{"id":961,"date":"2020-11-17T11:15:20","date_gmt":"2020-11-17T11:15:20","guid":{"rendered":"https:\/\/wordpress.peters-research.com\/?page_id=961"},"modified":"2020-11-17T11:21:38","modified_gmt":"2020-11-17T11:21:38","slug":"enhancements-to-the-etd-dispatcher-algorithm","status":"publish","type":"page","link":"https:\/\/wordpress.peters-research.com\/index.php\/papers\/enhancements-to-the-etd-dispatcher-algorithm\/","title":{"rendered":"Enhancements to the ETD Dispatcher Algorithm"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-page\" data-elementor-id=\"961\" class=\"elementor elementor-961\">\n\t\t\t\t\t\t<section class=\"has_eae_slider elementor-section elementor-top-section elementor-element elementor-element-2bd44fca elementor-section-full_width elementor-section-height-default elementor-section-height-default\" data-eae-slider=\"52049\" data-id=\"2bd44fca\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container 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class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-624c56e elementor-widget elementor-widget-heading\" data-id=\"624c56e\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Enhancements to the ETD Dispatcher Algorithm\n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-550f1783 elementor-widget elementor-widget-text-editor\" data-id=\"550f1783\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"art-layout-wrapper\"><div class=\"art-content-layout\"><div class=\"art-content-layout-row\"><div class=\"art-layout-cell art-content\"><div class=\"item-page\"><article class=\"art-post\"><div class=\"art-postcontent clearfix\"><div class=\"art-article\"><p>Rory Smith, ThyssenKrupp Elevator Inc., USA<br \/>Richard Peters, Peters Research Ltd., UK<\/p><p><strong>Key Words:<\/strong>\u00a0Simulation, dispatching, destination dispatch, boosters, energy, TWIN, Vmax<br \/><br \/><em>This paper was presented at ELEVCON\u00a0ISTANBUL 2004, The International Congress on Vertical Transportation Technologies\u00a0and first published in the IAEE book &#8220;Elevator Technology 14&#8221;, edited by A. Lustig.\u00a0 It is reproduced with permission from The International Assocication of Elevator Engineers.\u00a0\u00a0This web version \u00a9 Peters Research Ltd 2009.<\/em><\/p><h3><br \/>Abstract<\/h3><p>ETD, Estimated Time to Destination, is a patented ThyssenKrupp Elevator algorithm which can be used in three modes: (i) destination dispatch, (ii) conventional dispatching with up\/down hall call buttons, (iii) a combination of destination input on busy floors, with conventional hall call buttons on other floors.\u00a0 Improvements have been made to the ETD optimization function so that it can adjust the relative importance of waiting and transit times.\u00a0 Other optimization parameters can be introduced, such as a function to minimize the energy consumption arising from alternative dispatching decisions.\u00a0 ETD is also being adapted to the Vmax controlled over-speed technology, and to TWIN, which utilizes two cars in one shaft.Current methods for calculating lift energy consumption rely on rules of thumb and generalisation.\u00a0 The accuracy of these models is very limited.\u00a0 In this paper a new general energy modeling approach is proposed. This approach has been applied to design a new lift energy model, which can be used to calculate the energy consumption of any individual lift trip.\u00a0 The energy model is linked to a lift traffic simulation program, which enables the energy consumption of a lift installation to be calculated in any building, and for any passenger traffic scenario.<\/p><h3><br \/>1.\u00a0\u00a0\u00a0\u00a0\u00a0Introduction<\/h3><p>The patented ThyssenKrupp ETD (Estimated Time to Destination) dispatching concept was presented at Elevcon 2002 [1], together with simulation demonstrations using the non-proprietary elevator simulation software, Elevate[TM].