Demand Points Research Articles

A parallel mayfly algorithm for the [formula omitted]-neighbor p-center problem

This study presents a competitive algorithm in which the mayfly optimization algorithm (MA) is combined with a local search procedure to provide high-quality solutions to the α-neighbor p-center problem (α – pCP). The aim of this problem is to locate p facilities on a plane such that the maximum distance between each demand point and its αth closest facility is minimized. Although α – pCP is a discrete optimization problem, it is addressed in this study from the perspective of a continuous optimization problem. The major reason for this choice is that the MA is a very suitable technique for finding the optimum point on a plane. However, it is very difficult to successfully apply this technique to a discrete optimization problem. Hence, additional efforts are made to reduce the computational burden that arises from approaching this from the perspective of a continuous problem. Thus, an effective solution method based on the MA for finding the optimum point on a plane is presented for the discrete α – pCP. The proposed MA method is compared to the best alternatives found in the literature, which are an extremely efficient exact procedure for the continuous variant of the problem, and a GRASP algorithm that includes a tabu search with strategic oscillation (SO) post-processing. A comparative analysis shows that the performance of the proposed algorithm is similar to that of these state-of-the-art methods. A more extensive comparison shows that the two best performing algorithms, the MA and the SO, complement each other. In addition, the proposed method performs very well for cases where the number of facilities are low. If we consider only the cases where p≤ 50, we see that the MA performs best 5 times, 14 times and 14 times for α= 1, 2, 3, respectively, and loses twice, three times, and three times.

Allocation strategy of medical supplies during a public health emergency: a tripartite evolutionary game perspective

Ensuring the rational and orderly circulation of medical supplies during a public health emergency is crucial to quickly containing the further spread of the epidemic and restoring the order of rescue and treatment. However, due to the shortage of medical supplies, there are challenges to rationalizing the allocation of critical medical supplies among multiple parties with conflicting interests. In this paper, a tripartite evolutionary game model is constructed to study the allocation of medical supplies in the rescue environment of public health emergencies under conditions of incomplete information. The game’s players include Government-owned Nonprofit Organizations (GNPOs), hospitals, and the government. By analyzing the equilibrium of the tripartite evolutionary game, this paper makes an in-depth study on the optimal allocation strategy of medical supplies. The findings indicate that: (1) the hospital should reasonably increase its willingness to accept the allocation plan of medical supplies, which can help medical supplies allocate more scientifically. (2) The government should design a reasonable reward and punishment mechanism to ensure the rational and orderly circulation of medical supplies, which can reduce the interference of GNPOs and hospitals in the allocation process of medical supplies. (3) Higher authorities should strengthen the supervision of the government and the accountability for loose supervision. The findings of this research can guide the government in promoting better circulation of medical supplies during public health emergencies by formulating more reasonable allocation schemes of emergency medical supplies, as well as incentives and penalties. At the same time, for GNPOs with limited emergency medical supplies, the equal allocation of emergency supplies is not the optimal solution to improve the efficiency of emergency relief, and it is simpler to achieve the goal of maximizing social benefits by allocating limited emergency resources to the demand points that match the degree of urgency. For example, in Corona Virus Disease 2019, emergency medical supplies should be prioritized for allocation to government-designated fever hospitals that are have a greater need for medical supplies and greater treatment capacity.

Considering the Research on The Distribution Priority of Materials at The Disaster-Stricken Points After the Earthquake

After the earthquake, it is necessary to effectively and quickly carry out the distribution of emergency materials. This paper considers the different disaster situations of different disaster areas, selects the attributes that affect the material demand in the disaster area, and uses fuzzy clustering technology to classify the demand points; then, uses the entropy The right method sorts different classifications to obtain the priority of material distribution at disaster-stricken points; determines the material satisfaction requirements of each demand point in the distribution system, and finally takes the Yushu earthquake in Sichuan as an example to verify.

A Location-allocation Model for Bio-waste Management in the Hospitality Sector

Tourism generates huge amounts of waste. It has been estimated that about half of the waste generated by hotels is food and garden bio-waste. This bio-waste can be used to make compost and pellets. In turn, pellets can be used as an absorbent material in composters and as an energy source. In this paper, we consider the problem of locating composting and pellet-making facilities so that the bio-waste generated by a chain of hotels can be managed at or close to the generation points. The general objective is twofold: i) to avoid waste transportation from generation to treatment points and product transportation from production to demand points, and ii) to implement a circular model in which the hotels themselves become the suppliers of the products they need (compost and pellets) by transforming the bio-waste that they generate. Any bio-waste not processed by the hotels has to be treated at private or state-run plants. A mathematical optimization model is presented to locate the facilities and allocate the waste and products. The application of the proposed location-allocation model is illustrated with an example.

