Demand Nodes Research Articles

Efficient Neutrosophic Optimization for Minimum Cost Flow Problems

In the domain of optimization, linear programming (LP) is recognized as an exceptionally effective method for ensuring the most favorable outcomes. Within the context of LP, the minimum cost flow (MCF) problem is fundamental, with its primary objective being to reduce the transportation costs for a single item moving through a network, under the constraints related to capacity. This network is made up of supply nodes, directed arcs, and demand nodes and each arc has an associated cost and capacity constraint, these factors are certain. However, in practical scenarios, these factors are susceptible to variation due to causal uncertainty. The neutrosophic set theory has surfaced as a challenging approach to tackle the uncertainty that is often encountered in optimization processes. In this manuscript, our primary objective is to address the minimal cost flow (MCF) problem while accounting for the uncertainty inherent in the neutrosophic set. We specifically focus on the cost aspect as SVTN numbers and introduce a new approach based on a customized ranking function handmade for the MCF problem a pioneering endeavor within the field of neutrosophic sets. Additionally, we present numerical example to validate the effectiveness and robustness of our model.

Hub-and-spoke network design considering multiple delivery requirements under uncertainties

To satisfy customers' personalized delivery demands and expand the application of hub location models, this paper introduces a hub location problem with multiple service levels and mixture uncertainties, in which two or more allowed delivery times are provided for shipment. In an expected cost-minimization context with delivery-time restrictions, the researched problem explicitly considers fixed hub number, single-assignment pattern from hubs to demand nodes, diversified transportation modes, and two types of real-life uncertainties (stochastic customer demand and fuzzy transportation cost). To help eliminate the mixture uncertainties in the proposed mathematical model, the expected value model and fuzzy chance constrained programming are utilized. By employing the existing fuzzy arithmetic and the newly-proposed operational law of γ-pessimistic value for functions of regular fuzzy numbers, two novel two-phase solution approaches are designed, respectively. The effectiveness of solution approaches and the necessity of uncertain parameters setting in the model are comprehensively illustrated by a series of numerical experiments based on Turkish data set.

An optimisation model of hierarchical facility location problem for urban last-mile delivery with drones

Drones have, in recent years, been considered as a promising way for the last-mile delivery of urban logistics. In view of limited or short battery life of drones in every operation, the drone recharging stations and their locations are crucial to ensure delivery accessibility and to accomplish the required mission. Therefore, in addition to considering the selection of service centres and demand nodes, the location of recharging stations should also be incorporated into the proposed model of hierarchical facility location problem (HFLP). Many existing researches on drone recharging station locations assume that the delivery demand is known and only a single path is designed to meet the demand by using queuing theory models to describe node-based service waiting time. However, the reality is that we are still in the infrastructure planning stage for drone delivery, in which the demand is unknown in most cases. Our research proposes a novel mix-integer programming model to solve the HFLP for the last-mile drone delivery in urban areas, aiming to minimise the deployment, operation, and maintenance (DOM) costs of recharging stations, service centres, and paths. We have also incorporated a demand satisfaction constraint into the selection of effective alternative paths that meet the delivery accessibility. On the other hand, a sensitivity analysis in our study reveals varying degrees of impact on DOM costs and effective alternative path quantities, whose results would be useful to practical drone logistics delivery in urban areas.

Optimizing Multi-Echelon Delivery Routes for Perishable Goods with Time Constraints