\u00a0 ETD as described at Elevcon 2002 is currently available with the ThyssenKrupp TAC50 control system.\u00a0 This paper describes some additional enhancements to ETD, again supported by Elevate simulation results.<\/p><p>ETD can operate as a full destination control system, where passengers register their destination floors at landings.\u00a0 ETD can also operate with conventional up\/down hall call buttons, or with a combination of up\/down hall call buttons and &#8220;booster&#8221; destination call stations on peak floors.\u00a0 For systems without destination input, ETD infers the number of people behind each hall call, and probable future car calls based on traffic learning algorithms.<\/p><p>ETD aims to minimize total passenger time to destination, which is the time passengers are waiting for and traveling in the elevators.\u00a0 It takes account of the time it will take for each elevator to serve the new call.\u00a0 It also takes full account of the impact of the new allocation on all other passengers in the system.<\/p><p>To understand how the ETD algorithm differs from other algorithms, consider the following scenario: there are three elevators, and a number of calls on the system.\u00a0 A new down hall call is registered at level 7.\u00a0 Which elevator should serve the call?<\/p><p>A conventional ETA (Estimated Time of Arrival) dispatching decision is presented in Figure 1.<\/p><p>\u00a0<\/p><p><img decoding=\"async\" src=\"https:\/\/wordpress.peters-research.com\/images\/stories\/papers\/EnhancementstotheETDdispatcheralgorithm\/lift.3.gif\" alt=\"\" border=\"0\" \/><br \/>Car 1 is 15 s from the call.\u00a0 It has to stop at level 8, which will delay it 10 s on its journey to level 7.\u00a0 So the ETA of Car 1 is 15 s plus 10 s, which is 25 s.<\/p><p>Car 2 is 10 s from the call.<\/p><p>Car 3 is 5 s from the call.<\/p><p>Based on the above analysis, the ETA algorithm allocates Car 3.<\/p><p><strong><em>Figure 1\u00a0 Example allocation using an ETA dispatcher<\/em><\/strong><\/p><p><br \/>A Fuzzy Logic or other intelligent controller may make a more sophisticated analysis, as suggested in Figure 2.<\/p><p><img decoding=\"async\" src=\"https:\/\/wordpress.peters-research.com\/images\/stories\/papers\/EnhancementstotheETDdispatcheralgorithm\/lift.1.gif\" alt=\"\" border=\"0\" \/><\/p><p>There are many variations in implementation, but the deciding logic may be as follows:<\/p><p>Car 1 is far and almost empty<\/p><p>Car 2 is close and almost empty<\/p><p>Car 3 is very close and almost full.<\/p><p>Car 2 is allocated in preference to Car 3 as the more intelligent controller realizes that minimizing ETA is not always the best strategy.\u00a0 It is worse to delay the almost full car to pick up the new passenger, even though it is the closest.<\/p><p><strong><em>Figure 2\u00a0 Example allocation using a Fuzzy Logic or other intelligent dispatcher<\/em><\/strong><\/p><p><br \/>Using ThyssenKrupp ETD the analysis is as follows in Figure 3.<\/p><p><img decoding=\"async\" src=\"https:\/\/wordpress.peters-research.com\/images\/stories\/papers\/EnhancementstotheETDdispatcheralgorithm\/lift.2.gif\" alt=\"\" border=\"0\" \/><br \/><br \/>If Fred is allocated to Car 1, then<\/p><ul><li>The delay to Anna is 0 s.<\/li><li>Fred waits 15 s for the elevator to travel to him, plus 10 s to drop off Anna.<\/li><li>Once picked up, Fred then has to complete his journey, which will take 25 s.<\/li><li>The Total Cost is 50 s.<\/li><\/ul><p>If Fred is allocated to Car 2, then<\/p><ul><li>Simon\u2019s trip is delayed by 10 s while Fred is picked up.<\/li><li>Fred has to wait 10 s to be picked up.