Method for collaborative optimization of elevator layout scheduling based on slp and simulation

Abstract

The present application relates to the field of building vertical transportation system optimization, in particular to an elevator layout and dispatching co-optimization method based on SLP and simulation, which first acquires elevator operation data, determines the uplink dominant characteristics specific to the hospital through flow pattern analysis, and models passenger arrival pattern using Weibull distribution; then constructs the inter-floor relationship strength matrix using SLP method, and optimizes the floor layout based on the total travel distance minimization objective function; further constructs the discrete event simulation model of the elevator system, designs and evaluates multiple zoning strategies, among which the demand-based cross-zone zoning scheme is the best; finally, the layout and dispatching co-optimization is realized through the SLP-simulation bidirectional data exchange mechanism, which can be implemented without replacing equipment, and 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 is established between the SLP method and the simulation, the SLP provides the floor layout scheme and the traffic matrix to the simulation, and the simulation feeds back the performance evaluation results to the 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, 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, the elevator zoning strategy is optimized first, and then the floor layout is adjusted;

[0061] A visual guidance system and queue management facilities are designed, including dynamic electronic display screens and physical isolation signs;

[0062] A continuous optimization mechanism is established, 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; the SLP method is combined to optimize the floor layout, minimizing the total passenger travel distance; the discrete event simulation is used to evaluate various elevator zoning strategies, and 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, about 38.8% reduction; the maximum waiting time is reduced from 703.03 seconds to 232.19 seconds, about 67.0% reduction. 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, about 27.7% reduction, 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, about 46.6% increase; the average queue length is reduced from 22 people to 16 people, about 27.3% reduction; the maximum queue length is reduced from 45 people to 28 people, about 37.8% reduction. 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: is the floor height (unit: meter, 3.5 meters in this example); is the floor height (unit: meter, 3.5 meters in this example); is the number of floors between and is the time cost of the acceleration phase; is the time cost of the deceleration phase; is the number of floors required for the acceleration phase; is the number of floors required for the deceleration phase; is the speed of the uniform phase (unit: meter / second). According to the measured data, seconds, floors, meter / second.

[0102] This method establishes a target function that minimizes the total moving distance. In this example, the target function takes into account passenger flow, physical distance between floors, and time period weight, and is in the following form:

[0103] ,

[0104] where: is the total moving distance of the kth layout scheme; and are the weights of the morning peak and the peak period between floors (set to 0.7 and 0.3); is the number of passengers from floor i; is the proportion of passengers from floor i to floor j; is the equivalent distance between floor i and j; is the total number of floors; represents the weighted distance sum from the 1st floor (lobby) to each floor; represents the weighted distance sum of all inter-floor movements from the 2nd floor and above. The constant 0.64 is the early morning peak period flow proportion coefficient determined according to historical data, reflecting the characteristics that about 64% of the flow in the early morning peak period is concentrated in the 1st floor going to other floors.

[0105] This example sets medical process constraints and department adjacency requirements as constraint conditions. For example, operating rooms, ICUs, and imaging departments need to maintain convenient contact; outpatient areas 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 is performed by the pairwise exchange method, and the iteration process is as follows:

[0108] 1. Initialization: take the current layout as the initial solution , calculate its target function value ;

[0109] 2. Iterative optimization: for the tth iteration;

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

[0111] b. Check if the constraints C are satisfied;

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

[0113] d. If , accept the new solution; otherwise, keep the original solution

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

[0115] Preferably, to avoid falling into local optimum, a multi-start strategy is adopted, starting optimization from 3 different initial layouts, and selecting the global optimal solution. In this embodiment, the final optimization scheme repositions the high-demand floors (such as floors 9, 16, and 19) to the central area, significantly reducing the total moving distance.

[0116] Based on the optimized floor layout, the 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, Flexsim2022 is selected to build the hospital elevator system model, as shown in Figure 4 . The model includes three types of core objects: the Source object simulates passenger generation, and generates passenger arrivals according to the aforementioned Weibull distribution; the Processor object simulates elevator operation, including acceleration, uniform speed, deceleration, and stopping physical processes; the Queue object simulates the waiting queue.

