Network Design Approach Research Articles

Opportunity Research of using Network Project Groups in Organizing the Production of IT- Products and the Principles of Motivation for the Effectiveness of the Group's Functioning

The article shows the relevance of creating network project groups in the organization of production of Information Technology (IT)-products. The goal of the study was identified, the decomposition of this goal was carried out, and other components of the logical structure of the study were identified. The hypothesis of the study is the following: when transforming an ordinary project group into a network one, characteristics are acquired that ensure the success of the project group and increase the competitiveness of IT-companies, using appropriate methods of motivation. The scientific novelty of the work lies in the fact that the relationship between the use of the network design approach and the company's competitiveness in the implementation of complex and non-standard projects in the IT-companies’ organization of production is revealed. Features of employees’ motivation of such divisions are revealed and fundamentally new solutions in the field of employees’ motivation of IT-companies are defined.

Soil moisture sensor network design for hydrological applications

Abstract. Soil moisture plays an important role in the partitioning of rainfall into evapotranspiration, infiltration, and runoff, hence a vital state variable in hydrological modelling. However, due to the heterogeneity of soil moisture in space, most existing in situ observation networks rarely provide sufficient coverage to capture the catchment-scale soil moisture variations. Clearly, there is a need to develop a systematic approach for soil moisture network design, so that with the minimal number of sensors the catchment spatial soil moisture information could be captured accurately. In this study, a simple and low-data requirement method is proposed. It is based on principal component analysis (PCA) for the investigation of the network redundancy degree and K-means cluster analysis (CA) and a selection of statistical criteria for the determination of the optimal sensor number and placements. Furthermore, the long-term (10-year) 5 km surface soil moisture datasets estimated through the advanced Weather Research and Forecasting (WRF) model are used as the network design inputs. In the case of the Emilia-Romagna catchment, the results show the proposed network is very efficient in estimating the catchment-scale surface soil moisture (i.e. with NSE and r at 0.995 and 0.999, respectively, for the areal mean estimation; and 0.973 and 0.990, respectively, for the areal standard deviation estimation). To retain 90 % variance, a total of 50 sensors in a 22 124 km2 catchment is needed, and in comparison with the original number of WRF grids (828 grids), the designed network requires significantly fewer sensors. However, refinements and investigations are needed to further improve the design scheme, which are also discussed in the paper.

A robust mixed flexible-possibilistic programming approach for multi-objective closed-loop green supply chain network design

In recent years, due to governmental legislation, environmental groups’ pressures, customer green expectation, etc., closed-loop green supply chains have gained paramount consideration. Accordingly, this study develops a novel multi-objective mixed integer nonlinear programming model for a closed-loop green supply chain network design problem. The proposed model aims to minimize the total costs, total CO2 emissions, and robustness costs in both forward and reverse directions, simultaneously. To cope with flexible constraints and epistemic uncertainty in the model’s parameters, a robust flexible-possibilistic programming approach is tailored. The model is solved using an efficient interactive solution approach, in which, the presented model is analyzed under various carbon emission mechanisms to assess the influence of these mechanisms on the achieved solution. An illustrative example in the copier industry is also provided to validate the applicability of the presented optimization model. Numerical results indicate the superiority of the carbon cap-and-trade policy in most of the cases.

A Benders decomposition-based approach for logistics service network design

We propose an exact solution method for a Logistics Service Network Design Problem (LSNDP) inspired by the management of restaurant supply chains. In this problem, a distributor seeks to source and fulfill customer orders of products (fruits, meat, napkins, etc.) through a multi-echelon distribution network consisting of supplier locations, warehouses, and customer locations in a cost-effective manner. As these products are small relative to vehicle capacity, an effective strategy for achieving low transportation costs is consolidation. Specifically, routing products so that vehicles transport multiple products at a time, with each product potentially sourced by a different supplier and destined for a different customer. As instances of this problem of sizes relevant to the operations of an industrial partner are too large for off-the-shelf optimization solvers, we propose a suite of techniques for enhancing a Benders decomposition-based algorithm, including a strengthened master problem, valid inequalities, and a heuristic. Together, these enhancements enable the resulting method to produce provably high-quality solutions to multiple variants of the problem in reasonable run-times.