As the logistics industry modernizes, living standards improve, and consumption patterns shift, the demand for fresh food continues to grow, making cold chain logistics for perishable goods a critical component in ensuring food quality and safety. However, the presence of both soft and hard time windows among demand nodes can complicate the single-network distribution of perishable goods. In response to these challenges, this paper proposes an optimization model for multi-distribution center perishable goods delivery, considering both one-echelon and two-echelon network joint distributions. The model aims to minimize total costs, including transportation, fixed, refrigeration, goods damage, and penalty costs, while measuring customer satisfaction by the start time of service at each demand node. A two-stage heuristic algorithm is designed to solve the model. In the first stage, an initial solution is constructed using a greedy approach based on the principles of the k-medoids clustering algorithm, which considers both spatial and temporal distances. In the second stage, the initial routing solution is optimized using a linear programming approach from the Ortools solver combined with an Improved Adaptive Large Neighborhood Search (IALNS) algorithm. The effectiveness of the proposed model and algorithm is validated through a case study analysis. The results demonstrate that the initial solutions obtained through the k-medoids clustering algorithm based on spatio-temporal distance improved the overall cost optimization by 1.85% and 4.74% compared to the other two algorithms. Among the three two-stage heuristic algorithms, the Ortools-IALNS proposed here showed enhancements in the overall cost optimization over the IALNS, with improvements of 3.24%, 1.12%, and 0.41%, respectively. The two-stage heuristic algorithm designed in this study also converged faster than the other two heuristic algorithms, with overall optimization improvements of 1.55% and 1.28%, further validating the superior performance of the proposed heuristic algorithm.

Delivery optimization for collaborative truck–drone routing problem considering vehicle obstacle avoidance

Drone participating in logistics delivery has gained much concern due to its potential superiority of operational efficiency and service costs. How to optimize the hybrid vehicle delivery trip is still a crucial issue. Besides, complex urban environment and regulatory no-through zones bring more rigorous challenges. The aim is to develop an efficient routing solution that considers obstacles encountered by the truck and drone, ensuring the timely delivery. This paper proposes an efficient deep reinforcement learning to optimize the collaborative truck–drone routing problem (C-TDRP) under vehicle obstacle avoidance trail. Specifically, the trucks deploy a fleet of drones to serve the random distributed consumers subject to minimum delivery cost. Homogeneous drones serve multiple demand nodes per trip with limited energy carrying capacity. We design the C-TDRP formulation with studying the feasibility of obstacle avoidance routing. Owing to the computational complexity of the proposed mathematical model, an intelligent optimization algorithm based on deep reinforcement learning is developed, named pointer network (Ptr-Net), which can capture the position weights associated with network sequence automatically. Through rigorous experimental validations, the results demonstrate that the vehicle energy consumption of final trajectory reduces by more than 42%, even for large-scale delivery scenarios. Our proposed method not only enhances the optimization performance but also lays the foundation for more intelligent and adaptable logistics delivery in complex urban environments.

Optimization of a “Social‒Ecological” System Pattern from the Perspective of Ecosystem Service Supply and Demand: A Case Study of Jilin Province

This study establishes and refines a social-landscape ecological security pattern that integrates the demand and supply of ecosystem services, providing a substantial foundation for the ecological restoration of territorial spaces. This foundation is crucial for enhancing the effectiveness of “social–ecological” systems in achieving sustainable development. Jilin Province, serving as a national ecological security buffer and experiencing rapid economic growth, exhibits a significant spatial imbalance between social and economic progress and ecological conservation. The balance of ecosystem service demand and supply is pivotal in this context, making Jilin Province an ideal study area. We employed a multifaceted approach, including MSPA, the InVEST model, landscape connectivity assessment, circuit theory, and ecological network integrity evaluation, to elucidate the spatial disparities between the demand and supply of ecosystem services. We then developed and optimized social and landscape ecological security patterns to meet human demands and safeguard ecological integrity, thereby promoting the sustainable development of “social–ecological” systems. The key findings are as follows: (1) The supply of ecosystem services shows a clear spatial gradient, with lower values in the west and higher in the east, while demand is concentrated in the central region with lower values in the east and west, indicating a pronounced spatial mismatch in Jilin Province. (2) The landscape ecological security pattern includes 18 barrier points, 33 pinch points, 166 ecological corridors, and 101 ecological sources. (3) The social–ecological security pattern comprises 119 demand sources and 150 supply–demand corridors. (4) The study introduces 14 supply–demand nodes and 47 optimization corridors, proposing zoning schemes for the eastern core protection area, the central ecological demand area, and the western core restoration area. Additionally, recommendations are concerning the optimization of the “social–ecological” system pattern. This research advances the theoretical understanding of “social–ecological” system development in Jilin Province and offers insights for more harmonized development strategies.

