Realistic Variant Research Articles

Modeling a robust 2-service community healthcare network design problem with central hospital congestion

We address a more realistic variant of the community healthcare network design problem, considering a 2-service network framework and hospital congestion structures. Our objective is to minimise the total expected cost through strategic location of central hospitals and allocation of community health stations. Notably, each community health station is allocated to exactly two central hospitals. We also account for uncertainties in resident medical demands, which are challenging to predict accurately due to their volatility. To tackle this, we propose an adaptive distributionally robust optimisation approach that confines uncertain variables to an ambiguity set reflecting demand distribution characteristics. To enhance tractability, we reformulate the distributionally robust model as a mixed-integer linear programme within the specified ambiguity set. Numerical experiments on a real case demonstrate the validity and superiority of the proposed model. Finally, the impact of uncertainty is discussed in depth and some managerial insights for healthcare managers are summarised.

Adaptive distributionally robust hub location and routing problem with a third-party logistics strategy

In this paper, we study an essential and more realistic variant of the hub location-routing problem in which third-party logistics (3PL) are incorporated into a self-built distribution hub network. A two-stage mixed-integer programming model is developed to address the proposed hub location-routing problem with mixed logistical strategies. In the strategic stage, the hub location and allocation decisions are made before the realization of uncertain demands to minimize the total construction and transportation costs. In the operational stage, the managers make vehicle routing decisions with and without the 3PL strategy, and route planning is considered for multiple owned and 3PL vehicles on the basis of simultaneous pickup and delivery. We investigate the effects of uncertain factors on the network design of this framework, as the node demands are often unobservable or difficult to obtain accurately in practice. To address the inherent uncertainty of the problem, we propose an adaptive distributionally robust model by limiting uncertain demands to a specified ambiguity set. Moreover, we reformulate the proposed model into tractable robust counterpart forms under the ambiguity set with information about the support, mean and upper bounds on the dispersion. We design a genetic algorithm with local search to solve the reformulated model on a large scale. Finally, we conduct a case study for a leading retail enterprise in the Beijing–Tianjin–Hebei region to demonstrate the effectiveness and applicability of our model.

A parallel adaptive memory algorithm for the capacitated modular hub location problem

The capacitated modular hub location problem is a realistic variant of the popular hub location problem arising from the design of telecommunications networks. Given a set of demand nodes, the problem consists in selecting a subset of nodes to represent hubs and assigning the rest of the nodes to the hub nodes, such that the transportation cost is minimized while satisfying the capacity constraints. The adaptive memory algorithm is a hybrid evolutionary heuristic that uses a central memory to store blocks of solutions. At each iteration, the recombination operator uses these solution blocks to create an offspring solution. In this paper, we present a parallel adaptive memory algorithm for the capacitated modular hub location problem that stores both solution blocks and complete solutions in a shared memory for the creation of an offspring. Other distinguishing features of our proposed algorithm include specially designed recombination and mutation operators for search diversification, an effective tabu search procedure for search intensification, and parallel computing for global optimization. Extensive computational results on three well-known data sets of 170 benchmark instances show that the proposed algorithm competes very favorably with the state-of-the-art heuristics from the literature. In particular, it finds 115 improved best-known solutions (for more than 67% of the cases). Furthermore, we analyze the key algorithmic components and shed lights on their impact on the proposed algorithm.

Metaheuristic Technique based on Realistic Mimicking of Grey Wolf’s Hunting Process for Solving Dynamic Economic Dispatch Problem with Electric Vehicles

This paper proposes a realistic variant of grey wolf optimizer with the dynamic behavior of prey position and hence called as Realistic Grey Wolf Optimizer (RGWO). The proposed method is employed to solve 23 benchmark problems and a well-known complex power system problem of dynamic economic dispatch of power by generating units taking into consideration of electric vehicles with valve point effect, ramp-rate, transmission losses for a time interval of 24 hours. The prey position is modeled dynamic i.e., it is considered to be moving and is not static as considered in an earlier version of GWO with the help of probability distribution function. The probability distribution function which is considered here is Levy Flight, Cauchy, Gamma, Gaussian, and Weibull. Thereafter, the statistical testing is done with the help of the Wilcoxon Rank test and Wilcoxon Signed Rank test to investigate the significance of the different variants of RGWO. The experimental results and statistical testing show that the modification proposed in RGWO significantly improves the performance of GWO and hence the algorithm is utilized to find dynamic economic dispatch along with electric vehicles considering different constraints effectively.