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

[0118] ,

[0119] wherein: is the expected waiting time of the elevator service floor 's call request; is the expected time for the elevator to move from the current position to the floor ; Additional time for each stop (including deceleration and acceleration) is 5 seconds; Number of expected stops before reaching the target floor for the elevator; Door opening and closing time is 6 seconds; Number of door openings and closings. The system will assign the elevator with the shortest to the request.

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

[0121] Scheme 1: Full floor service (2) combined with odd / even layering (2)

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

[0123] Elevator 3: Service odd floors (7, 9, 11, 13, 15, 17, 19);

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

[0125] Scheme 2: Full floor service (2) combined with low / high zone zoning (2)

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

[0127] Elevator 3: Service low zone (6-12);

[0128] Elevator 4: Service high zone (13-19).

[0129] Scheme 3: Full floor service (1) combined with demand-based cross-zone zoning (3)

[0130] Elevator 1: Service all floors (6-19);

[0131] Elevator 2: Service low zone (6-10);

[0132] Elevator 3: Service mid zone (11-15);

[0133] Elevator 4: Service high zone (16-19).

[0134] Scheme 4: Full floor service (1) combined with uniform zoning (3)

[0135] Elevator 1: Service all floors (6-19);

[0136] Elevator 2: Service low zone (6-9);

[0137] Elevator 3: Service mid zone (10-14);

[0138] Elevator 4: serves high zone (15-19th floor).

[0139] Among them, the demand-based cross-zone partitioning of scheme 3 is one of the innovations of the present application. This scheme partitions the zones by analyzing the floor demand probability density to carry out zone division:

[0140] ,

[0141] Among them: is the demand probability density of the floor ; is the number of passengers going to the floor ; is the total number of passengers going to all floors. In this embodiment, the of the 9th floor, the of the 16th floor, and the of the 19th floor are the key points of the partitioning design.

[0142] Set overlapping service floors at the boundaries of adjacent zones to form flexible service boundaries. For example, the 10th floor can be served by elevator 2 (low zone) and elevator 3 (middle zone), and the 15th floor can be served by elevator 3 (middle zone) and elevator 4 (high zone). The service allocation of the overlapping zone adopts a load balancing strategy:

[0143] ,

[0144] Among them: is the probability of assigning the request of floor to elevator ; and are the current loads (in terms of assigned call requests) of elevator and , respectively; is the overlapping service zone of elevator and . This dynamic allocation mechanism can adjust the service boundary according to the real-time load, improving the system's ability to respond to traffic fluctuations.

[0145] This method evaluates the performance of each partitioning strategy scheme through simulation. The simulation running parameters are set as follows: simulation duration of 2 hours, transient period of 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 effect of the initial 15 minutes, and only the performance data of the steady-state stage 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] where: is the total number of passengers; is the arrival time of passenger ; is the time when passenger enters the elevator; is the time when passenger leaves the elevator.

[0149] 2. Energy consumption indicator, running time proportion (RTP): ; Number of starts and stops (NSS): the total number of times the elevator goes from still to moving and back to still; Total travel distance (TTD): ;

[0150] where: is the running time of the elevator; is the total simulation time; is the travel distance of the th run of the elevator; is the number of runs of the elevator. 3. Comfort indicator, average queue length (AQL): ; Maximum queue length (MQL):

[0151] ; where: is the queue length at time

[0152] ; is the total simulation time. 4. Service capability indicator, total throughput (TTP): the total number of passengers successfully transported; System throughput rate (STR): ;

[0153] where: is the total throughput;

[0154] is the total simulation time (unit: hour). The statistical significance of the performance differences of each scheme was evaluated by one-way analysis of variance (ANOVA) and post-hoc Tukey test (significance level ). The ANOVA test statistic

[0155] is calculated as follows:

[0156] ,​​

[0157] Wherein: is the inter-group mean square deviation; is the intra-group mean square deviation. When the value is greater than the critical value and the p-value is less than 0.05, it is considered that there is a significant difference between the schemes.

