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Showing papers on "Network planning and design published in 2022"


Journal ArticleDOI
TL;DR: In this paper , an integrated multi-objective mixed-integer linear programming (MOMILP) model is proposed to design sustainable closed-loop supply chain networks with cross-docking, location-inventory-routing, time window, supplier selection, order allocation, transportation modes with simultaneous pickup, and delivery under uncertainty.

32 citations


Journal ArticleDOI
TL;DR: In this article , a multi-objective Transportation-Location-Inventory-Routing (TLIR) formulation for an emergency blood supply chain network design problem is proposed to design effective blood supply chains in emergencies.
Abstract: World Health Organization (WHO) declared COVID-19 as a pandemic On March 12, 2020. Up to January 13, 2022, 320,944,953 cases of infection and 5,539,160 deaths have been reported worldwide. COVID-19 has negatively impacted the blood supply chain by drastically reducing blood donation. Therefore, developing models to design effective blood supply chains in emergencies is essential. This research offers a novel multi-objective Transportation-Location-Inventory-Routing (TLIR) formulation for an emergency blood supply chain network design problem. We answer questions regarding strategic, operational, and tactical decisions considering disruption in the network and blood shelf-life. Since, in real-world applications, the parameters of the proposed mathematical formulation are uncertain, two flexible uncertain models are proposed to provide risk-averse and robust solutions for the problem. We applied the proposed formulations in a case study. Under various scenarios and realizations, we show that the offered robust model handles uncertainties more efficiently and finds solutions that have significantly lower costs and delivery time. To make a reliable conclusion, we performed extensive worst-case analyses to demonstrate the robustness of the results. In the end, we provide critical managerial insights to enhance the effectiveness of the supply chain.The online version contains supplementary material available at 10.1007/s10479-022-04673-9.

21 citations


Journal ArticleDOI
TL;DR: In this article , a fuzzy mathematical model for a distribution network design problem in a multi-product, multi-period, mult-echelon, multi plant, multi retailer, and multi-mode of transportation green supply chain system is proposed.
Abstract: This paper proposes a novel fuzzy mathematical model for a distribution network design problem in a multi-product, multi-period, multi-echelon, multi-plant, multi-retailer, multi-mode of transportation green supply chain system. The three purposes of the model are to minimise total network cost, maximise net profit per capita for each human resource, and diminish CO2 emission throughout the network. P-hub median location with multiple allocations is used for locating the distribution centres. One scenario is designed for fuzzy customer demands with a trapezoidal membership function. Furthermore, the model determines the design of the network (selecting the optimum numbers, locations of plants, and distribution centres to open), finding the best strategy for material transportation through the network with the availability of different transportation modes, the capacities level of the facilities (plants or distribution centres (DCs)), and the number of outsourced products. Finally, all uncertain customer demands for all product types can be satisfied based on the methods mentioned above. This multi-objective mixed-integer non-linear mathematical model is solved by NSGA-II, MOPSO and a hybrid meta-heuristic algorithm. The results show that NSGA-II is the exclusive algorithm that obtains the best result according to the evaluation criteria.

18 citations


Journal ArticleDOI
TL;DR: In this article , a novel bi-objective Mixed-Integer Linear Programming (MILP) model is suggested to formulate the problem which aims to minimize network costs and maximize job opportunities while considering the adverse effects of the pandemic.
Abstract: In uncertain circumstances like the COVID-19 pandemic, designing an efficient Blood Supply Chain Network (BSCN) is crucial. This study tries to optimally configure a multi-echelon BSCN under uncertainty of demand, capacity, and blood disposal rates. The supply chain comprises blood donors, collection facilities, blood banks, regional hospitals, and consumption points. A novel bi-objective Mixed-Integer Linear Programming (MILP) model is suggested to formulate the problem which aims to minimize network costs and maximize job opportunities while considering the adverse effects of the pandemic. Interactive possibilistic programming is then utilized to optimally treat the problem with respect to the special conditions of the pandemic. In contrast to previous studies, we incorporated socio-economic factors and COVID-19 impact into the BSCN design. To validate the developed methodology, a real case study of a Blood Supply Chain (BSC) is analyzed, along with sensitivity analyses of the main parameters. According to the obtained results, the suggested approach can simultaneously handle the bi-objectiveness and uncertainty of the model while finding the optimal number of facilities to satisfy the uncertain demand, blood flow between supply chain echelons, network cost, and the number of jobs created.