The Improvement of the Honey Badger Algorithm and Its Application in the Location Problem of Logistics Centers

Aiming at the problems of low resource utilization and high distribution cost of urban logistics enterprises, this paper introduces the threshold setting of large parcels, comprehensively considers the processing links of large parcels and standard parcels in loading, unloading, sorting, and other processing links, and constructs a logistics planning model with the type of multi-functional transit center as the variable and the total cost of the logistics system as the goal. Aiming at the shortcomings of the honey badger algorithm, three optimization strategies are used to improve the logistics model, and the effectiveness of the improved algorithm is verified by comparing with the CPLEX operation results. Based on the operation data of SF Jinzhou, this paper obtains the optimization results of large parcel threshold, multi-function transit center location layout, and terminal demand point allocation. From the results, the introduction of the threshold setting for large parcels has played a significant role in the joint optimization of multi-functional center location selection and terminal demand point allocation under multi-parcel distribution and provides theoretical data support for the existing urban logistics location problem.

Achieving Maximum Power Point Tracking with Partial Shading

Abstract: In the past, gasoline consumption has been high and on the rise, resulting in a massive increase in the production and use of renewable energy. The world's interest in greener energy is increasing, and photovoltaic (PV) solar is becoming more common and expanding in value as a choice. PV energy management and harvesting provide many benefits, but there are some drawbacks, for instance, poor energy conversion efficiency brought on by electrical and optical losses. The rest of these issues might be the most difficult to handle because of the chance element connected to its creation and the partial shading phenomena. Therefore, partial shading reduces the impact on the functionality and is a crucial component of the load torque voltage regulation unit. Even though each method has a unique set of potential downsides, different approaches have been proposed and evaluated in the literature up to this point. The suggested MATLAB computation in this paper creates a PV array structure in three distinct settings under varied partial shading situations. Also, it investigates the performance spanning various colouring patterns of numerous power demand point target tracking.

Optimization methods for planning in electric power grids

A rise in the rate of population growth leads to higher demand rates for electric power consumption. Therefore, it is necessary that the electrical energy generation, transmission and distribution system be planned in an optimized way, in order to provide an excellent service to the consumer and reduce costs for the supply of electrical energy distribution and transmission. For this, the location and load balancing of each substation is extremely important in the distribution systems. The problem can be modeled as a combinatorial optimization problem. In this paper, we propose an allocation model using the p-median for this problem. The heuristic methods developed, to solve this problem, were based on algorithms from Teitz and Bart (1968), to find the best location of the substations, and Gillett and Jhonson (1976), which it designates demand points to be connected to each substation. Finally, an exact method using the branch-and-bound algorithm was used to compare the computational results. The tests used several scenarios applied to the electrical energy distribution networks in a metropolitan region. The computational results were promising in terms of quality as well as in computational time. Finally, the validated algorithm were applied in a database for planning the transmission sector.

The impact of dynamic traffic conditions on the sustainability of urban fire service

Frequent fires pose a significant threat to the safety and sustainability of society. The urban fire service is critical for protecting against fire hazards. Traffic congestion increases the response time of fire service seriously, and dynamic traffic conditions can affect the sustainability of urban fire service. In this study, to evaluate the impact of dynamic traffic conditions on fire service comprehensively, we proposed a method for estimating the total effective coverage area of fire service (TECA) based on real-time traffic data extracted from online maps. To validate the proposed method, a fire station (FS-HN) in Hunan Province, China was selected as the study case. In total, 6625 POIs around FS-HN were characterized as the fire service demand points, and 336 estimating scenarios throughout a week were set. By analyzing the extracted 2,226,000 groups of sample data, TECA for FS-HN was estimated as 7.63 km2 with the assumption that the average travel time of fire trucks is 80% of that of ordinary vehicles. Visual results show that traffic congestion and the lack of transportation infrastructure (only one bridge over a river near FS-HN) seriously reduce the service coverage of FS-HN. To improve fire service, it is necessary to improve traffic conditions and build more transportation infrastructures.