On Multi-Objective Minimum Spanning Tree Problem under Uncertain Paradigm

Minimum spanning tree problem (MSTP) has allured many researchers and practitioners due to its varied range of applications in real world scenarios. Modelling these applications involves the incorporation of indeterminate phenomena based on their subjective estimations. Such phenomena can be represented rationally using uncertainty theory. Being a more realistic variant of MSTP, in this article, based on the principles of the uncertainty theory, we have studied a multi-objective minimum spanning tree problem (MMSTP) with indeterminate problem parameters. Subsequently, two uncertain programming models of the proposed uncertain multi-objective minimum spanning tree problem (UMMSTP) are developed and their corresponding crisp equivalence models are investigated, and eventually solved using a classical multi-objective solution technique, the epsilon-constraint method. Additionally, two multi-objective evolutionary algorithms (MOEAs), non-dominated sorting genetic algorithm II (NSGAII) and duplicate elimination non-dominated sorting evolutionary algorithm (DENSEA) are also employed as solution methodologies. With the help of the proposed UMMSTP models, the practical problem of optimizing the distribution of petroleum products was solved, consisting in the search for symmetry (balance) between the transportation cost and the transportation time. Thereafter, the performance of the MOEAs is analyzed on five randomly developed instances of the proposed problem.

Generalized Zero-Shot Learning via Disentangled Representation

Zero-Shot Learning (ZSL) aims to recognize images belonging to unseen classes that are unavailable in the training process, while Generalized Zero-Shot Learning (GZSL) is a more realistic variant that both seen and unseen classes appear during testing. Most GZSL approaches achieve knowledge transfer based on the features of samples that inevitably contain information irrelevant to recognition, bringing negative influence for the performance. In this work, we propose a novel method, dubbed Disentangled-VAE, which aims to disentangle category-distilling factors and category-dispersing factors from visual as well as semantic features, respectively. In addition, a batch re-combining strategy on latent features is introduced to guide the disentanglement, encouraging the distilling latent features to be more discriminative for recognition. Extensive experiments demonstrate that our method outperforms the state-of-the-art approaches on four challenging benchmark datasets

Combining Heuristics with Simulation and Fuzzy Logic to Solve a Flexible-Size Location Routing Problem under Uncertainty

The location routing problem integrates both a facility location and a vehicle routing problem. Each of these problems are NP-hard in nature, which justifies the use of heuristic-based algorithms when dealing with large-scale instances that need to be solved in reasonable computing times. This paper discusses a realistic variant of the problem that considers facilities of different sizes and two types of uncertainty conditions. In particular, we assume that some customers’ demands are stochastic, while others follow a fuzzy pattern. An iterated local search metaheuristic is integrated with simulation and fuzzy logic to solve the aforementioned problem, and a series of computational experiments are run to illustrate the potential of the proposed algorithm.

Experimental Study of Methods of Scenario Lattice Construction for Stochastic Dual Dynamic Programming

The stochastic dual dynamic programming (SDDP) algorithm is becoming increasingly used. In this paper we present analysis of different methods of lattice construction for SDDP exemplifying a realistic variant of the newsvendor problem, incorporating storage of production. We model several days of work and compare the profits realized using different methods of the lattice construction and the corresponding computer time spent in lattice construction. Our case differs from the known one because we consider not only a multidimensional but also a multistage case with stage dependence. We construct scenario lattice for different Markov processes which play a crucial role in stochastic modeling. The novelty of our work is comparing different methods of scenario lattice construction. We considered a realistic variant of the newsvendor problem. The results presented in this article show that the Voronoi method slightly outperforms others, but the k-means method is much faster overall.

The Multi-period Multi-trip Container Drayage Problem with Release and Due Dates

The Container Drayage Problem (CDP) aims at routing a fleet of trucks, based at a common terminal, to serve customers while minimizing the total travel distance. Each trip starts from and ends at the terminal, and handles a subset of customers. Each customer requires either that a container is picked up or delivered. We introduce a more realistic variant, i.e., the Multi-trip Multi-period CDP with Release and Due Dates (MM-CDP-RDD), in which the planning horizon is composed of several periods (days). On each day, each truck may perform more than one trip respecting the Release and Due Dates (RDD) associated with customer services, corresponding to the first and the last day on which the service can be carried out, respectively. Drivers’ contracts impose limitations on the maximum driving time allowed on each day, on two consecutive days and on the whole weekly planning horizon. To model the MM-CDP-RDD, we propose both an Arc-based Integer Linear Programming (ILP) formulation and a Trip-based ILP formulation that receives as input all the feasible non-dominated trips. To efficiently address medium/large-sized instances of the problem, we also design six Combinatorial Benders’ Cuts approaches. All the methods are compared on a rich set of instances generated for this new problem.