18 citations


Journal ArticleDOI
TL;DR: In this article, the authors proposed a distributionally robust risk-averse model to safeguard the profits of investors in extreme situations, which is enhanced through valid inequalities, local branching, in-out variant methods and scenario-based aggregated cuts.

18 citations


Journal ArticleDOI
TL;DR: In this paper , the authors investigated the multi-facility green RL network design problem, integrating carbon footprint and vehicle selection, entailing allocation between the facilities in the multiscale setting to incorporate the dynamic characteristics.

16 citations


Journal ArticleDOI
TL;DR: In this article , the authors proposed a distributionally robust risk-averse model to safeguard the profits of investors in extreme situations, which is enhanced through valid inequalities, local branching, in-out variant methods and scenario-based aggregated cuts.

15 citations


Journal ArticleDOI
TL;DR: In this paper , a multi-objective optimization model for a green dual-channel supply chain network that handles economic and environmental objectives to optimize network flow is presented, where the main objective of the generated MILP model is to investigate the optimal selection of echelons and the optimal choice of transportation alternatives between these echelon in a closed-loop supply chain (CLSC) network that includes an e-commerce channel structure.

15 citations


Journal ArticleDOI
Shiqing Gao1, Xu Xin1, Cui Li1, Yanran Liu1, Kang Chen2 
TL;DR: In this article, a bi-level programming model is developed to jointly optimize the liner type, calling sequence, calling frequency, and sailing speed for a container ocean shipping network design problem.

14 citations


Journal ArticleDOI
TL;DR: In this paper , the authors explore systematically the topological limitations of urban bicycle network development and find that initially decreasing returns on investment until a critical threshold, posing fundamental consequences to sustainable urban planning.
Abstract: Cycling is a promising solution to unsustainable urban transport systems. However, prevailing bicycle network development follows a slow and piecewise process, without taking into account the structural complexity of transportation networks. Here we explore systematically the topological limitations of urban bicycle network development. For 62 cities we study different variations of growing a synthetic bicycle network between an arbitrary set of points routed on the urban street network. We find initially decreasing returns on investment until a critical threshold, posing fundamental consequences to sustainable urban planning: cities must invest into bicycle networks with the right growth strategy, and persistently, to surpass a critical mass. We also find pronounced overlaps of synthetically grown networks in cities with well-developed existing bicycle networks, showing that our model reflects reality. Growing networks from scratch makes our approach a generally applicable starting point for sustainable urban bicycle network planning with minimal data requirements.

14 citations


Journal ArticleDOI
TL;DR: In this article , a bi-level programming model is developed to jointly optimize the liner type, calling sequence, calling frequency, and sailing speed for a container ocean shipping network design problem.

Journal ArticleDOI
TL;DR: In this article , a flexible scheduled service network design problem is proposed to identify the shipments for which these times should be changed to minimize total transportation and handling costs, and a solution approach that outperforms a commercial optimization solver on instances derived from the operations of a U.S. less-than-truckload freight transportation carrier is presented.
Abstract: The scheduled service network design problem (SSNDP) can support planning the transportation operations of consolidation carriers given shipment-level service commitments regarding available and due times. These available and due times impact transportation costs by constraining potential consolidation opportunities. However, such available and due times may be changed, either because of negotiations with customers or redesigned internal operations to increase shipment consolidation and reduce transportation costs. As changing these times can lead to customer service and operational issues, we presume a carrier seeks to do so for a limited number of shipments. We propose a new variant of the SSNDP, the flexible scheduled service network design problem, that identifies the shipments for which these times should be changed to minimize total transportation and handling costs. We present a solution approach for this problem that outperforms a commercial optimization solver on instances derived from the operations of a U.S. less-than-truckload freight transportation carrier. With an extensive computational study, we study the savings potential of leveraging flexibility and the operational settings that are fertile ground for doing so.

