Showing papers in "Journal of Heuristics in 2020"

A variable neighborhood search simheuristic for project portfolio selection under uncertainty

Abstract: With limited financial resources, decision-makers in firms 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 flows 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.

A hybrid genetic algorithm for the traveling salesman problem with drone

Abstract: This paper addresses the traveling salesman problem with drone (TSP-D), in which a truck and drone are used to deliver parcels to customers. The objective of this problem is to either minimize the total operational cost (min-cost TSP-D) or minimize the completion time for the truck and drone (min-time TSP-D). This problem has gained a lot of attention in the last few years reflecting the recent trends in a new delivery method among logistics companies. To solve the TSP-D, we propose a hybrid genetic search with dynamic population management and adaptive diversity control based on a split algorithm, problem-tailored crossover and local search operators, a new restore method to advance the convergence and an adaptive penalization mechanism to dynamically balance the search between feasible/infeasible solutions. The computational results show that the proposed algorithm outperforms two existing methods in terms of solution quality and improves many best known solutions found in the literature. Moreover, various analyses on the impacts of crossover choice and heuristic components have been conducted to investigate their sensitivity to the performance of our method.

Bilevel optimization based on iterative approximation of multiple mappings

Abstract: A large number of application problems involve two levels of optimization, where one optimization task is nested inside the other. These problems are known as bilevel optimization problems and have been studied by both classical optimization community and evolutionary optimization community. Most of the solution procedures proposed until now are either computationally very expensive or applicable to only small classes of bilevel optimization problems adhering to mathematically simplifying assumptions. In this paper, we propose an evolutionary optimization method that tries to reduce the computational expense by iteratively approximating two important mappings in bilevel optimization; namely, the lower level rational reaction mapping and the lower level optimal value function mapping. The algorithm has been tested on a large number of test problems and comparisons have been performed with other algorithms. The results show the performance gain to be quite significant. To the best knowledge of the authors, a combined theory-based and population-based solution procedure utilizing mappings has not been suggested yet for bilevel problems.

Vehicle routing for the urgent delivery of face shields during the COVID-19 pandemic

Abstract: The speed by which the COVID-19 pandemic spread throughout the world caught some national and local governments unprepared. Healthcare systems found themselves struggling to increase capacity and procure key supplies, such as personal protective equipment. Protective face shields became essential for healthcare professionals. However, most hospitals and healthcare facilities did not have them in adequate quantities. The urgency of producing and delivering face shields increased as the number of COVID-19 cases rapidly multiplied. This was the situation that we encountered in the city and province of Burgos (Spain). Since there was no time to wait for a large manufacturer to produce face shields, private citizens and small companies volunteered to make them using technologies such as 3D printers. Nonprofits, citizens, and governments agencies volunteered to deliver materials to the face shield makers and to pick up and deliver the face shields to health centers and other locations where they were needed. This resulted in a vehicle routing problem with some special characteristics that made it different from models used for commercial purposes. We describe the development of a heuristic to find feasible and efficient routes for this problem. We highlight the advantages of using heuristics in an emergency context like the one triggered by the COVID-19 pandemic. In particular, the heuristic approach allowed us to design, implement, test, and delivery a routing system in less than 1 week from the time that the local government contacted us with what they described as a logistics nightmare.

IoT networks 3D deployment using hybrid many-objective optimization algorithms

Abstract: When resolving many-objective problems, multi-objective optimization algorithms encounter several difficulties degrading their performances. These difficulties may concern the exponential execution time, the effectiveness of the mutation and recombination operators or finding the tradeoff between diversity and convergence. In this paper, the issue of 3D redeploying in indoor the connected objects (or nodes) in the Internet of Things collection networks (formerly known as wireless sensor nodes) is investigated. The aim is to determine the ideal locations of the objects to be added to enhance an initial deployment while satisfying antagonist objectives and constraints. In this regard, a first proposed contribution aim to introduce an hybrid model that includes many-objective optimization algorithms relying on decomposition (MOEA/D, MOEA/DD) and reference points (Two_Arch2, NSGA-III) while using two strategies for introducing the preferences (PI-EMO-PC) and the dimensionality reduction (MVU-PCA). This hybridization aims to combine the algorithms advantages for resolving the many-objective issues. The second contribution concerns prototyping and deploying real connected objects which allows assessing the performance of the proposed hybrid scheme on a real world environment. The obtained experimental and numerical results show the efficiency of the suggested hybridization scheme against the original algorithms.