Optimization Method of Charging Station Layout Based on Internet of Things Under the Background of Sustainable Development

In the context of sustainable development, the research on the optimization method of charging station layout based on the Internet of things can effectively shorten the distance between the charging demand point and the charging station candidate point. Based on the perception of the charging status of the electric station and the transmission layer of the RFID, the charging system is designed to collect and store the relevant information from the charging system of the electric station in real time according to the charging status of the electric station and the transmission layer of the RFID. Based on the above information, taking the minimum distance from the user to the charging station, the expected waiting time and the construction cost as the objective function, all demand points are allocated to the corresponding charging station, charging can be provided to users only by building a charging station at the candidate point, and users at all demand points can only enjoy charging services at a specific charging station as the constraint. The optimization model of charging station layout is constructed and solved by genetic algorithm to obtain the best charging station layout. The experimental results show that the layout scale of electric vehicle charging stations based on this method has the advantages of global optimization, strongest adaptability and good economic benefits, and the increase in the number of charging stations can effectively improve user satisfaction.

A location-inventory-routing model for distributing emergency supplies

Dynamic last-mile distribution of emergency supplies to affected people in post-disaster situations is a key emergency response task, which however is seldom addressed in the literature. To fill this gap, we present a path-based location-inventory-routing model for developing a dynamic last-mile emergency supply distribution plan, including a local distribution center, points of distribution, and demand points. The model can be applied to evolving post-hurricane situations, where candidate point of distribution sites and demands dynamically change. Both cost and equity are incorporated into the planning goal to ensure that distribution of emergency supplies is cost-efficient and fair. With a case study of the previously flooded Red Hook neighborhood in New York City during Hurricane Sandy, we show that our model outperforms different comparison models significantly. Moreover, we obtain managerial insights regarding location, inventory, and routing flexibility, which could be leveraged for post-hurricane distribution practice.

Comparing Two Strategies for Locating Hydrogen Refueling Stations under High Demand Uncertainty

This research aims to model and compare two strategies for locating new hydrogen refueling stations (HRS) in a context of high uncertainty on H<sub>2</sub> demand and on the spatial distribution of demand points. The first strategy S1 represented by an agent-based model integrating a particle swarm optimization metaheuristic consists of finding the best HRS locations by adapting to the real evolution of the demand. A second strategy S2 consists in solving a classical capacitated <em>p</em>-median problem based on H<sub>2</sub> consumption forecasts over a given deterministic horizon in order to define in advance <em>p</em> optimal future HRS locations. Assuming that the same distributor gradually implements future HRSs in a given area between 2023 and 2030, both models minimize the sum of travel distances between each demand point and its assigned SRH. The results show that during the growth phase of the fuel cell electric vehicle (FCEV) market, with two different compound annual growth rates (medium and strong), the conservative S1 strategy performs better than S2 as these rates increase. However, while S2 remains suboptimal throughout the sales growth period, it becomes more effective once demand stabilizes. Another finding is that different uniform distributions of H<sub>2</sub> demand points in the same space have only a small long-term influence on the performance of these two models. This research advises investors to study the influence of different location strategies and models on the performance of a final HRS network in a given region. Models can be easily configured and adapted to a particular spatial distribution of demand points in a specific environment, more flexible H<sub>2</sub> production capabilities, or different behaviors of FCEV drivers that could be geo-located.

SOLUTION APPROACH TO P-MEDIAN FACILITY LOCATION PROBLEM WITH INTEGRATED PROGRAMMING AND GENETIC ALGORITHM

In the study, the P-median problem that Hakimi brought to science was discussed. With this problem, in short, it is ensured that the locations for the supply of n demand points of p facilities with the lowest cost are determined and the demand points to be served are allocated to these locations. The solution of the p-median problem with the integer programming approach is in the NP (Non-Polynominal) difficult class, and the solution time increases exponentially as the size of the problem increases. For this, the use of heuristic approaches in the P-median problem significantly shortens the solution time of large-sized problems. The data used in the application were taken from the Operation Research-Library site. In practice, the P-median is considered as capacity-constrained and unconstrained. Euclidean distances were calculated in Excel. The solution of mixed integer programming is obtained by using the CPLEX program. In addition to mixed integer programming, genetic algorithm, which is a meta-heuristic method, is applied to the capacity-constrained P-median problem. Different solutions were obtained by changing the population size, maximum number of iterations, crossover and mutation probability values. It has been observed that there is more than one optimum solution in repeated studies. When working with larger data sets, this algorithm will be less likely to find the global optimum. However, when compared to mixed integer programming, the genetic algorithm gave results very close to the optimum result. The use of heuristic algorithms in real-world complex problems will both shorten the solution time considerably compared to the integer programming method and provide results that are very close to the optimum result.

