Proposed Solution Methodology Research Articles

Tracking Nosocomial Diseases at Individual Level with a Real-Time Indoor Positioning System.

Our research is motivated by the rapidly-evolving outbreaks of rare and fatal infectious diseases, for example, the severe acute respiratory syndrome (SARS) and the Middle East respiratory syndrome. In many of these outbreaks, main transmission routes were healthcare facility-associated and through person-to-person contact. While a majority of existing work on modelling of the spread of infectious diseases focuses on transmission processes at a community level, we propose a new methodology to model the outbreaks of healthcare-associated infections (HAIs), which must be considered at an individual level. Our work also contributes to a novel aspect of integrating real-time positioning technologies into the tracking and modelling framework for effective HAI outbreak control and prompt responses. Our proposed solution methodology is developed based on three key components – time-varying contact network construction, individual-level transmission tracking and HAI parameter estimation – and aims to identify the hidden health state of each patient and worker within the healthcare facility. We conduct experiments with a four-month human tracking data set collected in a hospital, which bore a big nosocomial outbreak of the 2003 SARS in Hong Kong. The evaluation results demonstrate that our framework outperforms existing epidemic models for characterizing macro-level phenomena such as the number of infected people and epidemic threshold.

Prediction of fluid flow and acoustic field of a supersonic jet using vorticity confinement.

In this study, the numerical simulation of the fluid flow and acoustic field of a supersonic jet is performed by using high-order discretization and the vorticity confinement (VC) method on coarse grids. The three-dimensional Navier-Stokes equations are considered in the generalized curvilinear coordinate system and the high-order compact finite-difference scheme is applied for the space discretization, and the time integration is performed by the fourth-order Runge-Kutta scheme. A low-pass high-order filter is applied to stabilize the numerical solution. The non-reflecting boundary conditions are adopted for all the free boundaries, and the Kirchhoff surface integration is utilized to obtain the far-field sound pressure levels in a number of observer locations. Comparisons of the jet mean flow and jet aeroacoustics results with the other numerical and experimental data at similar flow conditions are made and show a reasonable agreement. The study shows that the proposed solution methodology based on the high-order compact finite-difference scheme in conjunction with the VC method can reasonably predict the near-field flow and the far-field noise of high Reynolds number jets with a fairly coarser grid than that used in the large eddy simulations and, thus, the computational cost can be significantly decreased.

Collaboration and transportation resource sharing in multiple centers vehicle routing optimization with delivery and pickup

The adoption of collaboration strategies among logistics facilities and the formation of one or multiple coalitions constitute a sustainable approach to vehicle routing network optimization. This paper introduces a collaborative multiple centers vehicle routing problem with simultaneous delivery and pickup (CMCVRPSDP) to minimize operating cost and the total number of vehicles in the network. Distribution and pickup centers are allowed to share vehicles and customers in order to increase the entire network's efficiency and maximize profit. To provide the coalition coordinators with good routing solutions, we propose a hybrid heuristic algorithm which properly combines k-means and Non-dominated Sorting Genetic Algorithm-II (NSGA-II). Based on clustering solutions, the proposed Hybrid NSGA-II (HNSGA-II) first generates a real coded population to bind our mathematical model constraints and to obtain a large number of feasible solutions which converge to optimality. Chromosomes are divided for genetic operations with partial mapped crossover and swap mutation algorithms, before their recombination to ensure the quality of our results. Comparisons with the traditional NSGA-II and the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm indicate better performances of HNSGA-II in terms of objective function values. We also apply Cost Gap Allocation method (CGA) and the strictly monotonic path selection principle to examine profit allocation schemes. Numerical analyses on part of Chongqing city's logistics network show the superiority of HNSGA-II over MOPSO and NSGA-II on the practical case study, as well that of CGA over the Minimum Costs-Remaining Savings (MCRS), Shapley and Game Quadratic Programming (GQP) methods. In addition, the proposed profit allocation approach has supported the establishment of a grand coalition instead of two sub-coalitions. CMCVRPSDP optimization reduces long-haul transportation, improves the vehicle loading rate and facilitates sustainable development. Through the rational allocation of profits, the proposed solution methodology assures the stability and fairness among coalition members. The implementation is also important to design sustainable urban transportation networks.