<\/li><li>Fred then takes 25 s to complete his journey, plus 10 s to drop off Simon.<\/li><li>The Total Cost is 55 s.<\/li><\/ul><p><br \/>If Fred is allocated to Car 3, then<\/p><ul><li>A group of 8 people are each delayed 10 s to pick up Fred and 10 s to drop off Fred.<\/li><li>Fred has to wait 5 s to be picked up.<\/li><li>Fred takes 25 s to reach his destination once he has been picked up.<\/li><li>The Total Cost is 190 s.<\/li><\/ul><p><br \/>Fred is allocated to Car 1, as this allocation is the best overall solution.<br \/><br \/><strong><em>Figure 3\u00a0 Example allocation using an ETD dispatcher<\/em><\/strong><\/p><p><br \/>Figures 1 to 3 are only indicative of how the different systems may evaluate the same scenario and make a different decision.\u00a0 For other scenarios, the different systems may or may not make the same allocation.<\/p><h3><br \/>2.\u00a0\u00a0\u00a0\u00a0 ETD optimization function<\/h3><p>The goal of the ETD optimization algorithm is to minimize the total time to destination.\u00a0 Consider the scenario where the dispatcher is choosing between the allocation options which will give rise to the waiting and transit times given in Table 1 and Table 2.<br \/><br \/><br \/><\/p><p><img decoding=\"async\" src=\"https:\/\/wordpress.peters-research.com\/images\/stories\/papers\/EnhancementstotheETDdispatcheralgorithm\/table%205.gif\" alt=\"\" border=\"0\" \/><br \/><strong><em>Table 1\u00a0\u00a0 Predicted waiting, transit and time to Destination for allocation option A<\/em><\/strong><br \/><br \/><br \/><img decoding=\"async\" src=\"https:\/\/wordpress.peters-research.com\/images\/stories\/papers\/EnhancementstotheETDdispatcheralgorithm\/table%206.gif\" alt=\"\" border=\"0\" \/><br \/><strong><em>Table 2\u00a0\u00a0 Predicted waiting, transit and time to destination for allocation option B<\/em><\/strong><\/p><p>Note that allocation option A gives a total time to destination less than allocation option B.\u00a0 Thus option A would be selected by standard ETD.\u00a0 However, Anna is being penalized by giving Fred excellent service.\u00a0 The overall perceived level of service may be better if we selected allocation option B.\u00a0 This raises a number of questions:\u00a0 (i) What is more \u201cpainful\u201d for passengers, waiting time or transit time.\u00a0 (ii) Is the 1st second of waiting time less painful than the 15th or the 155th second?<\/p><p>To optimize the perceived level of service, we need a way of defining how undesirable each additional second of waiting or transit time is.\u00a0 One way to achieve this is to use a \u201cpain index\u201d to define the relative importance of waiting and transit times.\u00a0 And then select allocations to minimize the \u201cpain\u201d.\u00a0 The technique, which is akin to Fuzzy Logic, is very flexible.\u00a0 For example, conventional ETD, minimizing strictly on \u201ctime to destination\u201d could be represented by the graphs in Figure 4.\u00a0 Applying these graphs, a passenger waiting 10 seconds and traveling for 50 seconds experiences 60 units of pain.<\/p><p><img decoding=\"async\" src=\"https:\/\/wordpress.peters-research.com\/images\/stories\/papers\/EnhancementstotheETDdispatcheralgorithm\/image036.gif\" alt=\"\" border=\"0\" \/><\/p><p><img decoding=\"async\" src=\"https:\/\/wordpress.peters-research.com\/images\/stories\/papers\/EnhancementstotheETDdispatcheralgorithm\/image038.gif\" alt=\"\" border=\"0\" \/><br \/><strong><em>Figure 4\u00a0\u00a0 Optimization functions based on original ETD<\/em><\/strong><\/p><p>If we considered waiting time to be three times as painful as transit time, then the optimization functions could be represented as shown in Figure 5.\u00a0 In this instance a passenger waiting 10 seconds and traveling for 90 seconds would experience 40 units of pain.