A general variable neighborhood search for solving the multi-objective open vehicle routing problem

Abstract: The multi-objective open vehicle routing problem (MO-OVRP) is a variant of the classic vehicle routing problem in which routes are not required to return to the depot after completing their service and where more than one objective is optimized. This work is intended to solve a more realistic and general version of the problem by considering three different objective functions. MO-OVRP seeks solutions that minimize the total number of routes, the total travel cost, and the longest route. For this purpose, we present a general variable neighborhood search algorithm to approximate the efficient set. The performance of the proposal is supported by an extensive computational experimentation which includes the comparison with the well-known multi-objective genetic algorithm NSGA-II.

A biased-randomized variable neighborhood search for sustainable multi-depot vehicle routing problems

Abstract: Urban freight transport is becoming increasingly complex due to a boost in the volume of products distributed and the associated number of delivery services. In addition, stakeholders’ preferences and city logistics dynamics affect the freight flow and the efficiency of the delivery process in downtown areas. In general, transport activities have a significant and negative impact on the environment and citizens’ welfare, which motivates the need for sustainable transport planning. This work proposes a metaheuristic-based approach for tackling an enriched multi-depot vehicle routing problem in which economic, environmental, and social dimensions are considered. Our approach integrates biased-randomization strategies within a variable neighborhood search framework in order to better guide the searching process. A series of computational experiments illustrates how the aforementioned dimensions can be integrated in realistic transport operations. Also, the paper discusses how the cost values change as different dimensions are prioritized.

The parking allocation problem for connected vehicles

Abstract: In this paper, we propose a parking allocation model that takes into account the basic constraints and objectives of a problem where parking lots are assigned to vehicles. We assume vehicles are connected and can exchange information with a central intelligence. Vehicle arrival times can be provided by a GPS device, and the estimated number of available parking slots, at each future time moment and for each parking lot is used as an input. Our initial model is static and may be viewed as a variant of the generalized assignment problem. However, the model can be rerun, and the algorithm can handle dynamic changes by frequently solving the static model, each time producing an updated solution. In practice this approach is feasible only if reliable quality solutions of the static model are obtained within a few seconds since the GPS can continuously provide new input regarding the vehicle’s positioning and its destinations. We propose a 0–1 programming model to compute exact solutions, together with a variable neighborhood search-based heuristic to obtain approximate solutions for larger instances. Computational results on randomly generated instances are provided to evaluate the performance of the proposed approaches.

A coordinated production and transportation scheduling problem with minimum sum of order delivery times

Abstract: In this paper, a coordinated production scheduling and vehicle routing problem aiming at minimizing the sum of order delivery times is considered, where there are a single machine for production and limited number of homogenous capacitated vehicles for transportation. Given the complexity of the studied problem, a variable neighborhood search (VNS) algorithm is proposed to address this problem. To construct initial solution, vehicle routing is determined with nearest insertion first, whereby the order batch production sequence is determined based on three propositions. Moreover, ten neighborhood structures are designed and a local search algorithm based on tabu search algorithm is proposed for intensification. The effectiveness of the proposed VNS algorithm is validated by comparing it with CPLEX and two heuristic algorithms in the existing literature. The computational results show that the proposed VNS algorithm can obtain optimal or near optimal solutions of the problem and is superior to the two heuristic algorithms proposed in the existing literature.

Hybrid adaptive large neighborhood search algorithm for the mixed fleet heterogeneous dial-a-ride problem

Abstract: The mixed fleet heterogeneous dial-a-ride problem (MF-HDARP) consists of designing vehicle routes for a set of users by using a mixed fleet including both heterogeneous conventional and alternative fuel vehicles. In addition, a vehicle is allowed to refuel from a fuel station to eliminate the risk of running out of fuel during its service. We propose an efficient hybrid adaptive large neighborhood search (hybrid ALNS) algorithm for the MF-HDARP. The computational experiments show that the algorithm produces high quality solutions on our generated instances and on HDARP benchmarks instances. Computational experiments also highlight that the newest components added to the standard ALNS algorithm enhance intensification and diversification during the search process.