Development of a high-order compact finite-difference total Lagrangian method for nonlinear structural dynamic analysis

• A compact finite-difference total Lagrangian method (CFDTLM) is developed for nonlinear structural dynamics analysis. • The large deformation of elastic structures for Hookean and neo-Hookean materials is accurately simulated using the CFDTLM. • The CFDTLM is extended and assessed for simulating thermo-elastodynamic problems. • The numerical solution procedure adopted is simpler than high-order finite volume and finite element formulations . • The solution methodology proposed is accurate for simulating nonlinear structural dynamics problems. A high-order compact finite-difference total Lagrangian method (CFDTLM) is developed and applied to nonlinear structural dynamic analysis. The two-dimensional simulation of thermo-elastodynamics is numerically performed in generalized curvilinear coordinates by taking into account the geometric and material nonlinearities. The spatial discretization is carried out by a fourth-order compact finite-difference scheme and an implicit second-order accurate dual time-stepping method is applied for the time integration. The accuracy and capability of the proposed solution methodology for the nonlinear structural analysis is investigated through simulating different static and dynamic benchmark problems including large deformations, large displacements and Hookean/neo-Hookean materials. The solution method is demonstrated to be free of shear-locking behavior. The results obtained by the present solution algorithm are compared with the analytical solution and the numerical results of the finite element and finite volume methods to examine the accuracy and robustness of the solution method proposed. A grid study is also performed to investigate the grid size effect on the accuracy and performance of the solution. Indications are that the solution methodology proposed is accurate for simulating nonlinear structural dynamics problems.

Solving closed-loop supply chain problems using game theoretic particle swarm optimisation

In this paper, we propose a closed-loop supply chain network configuration model and a solution methodology that aim to address several research gaps in the literature. The proposed solution methodology employs a novel metaheuristic algorithm, along with the popular gradient descent search method, to aid location-allocation and pricing-inventory decisions in a two-stage process. In the first stage, we use an improved version of the particle swarm optimisation (PSO) algorithm, which we call improved PSO (IPSO), to solve the location-allocation problem (LAP). The IPSO algorithm is developed by introducing mutation to avoid premature convergence and embedding an evolutionary game-based procedure known as replicator dynamics to increase the rate of convergence. The results obtained through the application of IPSO are used as input in the second stage to solve the inventory-pricing problem. In this stage, we use the gradient descent search method to determine the selling price of new products and the buy-back price of returned products, as well as inventory cycle times for both product types. Numerical evaluations undertaken using problem instances of different scales confirm that the proposed IPSO algorithm performs better than the comparable traditional PSO, simulated annealing (SA) and genetic algorithm (GA) methods.

Balancing stochastic parallel assembly lines

In industrial systems, efficient design of production units is important for productivity. An assembly line is a special type of industrial systems, and it has been widely used in various manufacturing industries. One possible way to achieve high efficiency in an assembly line design is to use two or more assembly lines, referred to as parallel assembly lines. Additionally, the occurrence of random events in industrial systems is inevitable. Among these random events that disturb the production process are operator unavailability, technical failures in the production line, loss of motivation, lack of training, non-qualified operators, complex tasks, etc. So, it is more realistic to consider industrial systems as stochastic context rather than deterministic one. For this purpose, in this paper, the problem of balancing parallel assembly lines with stochastic task times (SPALBP) is introduced and characterised for the first time. A chance-constrained, piecewise-linear, mixed integer programming (CPMIP) formulation and a tabu search (TS) algorithm are proposed to model and to solve the SPALBP. The objective of CPMIP and TS algorithm is minimizing the number of stations for a given cycle time. A simple lower bound calculation on the number of stations for SPALBP is proposed, and an example problem is solved using the proposed CPMIP. A set of test problems taken from the literature is solved in order to test the proposed TS algorithm, and some computational properties of the proposed solution methodologies are given. The computational results show that the proposed algorithm is very effective and successful for the SPALBP.