A data-driven approach for supply chain network design under uncertainty with consideration of social concerns

Although supply chain network design under uncertainty has been studied by many researchers, most stochastic programming approaches in this area assume uncertain parameters follow certain distribution functions. However, in practice, the true distributions may be ambiguous and some historical data are available. This study proposes a data-driven two-stage stochastic programming model to obtain robust decisions among all possible distributions in a defined ambiguity set based on the moments of available data. In accordance with the proposed stochastic program, a solution algorithm based on Benders’ decomposition is developed. Further, the social concerns corresponding to the supply chain network are derived and quantified by the social life cycle assessment methodology. The proposed model is applied for designing a recovery network in which various technologies use generated municipal solid wastes for the power generation. Computational results on a real-life case study demonstrate the applicability of the proposed data-driven two-stage stochastic model as well as the impact of considering social concerns on the design decisions.

Two-Stage Stochastic Choice Modeling Approach for Electric Vehicle Charging Station Network Design in Urban Communities

Electric vehicles (EVs) provide a cleaner alternative that not only reduces greenhouse gas emissions but also improves air quality and reduces noise pollution. The consumer market for electrical vehicles is growing very rapidly. Designing a network with adequate capacity and types of public charging stations is a challenge that needs to be addressed to support the current trend in the EV market. In this research, we propose a choice modeling approach embedded in a two-stage stochastic programming model to determine the optimal layout and types of EV supply equipment for a community while considering randomness in demand and drivers' behaviors. Some of the key random data parameters considered in this study are: EV's dwell time at parking {\sv location}, battery's state of charge, distance from home, willingness to walk, drivers' arrival patterns, and traffic on weekdays and weekends. The two-stage model uses the sample average approximation method, which asymptotically converges to an optimal solution. To address the computational challenges for large-scale instances, we propose an outer approximation decomposition algorithm. We conduct extensive computational experiments to quantify the efficacy of the proposed approach. In addition, we present the results and a sensitivity analysis for a case study based on publicly available data sources.

A Novel Information Theoretic Measure Based Sensor Network Design Approach for Steady State Linear Data Reconciliation

The current work proposes a novel information theoretic based sensor network design (SND) approach for data reconciliation in a steady state linear process. The proposed approach is based on Kullback-Leibler divergence (KLD), which measures the difference of a density function from a reference density function. In particular, the optimal design is the one that leads to the smallest KLD value of the designed density function of the estimates from a reference density function. This reference density function can be provided by the end-user, and the approach thus enables explicit incorporation of the end-user’s preference in the SND procedure. Additionally, the approach does not assume specific forms for the density functions of the estimates and is thus also applicable for cases when the estimates have non-Gaussian density. The significance of the approach is illustrated on a small example. To demonstrate its utility in obtaining optimal sensor networks, it is also applied to a popular case study from SND literature and results are compared with existing approaches.

A Multi-Objective Optimization and Hybrid Heuristic Approach for Urban Bus Route Network Design

This study proposed a hybrid optimization model for urban bus transit route network design problem (TRNDP). Although several mathematical methods had been developed to make the problem tractable, the methods relied on excessive assumptions, which resulted in over-simplification or idealization of the problems. In light of these considerations, a multi-level and multi-mode network design method was introduced in this study. A multi-level network consisted of three levels: skeleton network, arterial network, and feeder network. The different levels of network were associated with different modes of public transportation (such as subway, light rail transit, trolleybus, BRT, normal bus, and community branch) based on the features of bus routes, city sizes, etc. In addition, according to the respective features of urban transit route network structures, we developed different optimization models for different levels. Finally, the proposed methodology was applied to case studies of the city of Zhaoyuan in China and the transit network of Mandl benchmark. The results showed that the total travel time for the proposed method was significantly lower than that of the competing method, with a 21.51% reduction. In addition, the proposed method provided 85.23% direct travelers, 14.65% travelers with one transfer, 0.12% travelers with two transfers, and no unsatisfied demand, which were better than the results from the compared method.