Impact of various fairness-based distribution models on supply chain networks from the perspective of customer satisfaction

This study investigates the effectiveness of two fairness-based distribution approaches—type I and type II—in enhancing the resilience of supply chain networks (SCNs) during disruptions. The research contributes to the growing body of knowledge on supply chain management by offering insights into how fairness principles can be applied to improve the restoration of disrupted networks. A mixed-integer programming model was developed to simulate these fairness-based distribution strategies, focusing on a water supply chain network of a privet company in Saudi Arabia. The SCN consists of a single supplier and ten demand nodes, each requiring multiple commodities. The model was tested under 100 random disruption scenarios, each reducing the capacity of randomly selected network segments. The performance of each fairness-based distribution approach was evaluated based on how quickly and effectively the SCN returned to its required service levels (SLs) across all demand nodes. Results indicate that Fairness Distribution Type I, which aims to minimize unmet demand across the entire network, generally outperformed Type II in terms of speed and efficiency. Type I was more effective at restoring SLs quickly at most nodes, while Type II showed localized advantages, particularly in restoring SLs for specific commodities at select nodes. The study concludes that while Type I is more suited for overall supply chain recovery, Type II may be beneficial in scenarios requiring focused recovery at specific demand nodes. These findings provide actionable insights for supply chain managers seeking to enhance network resilience through fairness-based distribution strategies, and suggest avenues for future research on hybrid and context-specific approaches.

Research on mobile energy storage scheduling strategy for emergency power supply protection of post-disaster isolated loads

Aiming at the problem of insufficient power supply capacity of isolated loads in oceanic islands, a concept based on mobile energy storage and power conservation is proposed in this paper. Firstly, the energy flow and traffic flow characteristics of mobile energy storage are quantitatively analyzed, and an accurate mathematical model is established. Furthermore, the time-space distribution characteristics of load are carefully considered, and the Voronoi diagram and Tyson polygon method are introduced to determine cell differentiation. On this basis, combined with the power demand of load nodes and the energy storage characteristics of mobile energy storage vehicles, the evaluation indicators of cell energy and relative surplus energy of cells are designed, and the agent-cellular automata algorithm is used to schedule the mobile energy storage. The simulation results show that the power supply mode based on mobile energy storage can effectively improve the reliability of isolated loads. This paper provides a new perspective for solving the reliable power supply problem of isolated loads.

An assessment of node–head–flow relationships at lumped demand nodes for pressure-dependent analysis of water distribution networks

ABSTRACT Pressure-dependent analysis (PDA) is preferred over demand-dependent analysis (DDA) to evaluate the performance of any water distribution network under pressure-deficient conditions. The PDA requires a relationship, called node–head–flow relationship (NHFR) between available head and available flow at a node to know whether the demand is fulfilled completely, partially, or not at all, depending on the available pressure. Several types of NHFRs have been suggested in the past and mostly a single type of NHFR is used at different nodes. An assessment of these NHFRs is carried out to know how accurate the use of a single type of NHFR is for the lumped nodes. The errors in prediction of the available nodal flows through the available NHFRs and that obtained through the detailed analysis are quantified using chi square. It is observed that actual NHFR is not as smooth as approximated by the different available NHFRs. A nonlinear behaviour between available flows and available heads exists for partial flow conditions and requires proper values of constants to define any NHFR. Furthermore, NHFRs at different lumped nodes in a network are different. This study recommends the consideration of different relationships at different lumped nodes of the network for PDA.