A framework to expedite joint energy-reserve payment cost minimization using a custom-designed method based on Mixed Integer Genetic Algorithm

In this paper, a methodology has been proposed to decrease the solution time of the joint energy-reserve market clearing problem under Payment Cost Minimization (PCM) mechanism. PCM mechanism is recently proposed to replace the available economically-inefficient clearing mechanisms in electricity markets. While the exact methods have been successfully applied to solve the market clearing problem under available mechanisms, their computational efficiency is poor when applied to solve this problem under PCM mechanism due to special characteristics of PCM formulation. The proposed solution methodology uses linear programming along with Mixed Integer Genetic Algorithm (MIGA) to minimize the payment cost. Most of efforts are focused on speeding up the applied optimization technique, since the available frameworks for solving the PCM problem suffer from very slow convergence. Different custom-designed functions have been added to the basic MIGA to decrease the solution time. The results are compared to those obtained using branch and cut technique.

An improved way to calculate imperfect items during long-run production in an integrated inventory model with backorders

This paper investigates an improved way to calculate imperfect items in an integrated inventory model with distribution free approach for lead time demand to simultaneously optimize lot size, safety factor, number of shipments, and lead time. The backorder rate depends on the reduced lead time. Due to the quality of products, each item is inspected by the buyer and defective items are sent to the supplier during delivery of the next lot. Some investments are used to improve the quality of products and reduce the setup cost. The model is solved by using distribution free approach with known mean and standard deviation. An improved solution methodology is designed and a lemma is constructed to obtain global optimum solution of the integrated model. An improved algorithm is designed to approach the global optimal solution. Five numerical examples and two special cases are provided to validate the suggested model. A case study is conducted for which the global optimum solution is obtained using the proposed solution methodology. Simplified versions of the proposed model, illustrated through numerical examples as special cases, converged over the existing theories. Some managerial insights are provided to help managers implement the suggested model in real time situations.

A linear framework for dynamic user equilibrium traffic assignment in a single origin-destination capacitated network

The dynamic traffic assignment (DTA) problem has been studied intensively in the literature. However, there is no existing linear framework to solve the user equilibrium (UE) DTA problem. In this paper, we develop a novel linear programming framework to solve the UE-DTA problem in a dynamic capacity network that exploits the linkage between the UE and system optimal (SO) solutions underpinned by a first-in-first-out (FIFO) principle. This important property enables us to develop an incremental loading method to obtain the UE solutions efficiently by solving a sequence of linear programs. The proposed solution methodology possesses several nice properties such as a predictable number of iterations before reaching the UE solution, and a linear system of equations to be solved in each of the iterations.In contrast to the related iterative methods, such as Frank–Wolfe algorithm, successive average (MSA) or projection and their extensions where the purpose of iteration is to seek the solution convergence, whereas ours is to solve a linear problem over multiple iterations but only for a single unit of demand in each iteration. Furthermore, we provide a theoretical proof that in the limit, the SO objective can be used to obtain the UE solution as the system time step goes to zero given the satisfaction of the FIFO constraint. We show via numerical examples the significant improvements in the obtained UE solutions both in terms of accuracy and computational complexity.

A novel parallel hurricane optimization algorithm for secure emission/economic load dispatch solution

This paper proposes a parallel hurricane optimization algorithm (PHOA) for solving economic emission load dispatch (EELD) problem in modern power systems. In PHOA, several sub-populations moving independently in the search space with the aim of simultaneously optimizing the problem objectives considering the local behavior between sub-populations. By this way, it is intended to search for the Pareto optimal solutions which are contrasting to the single optimal solution. The inherent characteristics of parallelization strategy can enhance the Pareto solutions and increase the convergence to reach the Pareto optimal solutions. Simulations are conducted on three test systems and comparisons with other optimization techniques that reported in the literature are demonstrated. The obtained results demonstrate the superiority of the proposed PHOA compared to other optimization techniques. Additional economic benefits with secure settings are fulfilled, while preserving all system constraints within their permissible limits. Added to that, two security indices are proposed from generation units and transmission lines. The highest security index from generation units reflects that the operating condition achieves more power reserve. In transmission lines, the highest security index means that the transmission lines operated beyond their congestion limits. For justification of the proposed security indices, the proposed solution methodology is employed to assure their benefits in terms of economical and environmental issues. The proposed algorithm improves the economic issue as well as enhances the power system operation in the technical point of view with acceptable levels of emissions. Moreover, design of experiments using the Taguchi approach is employed to calibrate the parameters of the algorithms. So, it can be considered as a promising alternative algorithm for solving problems in practical large-scale power systems.