Analysis of Transit Network Design Using GIS and Honeybee Algorithm in the City of Sanandaj, Iran

Public transit planning is a user-oriented problem, respectful of financial issues and involves different stakeholders such as the general public, the transportation provider and the local government. One of the main components of public transit planning is the transit network design (TND) problem. This research is an attempt to perform transit network design and analysis in the city of Sanandaj, Iran using the capabilities of GIS and Honeybee algorithm. Objectives of this study are formulating a multi-objective model of the TND problem, developing a GIS-based procedure for solving the TND problem and examination of the solutions using artificial metaheuristic methods such as honeybee algorithm. The transit network design approach in this research, aims to reduce the walking distance, the total travel distance and the total number of stops needed for a suitable transit service in Sanandaj, Iran. One of the contributions of this research is developing a transit network design with utilizing a spectrum of GIS software modelling functionalities and using the abilities of the artificial intelligence in modelling and assessment of the transit network.

A Novel Broadcast Network Design for Routing in Mobile Ad-Hoc Network

MANET is an infrastructure-less, fixed topology less, multi-hop and bandwidth constraint wireless network. In MANET optimized control message broadcasting through an efficient routing can results in bandwidth utilization and efficient data transmission with minimum numbers of hops. An efficient routing technique using a broadcast network, designed by existing minimum cost spanning tree (MCST) is also unable to provide all-nodes-to-all shortest route and also have issues about data transmission efficiency. In this article, our broadcast network design approaches can provide a solution for optimized control message broadcast and data transmission efficiency by considering Kruskalś MCST based broadcast network as a base network. Initially, we consider high-frequency links (HF-Link) to be added in the base network by considering the Dijkstra algorithm. Secondly, the broadcast network is designed by including those links with the neighbors who have at least a fixed number of neighbors (CF-Link) and finally, only those links are added with the base network which gives a shorter path to all other nodes (ST-Link) in the network. Using Omnet++ we have compared individual broadcast network design with the base network and observe that a slight increment in control overhead in existing based techniques could increase data efficiency significantly through a given suboptimal path.

A continuous approximation approach for supply network design considering radio frequency identification adoption

The design and management of multiechelon supply networks is one of the most crucial and challenging problems that managers encounter. In this study, supply networks are designed for both single-item and multi-item cases by considering RFID adoption. To maximize total profits, the objective is to determine the following: (1) the locations of distribution centers, (2) the allocation of retail stores to distribution centers, (3) the replenishment cycle time at distribution centers, and (4) whether RFID was adopted. In this study, a continuous approximation model is developed and an algorithm for solving this problem is proposed. Numerical studies are adopted to demonstrate the solution procedure and the effects of changing parameters on decisions and profits. The results also show how RFID adoption affects the design of supply networks. This paper provides novel management implications that business managers can use as a reference.

Context-Aware Convolutional Neural Network for Object Detection in VHR Remote Sensing Imagery

Object detection in very-high-resolution (VHR) remote sensing imagery remains a challenge. Environmental factors, such as illumination intensity and weather, reduce image quality, resulting in poor feature representation and limited detection accuracy. To enrich the feature representation and mine the underlying context information among objects, this article proposes a context-aware convolutional neural network (CA-CNN) model for object detection that includes proposal generation, context feature extraction, feature fusion, and classification. During feature extraction, we propose integrating a context-regions-of-interests (Context-RoIs) mining layer into the CNN model and extracting context features by mapping Context-RoIs mined from the foreground proposals to multilevel feature maps. Finally, the context features extracted from multilevel layers are fused into a single layer, and the proposals represented by the fused features are classified by a softmax classifier. In this article, through numerous experiments, we thoroughly explore the influence of key factors, such as Context-RoIs, different feature scales, and different spatial context window sizes. Because of the end-to-end network design approach, our proposed model simultaneously maintains high efficiency and effectiveness. We conducted all model testing on the public NWPU VHR-10 data set. The experimental results demonstrate that our proposed CA-CNN model achieves significantly improved model performance and better detection results compared with the state-of-the-art methods.