A genetic algorithm for finding realistic sea routes considering the weather

Abstract: The weather has a major impact on the profitability, safety, and environmental sustainability of the routes sailed by seagoing vessels. The prevailing weather strongly influences the course of routes, affecting not only the safety of the crew, but also the fuel consumption and therefore the emissions of the vessel. Effective decision support is required to plan the route and the speed of the vessel considering the forecasted weather. We implement a genetic algorithm to minimize the fuel consumption of a vessel taking into account the two most important influences of weather on a ship: the wind and the waves. Our approach assists route planners in finding cost minimal routes that consider the weather, avoid specified areas, and meet arrival time constraints. Furthermore, it supports ship speed control to avoid areas with weather conditions that would result in high fuel costs or risk the safety of the vessel. The algorithm is evaluated for a variety of instances to show the impact of weather routing on the routes and the fuel and travel time savings that can be achieved with our approach. Including weather into the routing leads to a savings potential of over 10% of the fuel consumption. We show that ignoring the weather when constructing routes can lead to routes that cannot be sailed in practice. Furthermore, we evaluate our algorithm with stochastic weather data to show that it can provide high-quality routes under real conditions even with uncertain weather forecasts.

Logistics optimization for a coal supply chain

Abstract: The Hunter Valley coal export supply chain in New South Wales, Australia, is of great importance to the Australian economy. Effectively managing its logistics, however, is challenging, because it is a complex system, covering a large geographic area and comprising a rail network, three coal terminals, and a port, and has many stakeholders, e.g., mining companies, port authorities, coal terminal operators, rail infrastructure providers, and above rail operators. We develop a matheuristic logistics planning system which integrates, amongst other concerns, train scheduling, stockpile management, and vessel scheduling. Different components of the supply chain are modeled at different levels of granularity. An extensive computational study has generated insights into the bottlenecks in the logistics system, which are used to guide changes in operating policies and future investments. The planning system uses a solver-independent modeling technology. This allowed us to observe differences between the performance of constraint programming and mixed-integer programming in the context of a rolling-horizon approach, due to custom search heuristics.

The selective traveling salesman problem with draft limits

Abstract: This paper introduces the selective traveling salesman problem with draft limits, an extension of the traveling salesman problem with draft limits, wherein the goal is to design a maximum profit tour respecting draft limit constraints at the visited nodes. We propose a mixed integer linear programming (MILP) formulation for this problem. This MILP model is used to solve—to optimality—small size instances and to assess the quality of solutions obtained using a general variable neighborhood search heuristic that explores several neighborhood structures. Our extensive computational experiments confirm the efficiency of the method and the quality of the reported solutions.

New heuristic algorithms for the Dubins traveling salesman problem

Abstract: The problem of finding a shortest curvature-constrained closed path through a set of targets in the plane is known as Dubins traveling salesman problem (DTSP). Applications of the DTSP include motion planning for kinematically constrained mobile robots and unmanned fixed-wing aerial vehicles. The difficulty of the DTSP is to simultaneously find an order of the targets and suitable headings (orientation angles) of the vehicle when passing the targets. Since the DTSP is known to be NP-hard there is a need for heuristic algorithms providing good quality solutions in reasonable time. Inspired by standard methods for the TSP we present a collection of such heuristics adapted to the DTSP. The algorithms are based on a technique that optimizes the headings of the targets of an open or closed subtour with given order. This is done by discretizing the headings, constructing an auxiliary network and computing a shortest path in the network. The first algorithm for the DTSP uses the order of the targets obtained from the solution of the Euclidean TSP. A second class of algorithms extends an open subtour by successively adding a new target and closes the tour if all targets have been added. A third class of algorithms starts with a closed subtour consisting of few targets and successively inserts a new target into the tour. The individual algorithms differ by the number of headings to be optimized in each iteration. Extensive simulation results show that the proposed methods are competitive with state-of-the-art methods for the DTSP concerning performance and superior concerning running time, which makes them applicable also to large-scale scenarios.

Meta-heuristics for the one-dimensional cutting stock problem with usable leftover

Abstract: This work considers the one-dimensional cutting stock problem in which the non-used material in the cutting patterns may be used in the future, if large enough. We show that a multiobjective criteria to classify the solutions could be more accurate than previous classifications attempts, also we give a heuristic algorithm and two meta-heuristic approaches to the problem and we use them to solve practical and randomly generated instances from the literature. The results obtained by the computational experiments are quite good for all the tested instances.