Journal ArticleDOI
TL;DR: In this article, a fix-and-optimize (FO) approach is proposed for solving the healthcare facility location/network design problem considering equity and accessibility, which aims to minimize system costs, maximize accessibility, and minimize inequality among all demand nodes.

Journal ArticleDOI
TL;DR: In this paper , a multi-objective robust possibilistic programming model is proposed to solve the closed-loop supply chain network design problem considering the environmental and responsiveness features.
Abstract: This study aims to investigate the closed-loop supply chain network design problem considering the environmental and responsiveness features. For this purpose, a multi-objective mathematical model is suggested that minimizes the carbon emissions and the total costs and maximizes the responsiveness of the system. Due to the dynamic space of the business environment, uncertainty is an integral part of the supply chain problem. Therefore, this research applies the robust possibilistic programming method to cope with uncertainty. Afterwards, since the research problem has a high level of the complexity, a hybrid solution approach based on a heuristic method and the meta-goal programming method is developed to solve the research problem in a reasonable time. Then, due to the importance of the ventilator device during the recent pandemic (COVID-19), this study considers this product as a case study. The main contribution of the current study is to design a green-responsive closed-loop supply chain network under uncertainty using a multi-objective robust possibilistic programming model, for the first time in the literature, especially in the medical devices industry. On the other side, the other contribution of this study is to develop an efficient hybrid solution method. The achieved results demonstrate the efficiency of the offered model and the developed hybrid method. Eventually, by carrying out sensitivity analysis, the impact of some of the critical parameters on the model is investigated. Based on the obtained results, an increase in the demand sizes leads to increasing the environmental damages and the total costs while reducing the responsiveness level. On the other side, an increase in the rate of return leads to an increase in all of the objective functions. Also, the achieved results show that when the capacity parameter is increased, the total costs are decreased, but the responsiveness and environmental impacts are increased.

Journal ArticleDOI
TL;DR: The purpose is to create a relatively complete project planning decision model and optimization algorithm that can select multiple decision spaces at the same time through the above experimental research.
Abstract: In many cases, some programs often have some milestone activities that decision makers need to pay attention to. In this case, managers must efficiently describe these different decision-making spaces and use this as a basis to optimize possible solutions. Choosing a suitable decision-making space has always been a major factor in decision-making management, and it is also one of the key factors that managers must determine when making plans. Multi-objective optimization is to optimize multiple objectives at the same time. Multi-objective optimization algorithms have been widely used in many technology industries. This article researched from two parts: model display and optimization algorithm. The first is to deepen the basic characteristics of project planning and decision-making when there are more decision-making spaces. Based on the existing decision-making network planning methods, combined with the situation analysis method, a decision-making network planning model that can show more decision-making space is established. Secondly, based on the actual improvement requirements, the multi-objective optimization method in the multi-decision network design model and the simulation model of activity exercise data are established. The purpose is to create a relatively complete project planning decision model and optimization algorithm that can select multiple decision spaces at the same time through the above experimental research.

Journal ArticleDOI
TL;DR: The proposed clustering approach has two main advantages over traditional methods for evaluating power system frequency robustness: a spatial awareness of a power system network in terms of frequency-stable areas and the ease of interpretation of the metric result for robust network planning.

