ABSTRACT The hazardous orienteering problem is the topic of this study. It is a variant of the well‐studied orienteering problem, where a vehicle, given a maximum mission time, has to select and visit customers out of a set of requests, aiming at maximizing the total profit associated with the customers selected. In the hazardous version of the problem we consider, the customers are associated with parcels that have to be collected, and some of them might explode during the transportation, after having been picked up. The probability of explosion depends on the characteristics of the parcel and on the time spent by the dangerous parcel itself on the vehicle. If an explosion happens, the collected profit is totally lost. The target becomes then to select the tour that maximizes the expected profit, taking into account the probability of catastrophic events happening. In this article, we propose an exact solving approach based on a mixed integer linear programming model which is dynamically modified by adding new constraints. The computational results substantially improve the state‐of‐the‐art for the problem.

A polynomial-size mixed integer linear programming model for the Precedence-Constrained Minimum-Cost Arborescence Problem with Waiting-Times was recently proposed in the literature, that uses a smaller number of variables and constraints compared to previously proposed polynomial-size models.In this work, we extend this model with constraint programming constructs to further enhance its performance.An extensive computational study support that modern constraint programming solvers are the best tool available at solving the models proposed.Several improvements to state-of-the-art results are finally reported.

Abstract This article considers a version of the flying sidekick traveling salesman problem in which parcels are delivered to customers by either a truck or a set of identical flying drones. The flights of the drones are limited by the battery endurance and each flight is made of a launch, a service to a customer, and a return: launch and return must happen when the truck is stationary. These operations require time and when multiple drones are launched and/or collected at the same node, their order becomes relevant. The proposed model takes into account the order of these operations as a scheduling problem, because ignoring it could lead to infeasible solutions in the reality due to the possible exceeding of the drones endurance. We propose a set of novel formulations for the problem that can improve the size of the largest instances solved in the literature. We provide a comparison among the formulations, between the multiple drones solutions and the single drone ones, and among different variants of the model.

With the recent technological developments a vast amount of high-throughput data has been profiled to understand the mechanism of complex diseases. The current bioinformatics challenge is to interpret the data and underlying biology, where efficient algorithms for analyzing heterogeneous high-throughput data using biological networks are becoming increasingly valuable. In this paper, we propose a software package based on the Prize-collecting Steiner Forest graph optimization approach. The PCSF package performs fast and user-friendly network analysis of high-throughput data by mapping the data onto a biological networks such as protein-protein interaction, gene-gene interaction or any other correlation or coexpression based networks. Using the interaction networks as a template, it determines high-confidence subnetworks relevant to the data, which potentially leads to predictions of functional units. It also interactively visualizes the resulting subnetwork with functional enrichment analysis.

This study is a novel contribution to the field of optimization in home health care services, both model and solution approach. We address an integration of interrelated optimization problems: rostering, assignment, routing, and scheduling in multi-period workforce planning under uncertainty in nurse availability. Our model explicitly handles the constraints related to workload balancing and multi-period planning, and the principles of robust optimization approach are followed to find a robust solution. We introduce a matheuristic algorithm that works based on a genetic algorithm mechanism to tackle four optimization problems sequentially but interactively. Two nested genetic algorithms are integrated. Steady-state reproduction is applied to both the inner and outer ones to reduce computational time/memory requirements. Two replacement strategies are carried out: replacing solutions at random and replacing the worst solutions. Experiments are conducted on instances based on real historical data from a company operating in Lugano, Switzerland. The obtained results show that, in genetic algorithm, the strategy of replacing solutions at random outperforms the strategy of replacing the worst solutions in our case. Addressing the four optimization problems in a unified approach results in a more efficient solution. In addition, the proposed algorithm: i) is able to handle large instances and to provide a weekly workforce planning solution in a reasonable time, which is reliable against uncertainty in nurse availability; ii) can be used to efficiently support managers in evaluating the trade off between the robustness and the operational cost of a solution.

Stochastic, meta-heuristic and linear construction algorithms for the design of DNA strands satisfying Hamming distance and reverse-complement constraints often use a GC-content constraint to pre-process the DNA strands. Since GC-content is a poor predictor of DNA strand hybridization strength the strands can be filtered by post-processing using thermodynamic calculations. An alternative approach is considered here, where the algorithms are modified to remove consideration of GC-content and rely on post-processing alone to obtain large sets of DNA strands with satisfactory melting temperatures. The two approaches (pre-processing GC-content and post-processing melting temperatures) are compared and are shown to be complementary when large DNA sets are desired. In particular, the second approach can give significant improvements when linear constructions are used.