Sample size calculations for the experimental comparison of multiple algorithms on multiple problem instances

Abstract: This work presents a statistically principled method for estimating the required number of instances in the experimental comparison of multiple algorithms on a given problem class of interest. This approach generalises earlier results by allowing researchers to design experiments based on the desired best, worst, mean or median-case statistical power to detect differences between algorithms larger than a certain threshold. Holm’s step-down procedure is used to maintain the overall significance level controlled at desired levels, without resulting in overly conservative experiments. This paper also presents an approach for sampling each algorithm on each instance, based on optimal sample size ratios that minimise the total required number of runs subject to a desired accuracy in the estimation of paired differences. A case study investigating the effect of 21 variants of a custom-tailored Simulated Annealing for a class of scheduling problems is used to illustrate the application of the proposed methods for sample size calculations in the experimental comparison of algorithms.

Evolutionary multi-level acyclic graph partitioning

Abstract: Directed graphs are widely used to model data flow and execution dependencies in streaming applications. This enables the utilization of graph partitioning algorithms for the problem of parallelizing execution on multiprocessor architectures under hardware resource constraints. However due to program memory restrictions in embedded multiprocessor systems, applications need to be divided into parts without cyclic dependencies. We found that this can be done by a subsequent second graph partitioning step with an additional acyclicity constraint. We have four main contributions. First, we show that this more constrained version of the graph partitioning problem is NP-complete and present linear time heuristics. We then integrate them into an existing multi-level graph partitioning framework to better handle large graphs. This achieves a 9% reduction of the edge cut compared to the previous single-level algorithm. Based on this, we engineer an evolutionary algorithm to further reduce the cut, achieving a 30% reduction on average compared to the state of the art. Finally, we integrate the partitioning heuristics into a graph compiler for an embedded multiprocessor architecture and show that this can reduce the amount of communication for a real-world imaging application and thereby accelerate it by an average of 11%. It is shown that the compiler can emit optimized code for vastly different hardware platforms using the heuristics. In addition, we demonstrate how a custom fitness function for the evolutionary algorithm can be used to optimize other objectives like load balancing if the communication volume is not predominantly important on a given hardware platform.

A comparative study of multi-objective machine reassignment algorithms for data centres

Abstract: At a high level, data centres are large IT facilities hosting physical machines (servers) that often run a large number of virtual machines (VMs)—but at a lower level, data centres are an intricate collection of interconnected and virtualised computers, connected services, complex service-level agreements. While data centre managers know that reassigning VMs to the servers that would best serve them and also minimise some cost for the company can potentially save a lot of money—the search space is large and constrained, and the decision complicated as they involve different dimensions. This paper consists of a comparative study of heuristics and exact algorithms for the multi-objective machine reassignment problem. Given the common intuition that the problem is too complicated for exact resolutions, all previous works have focused on various (meta)heuristics such as First-Fit, GRASP, NSGA-II or PLS. In this paper, we show that the state-of-art solution to the single objective formulation of the problem (CBLNS) and the classical multi-objective solutions fail to bridge the gap between the number, quality and variety of solutions. Hybrid metaheuristics, on the other hand, have proven to be more effective and efficient to address the problem—but as there has never been any study of an exact resolution, it was difficult to qualify their results. In this paper, we present the most relevant techniques used to address the problem, and we compare them to an exact resolution ($$\epsilon $$-Constraints). We show that the problem is indeed large and constrained (we ran our algorithm for 30 days on a powerful node of a supercomputer and did not get the final solution for most instances of our problem) but that a metaheuristic (GeNePi) obtains acceptable results: more (+ 188%) solutions than the exact resolution and a little more than half (52%) the hypervolume (measure of quality of the solution set).

MIQP model and improvement heuristic for power loss minimization in distribution system with network reconfiguration

Abstract: The problem of reconfiguration in electrical power distribution systems deals with changes in the network topology using maneuvers switches. This is an optimization problem where one of the goals is to minimize losses following constraints such as faults isolation, load feeders balancing and voltage profile improvement. The present paper solves such problem by introducing a mixed-integer quadratic program (MIQP) model, which aims to return optimal configurations. An improvement heuristic, based on the solution of MIQP sub models, is also introduced. The MIQP model and improvement heuristic are validated over eight benchmark power systems, and the results achieved are compared against those recently reported by literature. A new set of 10 large sized bus-systems is also defined as replication of a benchmark bus system from literature. The computational results show that our MIQP model and heuristic are able to find optimal configurations for those benchmark systems, as well as to provide good quality solution for the large sized bus-systems within a reduced computational time.