Comparative sustainability assessment of energy storage technologies in Qatar

Energy storage is a supporting technology for the penetration of intermittent renewable energy systems. The State of Qatar is a hub of natural gas production and planning to increase the utilization of its abundant clean solar energy resources. The tendency towards clean energy utilization necessitates the retrofit of energy storage technologies (ESTs) to stabilize the electricity supply sustainably. The key objective of the current paper is to study comparatively the sustainability of ETS pathways after adapting to the conditions of Qatar through an optimization model. Three different scenarios were conducted to focus on the concept of economic feasibility through a cost-effective (CE) scenario, a sustainable (ST) scenario of a minimum of each storage cost, natural resource (water and land) use, and greenhouse gas (GHG) emissions. The third stringent (STR) scenario is set with a constant GHG emissions constraint over different energy storage power. Qatar's daily energy storage demand is set in the range of 250–3000 MWh and could be fully (100 %) covered by the compressed air energy storage (CAES) pathway based on the CE scenario constraints. The ST scenario is satisfied by 79.21 % from flywheel energy storage systems (FESS), 20.75 % from CAES, and 0.04 % from pumped storage hydropower (PSH). The STR scenario constraints are fully met by CAES up to the daily demand point of 2500 MWh, after which PSH is merged by a range of 11.4 %–24.7 % to maintain the total GHG emissions from EST in Qatar constant at 924 t of CO2 equivalent.

Research on Distribution Optimization of Emergency Perishable Materials Considering Periodic Changes in Freshness

The distribution of emergency perishable materials is crucial for rescue operations in disaster-stricken areas. However, the freshness of these materials changes over time, affecting the quantity of materials that can be distributed to demand points at different stages. To address this issue, this paper proposes a novel approach. Firstly, a piecewise function is constructed to describe the impact of freshness on the quality and quantity of emergency perishable materials at different time stages. Secondly, the paper establishes a vehicle distribution optimization model with the goal of maximizing the sum of the freshness of all emergency perishable materials delivered to different disaster-affected locations, taking into account the different minimum freshness constraints for the same kind of materials in different locations. Thirdly, an approximate algorithm is designed to solve the model, with the time complexity and the upper and lower bounds of the approximate ratio analyzed. Finally, an example analysis is conducted to demonstrate the validity of the proposed model and algorithm.

IoT-assisted machine learning model for engineering project warehouse management

The engineering project warehouse can combine various sensor devices and smart devices under the Internet of Things technology to realize the convenient management of the warehouse. The Internet of Things technology combined with the machine learning model can make various industries transform to intelligence. The use of IoT technology and machine learning models for warehouse management can achieve an unattended warehouse effect. The Internet of Things has gradually developed into the intelligent technology that attracts the most attention in various industries with the advancement of electronic components required by various sensors. Combining various sensor devices and smart devices under the Internet of Things technology to achieve convenient management of warehouses, its intelligent management of warehouse security, environment, fire protection, energy consumption, etc. can save a lot of manpower and material resources, and improve the project warehouse, Utilization of supplies. Machine learning technology is a new type of computer self-learning technology produced along with artificial intelligence technology, which can combine the corresponding algorithm and the definition of the developer to realize the learning of the defined transaction. Because the way of this operation is similar to the human learning ability, machine learning Warehouse management for engineering projects will maximize the unattended effect of the warehouse. The research adopts a combination of questionnaire survey and modeling analysis. At first, analyzes the current demand points of engineering project warehouse management in various industries through a questionnaire survey, and designs the Internet of Things auxiliary scheme for engineering project warehouse management according to the demand points. The IoT-assisted machine learning model compares the IoT engineering project warehouse management solution without machine learning model with the IoT engineering project warehouse management solution with machine learning. It is found that the solution with machine learning model can effectively improve the engineering project warehouse management. abilities in all aspects including the effective utilization rate of necessities, manpower saving and safety accident control, which proves that the research has high use value.

A reactive path relinking algorithm for solving the bi-objective p-Median and p-Dispersion problem

This paper deals with an interesting facility location problem known as the bi-objective p-Median and p-Dispersion problem (BpMD problem). The BpMD problem seeks to locate p facilities to service a set of n demand points, and the goal is to minimize the total distance between facilities and demand points and, simultaneously, maximize the minimum distance between all pairs of hosted facilities. The problem is addressed with a novel path relinking approach, called reactive path relinking, which hybridizes two of the most extended path relinking variants: interior path relinking and exterior path relinking. Additionally, the proposal is adapted to a multi-objective perspective for finding a good approximation of the Pareto front. Computational results prove the superiority of the proposed algorithm over the best procedures found in the literature.

