Elevator layout scheduling collaborative optimization method based on SLP and simulation

Abstract

The invention relates to the field of building vertical traffic system optimization, in particular to an elevator layout scheduling collaborative optimization method based on SLP and simulation, and the method comprises the steps: firstly obtaining elevator operation data, determining the unique uplink dominant characteristics of a hospital through flow mode analysis, and modeling a passenger arrival mode through Weibull distribution; then, building an inter-floor relation strength matrix by using an SLP method, and carrying out floor layout optimization based on a total movement distance minimization objective function; an elevator system discrete event simulation model is further constructed, multiple partitioning strategies are designed and evaluated, and the effect of a requirement-based cross region partitioning scheme is optimal; finally, layout and scheduling collaborative optimization is achieved through an SLP-simulation two-way data exchange mechanism, implementation can be achieved without replacing equipment, and the method is suitable for elevator system optimization of various high-rise hospitals and other public buildings.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of building vertical transportation system optimization, and particularly to an elevator layout scheduling co-optimization method based on SLP and simulation, which is particularly suitable for the optimization design and operation management of high-rise hospital elevator systems. BACKGROUND

[0002] With the acceleration of urbanization and the centralization of medical resources, high-rise hospital buildings are increasingly popular. In such buildings, the elevator system as an important vertical transportation tool, its operation efficiency directly affects the overall service quality and patient satisfaction of the hospital. According to relevant research, more than 70% of hospital users rely on elevators for floor movement, which makes the performance of the elevator system a key factor affecting the operation of the hospital.

[0003] Currently, research on elevator system optimization mainly focuses on two directions: on the one hand, optimization of elevator scheduling algorithms, such as collective control, destination dispatching, etc.; on the other hand, evaluation of running strategies based on discrete event simulation. However, the existing research has the following shortcomings:

[0004] 1. Most of the scheduling algorithm researches are aimed at office buildings or residential buildings, and the elevator flow patterns of these buildings are significantly different from hospitals. Hospital elevators usually exhibit obvious early morning peak uplink dominant characteristics and concentrated destination floor distribution, and existing algorithms are difficult to effectively cope with this special pattern.

[0005] 2. Traditional optimization methods often treat elevator scheduling and building layout as independent problems, ignoring the close relationship between space layout and elevator operation efficiency. Although the System Layout Planning (SLP) method has been widely applied to hospital department layout optimization, the integration of its elevator system is still lacking.

[0006] 3. Existing elevator zoning strategies usually adopt simple odd-even zoning or high-low zoning, and these general strategies fail to fully consider the specific traffic characteristics of hospitals, resulting in uneven resource allocation and low service efficiency.

[0007] The above problems are particularly prominent in high-rise hospital environments, especially during the early morning peak period, and traditional methods are difficult to effectively alleviate elevator congestion, resulting in long waiting times for patients and medical staff, affecting the timeliness and satisfaction of medical services. Therefore, there is an urgent need for a new method that can comprehensively consider the specific traffic characteristics of hospitals and co-optimize space layout and elevator scheduling. SUMMARY

[0008] The application aims to provide a method for collaborative optimization of elevator layout and dispatching based on SLP and simulation, which realizes the collaborative optimization of floor function layout and elevator dispatching strategy by fusing system layout planning (SLP) and discrete event simulation technology, and effectively solves the elevator congestion problem during the morning rush hour in high-rise hospitals.

[0009] The application provides a method for collaborative optimization of elevator layout and dispatching based on SLP and simulation, which comprises the following steps:

[0010] Obtain hospital elevator system operation data, wherein the operation data comprises passenger flow data, starting floor data, destination floor data and timestamp data;

[0011] Based on the operation data, perform flow pattern analysis to obtain hospital elevator flow characteristics, wherein the flow characteristics comprise uplink flow proportion, inter-floor passenger flow matrix and time distribution characteristics;

[0012] Based on the flow characteristics, perform floor layout optimization by using a system layout planning (SLP) method, wherein the floor layout optimization comprises the following steps:

[0013] Construct an inter-floor relationship strength matrix based on the inter-floor passenger flow matrix;

[0014] Establish a target function for minimizing total moving distance;

[0015] Determine a floor function reorganization scheme by using a pair-wise exchange method;

[0016] Based on the floor function reorganization scheme, perform elevator zoning strategy optimization by using discrete event simulation, wherein the elevator zoning strategy optimization comprises the following steps:

[0017] Construct a discrete event simulation model of the elevator system;

[0018] Design multiple elevator zoning strategy schemes;

[0019] Evaluate the performance of each zoning strategy scheme by simulation;

[0020] Select an optimal zoning strategy scheme;

[0021] Based on the optimal zoning strategy scheme, realize the collaborative optimization of elevator layout and dispatching.