<\/p><p>Figure 6 shows an optimization function taking into account the hypothesis that the initial 10 seconds of waiting and 30 seconds of transit time are of minimal concern.\u00a0 After this, each additional second of waiting is more painful than the last.\u00a0 The curve is not exponential.\u00a0 This avoids an overloaded system degrading to the point where it is always chasing the oldest call; which then creates even more old calls.<\/p><p><img decoding=\"async\" src=\"https:\/\/wordpress.peters-research.com\/images\/stories\/papers\/EnhancementstotheETDdispatcheralgorithm\/image040.gif\" alt=\"\" border=\"0\" \/><br \/><img decoding=\"async\" src=\"https:\/\/wordpress.peters-research.com\/images\/stories\/papers\/EnhancementstotheETDdispatcheralgorithm\/image042.gif\" alt=\"\" border=\"0\" \/><br \/><strong><em>Figure 5\u00a0\u00a0 Optimization functions where waiting time is three times as painful as transit time<\/em><\/strong><\/p><p><img decoding=\"async\" src=\"https:\/\/wordpress.peters-research.com\/images\/stories\/papers\/EnhancementstotheETDdispatcheralgorithm\/image044.gif\" alt=\"\" border=\"0\" \/><\/p><p><img decoding=\"async\" src=\"https:\/\/wordpress.peters-research.com\/images\/stories\/papers\/EnhancementstotheETDdispatcheralgorithm\/image052.gif\" alt=\"\" border=\"0\" \/><br \/><strong><em>Figure 6\u00a0\u00a0 Optimization functions based on subjective opinions about the importance of\u00a0 waiting and transit times<\/em><\/strong><\/p><p><br \/>The choice of optimization function is a subjective decision.\u00a0 The selection may be different according to building type and culture.\u00a0 To allow for this, the commercially available ETD algorithm will allow the customer to choose either a preset optimization function, or to define the function precisely.<\/p><p>Figure 7 shows average waiting and transit time results for a benchmark lunchtime simulation [1] where a heavy 15% of the traffic is being transported per 5 minutes.\u00a0 The original ETD Full Destination is optimizing on time to destination, and achieves this goal.\u00a0 The Alternative ETD Function is based on ETD Full Destination, but using the optimization function described in Figure 5, where waiting time is more important than transit time.\u00a0 As a result we are achieving significantly better waiting times.\u00a0 However, the price is a slightly longer total time to destination.<\/p><p><img decoding=\"async\" src=\"https:\/\/wordpress.peters-research.com\/images\/stories\/papers\/EnhancementstotheETDdispatcheralgorithm\/image054.gif\" alt=\"\" border=\"0\" \/><br \/><br \/><img decoding=\"async\" src=\"https:\/\/wordpress.peters-research.com\/images\/stories\/papers\/EnhancementstotheETDdispatcheralgorithm\/image056.jpg\" alt=\"\" border=\"0\" \/>\u00a0Average Waiting Time (s)<br \/><img decoding=\"async\" src=\"https:\/\/wordpress.peters-research.com\/images\/stories\/papers\/EnhancementstotheETDdispatcheralgorithm\/image058.jpg\" alt=\"\" border=\"0\" \/>\u00a0Average Transit Time (s)<br \/><br \/><strong><em>Figure 7\u00a0\u00a0 Simulation results for benchmark lunchtime simulation<\/em><\/strong><\/p><h3><br \/>3.\u00a0\u00a0\u00a0\u00a0 Energy optimization<\/h3><p>The ThyssenKrupp energy model developed by Al Sharif et al [2] has been integrated into the ETD dispatcher.\u00a0 Applying the energy model we can predict, in advance of making the allocation of a call to an elevator, the energy consumption arising from each alternative dispatching option.<\/p><p>Using the new optimization strategy available for ETD, we can control how important energy consumption is relative to the waiting and transit times of passengers.\u00a0 The pain function will always be a straight line as each additional joule of energy costs the same; there is not an issue with perception as there is with waiting and transit times.