A two-stage optimization approach for subscription bus services network design: the China case

Subscription bus services (SBS) is a convenient and low-carbon rapid transport mode for passengers’ daily commute. The network design becomes a vital problem because it closely relates to both operators’ profit and passengers’ daily convenient traveling. In this paper, a two-stage model is formulated to optimize the subscription bus services network design (SBSND). During the first stage we minimize the total service distance and the number of vehicles as a single target objective function; vehicle capacity utilization rate, service time, ratio of service distance and linear distance between origin and destination are limited by means of constraints. During the second stage we share the same objective function and similar constraints used during the first stage, but the parameter settings of the constraints are different. Correspondingly, a two-stage algorithm is also designed to solve the SBSND problem. Firstly, we obtain the possible service lines, match the passengers and bus capacity. Next, we use Dijkstra to obtain the shortest SBS operation lines. Finally, we form the SBS network. The three phases are the main processes about the algorithm during both stages. The comparison between existing SBS in Chengdu city and the optimized SBS shows the high efficiency of the optimization model and algorithm formulated in this paper: the operation line length increases from 250.6 to 300.9 km; only 40 passengers have no SBS after optimization; the average operation time is reduced from 56.8 to 50.2 min.

A hybrid egalitarian bargaining game-DEA and sustainable network design approach for evaluating, selecting and scheduling urban road construction projects

Selecting and scheduling urban road construction projects (URCPs) is inherently an Urban Network Design Problem (UNDP) with a complex decision making process. Recently some studies have focused on sustainable UNDP, using different mathematical methods. In this paper, first a new network data envelopment analysis (NDEA) model has been developed. Then, considering sustainability dimensions, by integrating data envelopment analysis (DEA), game theory and sustainable UNDP, a bi-level model has been proposed for selecting and scheduling URCPs. A meta-heuristic algorithm is proposed to solve the presented bi-level model. Different test instances are solved to show the acceptable performance of proposed algorithm in both solution quality and execution time. Afterwards, the proposed model is applied to study the problem of urban road construction projects selection in a real-world case study of urban transportation network of Isfahan city in Iran. The results show that by applying obtained solution the environmental and social performance of the network has been improved and the performance of the network is almost efficient in all evaluation periods.

DESAIN JARINGAN DISTRIBUSI BERBASIS E-BUSINESS PADA SISTEM RANTAI PASOK

In this paper, we provide a form of distribution network design and application in the field of e-commerce in the supply chain, with a distribution network design approach. Thus a good distribution network model can be determined in e-commerce business and provide quality service through the delivery period of equipment and materials to the consumer's location that is fast and low cost which can provide more competitive prices for customers. This is where the role of distribution is very important, because with the distribution of all materials both raw materials and finished products can be distributed to each party in need in the supply chain. In the supply chain itself there are several distribution designs available, namely Manufacturer Storage With Direct Shipping, Manufacturer of Storage with Direct Shipping and In-Transit Merge, Storage With Carrier Delivery Distributor, Storage With Last-Mile Delivery Distributor, Manufacturer Or Distributor Storage With Customer Pickup.

A Stochastic Programming Approach for Electric Vehicle Charging Network Design

The advantages of electric vehicles (EVs) include reduction of greenhouse gas and other emissions, energy security, and fuel economy. The societal benefits of large-scale adoption of EVs cannot be realized without adequate deployment of publicly accessible charging stations. We propose a two-stage stochastic programming model to determine the optimal network of charging stations for a community, considering uncertainties in the arrival and dwell times of vehicles, the state of charge of arriving vehicles’ batteries, drivers’ walking ranges and charging preferences, demand during weekdays and weekends, and the community’s rate of EV adoption. We conducted studies using the sample average approximation method, which asymptotically converges to an optimal solution for a two-stage stochastic problem. However, this method is computationally expensive for large-scale instances. Therefore, we also developed a heuristic to produce nearly optimal solutions quickly for our data instances. We conducted computational experiments using various publicly available data sources and evaluated the benefits of the solutions for a given community, both quantitatively and qualitatively.