[0022] Preferably, the flow pattern analysis comprises the following steps:

[0023] Perform time segmentation processing on the operation data to obtain flow data of different time periods;

[0024] Detect and process abnormal values in the flow data;

[0025] Temporal-spatial feature extraction is performed on the processed traffic data to obtain a start-destination matrix and time distribution features between floors;

[0026] Statistical distribution fitting is performed on the traffic data, and the optimal distribution model is determined by comparing the goodness of fitting of gamma distribution, normal distribution, lognormal distribution, exponential distribution and Weibull distribution;

[0027] The optimal distribution model is verified by using AIC criterion and KS test.

[0028] Preferably, the optimal distribution model is Weibull distribution, and the scale parameter λ and shape parameter k of the Weibull distribution are determined according to the maximum likelihood estimation method.

[0029] Preferably, the construction of the relationship strength matrix specifically includes:

[0030] The floors are regarded as operation units, the passengers are regarded as materials, and the elevator operation is regarded as handling cost;

[0031] The relationship strength between floors is determined based on the inter-floor passenger flow matrix;

[0032] Differentiated weights are set for the early morning peak and inter-floor peak period, wherein the early morning peak weight w1 and the inter-floor peak period weight w2 are determined by expert consultation;

[0033] The equivalent distance between floors is defined according to the elevator operation characteristics.

[0034] Preferably, the objective function is a total moving distance minimization function, specifically:

[0035] The total moving distance from the starting floor to each destination floor is calculated, considering passenger flow, inter-floor physical distance and time period weight;

[0036] The medical process and department adjacency requirement is set as a constraint condition;

[0037] The floor layout is iteratively optimized by pair exchange method until the termination condition is met, and the termination condition is no improvement for a predetermined number of consecutive iterations.

[0038] Preferably, the construction of the elevator system discrete event simulation model specifically includes:

[0039] The hospital elevator system model is built on the Flexsim platform;

[0040] The Source object is set to simulate passenger generation, the Processor object is set to simulate elevator operation, and the Queue object is set to simulate waiting queue;

[0041] According to the measured data, elevator operation parameters are set, including elevator speed, capacity, acceleration, door opening and closing time, and passenger boarding and alighting time;

[0042] According to the optimal distribution model, passenger arrivals are generated, and destination floors are assigned according to historical data.

[0043] As a preferred embodiment, the design of multiple elevator zoning strategy schemes specifically includes:

[0044] Design a scheme combining full-floor service with odd-even layering;

[0045] Design a scheme combining full-floor service with low / high zone zoning;

[0046] Design a scheme combining full-floor service with demand-based cross-zone zoning;

[0047] Design a scheme combining full-floor service with uniform zoning;

[0048] Among them, the demand-based cross-zone zoning is divided according to the floor demand probability density, and overlapping service floors are set at the boundaries of adjacent zones.

[0049] As a preferred embodiment, the performance evaluation of each zoning strategy scheme by simulation specifically includes:

[0050] Set simulation operation parameters, including simulation duration and transient period;

[0051] Run each scheme multiple times independently, using the same random seed to ensure comparability;

[0052] Apply the Redeletion method to remove the influence of the transient period;

[0053] Construct a multi-dimensional performance evaluation index system, including timeliness indicators, energy consumption indicators, comfort indicators, and service capacity indicators;

[0054] Use single-factor variance analysis and post-hoc Tukey test to evaluate the statistical significance of the performance differences between each scheme.

[0055] As a preferred embodiment, the timeliness indicators include average waiting time, average ride time, and maximum waiting time; the energy consumption indicators include running time proportion, start-stop times, and total travel distance; the comfort indicators include average queue length and maximum queue length; and the service capacity indicators include total throughput and system throughput rate.

[0056] As a preferred embodiment, the implementation of coordinated optimization of elevator layout and dispatching specifically includes:

[0057] A two-way data exchange mechanism between SLP and simulation is established, SLP provides floor layout plan and traffic matrix to simulation, and simulation feeds back performance evaluation results to SLP;

[0058] An iterative optimization control mechanism is designed, including convergence criteria, iteration depth control and multi-start strategy;

[0059] A comprehensive evaluation system is constructed, and the weights of each performance indicator are determined according to the hospital demand, and the standardized comparison of different schemes is carried out;

[0060] A phased implementation strategy is formulated, first optimizing the elevator zoning strategy, and then adjusting the floor layout;

[0061] A visual guidance system and queue management facility are designed, including dynamic electronic display screen and physical isolation mark;

[0062] A continuous optimization mechanism is established, and the system performance is evaluated regularly, and the parameters are continuously optimized based on the actual operation data.

