{"id":5123,"date":"2021-02-20T20:46:21","date_gmt":"2021-02-20T20:46:21","guid":{"rendered":"http:\/\/141.23.68.248\/wp\/?page_id=5123"},"modified":"2021-02-25T06:02:30","modified_gmt":"2021-02-25T06:02:30","slug":"multi-objective-optimization","status":"publish","type":"page","link":"http:\/\/141.23.68.248\/wp\/?page_id=5123","title":{"rendered":"Multi-Objective Optimization"},"content":{"rendered":"<div class=\"page\" title=\"Page 2\">\n<div class=\"layoutArea\">\n<div class=\"column\">\n<p class=\"p1\">As construction projects get larger and more complicated, it becomes more difficult to make the final decision because of several factors such as cost, time, environment. Multi-objective optimization model provides a solution to this problem by making use of the\u00a0Pareto front. This method can be used to assess multiple optimization objectives such as initial cost, maintenance cost, emission cost, etc. Subsequently, the most appropriate solution can be identified. It is implemented by following these steps [1]:<\/p>\n<ol>\n<li class=\"p1\">identification of the problem<\/li>\n<li class=\"p1\">identification of the optimization objectives<\/li>\n<li class=\"p1\">developing the data structure<\/li>\n<li class=\"p1\">standardization of the objectives<\/li>\n<li class=\"p1\">definition of the fitness function<\/li>\n<li class=\"p1\">establishment of the genetic algorithm.<\/li>\n<\/ol>\n<p style=\"text-align: center;\"><img loading=\"lazy\" decoding=\"async\" class=\"  wp-image-6695 aligncenter\" src=\"http:\/\/141.23.68.248\/wp\/wp-content\/uploads\/2021\/02\/Screenshot-2021-02-24-at-18.36.04.png\" alt=\"screenshot-2021-02-24-at-18-36-04\" width=\"538\" height=\"527\" srcset=\"http:\/\/141.23.68.248\/wp\/wp-content\/uploads\/2021\/02\/Screenshot-2021-02-24-at-18.36.04.png 1267w, http:\/\/141.23.68.248\/wp\/wp-content\/uploads\/2021\/02\/Screenshot-2021-02-24-at-18.36.04-300x294.png 300w, http:\/\/141.23.68.248\/wp\/wp-content\/uploads\/2021\/02\/Screenshot-2021-02-24-at-18.36.04-1024x1004.png 1024w, http:\/\/141.23.68.248\/wp\/wp-content\/uploads\/2021\/02\/Screenshot-2021-02-24-at-18.36.04-520x510.png 520w, http:\/\/141.23.68.248\/wp\/wp-content\/uploads\/2021\/02\/Screenshot-2021-02-24-at-18.36.04-740x725.png 740w\" sizes=\"auto, (max-width: 538px) 100vw, 538px\" \/>Figure 1. General optimization procedure for maintenance scheduling [2]<\/p>\n<p>&nbsp;<\/p>\n<div class=\"page\" title=\"Page 10\">\n<div class=\"layoutArea\">\n<div class=\"column\">\n<div class=\"page\" title=\"Page 9\">\n<div class=\"layoutArea\">\n<div class=\"column\">\n<p>The three\u00a0parameters that we follow in this integration are costs, total intervention time and the distance between the interventions. The best options would have a maximized distance between the interventions and minimized cost and distance. The pareto front described by the total duration of the interventions and the cost is shown below:<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<p><a href=\"http:\/\/141.23.68.248\/wp\/wp-content\/uploads\/2021\/02\/paretofront2.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-large wp-image-7438\" src=\"http:\/\/141.23.68.248\/wp\/wp-content\/uploads\/2021\/02\/paretofront2-1024x539.png\" alt=\"paretofront\" width=\"1024\" height=\"539\" srcset=\"http:\/\/141.23.68.248\/wp\/wp-content\/uploads\/2021\/02\/paretofront2-1024x539.png 1024w, http:\/\/141.23.68.248\/wp\/wp-content\/uploads\/2021\/02\/paretofront2-300x158.png 300w, http:\/\/141.23.68.248\/wp\/wp-content\/uploads\/2021\/02\/paretofront2-520x274.png 520w, http:\/\/141.23.68.248\/wp\/wp-content\/uploads\/2021\/02\/paretofront2-740x390.png 740w, http:\/\/141.23.68.248\/wp\/wp-content\/uploads\/2021\/02\/paretofront2.png 1538w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/a><\/p>\n<p style=\"text-align: center;\">Fig 2. Pareto Frontier &#8211; Duration, Distance and Cost<\/p>\n<p>To see the accumulated impact of the input parameters on the performance criteria, duration\u00a0and time gap between the interventions are visualised below. The red lines represent the optimal solutions\u00a0while the blue lines represent the non-optimal solutions.