Modelling and optimization of a bi-objective flow shop scheduling with diverse maintenance requirements

In real-world problems, machines cannot continuously operate and have to stop for maintenance before they fail. Lack of maintenance can also affect the performance of machines in processing jobs. In this paper, a permutation flow shop scheduling problem with multiple age-based maintenance requirements is modelled as a novel mixed-integer linear program in which the objectives are conflicting. In modelling the problem, we assume that infrequent maintenance can prolong job processing times. One of the objectives is to minimise the total maintenance cost by planning as few maintenance activities as possible to only meet the minimum requirements, and the other objective tries to minimise the total tardiness by sequencing the jobs and planning the maintenance activities in such a way that the processing times are not prolonged and unnecessary maintenance times are avoided. Because of this conflict, an interactive fuzzy, bi-objective model is introduced. Application of the model is illustrated through a case study for operations and maintenance scheduling of heavy construction machinery. An effective and efficient solution methodology is developed based on the structure of the problem and tested against commercial solvers and a standard GA. Computational results have verified the efficiency of the proposed solution methodology and show that unlike the proposed method, a generic metaheuristic that does not consider the unique structure of the problem can become ineffective for real-world problem sizes.

Evaluating airline service quality using a combined fuzzy decision-making approach

Developments in the airline industry in the last decade have been forcing airlines to evaluate the quality of their service performance with the goal of improving their competitive advantage. It has also become one of the most challenging tasks potentially influencing airlines' long-term success. This challenge calls for a systematic decision-aid tool that can not only handle the vagueness and conflicting nature of service quality evaluation criteria but also integrates the strengths of various multi-criteria decision-making (MCDM) methods in a fuzzy environment.This paper aims to evaluate airline service quality in Turkey, using a combined fuzzy decision-making approach, for the first time in the literature. For this purpose, it identifies sixteen airline service quality criteria under five dimensions. The paper then describes the fuzzy DEMATEL method to deal with the interactions among the evaluation criteria. In addition, it proposes a fuzzy ANP method to consider the interdependence and calculate the relative importance of each criterion. Finally, it introduces a fuzzy VIKOR method for evaluating and ranking the service quality performance of airlines in Turkey. The paper also implements a sensitivity analysis to validate the proposed solution methodology's usefulness and practicality by testing different parameters of index values.

Application of a preconditioned high‐order accurate artificial compressibility‐based incompressible flow solver in wide range of Reynolds numbers

SummaryIn the present study, the preconditioned incompressible Navier‐Stokes equations with the artificial compressibility method formulated in the generalized curvilinear coordinates are numerically solved by using a high‐order compact finite‐difference scheme for accurately and efficiently computing the incompressible flows in a wide range of Reynolds numbers. A fourth‐order compact finite‐difference scheme is utilized to accurately discretize the spatial derivative terms of the governing equations, and the time integration is carried out based on the dual time‐stepping method. The capability of the proposed solution methodology for the computations of the steady and unsteady incompressible viscous flows from very low to high Reynolds numbers is investigated through the simulation of different 2‐dimensional benchmark problems, and the results obtained are compared with the existing analytical, numerical, and experimental data. A sensitivity analysis is also performed to evaluate the effects of the size of the computational domain and other numerical parameters on the accuracy and performance of the solution algorithm. The present solution procedure is also extended to 3 dimensions and applied for computing the incompressible flow over a sphere. Indications are that the application of the preconditioning in the solution algorithm together with the high‐order discretization method in the generalized curvilinear coordinates provides an accurate and robust solution method for simulating the incompressible flows over practical geometries in a wide range of Reynolds numbers including the creeping flows.