[0063] The present application adopts data-driven traffic analysis to accurately identify the specific traffic mode of the hospital elevator system; combined with the SLP method for floor layout optimization, the total passenger moving distance is minimized; through discrete event simulation to evaluate various elevator zoning strategies, the optimal scheme is selected; finally, the overall solution of layout and scheduling collaborative optimization is formed.

[0064] The beneficial effects of the present application mainly include:

[0065] 1. Time efficiency is significantly improved: through collaborative optimization, the average elevator waiting time is reduced from 171.36 seconds to 104.90 seconds, reduced by about 38.8%; the maximum waiting time is reduced from 703.03 seconds to 232.19 seconds, reduced by about 67.0%. This greatly improves the efficiency of hospital vertical transportation, improves the patient experience and the work efficiency of medical staff.

[0066] 2. Energy efficiency is significantly improved: the elevator running time ratio is reduced from 48.2% to 36.65%, the total travel distance is reduced from 7850 meters to 5672 meters, reduced by 27.7%, the comprehensive energy saving effect is significant, and it meets the requirements of green hospital construction.

[0067] 3. Service capacity is fully enhanced: the system throughput is increased from 1023.5 person times / hour to 1500 person times / hour, increased by 46.6%; the average queue length is reduced from 22 people to 16 people, reduced by 27.3%; the maximum queue length is reduced from 45 people to 28 people, reduced by 37.8%. This means that the system can better meet the hospital vertical transportation demand during peak period.

[0068] 4. Theory and method innovation: the layout-scheduling collaborative optimization framework is proposed for the first time, which integrates system layout planning (SLP) and elevator dispatching optimization; the applicability of Weibull distribution in describing elevator passenger arrival pattern is verified for the first time in the hospital environment; the demand-based cross-zone partition strategy is developed, which breaks through the limitations of traditional partition methods.

[0069] 5. Wide applicability: the method does not need to replace existing elevator equipment, and can be realized through algorithms and management measures, with the characteristics of low investment and quick effect; at the same time, the method has strong generalizability, which is suitable for various high-rise hospitals, and can be extended to other public building types. BRIEF DESCRIPTION OF DRAWINGS

[0070] In order to more clearly illustrate the technical solutions of the present application, the following will briefly introduce the drawings needed to be used in the embodiments. Obviously, the drawings described in the following only constitute some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained from these drawings without creative labor.

[0071] Figure 1 The overall framework diagram of the elevator layout and scheduling collaborative optimization method based on SLP and simulation of the present application;

[0072] Figure 2 The uplink flow proportion statistical chart of hospital elevators of the present application;

[0073] Figure 3 The fitting comparison chart of elevator passenger arrival interval distribution of the present application;

[0074] Figure 4 The schematic diagram of the elevator discrete event simulation model of the present application;

[0075] Figure 5 The timeliness index comparison chart of each partition strategy scheme of the present application;

[0076] Figure 6 The energy consumption index comparison chart of each partition strategy scheme of the present application;

[0077] Figure 7 The service capacity index comparison chart of each partition strategy scheme of the present application;

[0078] Figure 8 The floor layout comparison chart before and after optimization of the present application. DETAILED DESCRIPTION

[0079] Please refer to Figures 1-8 , the present application will be further described in detail in combination with the drawings.

[0080] Refer to Figure 1The application provides a method for collaborative optimization of SLP and simulated elevator layout scheduling, which mainly includes four core steps of data acquisition, traffic pattern analysis, floor layout optimization and elevator zoning strategy optimization. The method is particularly suitable for optimization of high-rise hospital elevator system and can effectively solve the problem of elevator congestion during the morning rush hour.

[0081] The method first acquires hospital elevator system operation data, including passenger flow data, starting floor data, destination floor data and timestamp data. In this embodiment, a 20-story inpatient building of a certain first-class hospital is selected as the research object, and elevator operation data is collected from 7:00 to 12:00 on weekdays from June to August 2025 through a combination of elevator internal monitoring system and manual counting. Preferably, the data collection points are set at the 1st floor lobby and each floor elevator hall, and a combination of video monitoring and electronic counter is used to record data every 30 seconds to ensure the accuracy and comprehensiveness of the data.