Journal ArticleDOI
TL;DR: In this article , a distributionally robust optimization model is proposed with the objective of minimizing the preparedness cost and the expected penalty cost of demand shortage under the worst-case distribution over the ambiguity set.
Abstract: This paper focuses on an emergency rescue network design problem in response to disasters under uncertainty. Considering the limited distribution information of the uncertain demands extracted from the historical data, we use the mean absolute deviation (MAD) that can derive tractable reformulations and better capture outliers and small deviations, to construct a MAD-based ambiguity set. A distributionally robust optimization model is proposed with the objective of minimizing the preparedness cost and the expected penalty cost of demand shortage under the worst-case distribution over the ambiguity set. We analyze the constructed model and provide some features such as the theoretical bounds of the objective value. For large-scale cases, we reformulate the knotty model using the linear decision rule to obtain tight and tractable problems. Computational experiments verify that the out-of-sample performance of the proposed model is better than that of the stochastic optimization model, especially for extreme cases. The MAD-based ambiguity set combined with the approximation technique can reduce the solution time and obtain high-quality solutions. Moreover, the results show that the amount of data has a significant effect on model performance. These results provide references for decision-makers in the practice of emergency response network design.

Journal ArticleDOI
TL;DR: This paper investigates the use of Machine Learning to approximate a complex 5G path loss model and shows that the new approach is able to find a wireless network deployment solution with an equal, or smaller, amount of access points, while still providing the required coverage for at least 99.4% of the receiver locations.
Abstract: Accurate wireless network planning is crucial for the deployment of new wireless services. This usually requires the consecutive evaluation of many candidate solutions, which is only feasible for simple path loss models, such as one-slope models or multi-wall models. However, such path loss models are quite straightforward and often do not deliver satisfactory estimations, eventually impacting the quality of the proposed network deployment. More advanced models, such as Indoor Dominant Path Loss models, are usually more accurate, but as their path loss calculation is much more time-consuming, it is no longer possible to evaluate a large set of candidate deployment solutions. Out of necessity, a heuristic network planning algorithm is then typically used, but the outcomes heavily depend on the quality of the heuristic. Therefore, this paper investigates the use of Machine Learning to approximate a complex 5G path loss model. The much lower calculation time allows using this model in a Genetic Algorithm-based network planning algorithm. The Machine Learning model is trained for two buildings and is validated on three other buildings, with a Mean Absolute Error below 3 dB. It is shown that the new approach is able to find a wireless network deployment solution with an equal, or smaller, amount of access points, while still providing the required coverage for at least 99.4% of the receiver locations and it does this 15 times faster. Unlike a heuristic approach, the proposed one also allows accounting for additional design criteria, such as maximal average received power throughout the building, or minimal exposure to radiofrequency signals in certain rooms.

Journal ArticleDOI
TL;DR: In this paper , a multi-objective, multiproduct, multi-period mathematical model is developed for the sustainable phosphorus supply chain management in an uncertain environment, where parametric uncertainties such as demand and supply are aggravated by disruptions with devastating effects on strategic, tactical, and operational decisions.

Journal ArticleDOI
TL;DR: In this paper , the problem of optimization in several sample networks was defined with the objectives of cost minimization and minimization of pressure deficit in the whole network, and the results showed that these algorithms have a high ability to find optimal solutions and are able to optimize the network in terms of cost and pressure by finding the appropriate pipe diameter.
Abstract: Abstract Water distribution networks require huge investment for construction. Involved people, especially researchers, are always seeking to find a way for decreasing costs and achieving an efficient design. One of the main factors of the network design is the selection of proper diameters based on costs and deficit of flow pressure and velocity in the network. The reduction in construction costs is accomplished by minimizing the diameter of network pipes which leads to the pressure drop in the network. Supplying proper pressure in nodes is one of the important design principles, and low pressure will not provide a complete water supply at the consumption site. Therefore, in this research, the problem of optimization in several sample networks was defined with the objectives of cost minimization and minimization of pressure deficit in the whole network. The EPANET software was used for hydraulic analysis of sample networks, and the multi-objective optimization process was performed by coding NSGA-II and MOPSO algorithms in the MATLAB software environment and linking them to EPANET. The cost function was initially defined only by considering the relationship between cost and diameter and the length of pipes, and in the next definition, the cost resulted by violation of the allowable pressure range was added to this function In both cases, the schedule for achieving the optimal answer was executed. The results showed that these algorithms have a high ability to find optimal solutions and are able to optimize the network in terms of cost and pressure by finding the appropriate pipe diameter. The time for reaching convergence was reduced by considering the cost of violation of the allowable pressure limits significantly and the optimal answer is obtained in a small number of repetitions. In NSGA-II and MOPSO algorithms in two-looped network with 20 and 30 iterations and run time of 0.66 and 0.8 s, respectively, and in Lansey network with 150 and 250 iterations and run time of 5.7 and 9.5 s, the optimal solutions were obtained.