Regenerator Location Problem in Flexible Optical Networks

In this study, we introduce the regenerator location problem in flexible optical networks. With a given traffic demand, the regenerator location problem in flexible optical networks considers the regenerator location, routing, bandwidth allocation, and modulation selection problems jointly to satisfy data transfer demands with the minimum cost regenerator deployment. We propose a novel branch-and-price algorithm for this challenging problem. Using real-world network topologies, we conduct extensive numerical experiments to both test the performance of the proposed solution methodology and evaluate the practical benefits of flexible optical networks. In particular, our results show that, making routing, bandwidth allocation, modulation selection, and regenerator placement decisions in a joint manner, it is possible to obtain drastic capacity enhancements when only a very modest portion of the nodes is endowed with the signal regeneration capability.

Mixed integer biobjective quadratic programming for maximum-value minimum-variability fleet availability of a unit of mission aircraft

We consider the FMP problem encountered in the Hellenic Air Force (HAF), that is, the problem of issuing individual flight and maintenance plans for a group of aircraft comprising a unit, so as to maximize the fleet availability of the unit over a multi-period planning horizon while also satisfying various flight and maintenance related restrictions. The optimization models that have been developed to tackle this problem often perform unsatisfactorily, providing solutions for which the fleet availability exhibits significant variability. In order to handle this difficulty, in this work we develop a mixed integer programming model, which, besides the typical objective maximizing the fleet availability, also includes an additional objective that minimizes its variability. Motivated by the substantial computational difficulties the typical ε-constraint reduced feasible region approach is faced with, as a result of the solution complexity of the optimization models involved, we also develop two specialized solution methodologies for this problem. Both methodologies identify the entire frontier of non-dominated solutions, utilizing suitable relaxations of the original model and exploiting the fact that the domain comprising possible fleet availability values is a discrete set. The first one disaggregates the original FMP model into smaller subproblems whose solution is attained much more efficiently. The second one is a variant of the ε-constraint method, applied to a suitable relaxation of the original FMP model. We present extensive computational results assessing the efficiency of the proposed solution methodologies and demonstrating that their performance is significantly superior to that of the typical ε-constraint method applied directly to the original biobjective model.

Combining Worst Case and Average Case Considerations in an Integrated Emergency Response Network Design Problem

We study an emergency response network design problem that integrates relief (supply) and evacuation (demand) sides under disaster location and intensity uncertainties which, in turn, dictate uncertainty in terms of the location and amount of demand. Representing these uncertainties by discrete scenarios, we present a stochastic programming framework in which two second stage objectives, the average and worst case costs, are combined. In our model, we minimize, over all of the scenarios, the fixed costs of opening supply centers and shelters, and the weighted sum of average and worst case flow costs. Thus, the model gives the decision maker the flexibility to put relative emphasis on the worst case and average flow cost minimization and explore outcomes in terms of total costs and network configurations. To solve large scale instances with varying relative weights, we devise alternative Benders Decomposition approaches. We implement these by using an advanced callback feature of the solver while simultaneously incorporating several performance-enhancing steps that help to improve runtimes significantly. We conduct a detailed computational study to highlight the efficiency of our proposed solution methodology. Furthermore, we apply our approach in a realistic case study based on Geographical Information Systems data on coastal Texas and present interesting insights about the problem and the resulting network structures for varying weights assigned to objectives.

Optimal transportation and shoreline infrastructure investment planning under a stochastic climate future

This paper studies the problem of optimal long-term transportation investment planning to protect from and mitigate impacts of climate change on roadway performance. The problem of choosing the extent, specific system components, and timing of these investments over a long time horizon (e.g., 40–60 years) is modeled as a multi-stage, stochastic, bi-level, mixed-integer program wherein cost-effective investment decisions are taken in the upper level. The effects of possible episodic precipitation events on experienced travel delays are estimated from solution of a lower-level, traffic equilibrium problem. The episodic events and longer-term sea level changes exist on different time scales, making their integration a crucial element in model development. The optimal investment strategy is obtained at a Stackelberg equilibrium that is reached upon solution to the bilevel program. A recursive noisy genetic algorithm (rNGA), designed to address large-scale applications, is proposed for this purpose. The rNGA seeks the optimal combination of investment decisions to take now given only probabilistic information on the predicted sea level rise trend for a long planning horizon and associated likely extreme climatic events (in terms of their frequencies and intensities) that might arise over that planning period. The proposed solution method enables the evaluation of decisions concerning where, when and to what level to make infrastructure investments. The proposed rNGA has broad applicability to more general multi-stage, stochastic, bilevel, nonconvex, mixed integer programs that arise in many applications. The proposed solution methodology is demonstrated on an example representing a portion of the Washington, D.C. Greater Metropolitan area adjacent to the Potomac River.