\u00a0 The optimization function is then represented by three graphs.\u00a0 The first two may be as given in Figures 4 to 6.\u00a0 The third introduces the energy cost, as per the example in Figure 8.<br \/><br \/><img decoding=\"async\" src=\"https:\/\/wordpress.peters-research.com\/images\/stories\/papers\/EnhancementstotheETDdispatcheralgorithm\/image060.gif\" alt=\"\" border=\"0\" \/><br \/><strong><em>Figure 8\u00a0\u00a0 Example optimization function for energy cost<\/em><\/strong><\/p><p>The energy pain function could be varied dynamically during the day to raise performance in peak times, and focus more on reducing energy consumption at other times of the day.\u00a0 Figure 9 shows average waiting and transit time results for a benchmark lunchtime simulation [1].\u00a0 The simulations have been repeated with the energy pain function set at different gradients.\u00a0\u00a0 As would be expected, there is a tradeoff between energy savings and performance.<\/p><p><img decoding=\"async\" src=\"https:\/\/wordpress.peters-research.com\/images\/stories\/papers\/EnhancementstotheETDdispatcheralgorithm\/image062.gif\" alt=\"\" border=\"0\" \/><br \/><strong><em>Figure 9\u00a0\u00a0 Graph to demonstrate trade off between energy saving and traffic performance<\/em><\/strong><\/p><h3><br \/>4.\u00a0\u00a0\u00a0\u00a0 VMAX<\/h3><p><br \/><br \/><img decoding=\"async\" src=\"https:\/\/wordpress.peters-research.com\/images\/stories\/papers\/EnhancementstotheETDdispatcheralgorithm\/vmax.gif\" alt=\"\" border=\"0\" \/><\/p><p><em><strong>Figure 10\u00a0\u00a0 Elevator drives operating in 4 quadrants<\/strong><\/em><br \/><br \/>When an elevator leaves the ground floor full of passengers, it is motoring, requiring predominantly positive torque in a positive direction.\u00a0 As passengers are dropped off up the building, the counterweight becomes heavier than the elevator, so the motor is providing predominantly negative torque in a positive direction.\u00a0 Similarly for a journey down the building, a negative direction, the motor can be required to deliver both positive and negative torque.\u00a0<br \/><br \/>Thus the elevator is said to operate in \u201cfour quadrants\u201d, as represented graphically in Figure 10.<\/p><p>Assuming a conventional counterbalancing ratio is less than 50%, an elevator works hardest when it is travelling up and is fully loaded.\u00a0 At other times, the system is required to motor or to generate with less torque.\u00a0 It should be noted that an elevator lift\u00a0 car fully loaded with passengers is only carrying about 65% of its rated capacity.\u00a0 Vmax is the name given to the concept of over-speeding the car when we have \u201cspare\u201d torque available.\u00a0<br \/><br \/>Equation 1 is conventionally applied to calculate the design power of a drive motor in an elevator.<\/p><p>\u00a0<\/p><p><img decoding=\"async\" src=\"https:\/\/wordpress.peters-research.com\/images\/stories\/papers\/EnhancementstotheETDdispatcheralgorithm\/image069.gif\" alt=\"\" border=\"0\" \/>\u00a0(1)<\/p><p>where<\/p><ul><li><strong>HP<\/strong>\u00a0Power (in horsepower)<\/li><li><strong>cw<\/strong>\u00a0Counterweight (as a % of the maximum car capacity<\/li><li><strong>CAPA<\/strong>\u00a0Maximum car capacity (lbs.)<\/li><li><strong>VEL<sub><em>design<\/em><\/sub><\/strong>\u00a0Pre-set design velocity of the elevator (fpm)<\/li><li><strong>EFF<\/strong>\u00a0Efficiency of the elevator (%)<\/li><\/ul><p><br \/>The maximum theoretical velocity for an individual is determined by applying Equation 1 in reverse.\u00a0 Before the trip commences, the actual passenger load in the car Lactual is measured.\u00a0 Velopt, the optimized velocity is then determined using equation 2.