Heuristics for the capacitated dispersion problem

AbstractIn this paper, we investigate the adaptation of the greedy randomized adaptive search procedure (GRASP) and variable neighborhood descent (VND) methodologies to the capacitated dispersion problem. Dispersion and diversity problems arise in the placement of undesirable facilities, workforce management, and social media, among others. Maximizing diversity deals with selecting a subset of elements from a given set in such a way that the distance among the selected elements is maximized. We target here a realistic variant with capacity constraints for which a heuristic with a performance guarantee was previously introduced. In particular, we propose a hybridization of GRASP and VND implementing within the strategic oscillation framework. To evaluate the performance of our heuristic, we perform extensive experimentation to first set key search parameters, and then compare the final method with the previous heuristic. Additionally, we propose a mathematical model to obtain optimal solutions for small‐sized instances, and compare our solutions with the well‐known LocalSolver software.

The social and biological aspects of human death and social downshifting as a philosophical problem

Social downshifting is emerging as one of the key strategies of optimizing and maintaining the quality of human life in view of the prospect meet one's death with dignity. The aim of the issue is to conceptualize the term “social downshifting”, by analyzing the relationship between social and biological aspects of death and the connection of a person's understanding of his/her death with his/her social self-identification. In this study the functional approach was used in its value interpretation, that is, as a method of value functionalism. If social downshift emerges as a social value, it can have different functional consequences - institutional, organizational, communicative. On the other hand, the functional choice of a person in favor of social downshift as accepting a new value of the state of limited functional capacity can have very different value reasons - biological (anthropologic, environmental, etc.) or social (ethical, religious, political, economic, etc.). Social downshifting is not an outgrowth of social identity frameworks, but rather their artificial (possibly temporary) narrowing. While there may be objective reasons for this, social downshifting is still the result of the subjective, conscious position being taken by an individual, with which he or she is replacing his/her previous, socially aggressive and expansive position. Changing of the social identification is the best alternative to the uncompensated loss of grounds for the individual’s ordinary method of social identification. The uncompensated loss of identity is essentially social death of a person - full or partial, and it is not surprising, therefore, that it often entails overt or covert suicide, that is, premature, artificially caused physical death. Therefore, social downshifting as a realistic variant of changing the social identity of an individual in unfavorable circumstances, acts as a safeguard against suicide. With successful social downshifting, the person acquires a new meaning of life and new, acceptable living conditions. Thus, a person is motivated to use the technological life-extension capabilities that global civilization offers.

Benders decomposition for multiple-allocation hub-and-spoke network design with economies of scale and node congestion

This paper is the first to consider a novel and more realistic variant of the uncapacitated hub location problem where both flow-dependent economies of scale and congestion considerations are incorporated into the multiple-allocation version of the problem. Given an undirected graph with edge per-unit costs and flow requirements for a given set of source-destination pairs of nodes, the goal is to decide where to locate hub nodes and then to route flow through these hubs in a way that minimizes total cost. Transportation costs are influenced by two conflicting effects: consolidation discounts on hub-to-hub flow due to economies of scale, and congestion penalties due to excessive demand through hubs. The problem is formulated as a mixed-integer linear program by piecewise-linearizing non-linear cost elements. Since the resulting model is found to be difficult to solve directly using commercial solvers, a specialized Benders decomposition approach is devised to solve the problem. The proposed method is tested on two standard testbeds (the Civil Aeronautics Board and Australian Post datasets). While the commercial solver is unable to obtain any feasible solution for networks larger than 25 nodes, the proposed algorithm is shown to be able to obtain optimal solutions for networks as large as 70 nodes, and is able to obtain small-gap feasible solutions for networks with up to 100 nodes. Whenever both algorithms are able to solve instances to optimality, the Benders algorithm is able to do so in a fraction of the time needed by the commercial solver. The incorporation of congestion costs into the multiple-allocation topology as opposed to the single-allocation one is also found to lead to significant cost savings.