In transportation problems like the vehicle routing problem, the decision makers are increasingly adopting the idea that the problem data can be subject to uncertainty. The uncertainty can be encountered because of events that are not exactly predictable, like weather conditions, traffic jams, etc. In this paper, we study vehicle routing problem with uncertain travel costs. Then, to solve the problem, we propose a robust multiple ant colony system: a metaheuristic in which multiple ant colonies work in parallel to generate a collection of solutions with different levels of protection against the uncertainty. The uncertainty is handled by incorporating linear formulations from the field of robust optimization into the metaheuristic approach.

In this study, we consider a capacitated vehicle routing problem where the objective function is to minimize the total travel cost.We also consider that the travel costs between the locations are subject to uncertainty, therefore they are expressed as intervals, rather than fixed numbers. The motivation of this study is to solve this problem by using a metaheuristic approach. We base our approach on a variant of ant colony optimization metaheuristic, called ant colony system, which was originally implemented for solving the deterministic version of the problem (i.e. the classical version of the problem without the uncertainty), previously reported in the literature. We modify the algorithm to incorporate a robust optimization methodology, so that the uncertainty on traveling costs can be handled.

Constant weight binary codes are used in a number of applications. Constructions based on mathematical structure are known for many codes. However, heuristic constructions unrelated to any mathematical structure can become of greater importance when the parameters of the code are larger. This paper considers the problem of finding constant weight codes with the maximum number of codewords from a purely algorithmic perspective. A set of heuristic and metaheuristic methods is presented and developed into a variable neighborhood search framework. The proposed method is applied to 383 previously studied cases with lengths between 29 and 63. For these cases it generates 153 new codes, with significantly increased numbers of codewords in comparison with existing constructions. For 10 of these new codes the number of codewords meets a known upper bound, and so these 10 codes are optimal. As well as the ability to generate new best codes, the approach has the advantage that it is a single method capable of addressing many sets of parameters in a uniform way.

The sequential ordering problem is a version of the asymmetric traveling salesman problem where precedence constraints on vertices are imposed. A tour is feasible if these constraints are respected, and the objective is to find a feasible solution with minimum cost. The sequential ordering problem models a lot of real world applications, mainly in the fields of transportation and production planning. In this paper we propose an extension of a well known ant colony system for the problem, aiming at making the approach more efficient on large problems. The extension is based on a problem manipulation technique that heuristically reduces the search space. Computational results, where the extended ant colony system is compared to the original one, are presented

Recent developments in information technology have brought about important changes in distributed computing. New environments such as massively large-scale, wide-area computer networks and mobile ad hoc networks have emerged. Common characteristics of these environments include extreme dynamicity, unreliability, and large scale. Traditional approaches to designing distributed applications in these environments based on central control, small scale, or strong reliability assumptions are not suitable for exploiting their enormous potential. Based on the observation that living organisms can effectively organize large numbers of unreliable and dynamically-changing components (cells, molecules, individuals, etc.) into robust and adaptive structures, it has long been a research challenge to characterize the key ideas and mechanisms that make biological systems work and to apply them to distributed systems engineering. In this article we propose a conceptual framework that captures several basic biological processes in the form of a family of design patterns. Examples include plain diffusion, replication, chemotaxis, and stigmergy. We show through examples how to implement important functions for distributed computing based on these patterns. Using a common evaluation methodology, we show that our bio-inspired solutions have performance comparable to traditional, state-of-the-art solutions while they inherit desirable properties of biological systems including adaptivity and robustness.

We consider the problem of assigning transmission powers to the nodes of a wireless network in such a way that all the nodes are connected by bidirectional links and the total power consumption is minimized. Since no central authority (with a global vision of the network) exists in wireless networks, only distributed, swarm approaches can be used. We present a distributed protocol that embeds well-known centralized techniques for power minimization, here used in a local, distributed fashion. The result can be seen as a complex adaptive system (the global network), where global optimization emerges as a result of the behavior of local nodes, each one carrying out a myopic, local optimization. Computational results, proving the effectiveness of the new protocol, are finally presented.

Broadcasting in wireless networks, unlike wired networks, inherently reaches several nodes with a single transmission. For an omnidirectional wireless broadcast to a node, all nodes closer to the transmitting node are also reached. This property can be used to compute routing trees which minimize the sum of the transmitter powers. We present a mixed integer programming formulation and a simulated annealing algorithm for the problem. Extensive experimental results for the heuristic approach are presented. They show that the proposed algorithm is capable of improving the results of state-of-the-art algorithms for most of the problems considered. The solutions provided by the simulated annealing algorithm can be improved by applying a very fast post-optimization procedure. This leads to the best known mean results for the problems considered.