Structural characterisation for the synthesis of large‐scale combined electric–gas networks

AbstractIn this paper, the authors present a methodology for building realistic synthetic test cases to model combined electric and natural gas infrastructure networks. Because these networks are synthetic, that is, fictitious, they are able to be freely shared; hence, they serve to support research and development in the area of coupled‐infrastructure analysis with large‐scale realistic test cases that do not contain non‐public information. We anchor our process for building these synthetic networks in a structural characterisation of actual electric and natural gas networks. Supply and demand nodes are geographically placed, based on a combination of publicly available information from several sources and a clustering‐based method to match the right fraction of each node type, taking into account the intersection points between the electric and gas grids. Then the networks are connected with pipelines and transmission lines using a systematic graph construction method, validated against network properties including degree distribution, clustering, graph diameter, and degree of planarity, along with operational validation to ensure the simulated solution is realistic. The methodology is demonstrated by creating and validating a test case with 6717 electric buses and 2451 gas nodes.

Model for detecting metastatic deposits in lymph nodes of colorectal carcinoma on digital/ non-WSI images

IntroductionColorectal cancer (CRC) constitutes around 10% of global cancer diagnoses and death due to cancer. Treatment involves the surgical resection of the tumor and regional lymph nodes. Assessment of multiple lymph node demands meticulous examination by skilled pathologists, which can be arduous, prompting consideration for an artificial intelligence (AI)-supported workflow due to the growing number of slides to be examined, demanding heightened precision and the global shortage of pathologists.MethodThis was a retrospective cross-sectional study including digital images of glass slides containing sections of positive and negative lymph nodes obtained from radical resection of primary CRC. Lymph nodes from 165 previously diagnosed cases were selected from Agha Khan University Hospital, from Jan 2021 to Jan 2022. The images were prepared at 10X and uploaded into an open source software, Q path and deep learning model Ensemble was applied for the identification of tumor deposits in lymph node.ResultsOut of the 87 positive lymph nodes detected by AI, 73(84%) were true positive and 14(16%) were false positive. The total number of negative lymph nodes detected by AI was 78. Out of these, 69(88.5%) were true negative and 9 (11.5%) were false negative. The sensitivity was 89% and specificity 83.1%. The odds ratio was 40 with a confidence interval of 16.26–98.3. P-value was < 0.05 (< 0.0001).ConclusionThough it was a small study but its results were really appreciating and we encourage more such studies with big sample data in future.

Earthquake preparedness based on reliability and relief of the emergency supply network: Pre-storage and pre-matching

Abstract The efficiency of post-disaster relief operations is impacted by the design of the emergency supply networks in the earthquake preparedness phase. This paper proposes a bi-objective mixed integer programming model to improve the preparedness of the emergency supply network. The maximization of connectivity reliability and relief is taken as the objective function as the design perspectives were inclined towards prioritizing human survival over cost. The number and location of storage points, the preset level and the pre-matching of demand nodes with their supply storage points are determined based on this. Then, applicability is evaluated through numerical examples, and critical parameters are demonstrated by sensitivity analysis. Finally, practicality is evaluated through practical examples. The results demonstrate that the method in this paper designs an emergency supply network with high reliability and relief level. The reliability of the supply network is improved by 42.3% and the rescue level reaches 96.6% compared with the pre-optimization period. Results may provide theoretical support for urban pre-earthquake preparedness and can help traffic planners and local governments formulate better earthquake prevention strategies.

