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    <entry>
      <title></title>
      <link href="/2019/09/12/cp_and_mip/"/>
      <url>/2019/09/12/cp_and_mip/</url>
      
        <content type="html"><![CDATA[<h1><span id="integrating-constraint-programming-and-integer-programming">Integrating Constraint Programming and Integer Programming</span><a href="#integrating-constraint-programming-and-integer-programming" class="header-anchor">#</a></h1><p><em>I am also a newbee in the research on Constraint Programming (CP) and Integer Programming (IP), but they are both pretty important models on working for scheduling problems. I try to write what I have understood within these fields. If there exist some problems, mistakes and questions, please do not hesitate to contact me.</em> </p><p>During working on scheduling problems, our focus is put on the black dot in the following figure. Its reason lies within the fact that real-world industrial problems have very large scale variables/constraints to be solved/satisfied.</p><p>In the midst of search a trial soltuion is found to be unsatisfactory,</p>]]></content>
      
      
      
    </entry>
    
    
    
    <entry>
      <title></title>
      <link href="/2019/09/10/learning_to_choose/"/>
      <url>/2019/09/10/learning_to_choose/</url>
      
        <content type="html"><![CDATA[<h1><span id="learning-when-to-use-given-algorithms-i">Learning When to Use Given Algorithms (I)</span><a href="#learning-when-to-use-given-algorithms-i" class="header-anchor">#</a></h1><p>There have been many efficient algorithms for combinatorial optimization, especially in small scale ones. </p><p><strong>Learning to Perform Local Rewriting for Combinatorial Optimization</strong></p>]]></content>
      
      
      
    </entry>
    
    
    
    <entry>
      <title>Hello world</title>
      <link href="/2019/09/08/hello-world/"/>
      <url>/2019/09/08/hello-world/</url>
      
        <content type="html"><![CDATA[<p>During working on the interface between supply chain management and data-driven algorithms, I find a new interesting world. It is different from such fields as my master to PhD time. At then, I worked on (stochastic/nonlinear) partial differential equations and nonlinear dynamic systems. They are too far to be applied. But now, what we work is deployed on real-world supply chain system and you can see the real profits.</p><p>In my opinion, supply chain management has a wide working field. Within it, I focus on manufacturing planning, capacity planning, scheduling and the optimization problems relative to them. Meanwhile, we also make some works in decision making, risk management, and (beer/stackelberg) game. It is exciting to see how many profits can be obtained by algorithmic approaches and explore how much more can be obtained. </p><p>Thus, I start to write blogs. In these blogs, I focus on <strong>supply chain management</strong>, including manufacturing planning, capacity planning and scheduling, <strong>programming</strong>, including (mixed) integer programming and constraint programming, and <strong>game</strong>, mainly on Stackelberg game and beer game. Some works on <strong>combinatorial optimization</strong> may also be included. I hope to organize the issues I understand and the meaningful papers/works/results. Meanwhile, I want to exercise the writing. Making people to know my thoughts is another goal. At least, I hope to write for fun and find anyone who is also interested in similar topics. </p><p><img src="/images/hello_world.png" alt="My favorate movie. It tells me do NOT forget why I started."></p>]]></content>
      
      
      
        <tags>
            
            <tag> combinatorial optimization </tag>
            
            <tag> supply chain management </tag>
            
        </tags>
      
    </entry>
    
    
  
  
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