{"id":29862,"date":"2015-06-23T12:01:34","date_gmt":"2015-06-23T08:31:34","guid":{"rendered":"http:\/\/www.irantahgig.ir\/?p=29862"},"modified":"2015-06-30T22:48:42","modified_gmt":"2015-06-30T08:48:42","slug":"%d8%b1%d8%a7%d9%87%d9%83%d8%a7%d8%b1-%d8%a8%d8%b1%d9%86%d8%a7%d9%85%d9%87-%d8%b1%d9%8a%d8%b2%d9%8a-%d9%87%d8%af%d9%81-%d8%a8%d8%b1%d8%a7%d9%8a-%d8%ad%d9%84-%d9%85%d8%b4%d9%83%d9%84-%d8%b7%d8%b1%d8%a7","status":"publish","type":"post","link":"http:\/\/www.irantahgig.ir\/?p=29862","title":{"rendered":"\u0631\u0627\u0647\u06a9\u0627\u0631 \u0628\u0631\u0646\u0627\u0645\u0647 \u0631\u06cc\u0632\u06cc \u0647\u062f\u0641 \u0628\u0631\u0627\u06cc \u062d\u0644 \u0645\u0634\u06a9\u0644 \u0637\u0631\u0627\u062d\u06cc \u0634\u0628\u06a9\u0647 \u0628\u0627 \u0686\u0646\u062f\u06cc\u0646 \u0647\u062f\u0641 \u0648 \u0628\u06cc \u062b\u0628\u0627\u062a\u06cc \u062a\u0642\u0627\u0636\u0627"},"content":{"rendered":"

Goal programming approach to solving network design problem with multiple objectives and demand uncertainty<\/span><\/p>\n

\u062a\u0627\u0631\u06cc\u062e:<\/strong> \u06f2\u06f0\u06f1\u06f2<\/span><\/p>\n

\u067e\u0627\u06cc\u06af\u0627\u0647: <\/strong>\u0627\u0644\u0632\u0648\u06cc\u0631<\/span><\/p>\n

\u0644\u06cc\u0646\u06a9 \u062f\u0627\u0646\u0644\u0648\u062f \u0627\u0635\u0644 \u0645\u0642\u0627\u0644\u0647<\/a><\/span><\/p>\n

\"OK\"<\/span><\/p>\n

\t

\n\n\t\t\n\t\t
\n\t\t\t200,000 \u0631\u06cc\u0627\u0644 – \u0627\u0636\u0627\u0641\u0647 \u06a9\u0631\u062f\u0646 \u0628\u0647 \u0633\u0628\u062f \u062e\u0631\u06cc\u062f<\/span> <\/span><\/a>\u0646\u0647\u0627\u06cc\u06cc \u06a9\u0631\u062f\u0646 \u062e\u0631\u06cc\u062f<\/a>\n\t\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t<\/svg>\n\t\t\t\t\t\t\u0645\u0648\u0631\u062f \u0628\u0647 \u0633\u0628\u062f \u062e\u0631\u06cc\u062f \u0627\u0636\u0627\u0641\u0647 \u0634\u062f\t\t\t\t\t<\/span>\n\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t

<\/span><\/p>\n

\u0646\u0627\u0645 \u0645\u062c\u0644\u0647:\u00a0<\/strong>Expert Systems with Applications<\/span><\/span><\/p>\n

\u0642\u06cc\u0645\u062a:<\/strong> \u06f2\u06f0\u06f0,\u06f0\u06f0\u06f0 \u0631\u06cc\u0627\u0644<\/span><\/p>\n

\u062a\u0639\u062f\u0627\u062f \u0635\u0641\u062d\u0627\u062a \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc:<\/strong> \u06f1\u06f1<\/span><\/p>\n

\u062a\u0639\u062f\u0627\u062f \u0635\u0641\u062d\u0627\u062a \u0641\u0627\u0631\u0633\u06cc:<\/strong> \u06f2\u06f2<\/span><\/p>\n