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

[0159] Finally, the method realizes the collaborative optimization of elevator layout and dispatching. First, a bidirectional data exchange mechanism between SLP and simulation is established, and SLP provides floor layout scheme and traffic matrix to simulation, and simulation feeds back performance evaluation results to SLP. In this embodiment, standardized data structures in XML format are used to facilitate inter-module communication and ensure data consistency and integrity.

[0160] An iterative optimization control mechanism is designed, including convergence criteria, iteration depth control and multi-start strategy. The convergence criteria are defined as the performance index improvement amplitude being less than a threshold :

[0161] ,

[0162] Wherein: and are the performance index values of the first and the first iterations, respectively; is the improvement threshold, set to 2%. The iteration depth control sets the maximum number of iterations to 10. The multi-start strategy starts optimization from 3 different initial points and selects the global optimal solution.

[0163] A comprehensive evaluation system is constructed, and the weights of each performance index are determined according to the actual needs of the hospital: timeliness , energy consumption , comfort , service capacity . The comprehensive performance score is calculated as follows:

[0164] ,

[0165] wherein: is the comprehensive score of the scheme ; , , and are the average waiting time, total travel distance, average queue length and system throughput rate, respectively; the subscript represents the baseline value; the subscript represents the value of the scheme . The higher the score, the better the performance of the scheme.

[0166] The method adopts a phased implementation strategy, first optimizing the elevator zoning strategy, and then adjusting the floor layout. Preferably, the elevator zoning strategy of scheme 3 is implemented in the first phase, and the elevator control system is updated and managed; in the second phase, the floor layout is adjusted according to the SLP optimization result, as shown in Figure 8 , the high demand floors (9, 16, 19 floors) are repositioned to the central area to further improve system performance. The comprehensive evaluation result shows that the average waiting time after collaborative optimization is reduced to 104.90 seconds, which is 2.4% lower than that of scheme 3 alone (the significance is verified by paired t-test p<0.05).

[0167] To ensure the effective implementation of the scheme, the method designs a visual guidance system and a queue management facility. The visual guidance system includes dynamic electronic display screens that indicate the floor range served by each elevator in real time; the queue management facility includes physical barriers or ground markings to guide passengers to form a correct queue. At the same time, a continuous optimization mechanism is established to evaluate the system performance every quarter, continuously optimize the parameters based on actual operation data, and adjust the optimization strategy in a timely manner according to the changes in hospital functions.

[0168] Preferably, the 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 usually have morning uplink peak and afternoon downlink peak, so the weight distribution and zoning strategy can be adjusted accordingly. In addition, the method can also be combined with advanced elevator dispatching algorithms (such as destination dispatching) to further improve system performance.

[0169] As can be seen from the above examples, the elevator layout and dispatching collaborative optimization method based on SLP and simulation provided by the present application successfully solves the early morning peak elevator congestion problem in high-rise hospitals, and significantly improves the timeliness, energy efficiency and service capacity. The method combines system layout planning with discrete event simulation to achieve collaborative optimization of floor layout and elevator dispatching, providing a new technical path for the optimization of vertical transportation systems in high-rise buildings.

[0170] The above descriptions are only the preferred embodiments of the present application, not intended to limit the protection scope of the present application. Any modification or replacement within the technical range disclosed by the present application should be covered within the protection scope of the present application.