[0082] Based on the acquired operation data, the method performs traffic pattern analysis to obtain the elevator traffic characteristics of the hospital. The traffic pattern analysis specifically includes time segmentation processing, outlier detection and processing, spatiotemporal feature extraction, statistical distribution fitting and model verification.

[0083] In the time segmentation processing step, the embodiment segments the whole day data into 48 time segments according to 30-minute units. The outlier detection uses the quartile method, sets the upper limit as and the lower limit as , wherein: is the first quartile of the data, is the third quartile of the data, is the interquartile range, and the calculation formula is In a high-rise hospital elevator system, outliers usually represent a sharp increase or decrease in passengers in a short period of time, such as irregular events such as the end of surgery or the start of consultation, and these abnormal points need to be excluded to obtain the regular operation mode. Data points outside this range are marked as outliers and processed by the median replacement method.

[0084] In the spatiotemporal feature extraction step, the processed data is analyzed to construct a starting-destination (OD) matrix between floors, wherein the element represents the number of passengers from floor to floor . For example, in the research object hospital, represents the number of passengers from the 1st floor to the 9th floor, which accounts for about 10.12% of the total number of passengers from the 1st floor, and accounts for about 9.44%. Through OD matrix analysis, three key findings are obtained: first, the uplink dominant feature, such as Figure 2As shown in the figure, the proportion of up passengers is as high as 85.7% in the 7:30-9:30 period, which meets the definition of up peak in the elevator traffic system guide standard; secondly, there is no obvious down peak, unlike office buildings, the down passenger flow of the hospital is relatively stable, and does not form a concentrated peak; thirdly, the destination floor is concentrated, mainly concentrated in the middle floors (such as the 9th, 16th and 19th floors).

[0085] The statistical distribution fitting link is crucial. In this embodiment, the DistributionFitter tool of MATLAB2022b is used to compare the fitting of five kinds of distributions including gamma, normal, lognormal, exponential and Weibull, the maximum likelihood estimation method is used to determine the distribution parameters, and the AIC criterion is used for model comparison. The calculation formula of AIC criterion is:

[0086] ,

[0087] Among them: is the log-likelihood function value, is the number of model parameters. The smaller the AIC value, the better the model fitting effect, and the complexity of the model is also considered. As shown in Figure 3 , the Weibull distribution performs best, with an AIC value of 68.558, which is significantly lower than other distributions (gamma 82.701, normal 82.764, lognormal 82.770, exponential 112.264).

[0088] Further verify the applicability of Weibull distribution through KS test (significance level ), and the KS test statistic is calculated as follows:

[0089] ,

[0090] Among them: is the sample empirical distribution function, is the theoretical distribution function, is the maximum difference between the two. In this embodiment, the test statistic is 0.042 (p>0.05), which indicates that this distribution can well describe the hospital elevator passenger arrival interval.

[0091] The final determined Weibull distribution probability density function is:

[0092] ,

[0093] Among them: is the passenger arrival time interval (unit: second), is the scale parameter (value is 2.3), The shape parameter (value 1.8) is set to be 1.8. This distribution can accurately describe the randomness and time-dependent characteristics of passenger arrival in the hospital environment, providing a reliable mathematical foundation for the subsequent simulation model.

[0094] According to the analysis results of the flow characteristics, the method further utilizes the system layout planning (SLP) method to optimize the floor layout. First, a relationship strength matrix between floors is constructed. The core idea of the SLP method is to optimize the spatial layout by analyzing the relationship strength between operation units to minimize the material handling cost. In this application scenario, the floor is taken as the operation unit, the passenger is taken as the material, and the elevator operation is taken as the handling cost.

[0095] Relationship strength matrix Based on the passenger flow matrix between floors, the dimension is , where is the number of floors (in this example , corresponding to floors 6-20). The matrix element represents the passenger flow from floor to floor , and the calculation formula is:

[0096] ,

[0097] Among them: is the passenger flow from floor to floor during the morning peak; is the passenger flow between floors during the peak; and are the weights of the morning peak and the inter-floor peak, respectively.

[0098] Through the structured two-round Delphi expert consultation process, the morning peak weight and the inter-floor peak weight are determined, reflecting the greater impact of morning peak flow on system performance. Preferably, the expert group consists of 5 hospital facility management and elevator system experts, and after the first round of open questionnaire, the second round is based on anonymous feedback to adjust the score, and the average value is finally taken as the weight.