Objective Scaling Ensemble Approach for Integer Linear Programming

Abstract: The objective scaling ensemble approach is a novel two-phase heuristic for integer linear programming problems shown to be effective on a wide variety of integer linear programming problems The technique identifies and aggregates multiple partial solutions to modify the problem formulation and significantly reduce the search space An empirical analysis on publicly available benchmark problems demonstrate the efficacy of our approach by outperforming standard solution strategies implemented in modern optimization software

An adaptive large neighborhood search heuristic for the planar storage location assignment problem: application to stowage planning for Roll-on Roll-off ships

Abstract: This paper considers a generalized version of the planar storage location problem arising in the stowage planning for Roll-on/Roll-off ships. A ship is set to sail along a predefined voyage where given cargoes are to be transported between different port pairs along the voyage. We aim at determining the optimal stowage plan for the vehicles stored on a deck of the ship so that the time spent moving vehicles to enable loading or unloading of other vehicles (shifting), is minimized. We propose a novel mixed integer programming model for the problem, considering both the stowage and shifting aspect of the problem. An adaptive large neighborhood search (ALNS) heuristic with several new destroy and repair operators is developed. We further show how the shifting cost can be effectively evaluated using Dijkstra’s algorithm by transforming the stowage plan into a network graph. The computational results show that the ALNS heuristic provides high quality solutions to realistic test instances.

Basic variable neighborhood search for the minimum sitting arrangement problem

Abstract: The minimum sitting arrangement (MinSA) problem is a linear layout problem consisting in minimizing the number of errors produced when a signed graph is embedded into a line. This problem has been previously tackled by theoretical and heuristic approaches in the literature. In this paper we present a basic variable neighborhood search (BVNS) algorithm for solving the problem. First, we introduce a novel constructive scheme based on the identification of cliques from the input graph, when only the positive edges are considered. The solutions obtained by the constructive procedure are then used as a starting point for the proposed BVNS algorithm. Efficient implementations of the several configurations of the local search procedure within the BVNS are described. The algorithmic proposal is then compared with previous approaches in the state of the art for the MinSA over different sets of referred instances. The obtained results supported by non-parametric statistical tests, indicate that BVNS can be considered as the new state-of-the-art algorithm for the MinSA.

A multi-start local search algorithm for the Hamiltonian completion problem on undirected graphs

Abstract: This paper proposes a local search algorithm for a specific combinatorial optimisation problem in graph theory: the Hamiltonian completion problem (HCP) on undirected graphs. In this problem, the objective is to add as few edges as possible to a given undirected graph in order to obtain a Hamiltonian graph. This problem has mainly been studied in the context of various specific kinds of undirected graphs (e.g. trees, unicyclic graphs and series-parallel graphs). The proposed algorithm, however, concentrates on solving HCP for general undirected graphs. It can be considered to belong to the category of matheuristics, because it integrates an exact linear time solution for trees into a local search algorithm for general graphs. This integration makes use of the close relation between HCP and the minimum path partition problem, which makes the algorithm equally useful for solving the latter problem. Furthermore, a benchmark set of problem instances is constructed for demonstrating the quality of the proposed algorithm. A comparison with state-of-the-art solvers indicates that the proposed algorithm is able to achieve high-quality results.

Heuristic/meta-heuristic methods for restricted bin packing problem

Abstract: This paper addresses a special bin packing problem in which each item can only be assigned to a subset of the bins. We name this problem as the restricted bin packing problem (RBPP). This paper is designed to explore the relationships of RBPP with classic NP-complete problems, and to resolve the restrictions of assignment through heuristic and meta-heuristic algorithms. A new heuristic algorithm named ‘Max-fit Based on Zigzag Sorting with Retained Feasibility’ is proposed. In this heuristic algorithm, a feasibility retaining rule is constructed to assure the assignment of every item; a zigzag sorting method is designed to improve the performance of the algorithm. Our heuristic algorithm is able to generate better results in comparison with existing heuristics. Greedy Randomized Adaptive Search Procedure (GRASP) and Simulated Annealing (SA) are exploited to obtain better solutions for RBPP. A new construction method based on cliques and zigzag sorting are built for GRASP and SA. The proposed methods are shown to have higher efficiency than traditional ones through numeric examples.