Journal ArticleDOI
TL;DR: A novel channel-based integer linear programming (ILP) model for the problem of routing, space, and spectrum assignment (RSSA) in consideration of space lane change in SDM-EON has an overwhelming advantage over the previous slot-based one in computing time for the optimization process.

Journal ArticleDOI
01 Aug 2022
TL;DR: In this article , an integrated model of hub-and-spoke network design and fleet planning with the constraints of the passenger flow demand as well as the adaptation of different types of aircraft for each route, with the objective to minimize the total system cost (i.e., the sum of the hub setting cost and aircraft's related cost).
Abstract: Most extant studies on aviation’s hub-and-spoke network focus on the optimization problems from the perspective of economies of scale thanks to the inherent inter-hub connections, whereas the economies of scale on each route is contingent on the associated allocation of different types of aircraft. In this paper, we devise an integrated model of hub-and-spoke network design and fleet planning with the constraints of the passenger flow demand as well as the adaptation of different types of aircraft for each route, with the objective to minimize the total system cost (i.e., the sum of the hub setting cost and aircraft’s related cost). To tackle the complexity of the integrated model, we develop a heuristic solution algorithm based on a Genetic Algorithm framework and an improved Floyd–Warshall algorithm to solve the value of the fitness function. The proposed model and the developed algorithms are tested with the US flight network (i.e., the CAB dataset) and China’s aviation network. The sensitivity analysis reveals that the demand window is one of the critical factors that affect hub-and-spoke network design and fleet planning, while the utilization rate of specified aircraft fleet determines the fleet configuration, which poses relatively less impact on hub-and-spoke network design.

Journal ArticleDOI
TL;DR: In this article, the authors developed an optimization-based approach, referred to Integrated Connection Planning and Passenger Allocation Model (ICPPAM), to support low-cost airlines in the early stage of connection planning.

Journal ArticleDOI
TL;DR: In this paper , a mixed-integer linear programming (MILP) formulation is proposed to solve the maximal covering bicycle network design problem (MCBNDP), which involves making investment decisions to build a cycling network that is aimed at maximizing the coverage of cyclists, while maintaining a minimum total network cost at its minimum.
Abstract: Considering the lack of adequate cycling infrastructure networks in many cities, decision makers must face the challenge of designing connected bicycle facility networks to ensure safe and comfortable access to urban opportunities for cyclists and the usability of infrastructure. This paper addresses the maximal covering bicycle network design problem (MCBNDP). MCBNDP involves making investment decisions to build a cycling network that is aimed at maximizing the coverage of cyclists, while maintaining a minimum total network cost at its minimum. The derived network is subject to a budget limit and accounts for the entire connectivity and directness as fundamental bicycle network design criteria. Cyclists who are located at a given origin are considered covered by the network if a connected path of dedicated cycling infrastructure links them to their desired destination, within a maximum travel distance. We propose a mixed-integer linear programming (MILP) formulation, including a two-phase solution approach to solving the MCBNDP. In addition, using a commercial solver, our MILP formulation allows exact solutions to be obtained for large-scale instances with reasonable computing times for these types of problems. This MILP formulation is employed to solve a real instance that is applied to a wide territory of analysis in Medellin city (Colombia). The findings of this paper will contribute to existing literature and support urban policymakers to better spatially allocate the resources and, consequently, maximize the impact of their investments on connected cycling infrastructure networks. Our findings indicate that access to opportunities for cyclists can be easily favored by making small improvements to the existing infrastructure to guarantee safe, direct, and comfortable cycling infrastructure. Because we specify a maximum travel distance rather than a shortest path limitation, our problem ensures that cyclists may have several possibilities for their routes, which may go along the shortest path or any other alternative, however, without exceeding the maximum travel distance. In this direction, our findings confirm that prioritizing coverage, while accounting for full network connectivity, will benefit more cyclists because of the flexible configuration of the new network, which also may ensure its usability.