[0099] Inter-floor equivalent distance matrix The elevator operation characteristics are considered, including acceleration, constant speed, and deceleration three stages. For the equivalent distance between floors and , the calculation formula is:

[0100] ,

[0101] Among them: The floor height is shown in meters (3.5 meters in this example). For floors and The difference in the number of floors between them; and These are the time costs for the acceleration and deceleration phases, respectively. and These represent the number of floors required for the acceleration and deceleration phases, respectively. The speed during the constant-speed phase of the elevator (unit: meters per second). Based on measured data... Second, layer, meters per second.

[0102] This method establishes an objective function that minimizes the total travel distance. In this embodiment, the objective function considers passenger flow, physical distance between floors, and time period weights, and takes the following form:

[0103] ,

[0104] in: Let k be the total moving distance for the k-th layout scheme; and The weights for the morning rush hour and the peak hours between floors are set to 0.7 and 0.3, respectively. Let i be the number of passengers departing from floor i. The proportion of passengers from floor i to floor j; This represents the equivalent distance between floors i and j. Total number of floors; This represents the weighted total distance from the 1st floor (lobby) to each floor; This represents the weighted total distance traveled between all floors starting from the 2nd floor and above. The constant 0.64 is a traffic flow percentage coefficient determined based on historical data during the morning peak hours, reflecting the characteristic that approximately 64% of the traffic during the morning peak hours is concentrated on the 1st floor traveling to other floors.

[0105] This embodiment sets medical process constraints and departmental adjacency requirements as constraints. For example, convenient connections should be maintained between the operating room, ICU, and radiology department; outpatient and inpatient areas should be appropriately separated; special departments such as obstetrics and pediatrics should be relatively independent. These constraints can be expressed as:

[0106] ,

[0107] The floor layout optimization using a pairwise swapping method follows an iterative process as follows:

[0108] 1. Initialization: Use the current layout as the initial solution. Calculate its objective function value ;

[0109] 2. Iterative optimization: For the t-th iteration;

[0110] a. Randomly select two floors i and j to generate a new solution. ;

[0111] b. Check if constraint C is satisfied;

[0112] c. If the constraints are satisfied, calculate the new objective function value. ;

[0113] d. If If a new solution is accepted, the original solution is retained.

[0114] 3. Termination condition: The algorithm terminates when there is no improvement after 100 consecutive iterations or when the maximum number of iterations (1000) is reached.

[0115] Preferably, to avoid getting trapped in local optima, a multi-starting-point strategy is adopted, starting optimization from three different initial layouts and selecting the globally optimal solution. In this embodiment, the final optimized solution repositions high-demand floors (such as floors 9, 16, and 19) to the central area, significantly reducing the total movement distance.

[0116] Based on the optimized floor layout, this method further utilizes discrete event simulation to optimize the elevator zoning strategy. First, a discrete event simulation model of the elevator system is constructed. In this embodiment, Flexsim 2022 is used to build the hospital elevator system model, as follows... Figure 4 As shown, the model contains three core objects: the Source object simulates passenger generation, generating passenger arrivals based on the aforementioned Weibull distribution; the Processor object simulates elevator operation, including physical processes such as acceleration, constant speed, deceleration, and stopping; and the Queue object simulates the waiting queue.

[0117] The elevator operating parameters are set based on measured data: elevator speed is 1.5 m / s, rated capacity is 20 people, door opening / closing time is 6 seconds, passenger alighting / asking time is 2 seconds, and acceleration is 2 m / s². Elevator dispatching adopts a collective control principle and a minimum waiting time algorithm. The system calculates the estimated waiting time (EWT) for each call request.

[0118] ,

[0119] in: For elevator Service Floor The estimated waiting time for the elevator call request; For elevator Move from current location to floor The estimated time; This is the additional time for each stop (including deceleration and acceleration), with a value of 5 seconds. This represents the estimated number of stops the elevator will make before reaching the target floor. This represents the door opening and closing time, with a value of 6 seconds. This refers to the number of times the door is opened and closed. The system will have the shortest... The elevator was assigned to the request.

[0120] This method designs multiple elevator zoning strategy schemes:

[0121] Option 1: Combining full-floor service (2 units) with odd-even tiered service (2 units)

[0122] Elevators 1 and 2: serve all floors (floors 6-19);

[0123] Elevator 3: Serves odd-numbered floors (7, 9, 11, 13, 15, 17, 19).

[0124] Elevator 4: Serves even-numbered floors (6, 8, 10, 12, 14, 16, 18).