\u06a9\u062f: <\/strong>\u06f4\u06f0\u06f0\u06f9\u06f1<\/span><\/p>\n

\u0686\u06a9\u06cc\u062f\u0647 \u0641\u0627\u0631\u0633\u06cc<\/span><\/h3>\n

\u0645\u0634\u06a9\u0644 \u0637\u0631\u0627\u062d\u06cc \u0634\u0628\u06a9\u0647 \u062c\u0627\u0628\u062c\u0627\u06cc\u06cc (NDP) \u0628\u0627 \u0686\u0646\u062f\u06cc\u0646 \u0647\u062f\u0641 \u0648 \u0628\u06cc\u200c\u062b\u0628\u0627\u062a\u06cc \u062a\u0642\u0627\u0636\u0627 \u0628\u0639\u0646\u0648\u0627\u0646 \u0637\u06cc\u0641\u06cc \u0627\u0632 \u0627\u0644\u06af\u0648\u0647\u0627\u06cc \u0628\u0631\u0646\u0627\u0645\u0647\u200c\u0631\u06cc\u0632\u06cc \u0686\u0646\u062f \u0645\u0646\u0638\u0648\u0631\u0647 Stochastic\u200c \u062f\u0631 \u0686\u0627\u0631\u0686\u0648\u0628 \u0628\u0631\u0646\u0627\u0645\u0647\u200c\u0631\u06cc\u0632\u06cc \u062f\u0648 \u0633\u0637\u062d\u06cc \u0628\u0648\u062f. \u062d\u0644 \u0627\u06cc\u0646 \u0627\u0644\u06af\u0648\u0647\u0627\u06cc NDP \u0686\u0646\u062f\u200c\u0645\u0646\u0638\u0648\u0631\u0647 (Stochastic\u200c(SMONDP \u0645\u0633\u062a\u0642\u06cc\u0645\u0627\u064b \u0646\u06cc\u0627\u0632 \u0628\u0647 \u062a\u0648\u0644\u06cc\u062f \u0645\u062c\u0645\u0648\u0639\u0647\u200c\u0627\u06cc \u0627\u0632 \u0631\u0627\u0647\u200c\u062d\u0644\u200c\u0647\u0627\u06cc \u0628\u0647\u06cc\u0646\u0647 \u0645\u0639\u0631\u0648\u0641 \u0628\u0647 \u0645\u062c\u0645\u0648\u0639\u0647 \u0628\u0647\u06cc\u0646\u0647 pareto \u062f\u0627\u0631\u062f. \u0628\u0631\u0627\u06cc \u062a\u062d\u0642\u06cc\u0642 \u0639\u0645\u0644\u06cc\u060c \u0641\u0642\u0637 \u06cc\u06a9 \u0631\u0627\u0647\u200c\u062d\u0644 \u062e\u0648\u0628 \u06a9\u0647 \u0647\u062f\u0641\u200c\u0647\u0627\u06cc \u0633\u0647\u0627\u0645\u062f\u0627\u0631\u0627\u0646 \u0645\u062e\u062a\u0644\u0641 \u0631\u0627 \u062f\u0631 \u0646\u0638\u0631 \u062f\u0627\u0631\u062f \u0645\u0648\u0631\u062f \u0646\u06cc\u0627\u0632 \u0645\u06cc\u200c\u0628\u0627\u0634\u062f. \u0628\u0627 \u0627\u06cc\u0646 \u062f\u06cc\u062f \u0645\u0627 \u0631\u0627\u0647\u06a9\u0627\u0631 \u0628\u0631\u0646\u0627\u0645\u0647\u200c\u0631\u06cc\u0632\u06cc \u0647\u062f\u0641 \u0631\u0627 \u0628\u0631\u0627\u06cc \u062d\u0644 \u0627\u0644\u06af\u0648\u0647\u0627\u06cc SMONDP \u0645\u06cc\u200c\u067e\u0630\u06cc\u0631\u06cc\u0645. \u0631\u0627\u0647\u06a9\u0627\u0631 GP \u0627\u0647\u062f\u0627\u0641 \u062a\u0639\u0631\u06cc\u0641 \u0634\u062f\u0647 \u0645\u0635\u0631\u0641\u200c\u06a9\u0646\u0646\u062f\u0647 \u0648 \u0633\u0627\u062e\u062a\u0627\u0631 \u0628\u0631\u062a\u0631 \u0628\u06cc\u0646 \u0627\u0647\u062f\u0627\u0641 \u0686\u0646\u062f\u200c\u0645\u0646\u0638\u0648\u0631\u0647 \u062f\u0631 \u067e\u0631\u0648\u0633\u0647 \u062a\u0635\u0645\u06cc\u0645 NDP \u0631\u0627 \u062f\u0631 \u0646\u0638\u0631 \u0645\u06cc\u200c\u06af\u06cc\u0631\u062f. \u0628\u0627 \u062f\u0631 \u0646\u0638\u0631 \u06af\u0631\u0641\u062a\u0646 \u0627\u0647\u062f\u0627\u0641 \u0627\u0644\u06af\u0648\u0633\u0627\u0632\u06cc \u0645\u062a\u0641\u0627\u0648\u062a \u0645\u0627 \u0633\u0647 \u0627\u0644\u06af\u0648\u06cc GPStochastic\u200c \u0631\u0627 \u0628\u0627 \u0641\u0644\u0633\u0641\u0647\u200c\u0647\u0627\u06cc \u0645\u062a\u0641\u0627\u0648\u062a \u0628\u0631\u0627\u06cc \u0637\u0631\u0627\u062d\u0627\u0646 \u0627\u0644\u06af\u0648\u06cc \u062a\u0635\u0645\u06cc\u0645 NDP \u062a\u062d\u062a \u0628\u200c\u062b\u0628\u0627\u062a\u06cc \u062a\u0642\u0627\u0636\u0627 \u0627\u0633\u062a. \u0628\u0637\u0648\u0631 \u0645\u062b\u0627\u0644 \u0627\u0631\u0632\u0634 \u0645\u0648\u0631\u062f \u0646\u0638\u0631 \u0627\u0644\u06af\u0648\u06cc GP\u060c\u200c \u0627\u0644\u06af\u0648\u06cc GP \u0641\u0631\u0635\u062a \u063a\u06cc\u0631\u0637\u0628\u06cc\u0639\u06cc \u0648 \u0627\u0644\u06af\u0648\u06cc GP \u0641\u0631\u0635\u062a \u0648\u0627\u0628\u0633\u062a\u0647. \u062f\u0631 \u0636\u0645\u0646 \u0631\u0648\u0646\u062f \u0631\u0627\u0647\u200c\u062d\u0644 \u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645 \u062a\u06a9\u0648\u06cc\u0646\u06cc \u0628\u0631 \u067e\u0627\u06cc\u0647 \u0634\u0628\u06cc\u0647\u200c\u0633\u0627\u0632\u06cc (SGA) \u0628\u0631\u0627\u06cc \u062d\u0644 \u0647\u0631 \u0633\u0647 \u0645\u062f\u0644 GPStochastic\u200c \u062a\u0648\u0633\u0639\u0647 \u06cc\u0627\u0641\u062a\u0647 \u0627\u0633\u062a. \u0645\u062b\u0627\u0644\u200c\u0647\u0627\u06cc \u0639\u062f\u062f\u06cc \u0647\u0645\u0686\u0646\u06cc\u0646 \u0628\u0631\u0627\u06cc \u0646\u0634\u0627\u0646 \u062f\u0627\u062f\u0646 \u06a9\u0627\u0631\u0628\u0631\u06cc \u0628\u0648\u062f\u0646 \u0631\u0627\u0647\u06a9\u0627\u0631 GP \u062f\u0631 \u062d\u0644 \u0627\u0644\u06af\u0648\u0647\u0627\u06cc SMONDP \u0648 \u0647\u0645\u06cc\u0646\u0637\u0648\u0631 \u0627\u0633\u062a\u062d\u06a9\u0627\u0645 \u0631\u0648\u0646\u062f \u0631\u0627\u0647 \u062d\u0644 SGA \u0631\u0627 \u0627\u0631\u0627\u0626\u0647 \u0645\u06cc\u200c\u06a9\u0646\u062f.<\/span><\/p>\n