Journal ArticleDOI
TL;DR: In this article , the authors developed an optimization-based approach, referred to Integrated Connection Planning and Passenger Allocation Model (ICPPAM), to support low-cost airlines in the early stage of connection planning.

Journal ArticleDOI
TL;DR: Wang et al. as mentioned in this paper proposed a cluster-based network design for monitoring meteorological variables within the urban canopy layer, and examined its applications in Beijing and Hong Kong by using weather simulation data as ground truth.

Journal ArticleDOI
TL;DR: In this article , a new approach for ASP based on function-behavior-structure cells (FBSC) in OD is proposed to tackle this problem, and an approach for calculating the matching intensity between FBSC structure cells is developed for identifying the assembly relationships between SCs.

Journal ArticleDOI
TL;DR: In this paper , the authors present a survey of multilayer network design problems, focusing on applications in transportation and telecommunications, as well as on solution methods, and propose a general modeling framework which encompasses the models in the literature.

Journal ArticleDOI
TL;DR: In this article , a demand-driven inverse percolation approach is proposed to generate families of efficient bike path networks taking into account cyclist demand and safety preferences, and a comparison of the resulting families with those created for homogenized demand enables us to quantify the importance of the demand distribution for network planning.
Abstract: Cycling is crucial for sustainable urban transportation. Promoting cycling critically relies on sufficiently developed infrastructure; however, designing efficient bike path networks constitutes a complex problem that requires balancing multiple constraints. Here we propose a framework for generating efficient bike path networks, explicitly taking into account cyclists’ demand distribution and route choices based on safety preferences. By reversing the network formation, we iteratively remove bike paths from an initially complete bike path network and continually update cyclists’ route choices to create a sequence of networks adapted to the cycling demand. We illustrate the applicability of this demand-driven approach for two cities. A comparison of the resulting bike path networks with those created for homogenized demand enables us to quantify the importance of the demand distribution for network planning. The proposed framework may thus enable quantitative evaluation of the structure of current and planned cycling networks, and support the demand-driven design of efficient infrastructures. Designing efficient bike path networks requires balancing multiple constraints. In this study, a demand-driven inverse percolation approach is proposed to generate families of efficient bike path networks taking into account cyclist demand and safety preferences.

Journal ArticleDOI
TL;DR: In this article , the authors study a service network design problem for an urban same-day delivery system in which the number of vehicles that can simultaneously load or unload at a hub is limited.
Abstract: We study a new service network design problem for an urban same-day delivery system in which the number of vehicles that can simultaneously load or unload at a hub is limited. Due to the presence of both time constraints for the commodities and capacity constraints at the hubs, it is no longer guaranteed that a feasible solution exists. The problem can be modeled on a time-expanded network and formulated as an integer program. To be able to solve real-world instances, we design and implement three heuristics: (1) an integer programming–based heuristic, (2) a metaheuristic, and (3) a hybrid matheuristic. An extensive computational study using real-world instances (with different geographies, market sizes, and service offerings) from one of China’s leading comprehensive express logistics service providers demonstrates the efficacy of the three heuristics.