Generating simulated SNP array and sequencing data to assess genomic segmentation algorithms

We developed a tool, implemented in an R package called true and accurate clone generator (TACG), to simulate 'ground truth' and realistic SNP array and single nucleotide variant (SNV) data. We present TACG and use it to assess several different approaches to segmentation of copy number data from SNP arrays, with a particular interest in detecting copy number variations (CNVs) in cancer samples. We demonstrate that DNAcopy, an algorithm using circular binary segmentation, generally performs best, which is in agreement with previous research. We determine the conditions under which it and other methods break down. In particular, we assess how characteristics like clonal heterogeneity, presence of nested CNVs, and the type of aberration affect algorithm accuracy. The simulations we generated proved to be useful in determining not just the comparative overall accuracy of different algorithms, but also in determining how their efficacy is affected by the biological characteristics of samples from which the data was generated.

The green vehicle routing problem with capacitated alternative fuel stations

In this paper, we introduce the Green Vehicle Routing Problem with capacitated Alternative Fuel Stations (AFSs), a more realistic variant of the Green Vehicle Routing Problem where the capacity of the AFSs is addressed. Two Mixed Integer Linear Programming formulations, one based on arc-variables and one on path-variables are presented. In order to reduce the computational time required to solve the problem, two variants of an exact cutting planes method are proposed. All the proposed approaches are also extended to be applied in a scenario in which AFSs reservation is allowed, by introducing time windows at them. Computational experiments are carried out on both benchmark and challenging realistic instances for which the capacity of the AFSs is a crucial issue.

Fitting a stochastic fire spread model to data

Abstract. As the climate changes, it is important to understand the effects on the environment. Changes in wildland fire risk are an important example. A stochastic lattice-based wildland fire spread model was proposed by Boychuk et al. (2007), followed by a more realistic variant (Braun and Woolford, 2013). Fitting such a model to data from remotely sensed images could be used to provide accurate fire spread risk maps, but an intermediate step on the path to that goal is to verify the model on data collected under experimentally controlled conditions. This paper presents the analysis of data from small-scale experimental fires that were digitally video-recorded. Data extraction and processing methods and issues are discussed, along with an estimation methodology that uses differential equations for the moments of certain statistics that can be derived from a sequential set of photographs from a fire. The interaction between model variability and raster resolution is discussed and an argument for partial validation of the model is provided. Visual diagnostics show that the model is doing well at capturing the distribution of key statistics recorded during observed fires.

The multi-objective Steiner pollution-routing problem on congested urban road networks

This paper introduces the Steiner Pollution-Routing Problem (SPRP) as a realistic variant of the PRP that can take into account the real operating conditions of urban freight distribution. The SPRP is a multi-objective, time and load dependent, fleet size and mix PRP, with time windows, flexible departure times, and multi-trips on congested urban road networks, that aims at minimising three objective functions pertaining to (i) vehicle hiring cost, (ii) total amount of fuel consumed, and (iii) total makespan (duration) of the routes. The paper focuses on a key complication arising from emissions minimisation in a time and load dependent setting, corresponding to the identification of the full set of the eligible road-paths between consecutive truck visits a priori, and to tackle the issue proposes new combinatorial results leading to the development of an exact Path Elimination Procedure (PEP). A PEP-based Mixed Integer Programming model is further developed for the SPRP and embedded within an efficient mathematical programming technique to generate the full set of the non-dominated points on the Pareto frontier of the SPRP. The proposed model considers truck instantaneous Acceleration/Deceleration (A/D) rates in the fuel consumption estimation, and to address the possible lack of such data at the planning stage, a new model for the construction of reliable synthetic spatiotemporal driving cycles from available macroscopic traffic speed data is introduced. Several analyses are conducted to: (i) demonstrate the added value of the proposed approach, (ii) exhibit the trade-off between the business and environmental objectives on the Pareto front of the SPRP, (iii) show the benefits of using multiple trips, and (iv) verify the reliability of the proposed model for the generation of driving cycles. A real road network based on the Chicago's arterial streets is also used for further experimentation with the proposed PEP algorithm.