[0125] Option 2: Combining full-floor service (2 units) with low / high zone zoning (2 units).

[0126] Elevators 1 and 2: serve all floors (floors 6-19);

[0127] Elevator 3: Serves the lower area (floors 6-12);

[0128] Elevator 4: Serves the upper floors (floors 13-19).

[0129] Option 3: Combining full-floor service (1 unit) with demand-based cross-area zoning (3 units)

[0130] Elevator 1: Serves all floors (6-19).

[0131] Elevator 2: Serves the lower area (floors 6-10).

[0132] Elevator 3: Service area (floors 11-15);

[0133] Elevator 4: Serves the upper floors (floors 16-19).

[0134] Option 4: Combining full-floor service (1 unit) with evenly distributed zone service (3 units)

[0135] Elevator 1: Serves all floors (6-19).

[0136] Elevator 2: Serves the lower area (floors 6-9);

[0137] Elevator 3: Service area (floors 10-14);

[0138] Elevator 4: Serves the upper floors (floors 15-19).

[0139] Among them, the demand-based cross-region zoning of Scheme 3 is one of the innovative points of this invention. This scheme analyzes the probability density of floor demand. Divide the area into regions:

[0140] ,

[0141] in: For floors The probability density of demand; To go to the floor The number of passengers; This represents the total number of passengers traveling to all floors. In this embodiment, the 9th floor... , 16th floor , 19th floor These three floors became the key points of the zoning design.

[0142] Overlapping service floors are set at the boundaries of adjacent areas to form flexible service boundaries. For example, the 10th floor can be served by both elevator 2 (low zone) and elevator 3 (middle zone), and the 15th floor can be served by both elevator 3 (middle zone) and elevator 4 (high zone). Service allocation in overlapping areas employs a load balancing strategy.

[0143] ,

[0144] in: To make the floors The request was assigned to the elevator. The probability of; and elevator and The current load (in terms of the number of allocated elevator call requests); For elevator and This dynamic allocation mechanism allows for overlapping service areas. It adjusts service boundaries based on real-time load conditions, improving the system's ability to handle traffic fluctuations.

[0145] This method evaluates the performance of each partitioning strategy scheme through simulation. Simulation parameters are set as follows: simulation duration 2 hours, transient period 15 minutes. Each scheme is run 100 times independently, using the same random seed to ensure comparability. The Redeletion method is used to remove the transient effects of the initial 15 minutes, and only the performance data of the steady-state phase is analyzed.

[0146] This embodiment constructs a multi-dimensional performance evaluation index system, including:

[0147] 1. Timeliness indicator, Average Waiting Time (AWT): Average journey time (AJT): Maximum Waiting Time (MWT): ;

[0148] in: Total number of passengers; For passengers Arrival time; For passengers The time of entering the elevator; For passengers The time of leaving the elevator.

[0149] 2. Energy consumption indicators, operating time percentage (RTP): Start-stop count (NSS): The total number of times the elevator goes from a stationary position to a moving position and then back to a stationary position; Total travel distance (TTD): ;

[0150] in: Elevator running time; Total simulation time; For elevator No. The distance traveled in each run; For elevator The number of times it runs.

[0151] 3. Comfort index, average queue length (AQL): Maximum Queue Length (MQL): ;

[0152] in: For time The queue length at that time; This represents the total simulation time.

[0153] 4. Service capacity metrics: Total Throughput (TTP): Total number of passengers successfully transported; System Throughput (STR): (STR): ;

[0154] in: Total throughput; Total simulation time (in hours).

[0155] One-way ANOVA and post-hoc Tukey test (significance level) were used to analyze the variance. To assess the statistical significance of the performance differences among the various options, use the ANOVA test statistic. The calculation is as follows:

[0156] ,

[0157] in: The mean squared variance between groups; is the within-group mean squared variance. When Value greater than critical value Furthermore, a p-value less than 0.05 indicates a significant difference between the schemes.

[0158] Figures 5-7 The performance of each scheme in terms of timeliness, energy consumption, and service capacity is compared. The results show that Scheme 3 (cross-regional partitioning) performs best: the average waiting time is 107.49 seconds (95% confidence interval: 105.21–109.77 seconds), a reduction of 37.3% compared to the current situation and 40.4% compared to Scheme 1, with statistically significant differences (p<0.001); the elevator running time ratio is 36.65%, a reduction of 11.55 percentage points compared to the current situation; the total travel distance is 5672 meters, a reduction of 27.7% compared to the current situation; the system throughput reaches 1500 passengers per hour, meeting the morning peak demand; the average queue length is 16 people, a reduction of 27.3% compared to the current situation; and the maximum queue length is 28 people, a reduction of 37.8% compared to the current situation.