Claims

1. A method for collaborative optimization of elevator layout scheduling based on SLP and simulation, characterized in that, The method comprises the following steps: acquiring hospital elevator system operation data, the operation data comprising passenger flow data, starting floor data, destination floor data and timestamp data; based on the operation data, performing flow pattern analysis to obtain hospital elevator flow characteristics, the flow characteristics comprising uplink flow proportion, inter-floor passenger flow matrix and time distribution characteristics; the flow pattern analysis specifically comprises: performing time segmentation processing on the operation data to obtain flow data of different time periods; performing abnormal value detection and processing on the flow data; performing spatio-temporal feature extraction on the processed flow data to obtain starting-destination matrix and time distribution characteristics between floors; performing statistical distribution fitting on the flow data, and determining the optimal distribution model by comparing the goodness of fitting of gamma distribution, normal distribution, lognormal distribution, exponential distribution and Weibull distribution; verifying the optimal distribution model by using AIC criterion and KS test; based on the flow characteristics, performing floor layout optimization by using system layout planning (SLP) method, the floor layout optimization comprising: constructing an inter-floor relationship strength matrix, the relationship strength matrix being based on the inter-floor passenger flow matrix; the construction of the relationship strength matrix specifically comprises: regarding floors as operation units and passengers as materials, and regarding elevator operation as handling cost; determining the relationship strength between floors based on the inter-floor passenger flow matrix; setting differentiated weights for morning peak and inter-floor peak period, wherein the morning peak weight w1 and the inter-floor peak period weight w2 are determined through expert consultation; defining the equivalent distance between floors according to elevator operation characteristics; establishing a target function of minimizing total moving distance; determining a floor function reorganization scheme by pair-wise exchange method; based on the floor function reorganization scheme, performing elevator zoning strategy optimization by using discrete event simulation, the elevator zoning strategy optimization comprising: constructing a discrete event simulation model of the elevator system; designing multiple elevator zoning strategy schemes; evaluating the performance of each zoning strategy scheme through simulation; selecting the optimal zoning strategy scheme; based on the optimal zoning strategy scheme, realizing collaborative optimization of elevator layout and dispatching.

2. The method of claim 1, wherein, The optimal distribution model is Weibull distribution, and the scale parameter λ and the shape parameter k of the Weibull distribution are determined according to the maximum likelihood estimation method.

3. The method of claim 1, wherein, The target function is a total moving distance minimization function, specifically: calculating the total moving distance from the starting floor to each destination floor, considering passenger flow, inter-floor physical distance and time period weight; setting the requirement of medical process and department adjacency as a constraint condition; iteratively optimizing the floor layout by pair-wise exchange method until the termination condition is met, the termination condition being no improvement for a predetermined number of consecutive iterations.

4. The method of claim 1, wherein, The construction of the discrete event simulation model of the elevator system specifically comprises: building a hospital elevator system model on the Flexsim platform; setting a Source object to simulate passenger generation, a Processor object to simulate elevator operation and a Queue object to simulate waiting queue; setting elevator operation parameters according to measured data, including elevator speed, capacity, acceleration, door opening and closing time and passenger boarding and alighting time; Passenger arrivals are generated according to the optimal distribution model, and destination floors are assigned according to historical data.

5. The method of claim 1, wherein, The design of the multiple elevator zoning strategy scheme specifically includes: Design a scheme combining full-floor service and odd-even layering; Design a scheme combining full-floor service and low / high zone zoning; Design a scheme combining full-floor service and demand-based cross-zone zoning; Design a scheme combining full-floor service and uniform zoning; 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 regions.

6. The method of claim 1, wherein, The performance of each zoning strategy scheme is evaluated by simulation, specifically including: Set simulation running parameters, including simulation duration and transient period; Run each scheme multiple times independently, using the same random seed to ensure comparability; Apply the Redeletion method to remove the influence of the transient period; Construct a multi-dimensional performance evaluation index system, including timeliness indicators, energy consumption indicators, comfort indicators, and service capability indicators; Use single-factor variance analysis and post-hoc Tukey test to evaluate the statistical significance of the performance differences between each scheme.

7. The method of claim 6, wherein, 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; the service capability indicators include total throughput and system throughput rate.

8. The method of claim 1, wherein, The implementation of coordinated optimization of elevator layout and dispatching specifically includes: Establish a two-way data exchange mechanism between the SLP method and simulation, with the SLP providing floor layout schemes and traffic matrices to the simulation, and the simulation feeding back performance evaluation results to the SLP; Design an iterative optimization control mechanism, including convergence criteria, iteration depth control, and multiple starting point strategies; Construct a comprehensive evaluation system, determine the weights of each performance indicator based on hospital demand, and standardized compare different schemes; Develop a phased implementation strategy, first optimize the elevator zoning strategy, then adjust the floor layout; Design a visual guidance system and queue management facilities, including dynamic electronic display screens and physical isolation signs; Establish a continuous optimization mechanism, regularly evaluate system performance, and continuously optimize parameters based on actual operation data.

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