\u0686\u06a9\u06cc\u062f\u0647 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc<\/span><\/h3>\n

The transportation network design problem (NDP) with multiple objectives and demand uncertainty was originally formulated as a spectrum of stochastic multi-objective programming models in a bi-level programming framework. Solving these stochastic multi-objective NDP (SMONDP) models directly requires generating a family of optimal solutions known as the Pareto-optimal set. For practical implementation, only a good solution that meets the goals of different stakeholders is required. In view of this, we adopt a goal programming (GP) approach to solve the SMONDP models. The GP approach explicitly considers the user-defined goals and priority structure among the multiple objectives in the NDP decision process. Considering different modeling purposes, we provide three stochastic GP models with different philosophies to model planners\u2019 NDP decision under demand uncertainty, i.e., the expected value GP model, chance-constrained GP model, and dependent-chance GP model. Meanwhile, a unified simulation-based genetic algorithm (SGA) solution procedure is developed to solve all three stochastic GP models. Numerical examples are also presented to illustrate the practicability of the GP approach in solving the SMONDP models as well as the robustness of the SGA solution procedure<\/span><\/p>\n

\u0645\u0634\u062e\u0635\u0627\u062a \u0627\u0633\u062a\u0646\u0627\u062f\u06cc<\/strong><\/span><\/h3>\n

Chen, A., & Xu, X. (2012). Goal programming approach to solving network design problem with multiple objectives and demand uncertainty. Expert Systems with applications, 39(4), 4160-4170<\/span><\/p>\n

\u062f\u0627\u0646\u0644\u0648\u062f \u0627\u0635\u0644 \u0645\u0642\u0627\u0644\u0647<\/span><\/h3>\n

\"10002icon\"<\/a>\u0648\u06cc\u0698\u06af\u06cc\u200c\u0647\u0627\u06cc \u0645\u0642\u0627\u0644\u0647 \u0631\u0627\u0647\u06a9\u0627\u0631 \u0628\u0631\u0646\u0627\u0645\u0647 \u0631\u06cc\u0632\u06cc \u0647\u062f\u0641 \u0628\u0631\u0627\u06cc \u062d\u0644 \u0645\u0634\u06a9\u0644 \u0637\u0631\u0627\u062d\u06cc \u0634\u0628\u06a9\u0647 \u0628\u0627 \u0686\u0646\u062f\u06cc\u0646 \u0647\u062f\u0641 \u0648 \u0628\u06cc \u062b\u0628\u0627\u062a\u06cc \u062a\u0642\u0627\u0636\u0627<\/span><\/h1>\n