<\/p><p><img decoding=\"async\" src=\"https:\/\/wordpress.peters-research.com\/images\/stories\/papers\/EnhancementstotheETDdispatcheralgorithm\/image071.gif\" alt=\"\" border=\"0\" \/>\u00a0(2)<\/p><p>The maximum velocity for any journey between any two predefined floors is then selected from the lowest of three velocities as follows:<\/p><ul><li>The maximum velocity attainable according to Equation No. 2<\/li><li>The maximum velocity attainable for the distance between the two floors.\u00a0 This distance is defined by the\u00a0 acceleration rate and jerk rates, motor and drive capabilities, and by human comfort factors<\/li><li>The maximum velocity attainable with the mechanical equipment selected for the elevator.<\/li><\/ul><p>Figure 11 shows the down peak performance for a benchmark building [1]\u00a0 with and without Vmax.\u00a0 A counterweight ratio of 50% and a maximum over speed of 30% has been assumed.<br \/><img decoding=\"async\" src=\"https:\/\/wordpress.peters-research.com\/images\/stories\/papers\/EnhancementstotheETDdispatcheralgorithm\/image073.gif\" alt=\"\" border=\"0\" \/><br \/><img decoding=\"async\" src=\"https:\/\/wordpress.peters-research.com\/images\/stories\/papers\/EnhancementstotheETDdispatcheralgorithm\/image074.jpg\" alt=\"\" border=\"0\" \/>\u00a0Average Waiting Time (s)<br \/><img decoding=\"async\" src=\"https:\/\/wordpress.peters-research.com\/images\/stories\/papers\/EnhancementstotheETDdispatcheralgorithm\/image058.jpg\" alt=\"\" border=\"0\" \/>\u00a0Average Transit Time (s)<br \/><br \/><em><strong>Figure 11\u00a0 Example down peak performance with and without Vmax<\/strong><\/em><\/p><h3><br \/>5.\u00a0\u00a0\u00a0\u00a0 ETD Applied to twin<\/h3><p>The ThyssenKrupp TWIN system presented by Thumm [3] allows two independent elevators to run in a single shaft.\u00a0 In appropriate applications, this has the potential for dramatic savings in core space.\u00a0 The ETD algorithm has been extended to control these cars efficiently.\u00a0 TWIN ETD currently assumes destination input on every floor.<\/p><p>Running two cars in a single shaft presents some unique challenges in dispatching technology.\u00a0 The ETD strategy of plotting the path of every passenger and elevator forward in time before making an allocation lends itself well to a system which has to avoid collisions, and is making decisions about how stopping the car for a new call will impact on other passengers.\u00a0 The TWIN ETD dispatching strategy adds an additional rule set to the basic ETD concept.\u00a0 The rule sets performs two main functions: (i) it holds the back the lower car when the upper car is or will be in the way, and vice versa;\u00a0 (ii) it allows for the lower car to insert calls to move the upper car out of its way, and vice versa.\u00a0 A simplified diagram of the TWIN ETD rule set for the lower car is given in Figure 12.<\/p><p><img decoding=\"async\" src=\"https:\/\/wordpress.peters-research.com\/images\/stories\/papers\/EnhancementstotheETDdispatcheralgorithm\/image077.gif\" alt=\"\" border=\"0\" \/><\/p><p><em><strong>Figure 12\u00a0\u00a0 Example rule set for TWIN ETD dispatching<\/strong><\/em><\/p><p>TWIN ETD has been shown to perform well in simulation for all the scenarios proposed to date.\u00a0 This includes: (i) buildings with two entrances where the lower and upper cars are loaded simultaneously;\u00a0 (ii) buildings with single entrances and a virtual landing to \u201chide\u201d the lower car when the upper car is loading; (iii) zoned solutions where some cars serve different floors; (iv) non-zoned solutions where all cars serve all floors they can physically get to; (v) solutions with lower and upper elevators running at different speeds; (vi) scenarios with TWIN and conventional elevators in the same group.<\/p><p>\u00a0<\/p><h3>6.