors such as customers’ demands or traveling times. In order to deal with these stochastic variants of the VRP, a simheuristic algorithm is proposed. Our approach integrates Monte Carlo simulation inside a metaheuristic framework. Some computational experiments contribute to quantify the potential gains that can be obtained by the use of HC practices in modern city logistics.

Learnheuristics: hybridizing metaheuristics with machine learning for optimization with dynamic inputs

Abstract This paper reviews the existing literature on the combination of metaheuristics with machine learning methods and then introduces the concept of learnheuristics, a novel type of hybrid algorithms. Learnheuristics can be used to solve combinatorial optimization problems with dynamic inputs (COPDIs). In these COPDIs, the problem inputs (elements either located in the objective function or in the constraints set) are not fixed in advance as usual. On the contrary, they might vary in a predictable (non-random) way as the solution is partially built according to some heuristic-based iterative process. For instance, a consumer’s willingness to spend on a specific product might change as the availability of this product decreases and its price rises. Thus, these inputs might take different values depending on the current solution configuration. These variations in the inputs might require from a coordination between the learning mechanism and the metaheuristic algorithm: at each iteration, the learning method updates the inputs model used by the metaheuristic.

Optimizing Airline Crew Scheduling Using Biased Randomization: A Case Study

Various complex decision making problems are related to airline planning. In the competitive airline industry, efficient crew scheduling is hereby of major practical importance. This paper presents a metaheuristic approach based on biased randomization to tackle the challenging Crew Pairing Problem (CPP). The objective of the CPP is the establishment of flight pairings allowing for cost minimizing crew-flight assignments. Experiments are done using a real-life case with different constraints. The results show that our easy-to-use and fast algorithm reduces overall crew flying times and the necessary number of accompanying crews compared to the pairings currently applied by the company.

Multi criteria biased randomized method for resource allocation in distributed systems: Application in a volunteer computing system

Abstract Volunteer computing is a type of distributed computing in which a part or all the resources (processing power and storage) necessary to run the system are donated by users. In other words, participants contribute their idle computing resources to help running the system. Due to the fact that the nodes which compose the system are provided by a large number of users instead of a single (or a few) institution, a main drawback of volunteer computing is the unreliability of these nodes. For this reason, the selection of nodes to be involved in each task becomes a key issue. In this paper, we propose the Multi Criteria Biased Randomized (MCBR) method, a novel selection method for large-scale systems that use unreliable nodes. MCBR method is based on a multicriteria optimization strategy. We evaluated the method in a microblogging social network formed by a large number of microservices hosted in nodes voluntarily contributed by their participants. Simulation results show that our proposal is able to select nodes in a fast and efficient manner while requiring low computational power.