Verifying an improved intermittent water distribution network analysis method based on EPA-SWMM

ABSTRACT Modelling Intermittent Water Supply (IWS) presents challenges, as traditional hydraulic methods based on EPANET are often inadequate due to their inability to simulate the network filling process. While EPA-SWMM (EPA's Storm Water Management Model)-based methods enhance IWS analysis, they remain network-specific and lack universal applicability. This study aims to calibrate and verify an improved EPA-SWMM-based model on a 6 m × 5 m laboratory-scale IWS. Experiments were conducted to capture flow rate data from demand nodes under various conditions. The EPA-SWMM model, based on uncontrolled outlets with flow rate varying by pressure, was calibrated using, an automated procedure that integrated the Genetic Algorithm (GA) into the SWMM-toolkit for optimizing minor loss and pipe roughness coefficients. Comparing model results with experimental data demonstrated the model's capability to simulate the laboratory-scale IWS system behaviour. The model was also applied to a real case study, with results closely aligning with field data, affirming its reliability. The proposed IWS modelling method offers a versatile tool for applications, such as design and scenario analysis for tackling IWS challenges and managing IWS systems. Future research should focus on a large-scale laboratory experiment with pressure and flow sensors, considering air presence in the network to mitigate errors.

Analyzing the consequences of power factor degradation in grid-connected solar photovoltaic systems

This study examines the impact of integrating solar photovoltaic (PV) systems on power factor (PF) within low-voltage radial distribution networks, using empirical data from the Energy Self-Sufficiency for Health Facilities in Ghana (EnerSHelF) project sites in Ghana. The research included simulations focusing on optimal PV integration, with and without PF considerations, and the strategic placement of PV and shunt capacitors (SC). Three scenarios evaluated PV injection at high-load demand nodes, achieving penetration levels of 85.00 percent, 82.88 percent with high voltage drop, and 100.00 percent with high loss nodes. Additionally, three scenarios assessed SC allocation methods: proportional to the node's reactive power demand (Scenario I), even distribution (Scenario II), and proportional to installed PV capacity at PV nodes (Scenario III).The analysis used a twin-objective index (TOI), combining voltage deviations and power factor degradation. Results showed significant PV curtailment was necessary to achieve standard PF. Optimal penetration levels, considering TOI, reduced PV penetration from 85.00 percent to 63.75 percent, 82.88 percent to 57.38 percent, and 100.00 percent to 72.50 percent for high load, high voltage drops, and high loss nodes, respectively. Notably, all scenarios showed a concerning PF of 0.00 at dead-end nodes (P20, P21, P22).Scenario I achieved PF ranges of -0.26 to 1.00 with PV at high load, -0.69 to 1.00 with PV at high voltage drop, and 0.95 to 1.00 with PV at high loss nodes. Scenario II produced similar ranges, -0.48 to 1.00, -1.00 to 0.99, and 0.30 to 0.96, with PV placement at high load, voltage drops, and loss nodes, respectively. Scenario III yielded ranges of -0.19 to 0.97 (high load), -0.23 to 1.00 (high voltage drop), and 0.86 to 0.96 (high losses).The study concluded that the most effective strategy involves installing PVs at high-loss nodes and distributing SCs proportionally to the node's reactive power demand (Scenario I). This approach achieved a more uniform PF pattern throughout the network, highlighting the practical implications of strategic PV placement and targeted reactive power compensation for maintaining a healthy and efficient distribution system with solar PV integration.

Closure to “Analysis of Intermittent Water Distribution Networks Using a Dummy Emitter Device at Each Demand Node”

Closure to “Analysis of Intermittent Water Distribution Networks Using a Dummy Emitter Device at Each Demand Node”

The Cooperative Maximal Covering Location Problem with ordered partial attractions

The Maximal Covering Location Problem (MCLP) is a classical location problem where a company maximizes the demand covered by placing a given number of facilities, and each demand node is covered if the closest facility is within a predetermined radius. In the cooperative version of the problem (CMCLP), it is assumed that the facilities of the decision maker act cooperatively to increase the customers’ attraction towards the company. In this sense, a demand node is covered if the aggregated partial attractions (or partial coverings) of open facilities exceed a threshold. In this work, we generalize the CMCLP introducing an Ordered Median function (OMf), a function that assigns importance weights to the sorted partial attractions of each customer and then aggregates the weighted attractions to provide the total level of attraction. We name this problem the Ordered Cooperative Maximum Covering Location Problem (OCMCLP). The OMf serves as a means to compute the total attraction of each customer to the company as an aggregation of ordered partial attractions and constitutes a unifying framework for CMCLP models.We introduce a multiperiod stochastic non-linear formulation for the CMCLP with an embedded assignment problem characterizing the ordered cooperative covering. For this model, two exact solution approaches are presented: a MILP reformulation with valid inequalities and an effective approach based on Generalized Benders’ cuts. Extensive computational experiments are provided to test our results with randomly generated data and the problem is illustrated with a case study of locating charging stations for electric vehicles in the city of Trois-Rivières, Québec (Canada).