Pareto-based evolutionary multiobjective approaches and the generalized Nash equilibrium problem

Abstract: Pareto-based evolutionary multiobjective approaches are methods that use the Pareto dominance concept to guide the search of evolutionary algorithms towards the Pareto frontier of a problem. To address the challenge of providing an entire set of optimal solutions they use specially designed mechanisms for preserving search diversity and maintaining the non-dominated solutions set. The limitation of the Pareto dominance relation in high-dimensional spaces has rendered these methods inefficient for many-objective optimization. In this paper we aim to exploit existing Pareto-based methods to compute the generalized Nash equilibrium for multi-player games by replacing the Pareto dominance relation with an equilibrium generative relation. The generalized Nash equilibrium extends the Nash equilibrium concept by considering constraints over players’ strategies. Numerical experiments indicate that the selected methods can be employed for equilibria computation even for games with up to twenty players.

Reactive VNS algorithm for the maximum k-subset intersection problem

Abstract: This paper proposes a reactive VNS metaheuristic for the maximum intersection of k-subsets problem (kMIS). The kMIS is defined as: Given a set of elements, a subset family of the first set and an integer k. The problem consists of finding k subset so that the intersection is maximum. Our VNS metaheuristic incorporates strategies used in GRASP metaheuristics, such as the GRASP construction phase and the Reactive GRASP. Both were used in the shaking phase as a reactive components to VNS. We also propose what we call teh Dynamic Step, a new way to increase the VNS neighborhood. All of these strategies, as well as the Skewed VNS, were added to our Reactive VNS algorithm for kMIS. Computational results showed that the new algorithm outperforms the state-of-the-art algorithm.

MIP neighborhood search heuristics for a service network design problem with design-balanced requirements

Abstract: Service network design problems are used to address a variety of services in transportation and logistics planning. In the present paper, we consider the service network design problem with design-balanced requirements. This problem is particularly relevant to operations for consolidation transportation systems and determines the transportation network configuration and the characteristics of the corresponding services. We present a solution approach that combines a capacity scaling procedure for finding an initial feasible solution and a MIP neighborhood search for improving the solutions. Computational experiments on benchmark instances show that the proposed heuristic finds high-quality solutions in a short computation time.

Enhancement for human resource management in the ULD build-up process of air-cargo terminal: a strategic linkage approach

Abstract: This study proposes a strategic linkage method with mathematical model, simulation model and heuristic approaches to an adaptive workforce scheduling problem reflecting the work site situation of the air cargo terminal. For the application of the proposed method, we first generate an initial workforce schedule using the optimization model for minimizing labor costs. The simulation model is then used to verify that the initial schedule is applicable on-site. If the initial plan does not meet the service level expressed as a delay in the departure of aircrafts, by linking optimization and simulation models, additional required personnel are determined for reducing the frequency of departure delay. But, though the experiments, we were able to confirm that the linkage of optimization and simulation alone resulted in the creation of excess manpower. To compensate for the limitation of the linkage, we develop the Backward Removal Heuristics (BRHs) algorithm. In an experiment with BRHs algorithm, we showed that BRHs could reduce the number of unnecessarily added workers based on the interaction between utilization of workers and operation congestion. With case study on the ULD build-up process of K airline air-cargo terminal, the linkage model with BRHs algorithm is tested and proven its effectiveness. The experimental results show that the workforce schedule from the linkage model increases the service level and the cost, but when applied to the BRHs algorithm, the model maintains the service level but decreases the cost.

Heuristics for a vehicle routing problem with information collection in wireless networks

Abstract: We consider a wireless network where a given set of stations is continuously generating information. A single vehicle, located at a base station, is available to collect the information via wireless transfer. The wireless transfer vehicle routing problem (WTVRP) is to decide which stations should be visited in the vehicle route, how long shall the vehicle stay in each station, and how much information shall be transferred from the nearby stations to the vehicle during each stay. The goal is to collect the maximum amount of information during a time period after which the vehicle returns to the base station. The WTVRP is NP-hard. Although it can be solved to optimality for small size instances, one needs to rely on good heuristic schemes to obtain good solutions for large size instances. In this work, we consider a mathematical formulation based on the vehicle visits. Several heuristics strategies are proposed, most of them based on the mathematical model. These strategies include constructive and improvement heuristics. Computational experiments show that a strategy that combines a combinatorial greedy heuristic to design a initial vehicle route, improved by a fix-and-optimize heuristic to provide a local optimum, followed by an exchange heuristic, affords good solutions within reasonable amount of running time.