[0159] Finally, this method achieves collaborative optimization of elevator layout and scheduling. First, a bidirectional data exchange mechanism is established between the SLP method and simulation. SLP provides the simulation with floor layout schemes and flow matrices, while the simulation feeds back performance evaluation results to SLP. In this embodiment, a standardized data structure in XML format is used to facilitate communication between modules and ensure data consistency and integrity.

[0160] The design incorporates an iterative optimization control mechanism, including convergence criteria, iteration depth control, and a multi-starting point strategy. The convergence criterion is defined as the performance improvement being less than a threshold. :

[0161] ,

[0162] in: and The first Second and third Performance metrics for the next iteration; To improve the threshold, it is set to 2%. The iteration depth control is set to a maximum of 10 iterations. The multi-starting-point strategy optimizes from 3 different initial points and selects the globally optimal solution.

[0163] Construct a comprehensive evaluation system and determine the weights of each performance indicator based on the hospital's actual needs: Timeliness Energy consumption Comfort Service capabilities The overall performance score is calculated as follows:

[0164] ,

[0165] in: For the plan The overall score; , , and These are, respectively, average waiting time, total travel distance, average queue length, and system throughput; subscript Indicates the baseline value; subscript Representation scheme The higher the score, the better the performance of the solution.

[0166] This method employs a phased implementation strategy, first optimizing the elevator zoning strategy, and then adjusting the floor layout. Preferably, the first phase implements the elevator zoning strategy of Scheme 3, achieved through elevator control system updates and management measures; the second phase adjusts the floor layout based on the SLP optimization results, such as... Figure 8 As shown, the high-demand floors (floors 9, 16, and 19) were relocated to the central area to further improve system performance. The comprehensive evaluation results show that the average waiting time after collaborative optimization was reduced to 104.90 seconds, a further reduction of 2.4% compared to implementing Scheme 3 alone (significance verified by paired t-test, p < 0.05).

[0167] To ensure the effective implementation of the solution, this approach includes a visual guidance system and queue management facilities. The visual guidance system includes dynamic electronic displays that indicate the floor range served by each elevator in real time; the queue management facilities include physical barriers or ground markings to guide passengers to queue correctly. Simultaneously, a continuous optimization mechanism is established, with system performance evaluations conducted quarterly. Parameters are continuously optimized based on actual operational data, and optimization strategies are adjusted in a timely manner as hospital functions change.

[0168] Preferably, this method can be extended to other types of high-rise buildings, such as hotels, shopping malls, and office buildings, but the model parameters need to be adjusted according to their specific traffic characteristics. For example, office buildings typically experience morning uphill peaks and afternoon downhill peaks, so the weight allocation and zoning strategies can be adjusted accordingly. Furthermore, this method can also be combined with advanced elevator scheduling algorithms (such as destination dispatching) to further improve system performance.

[0169] As can be seen from the above embodiments, the elevator layout and scheduling collaborative optimization method based on SLP and simulation provided by this invention successfully solves the elevator congestion problem during the morning rush hour in high-rise hospitals, achieving significant improvements in timeliness, energy efficiency, and service capacity. This method combines system layout planning with discrete event simulation, realizing the collaborative optimization of floor layout and elevator scheduling, and providing a new technical path for the optimization of vertical transportation systems in high-rise buildings.

[0170] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A collaborative optimization method for elevator layout scheduling based on SLP and simulation, characterized in that, include: Acquire hospital elevator system operation data, including passenger flow data, starting floor data, destination floor data, and timestamp data; Based on the operational data, traffic pattern analysis is performed to obtain the hospital elevator traffic characteristics, which include the proportion of upward traffic, the passenger flow matrix between floors, and the time distribution characteristics. Based on the aforementioned traffic characteristics, the System Layout Planning (SLP) method is used to optimize the floor layout. The floor layout optimization includes: Construct a relationship strength matrix between floors, the relationship strength matrix being based on the passenger flow matrix between floors; Establish an objective function that minimizes the total travel distance; The floor function reorganization plan was determined by a pairwise exchange method; Based on the aforementioned floor function reorganization scheme, elevator zoning strategy optimization is performed using discrete event simulation. The elevator zoning strategy optimization includes: Construct a discrete event simulation model for an elevator system; Design multiple elevator zoning strategy schemes; The performance of each partitioning strategy scheme was evaluated through simulation. Choose the optimal partitioning strategy; Based on the aforementioned optimal partitioning strategy, the coordinated optimization of elevator layout and scheduling is achieved.