<\/p>\n<p><a href=\"http:\/\/141.23.68.248\/wp\/wp-content\/uploads\/2021\/02\/pareto.png\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone  wp-image-7028\" src=\"http:\/\/141.23.68.248\/wp\/wp-content\/uploads\/2021\/02\/pareto.png\" alt=\"pareto\" width=\"798\" height=\"457\" srcset=\"http:\/\/141.23.68.248\/wp\/wp-content\/uploads\/2021\/02\/pareto.png 1459w, http:\/\/141.23.68.248\/wp\/wp-content\/uploads\/2021\/02\/pareto-300x172.png 300w, http:\/\/141.23.68.248\/wp\/wp-content\/uploads\/2021\/02\/pareto-1024x587.png 1024w, http:\/\/141.23.68.248\/wp\/wp-content\/uploads\/2021\/02\/pareto-520x298.png 520w, http:\/\/141.23.68.248\/wp\/wp-content\/uploads\/2021\/02\/pareto-740x424.png 740w\" sizes=\"auto, (max-width: 798px) 100vw, 798px\" \/><\/a><\/p>\n<p style=\"text-align: center;\">Fig 3.\u00a0Accumulated Impact<\/p>\n<p>&nbsp;<\/p>\n<p><a href=\"http:\/\/141.23.68.248\/wp\/wp-content\/uploads\/2021\/02\/Screenshot-2021-02-25-at-10.48.48.png\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone  wp-image-7739\" src=\"http:\/\/141.23.68.248\/wp\/wp-content\/uploads\/2021\/02\/Screenshot-2021-02-25-at-10.48.48.png\" alt=\"screenshot-2021-02-25-at-10-48-48\" width=\"734\" height=\"459\" srcset=\"http:\/\/141.23.68.248\/wp\/wp-content\/uploads\/2021\/02\/Screenshot-2021-02-25-at-10.48.48.png 2880w, http:\/\/141.23.68.248\/wp\/wp-content\/uploads\/2021\/02\/Screenshot-2021-02-25-at-10.48.48-300x188.png 300w, http:\/\/141.23.68.248\/wp\/wp-content\/uploads\/2021\/02\/Screenshot-2021-02-25-at-10.48.48-1024x640.png 1024w, http:\/\/141.23.68.248\/wp\/wp-content\/uploads\/2021\/02\/Screenshot-2021-02-25-at-10.48.48-520x325.png 520w, http:\/\/141.23.68.248\/wp\/wp-content\/uploads\/2021\/02\/Screenshot-2021-02-25-at-10.48.48-740x463.png 740w\" sizes=\"auto, (max-width: 734px) 100vw, 734px\" \/><\/a><\/p>\n<p>&nbsp;<\/p>\n<p>Summary of approach:<\/p>\n<p><a href=\"http:\/\/141.23.68.248\/wp\/wp-content\/uploads\/2021\/02\/MOO.png\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-7569 \" src=\"http:\/\/141.23.68.248\/wp\/wp-content\/uploads\/2021\/02\/MOO-1024x251.png\" alt=\"Group 5 - Summary\" width=\"942\" height=\"231\" srcset=\"http:\/\/141.23.68.248\/wp\/wp-content\/uploads\/2021\/02\/MOO-1024x251.png 1024w, http:\/\/141.23.68.248\/wp\/wp-content\/uploads\/2021\/02\/MOO-300x73.png 300w, http:\/\/141.23.68.248\/wp\/wp-content\/uploads\/2021\/02\/MOO-520x127.png 520w, http:\/\/141.23.68.248\/wp\/wp-content\/uploads\/2021\/02\/MOO-740x181.png 740w\" sizes=\"auto, (max-width: 942px) 100vw, 942px\" \/><\/a><\/p>\n<p>REFERENCES<\/p>\n<p>[1] Choongwan Koo, Taehoon Hong &amp; Sangbum Kim\u00a0(2015)\u00a0An integrated multi-objective optimization model for solving the construction time-cost trade-off problem,\u00a0Journal of Civil Engineering and Management,\u00a021:3,\u00a0323-333,\u00a0DOI:\u00a010.3846\/13923730.2013.802733<\/p>\n<p>[2] Maintenance of Structures and Infrastructure Systems, Mohamed Soliman, A.M.ASCE* Dan M. Frangopol, Dist.M.ASCE\u2020<\/p>\n<\/div>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>As construction projects get larger and more complicated, it becomes more difficult to make the final decision because of several factors such as cost, time, environment. Multi-objective optimization model provides a solution to this problem<a class=\"read-more\" href=\"http:\/\/141.23.68.248\/wp\/?page_id=5123\">Continue reading<\/a><\/p>\n","protected":false},"author":93,"featured_media":0,"parent":4248,"menu_order":4,"comment_status":"closed","ping_status":"open","template":"","meta":{"footnotes":""},"class_list":["post-5123","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"http:\/\/141.23.68.248\/wp\/index.php?rest_route=\/wp\/v2\/pages\/5123","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/141.23.68.248\/wp\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"http:\/\/141.23.68.248\/wp\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"http:\/\/141.23.68.248\/wp\/index.php?rest_route=\/wp\/v2\/users\/93"}],"replies":[{"embeddable":true,"href":"http:\/\/141.23.68.248\/wp\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=5123"}],"version-history":[{"count":11,"href":"http:\/\/141.23.68.248\/wp\/index.php?rest_route=\/wp\/v2\/pages\/5123\/revisions"}],"predecessor-version":[{"id":7740,"href":"http:\/\/141.23.68.248\/wp\/index.php?rest_route=\/wp\/v2\/pages\/5123\/revisions\/7740"}],"up":[{"embeddable":true,"href":"http:\/\/141.23.68.248\/wp\/index.php?rest_route=\/wp\/v2\/pages\/4248"}],"wp:attachment":[{"href":"http:\/\/141.23.68.248\/wp\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=5123"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}