ETGuard: Detecting D2D attacks using wireless Evil Twins

In this paper, we demonstrate a realistic variant of wireless Evil Twins (ETs) for launching device to device (D2D) attacks over the network, particularly for Android. An ET can be defined as a rogue Access Point (AP) created by hackers to resemble the authentic AP in a network zone. The existing attacks that can be launched through ETs include sniffing, Man-in-the-Middle (MITM) attack, etc. However, these attacks affect the devices after their association and transmission of user traffic through an ET. We show an attack where an ET infects an Android device before the relay of network traffic through it, and disappears from the network immediately after inflicting the device. The attack leverages the captive portal facility of wireless networks to launch D2D attack. We configure an ET to launch a malicious component of an already installed app in the device on submission of the portal page. For example, the malicious component can be either a service which opens a port, or sends an SMS to premium number, or exfiltrates sensitive information to malicious server. Thus, the attack may lead to any number of consequences. The existing ET detection solutions on APs are incapable of preventing this attack due to two reasons – either they analyse an ET after the relay of user traffic through it, or they can detect this attack only for hardware ETs. In this paper, we present an online, incremental, automated, fingerprinting based pre-association detection mechanism named as ETGuard which works as a client-server mechanism in real-time. The fingerprints are constructed from the beacon frames transmitted by the wireless APs periodically to inform client devices of their presence and capabilities in a network. Once detected, ETGuard continuously transmits deauthentication frames to prevent clients from connecting to an ET. ETGuard outperforms the existing state-of-the-art techniques from various perspectives. Our technique does not require any expensive hardware, does not modify any protocols, does not rely on any network specific parameters such as Round Trip Time (RTT), number of hops, etc., can be deployed in a real network, is incremental, and operates passively to detect ETs in real-time. To evaluate the efficiency, we deploy ETGuard in 802.11a/b/g wireless networks. The experiments are conducted using 12 different attack scenarios where each scenario differs in the source used for introducing an ET. ETGuard effectively detects ETs introduced either through a hardware, software, or mobile hotspot with high accuracy, only one false positive scenario, and no false negatives.

Particle swarm optimization for uncapacitated multiple allocation hub location problem under congestion

In this paper we address the hub location problem in which capacity restrictions are introduced into the objective function as a penalty cost to represent their congestion effects on respective hubs. The goal of hub location problems is to locate an optimal set of hubs while minimizing sum of the transportation and opening costs under various constraints such as number of hubs, delay time and capacities of hubs. Moreover, we propose a more realistic variant of the problem where the capacities of hubs are not only a limitation in selecting the hubs. In practice, capacities of hubs are predicted by a strategic plan, but these predictions generally fail to meet the expectations due to changes in the flow in future and competitive nature of air transportation. A particle swarm optimization (PSO) is proposed to handle the complex nature of this problem and shorten the computing times. The performance of the proposed PSO is analyzed comparatively on the Australia Post (AP) data sets. The numerical results show that the proposed method can find the optimal solutions in less computation time in comparison with the earlier multiple allocation hub location models.

A variable neighborhood search simheuristic for the multiperiod inventory routing problem with stochastic demands

AbstractThe inventory routing problem (IRP) combines inventory management and delivery route‐planning decisions. This work presents a simheuristic approach that integrates Monte Carlo simulation within a variable neighborhood search (VNS) framework to solve the multiperiod IRP with stochastic customer demands. In this realistic variant of the problem, our goal is to establish the optimal refill policies for each customer–period combination, that is, those individual refill policies that minimize the total expected cost over the periods. This cost is the aggregation of both expected inventory and routing costs. Our simheuristic algorithm allows to consider the inventory changes between periods generated by the realization of the random demands in each period, which have an impact on the quantities to be delivered in the next period and, therefore, on the associated routing plans. A range of computational experiments are carried out in order to illustrate the potential of our simulation–optimization approach.

An iterative biased‐randomized heuristic for the fleet size and mix vehicle‐routing problem with backhauls

AbstractThis paper analyzes the fleet mixed vehicle‐routing problem with backhauls, a rich and realistic variant of the popular vehicle‐routing problem in which both delivery and pick‐up customers are served from a central depot using a heterogeneous and configurable fleet of vehicles. After a literature review on the issue and a detailed description of the problem, a solution based on a multistart biased‐randomized heuristic is proposed. Our algorithm uses an iterative method that relies on solving a series of smaller instances of the homogeneous‐fleet version of the problem and then using these subsolutions as partial solutions for the original heterogeneous instance. In order to better guide the exploration of the solutions space, the algorithm employs several biased‐randomized processes: a first one for selecting a vehicle type; a second one for sorting the savings list; and a third one to define the number of routes that must be selected from the homogenous‐fleet subsolution. The computational experiments show that our approach is competitive and able to provide 20 new best‐known solutions for a 36‐instance benchmark recently proposed in the literature.