Simulation for Appropriate Mean Selection for Can's Approximation Method in Transportation Models

Classical transportation models aim to minimize the total costs of homogeneous goods transport from supply points to demand points, taking into account unit transportation costs. They constitute a special case of network models and employ a technique based on linear programming. Suggested in 2015 and one of the early distribution methods, Tuncay Can’s Approximation Method (TCAM) is based on the geometric averages of unit transportation costs, although it is stated in the theorem that other means than geometric can be used. The aim of this study is to compare the total costs of a transportation model by solving a problem using geometric, arithmetic, square, and harmonic means based on TCAM. The coefficients of the transportation model were obtained randomly by simulation, and the method was repeated on the problem according to the different means and the appropriate means determined.

Optimal UAV Hangar Locations for Emergency Services Considering Restricted Areas

With unmanned aerial vehicle(s) (UAV), swift responses to urgent needs (such as search and rescue missions or medical deliveries) can be realized. Simultaneously, legislators are establishing so-called geographical zones, which restrict UAV operations to mitigate air and ground risks to third parties. These geographical zones serve particular safety interests but they may also hinder the efficient usage of UAVs in time-critical missions with range-limiting battery capacities. In this study, we address a facility location problem for up to two UAV hangars and combine it with a routing problem of a standard UAV mission to consider geographical zones as restricted areas, battery constraints, and the impact of wind to increase the robustness of the solution. To this end, water rescue missions are used exemplary, for which positive and negative location factors for UAV hangars and areas of increased drowning risk as demand points are derived from open-source georeferenced data. Optimum UAV mission trajectories are computed with an A* algorithm, considering five different restriction scenarios. As this pathfinding is very time-consuming, binary occupancy grids and image-processing algorithms accelerate the computation by identifying either entirely inaccessible or restriction-free connections beforehand. For the optimum UAV hangar locations, we maximize accessibility while minimizing the service times to the hotspots, resulting in a decrease from the average service time of 570.4 s for all facility candidates to 351.1 s for one and 287.2 s for two optimum UAV hangar locations.

Optimization Model and Algorithm of Logistics Vehicle Routing Problem under Major Emergency

The novel coronavirus pandemic is a major global public health emergency, and has presented new challenges and requirements for the timely response and operational stability of emergency logistics that were required to address the major public health events outbreak in China. Based on the problems of insufficient timeliness and high total system cost of emergency logistics distribution in major epidemic situations, this paper takes the minimum vehicle distribution travel cost, time cost, early/late punishment cost, and fixed cost of the vehicle as the target, the soft time window for receiving goods at each demand point, the rated load of the vehicle, the volume, maximum travel of the vehicle in a single delivery as constraints, and an emergency logistics vehicle routing problem optimization model for major epidemics was constructed. The convergence speed improvement strategy, particle search improvement strategy, and elite retention improvement strategy were introduced to improve the particle swarm optimization (PSO) algorithm for it to be suitable for solving global optimization problems. The simulation results prove that the improved PSO algorithm required to solve the emergency medical supplies logistics vehicle routing problem for the major emergency can reach optimal results. Compared with the basic PSO algorithm, the total cost was reduced by 20.09%.

Optimal Network of General Hospitals in Slovenia

This article explores how the minimum number of general hospital locations can be determined with optimal population coverage. Due to the increasing financial problems of hospitals and the poor organization of general hospital healthcare, Slovenia is currently working to reform the healthcare system. Defining the optimal network of hospital providers is one of the key elements in reforming the healthcare system. To define the optimal network of general hospitals, the allocation-location model was used, and the maximize attendance model was used as the central method. The chief purpose of maximize attendance model is to optimize the demand attendance with respect to distance and time spent getting to the point of demand. In the analysis of optimal locations and the number of Slovenian general hospitals, we used data on the locations of settlements with their number of inhabitants and data on the Slovenian road network, based on which we defined average travel speeds on the categorized road network. The hypothetical locations of general hospitals and the number of optimally located general hospitals that provide access to the nearest provider were determined in three different time intervals. We found that the same accessibility to hospital services as provided by the existing network of general hospitals can be achieved with only ten optimally located general hospitals within a 30-min time interval. This means that two general hospitals could be rationalized or reorganized, which would bring significant savings in the field of hospital activity, which creates a large loss in the health system in Slovenia.