2. The method according to claim 1, characterized in that, The traffic pattern analysis specifically includes: The operational data is segmented by time to obtain traffic data for different time periods; The traffic data is subjected to outlier detection and processing. Spatiotemporal features were extracted from the processed traffic data to obtain the origin-destination matrix and time distribution features between floors; The traffic data is subjected to statistical distribution fitting. The optimal distribution model is determined by comparing the goodness of fit of gamma distribution, normal distribution, log-normal distribution, exponential distribution and Weibull distribution. The optimal distribution model was validated using the AIC criterion and the KS test.

3. The method according to claim 2, characterized in that, The optimal distribution model is the Weibull distribution, and the scale parameter λ and shape parameter k of the Weibull distribution are determined by the maximum likelihood estimation method.

4. The method according to claim 1, characterized in that, The construction of the relationship strength matrix specifically includes: Treat the floors as operating units, passengers as materials, and elevator operation as handling costs; The strength of the relationship between floors is determined based on the inter-floor passenger flow matrix. Differentiated weights were set for the morning peak and the inter-floor peak, with the morning peak weight w1 and the inter-floor peak weight w2 determined through expert consultation. The equivalent distance between floors is defined based on the elevator's operating characteristics.

5. The method according to claim 1, characterized in that, The objective function is the function that minimizes the total travel distance, specifically: Calculate the total travel distance from the starting floor to each destination floor, taking into account passenger flow, physical distance between floors, and time period weighting; Set medical process and departmental adjacency requirements as constraints; The floor layout is iteratively optimized using a pairwise swapping method until a termination condition is met, which is that there is no improvement after a predetermined number of consecutive iterations.

6. The method according to claim 1, characterized in that, The construction of the discrete event simulation model of the elevator system specifically includes: Build a hospital elevator system model on the Flexsim platform; Set up a Source object to simulate passenger generation, a Processor object to simulate elevator operation, and a Queue object to simulate a waiting queue; Elevator operating parameters are set based on actual measurement data, including elevator speed, capacity, acceleration, door opening and closing time, and passenger boarding and alighting time. Passenger arrivals are generated based on the optimal distribution model, and destination floors are assigned based on historical data.

7. The method according to claim 1, characterized in that, The specific design of multiple elevator zone strategies includes: Design a solution that combines full-floor service with odd / even zoning; Design a solution that combines full-floor service with low / high zone zoning; Design a solution that combines full-floor service with demand-based cross-area zoning; Design a solution that combines full-floor service with even zoning; The demand-based cross-region partitioning is based on the probability density of floor demand, and overlapping service floors are set at the boundaries of adjacent regions.

8. The method according to claim 1, characterized in that, The specific steps of evaluating the performance of each partitioning strategy scheme through simulation include: Set simulation run parameters, including simulation duration and transient period; Each scheme was run independently multiple times, using the same random seed to ensure comparability; The Redeletion method is applied to remove the transient effects; Construct a multi-dimensional performance evaluation index system, including timeliness index, energy consumption index, comfort index, and service capability index; The statistical significance of the performance differences among the various schemes was assessed using one-way ANOVA and post-hoc Tukey tests.

9. The method according to claim 8, characterized in that, The timeliness indicators include average waiting time, average travel time, and maximum waiting time; the energy consumption indicators include the proportion of running time, number of start-stop cycles, and total travel distance; the comfort indicators include average queue length and maximum queue length; and the service capacity indicators include total throughput and system throughput rate.

10. The method according to claim 1, characterized in that, The specific aspects of achieving coordinated optimization of elevator layout and scheduling include: Establish a two-way data exchange mechanism between the SLP method and simulation. SLP provides the simulation with floor layout schemes and flow matrices, and the simulation feeds back the performance evaluation results to SLP. Design an iterative optimization control mechanism, including convergence criteria, iteration depth control, and multi-start point strategy; Construct a comprehensive evaluation system, determine the weight of each performance indicator based on the hospital's needs, and conduct standardized comparisons of different solutions; Develop a phased implementation strategy, first optimizing the elevator zoning strategy, and then adjusting the floor layout; Design visual guidance systems and queue management facilities, including dynamic electronic displays and physical isolation signage; Establish a continuous optimization mechanism, conduct regular system performance evaluations, and continuously optimize parameters based on actual operating data.

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