\u0645\u0642\u0627\u0644\u0647 “\u0631\u0627\u0647\u06a9\u0627\u0631 \u0628\u0631\u0646\u0627\u0645\u0647 \u0631\u06cc\u0632\u06cc \u0647\u062f\u0641 \u0628\u0631\u0627\u06cc \u062d\u0644 \u0645\u0634\u06a9\u0644 \u0637\u0631\u0627\u062d\u06cc \u0634\u0628\u06a9\u0647 \u0628\u0627 \u0686\u0646\u062f\u06cc\u0646 \u0647\u062f\u0641 \u0648 \u0628\u06cc \u062b\u0628\u0627\u062a\u06cc \u062a\u0642\u0627\u0636\u0627” \u062f\u0631 \u0633\u0627\u0644 \u06f2\u06f0\u06f1\u06f2 \u062f\u0631 \u0645\u062c\u0644\u0647\u00a0Expert Systems with Applications<\/span> \u0686\u0627\u067e \u0634\u062f\u0647 \u0648 \u062f\u0631 \u067e\u0627\u06cc\u06af\u0627\u0647 \u0627\u0637\u0644\u0627\u0639\u0627\u062a\u06cc \u0627\u0644\u0632\u0648\u06cc\u0631 \u0646\u0645\u0627\u06cc\u0647 \u0634\u062f\u0647 \u0627\u0633\u062a. \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0628\u0631\u0631\u0633\u06cc\u00a0\u0637\u0631\u0627\u062d\u06cc \u0634\u0628\u06a9\u0647 \u062c\u0627\u0628\u062c\u0627\u06cc\u06cc (NDP)\u060c\u00a0\u0627\u0644\u06af\u0648\u0647\u0627\u06cc \u0628\u0631\u0646\u0627\u0645\u0647\u200c\u0631\u06cc\u0632\u06cc \u0686\u0646\u062f \u0645\u0646\u0638\u0648\u0631\u0647 Stochastic\u060c \u0631\u0627\u0647\u200c\u062d\u0644 \u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645 \u062a\u06a9\u0648\u06cc\u0646\u06cc \u0628\u0631 \u067e\u0627\u06cc\u0647 \u0634\u0628\u06cc\u0647\u200c\u0633\u0627\u0632\u06cc (SGA) \u0648\u00a0\u0627\u0644\u06af\u0648\u0647\u0627\u06cc SMONDP \u067e\u0631\u062f\u0627\u062e\u062a\u0647 \u0627\u0633\u062a. \u0647\u0645\u0686\u0646\u06cc\u0646 \u0628\u0631\u0627\u0633\u0627\u0633 \u0627\u0637\u0644\u0627\u0639\u0627\u062a \u067e\u0627\u06cc\u06af\u0627\u0647 \u0627\u0637\u0644\u0627\u0639\u0627\u062a\u06cc \u06af\u0648\u06af\u0644 \u0627\u0633\u06a9\u0648\u0644\u0627\u0631 \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u06f3\u06f1 \u0628\u0627\u0631 \u0645\u0648\u0631\u062f \u0627\u0633\u062a\u0646\u0627\u062f \u0642\u0631\u0627\u0631 \u06af\u0631\u0641\u062a\u0647 \u0627\u0633\u062a.<\/span><\/p>\n

\t\n\n\t\t\n\t\t

\n\t\t\t200,000 \u0631\u06cc\u0627\u0644 – \u0627\u0636\u0627\u0641\u0647 \u06a9\u0631\u062f\u0646 \u0628\u0647 \u0633\u0628\u062f \u062e\u0631\u06cc\u062f<\/span> <\/span><\/a>\u0646\u0647\u0627\u06cc\u06cc \u06a9\u0631\u062f\u0646 \u062e\u0631\u06cc\u062f<\/a>\n\t\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t<\/svg>\n\t\t\t\t\t\t\u0645\u0648\u0631\u062f \u0628\u0647 \u0633\u0628\u062f \u062e\u0631\u06cc\u062f \u0627\u0636\u0627\u0641\u0647 \u0634\u062f\t\t\t\t\t<\/span>\n\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t

Goal programming approach to solving network design problem with multiple objectives and demand uncertainty \u062a\u0627\u0631\u06cc\u062e: \u06f2\u06f0\u06f1\u06f2 \u067e\u0627\u06cc\u06af\u0627\u0647: \u0627\u0644\u0632\u0648\u06cc\u0631 \u0644\u06cc\u0646\u06a9 \u062f\u0627\u0646\u0644\u0648\u062f \u0627\u0635\u0644 \u0645\u0642\u0627\u0644\u0647<\/p>\n","protected":false},"author":5,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[84,127,399,425,316],"tags":[],"_links":{"self":[{"href":"http:\/\/www.irantahgig.ir\/index.php?rest_route=\/wp\/v2\/posts\/29862"}],"collection":[{"href":"http:\/\/www.irantahgig.ir\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/www.irantahgig.ir\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/www.irantahgig.ir\/index.php?rest_route=\/wp\/v2\/users\/5"}],"replies":[{"embeddable":true,"href":"http:\/\/www.irantahgig.ir\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=29862"}],"version-history":[{"count":2,"href":"http:\/\/www.irantahgig.ir\/index.php?rest_route=\/wp\/v2\/posts\/29862\/revisions"}],"predecessor-version":[{"id":31043,"href":"http:\/\/www.irantahgig.ir\/index.php?rest_route=\/wp\/v2\/posts\/29862\/revisions\/31043"}],"wp:attachment":[{"href":"http:\/\/www.irantahgig.ir\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=29862"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.irantahgig.ir\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=29862"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.irantahgig.ir\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=29862"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}