A multi-objective mixed robust possibilistic flexible programming approach for sustainable seaport-dry port network design under an uncertain environment

This paper develops a multi-objective mixed robust possibilistic flexible programming (MOMRPFP) approach for the sustainable dry port network design under uncertain environment. The optimal number, location, and capacity of dry ports, and the optimal number of containers transferred through dry ports are determined with minimizing the economic costs and environmental and social impacts. Finally, numerical analyses indicate that the total network cost decrease about 1.14% with the proposed approach. Moreover, the MOMRPFP approach improves the efficiency about the average computational time for large-sized networks with the presence of uncertainty in flexible objective functions and constraints as well as in data.

A multi-objective meta-heuristic approach for transit network design and frequency setting problem in a bus transit system

This paper studies a multi-objective transit network design and frequency setting (TNDFS) problem with the aim of determining a set of routes and frequency on each route for public buses. The TNDFS problem is solved in two stages. First, the transit network design problem is solved to generate a set of routes using an initial route set generation (IRSG) procedure combined with genetic algorithm. Second, the frequency setting problem is formulated as a multi-objective model to assign the frequency on each route with the objectives of minimizing the passenger time and the operating cost simultaneously. This problem is solved using the multi-objective particle swarm optimization with multiple search strategies (MMOPSO) to generate a Pareto-Front between the passenger time and the operating cost. An extensive computational experiment is performed on the benchmark Mandl’s Swiss network (Mandl, 1980) considering different scenarios and instances. The results obtained using the proposed approach to solve the transit network design problem are compared with that of the state-of-the-art methodologies available in the literature (Arbex & Cunha, 2015; Kechagiopoulos & Beligiannis, 2014; Nikolić & Teodorović, 2013), which outperforms in terms of trip directness and average travel time of passengers. For the frequency setting problem, the solutions obtained using MMOPSO are justified over the NSGA-II.

A Benders decomposition approach for a real case supply chain network design with capacity acquisition and transporter planning: wheat distribution network

This paper considers a real case problem of supply chain network design inspired from a wheat distribution network in Iran. It generates a network with capacity acquisition and fleet management. The problem first is formulated as a mixed integer linear programming model. Then, a logic-based Benders decomposition algorithm is appropriately developed as the solution methodology. In the presented algorithm, the problem is decomposed into two models of master and subproblem. The master problem is improved by means of the preprocessing and valid inequalities. Moreover, three Benders cuts, one optimality and two feasibility cuts, are developed for the algorithm. The general and relative performance of the model and algorithm is experimentally evaluated. The wheat distribution system of Iran is considered here as the case study of this research. The model is developed based on Iran’s wheat distribution system. All the results show that the algorithm significantly outperforms the mathematical model of the case study. For example, the algorithm solves 95% of the tested instances to optimality, yet the model solves 29%.

A multi-stage supply chain network design problem with in-house production and partial product outsourcing

We propose a mixed-integer linear programming model for a novel multi-stage supply chain network design problem. Our model integrates location and capacity choices for plants and warehouses with supplier and transportation mode selection, and the distribution of multiple products through the network. The aim is to identify the network configuration with the least total cost subject to side constraints related to resource availability, technological conditions, and customer service level requirements. In addition to in-house manufacturing, end products may also be purchased from external sources and consolidated in warehouses. Therefore, our model identifies the best mix between in-house production and product outsourcing. To measure the impact of this strategy, we further present two additional formulations for alternative network design approaches that do not include partial product outsourcing. Several classes of valid inequalities tailored to the problems at hand are also proposed. We test our models on randomly generated instances and analyze the trade-offs achieved by integrating partial outsourcing into the design of a supply chain network against a pure in-house manufacturing strategy, and the extent to which it may not be economically attractive to provide full demand coverage.