In this paper, we propose a new mixed-integer linear programming (MILP) model ontology and a novel constraint typology of MILP formulations. MILP is a commonly used mathematical programming technique for modelling and solving real-life scheduling, routing, planning, resource allocation, and timetabling optimization problems providing optimized business solutions for industry sectors such as manufacturing, agriculture, defence, healthcare, medicine, energy, finance, and transportation. Despite the numerous real-life Combinatorial Optimization Problems found and solved and millions yet to be discovered and formulated, the number of types of constraints (the building blocks of a MILP) is relatively small. In the search for a suitable machine-readable knowledge representation structure for MILPs, we propose an optimization modelling tree built based upon an MILP model ontology that can be used as a guide for automated systems to elicit an MILP model from end-users on their combinatorial business optimization problems. Our ultimate aim is to develop a machine-readable knowledge representation for MILP that allows us to map an end-user's natural language description of the business optimization problem to an MILP formal specification as a first step towards automated mathematical modelling.
翻译:在本文中,我们提出了一个新的混合指数线性编程模型(MILP)模型肿瘤学和MILP配方的新制约类型。MILP是一种常用的数学编程技术,用于模拟和解决实际生活时间安排、路由安排、规划、资源分配和计时优化问题,为制造业、农业、国防、医疗保健、医药、能源、金融和运输等工业部门提供最佳商业解决办法。尽管发现并解决了无数实际的组合组合组合式优化问题,数百万个有待发现和拟订,但制约类型(MILP的构件)的数量相对较少。在为MILP寻找一个适合机器读的知识代表结构时,我们提议在MILP模型模型模型模型模型的基础上建立一个优化模型树,作为自动系统指南,从终端用户那里引出一个有关组合式企业优化问题的MILP模型。我们的最终目标是为MILP开发一个机器可读的知识代表,以便我们绘制最终用户第一个向IMP格式化问题的自动数学格式化要求步骤。