Fuzzy Fractional Minimal Cost Flow Problem

A new version of fractional minimal cost flow problem with fuzzy arc costs is focused in this study. The fuzzy arc costs are applied as in most of the real-world applications, the parameters have high degree of uncertainty. The goal of this problem is to determine the minimum fuzzy fractional cost of sending and passing a specified flow value into and from a network. A decomposition-based solution methodology is introduced to tackle this problem. The methodology applies Zadeh’s extension principle to decompose the problem to two upper bound and lower bound problems. These problems are capable of being solved for different \(\alpha\)-cut values to construct the fuzzy fractional minimal cost flow value as the objective function value. The efficiency of the proposed solution methodology is studied over some well-known examples of fractional minimal cost flow problem. The obtained results show the superiority of the proposed approach comparing to the methods of the literature.

Fuzzy MOGA for supply chain models with Pareto decision space at different α-cuts

Today’s global environment is highly competitive. The competition between two firms means the competition in their supply chain. The prime objective of decision makers are to optimize the supply chain. In every stage of supply chain, the cost and delivery time are involved. These criteria are uncertain in nature. We express these criteria in either the triangular or trapezoidal fuzzy numbers. While optimizing the time and cost simultaneously, the problem becomes bi-objective. Such problems can be solved by using Multiple Objective Genetic Algorithm (MOGA). We get the different Pareto front at different α-cuts which is called as the Pareto decision space. According to the uncertainty in the market, decision maker has a choice to take decision among the set of alternatives. Here, the proposed solution methodology is fusion of fuzzy set theory and MOGA. Fuzzy set theory is used for uncertainty in parameters and MOGA is used for obtaining non-dominated solutions. The numerical example is given to illustrate the proposed methodology and industrial case study is solved by using proposed methodology.

Chebyshev collocation spectral lattice Boltzmann method in generalized curvilinear coordinates

Abstract In this work, the Chebyshev collocation spectral lattice Boltzmann method is implemented in the generalized curvilinear coordinates to provide an accurate and efficient low-speed LB-based flow solver to be capable of handling curved geometries with non-uniform grids. The low-speed form of the D2Q9 and D3Q19 lattice Boltzmann equations is transformed into the generalized curvilinear coordinates and then the spatial derivatives in the resulting equations are discretized by using the Chebyshev collocation spectral method and the temporal term is discretized with the fourth-order Runge–Kutta scheme to provide an accurate and efficient low-speed flow solver. All boundary conditions are implemented based on the solution of the governing equations in the generalized curvilinear coordinates. The accuracy and robustness of the solution methodology presented are demonstrated by computing different benchmark and practical low-speed flow problems that are 2D Couette flow between concentric moving cylinders, 2D flow in a gradual expansion duct, 2D regularized trapezoidal cavity flow, and 3D flow in curved ducts of rectangular cross-sections. Results obtained for these test cases are in good agreement with the existing analytical and numerical results. The computational efficiency of the proposed solution methodology based on the Chebyshev collocation spectral lattice Boltzmann method implemented in the generalized curvilinear coordinates is also examined by comparison with the developed second-order finite-difference lattice Boltzmann method that indicates the proposed method provides more accurate and efficient solutions in terms of the CPU time and memory usage. The study shows the present solution methodology is robust and accurate for solving 2D and 3D low-speed flows over practical geometries. Indications are that the solution algorithm based on the CCSLBM in the generalized curvilinear coordinates does not need any filtering or numerical dissipation for stability considerations and thus high accuracy solutions obtained by applying the CCSLBM can be used as benchmark solutions for the evaluation of other LBM-based flow solvers.