\u00a0\u00a0\u00a0\u00a0 Summary and conclusions<\/h3><p>The original ThyssenKrupp ETD concept was presented in a earlier paper, and is now available in a commercial elevator controller.\u00a0 Extensions to the concept allow ETD to take into account the relative importance of waiting and transit times.\u00a0 Additionally, this extension looks at how \u201cpainful\u201d each extra second of waiting or transit time is to a passenger.\u00a0 This allows the dispatcher to improve the perceived performance of the system.\u00a0 Other factors can be introduced into the optimization process.\u00a0 It has been shown that energy can be saved purely through dispatching decisions, albeit with a corresponding deterioration in traffic performance.<\/p><p>Vmax is a technology exploiting the fact that most of the time an elevator motor is being under-utilized, as it is not carrying a full load up the building.\u00a0 Traffic performance can be improved by a controlled over-speed of the car.<\/p><p>The ETD concept has also been applied successfully to TWIN, where two cars share a single shaft.\u00a0 Additional rules have been developed to avoid collisions and to coordinate the relative movements of the cars.\u00a0 However, the fundamental ETD concept of modeling the impact of a new call on every other passenger in the system before making an allocation remains the same.<\/p><p><br \/><br \/><strong>REFERENCES<\/strong><\/p><ol><li>Smith R, Peters R\u00a0 ETD Algorithm with Destination Dispatch and Booster Options\u00a0\u00a0\u00a0 Elevator Technology 12, Proceedings of ELEVCON 2002 (The International Association of Elevator Engineers) (2002)<\/li><li>Al-Sharif L, Peters R, Smith R\u00a0 Elevator Energy Simulation Model\u00a0 Elevator Technology 14, Proceedings of ELEVCON 2004 (The International Association of Elevator Engineers) (2004)<\/li><li>Thumm G\u00a0 A Breakthrough in Lift Handling Capacity\u00a0 Elevator Technology 14, Proceedings of ELEVCON 2004 (The International Association of Elevator Engineers) (2004)<\/li><\/ol><p>\u00a0<\/p><\/div><\/div><\/article><\/div><\/div><\/div><\/div><\/div><footer class=\"art-footer\"><div class=\"art-nostyle\"><div class=\"custom\">\u00a0<\/div><\/div><\/footer>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Enhancements to the ETD Dispatcher Algorithm Rory Smith, ThyssenKrupp Elevator Inc., USARichard Peters, Peters Research Ltd., UK Key Words:\u00a0Simulation, dispatching, destination dispatch, boosters, energy, TWIN, Vmax This paper was presented at ELEVCON\u00a0ISTANBUL 2004, The International Congress on Vertical Transportation Technologies\u00a0and first published in the IAEE book &#8220;Elevator Technology 14&#8221;, edited by A. Lustig.\u00a0 It is [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":0,"parent":860,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"elementor_canvas","meta":{"footnotes":""},"class_list":["post-961","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.5 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Enhancements to the ETD Dispatcher Algorithm - Peters Research<\/title>\n<meta name=\"robots\" content=\"noindex, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Enhancements to the ETD Dispatcher Algorithm - Peters Research\" \/>\n<meta property=\"og:description\" content=\"Enhancements to the ETD Dispatcher Algorithm Rory Smith, ThyssenKrupp Elevator Inc., USARichard Peters, Peters Research Ltd., UK Key Words:\u00a0Simulation, dispatching, destination dispatch, boosters, energy, TWIN, Vmax This paper was presented at ELEVCON\u00a0ISTANBUL 2004, The International Congress on Vertical Transportation Technologies\u00a0and first published in the IAEE book &#8220;Elevator Technology 14&#8221;, edited by A. 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