A fairness-based multi-objective distribution and restoration model for enhanced resilience of supply chain transportation networks

Disruptive events play a significant role in the performance of supply chain networks (SCNs) due to their high dependency on transportation networks. Therefore, it is critical to develop coordinated strategies that consider (i) efficient restoration plans for the transportation network (TN) and (ii) distribution plans for downstream SCN, which consider critical aspects such as road congestion, travel time, and required service levels. Thus, we propose a novel multi-objective non-linear mixed-integer programming (NMIP) model to deal with disruptions in transportation networks while maximizing SCN performance. We developed a model for optimal restoration that reduces the effects of disruptions that disrupt the performance of the road TN and SCNs while considering a fair distribution of commodities. The model provides optimal restoration strategies for the TN after unpredicted events, not only based on a cost-effectiveness approach but also on the fair distribution of commodities among demand nodes on a complex network structure with a multi-supplier, multi-demand, and multi-commodity framework. To illustrate the model's capabilities, we study the transportation of commodities in Colombia and how disruptions impact their SCN performance. The results show that a fairness-based distribution helps to obtain satisfaction rates faster when compared to restoration plans based on cost-effectiveness only.

Hydraulic Connectiveness Metric for the Analysis of Criticality in Water Distribution Networks

Capturing the criticality of a water distribution network (WDN) is difficult because of its many constituent factors. In terms of operation, the arrangement of demand nodes and how they connect have a significant influence. This study aims to integrate hydraulic and topologic aspects into a single criticality measure by adapting the structural hole influence matrix concept. This method applies the nodal demand to the corresponding pipes to construct a weighted network. The matrix stores each node’s local and global connection information, and the criticality value is then assigned based on the adjacency information. The criticality value can reveal the locations in terms of nodes or pipes that are vital for maintaining a network’s level of service. By analyzing pipe-failure scenarios, the criticality value can be related to the loss of performance. Assessing the nodal criticality change behavior under an increased stress scenario can help uncover the impacted areas. The metric for district metered area (DMA) creation demonstrates its potential as a weighting to be considered. This unified criticality metric enables the evaluation of nodes and pipes in a WDN, thereby enabling resilient and sustainable development planning.

Marine Mammal Conflict Avoidance Method Design and Spectrum Allocation Strategy

Underwater wireless sensor networks play an important role in underwater communication systems. Communication through collaborative communication is an effective way to solve critical problems in underwater communication systems. Underwater sensors are often deployed in spaces that overlap with those of marine mammals, which can adversely affect them. For this reason, in this paper, a marine mammal conflict avoidance method that can be dynamically adjusted according to the channel idle time duration and sensor node demand is designed, and the derivation of the maximum occupancy time duration is performed. Meanwhile, in addition, combining the potential of reinforcement learning in adaptive management, efficient resource optimization, and solving complex problems, this study also proposes a reinforcement learning-based relay-assisted spectrum switching method (R2S), which aims to achieve a reasonable allocation of spectrum resources in relay collaborative communication systems. The experimental results show that the method proposed in this study can effectively reduce the disturbance to marine mammals while performing well in terms of conflict probability, interruption probability, and quality of service.

Minimizing the expense transmission time from the source node to demand nodes

Minimizing the expense transmission time from the source node to demand nodes