Strategic planning optimization for natural gas to liquid transportation fuel (GTL) systems

A strategic planning optimization model is proposed for a network of natural gas to liquids (GTL) systems, and it is solved using a rolling horizon strategy. The model formulation determines the strategic and tactical decisions of the GTL supply chain over a long time horizon. The decisions to build new GTL refineries may be made over the span of 30 years and their operations cover the span of 60 years. Multiple capacities of GTL refineries (i.e., 1, 5, 10, 50, and 200 thousand barrels per day) that produce gasoline, diesel, and kerosene commensurate to the United States demand ratio may exist in the network. The parameter inputs include the locations, availabilities, and prices of natural gas in the United States discretized by county, the delivery locations of fuel products, and the transportation costs of every input and output of the refinery, defined for each time period. Formulated as a large-scale mixed-integer linear optimization (MILP) model, the problem is solved using a rolling horizon strategy for tractability. Case studies on the state of Pennsylvania are presented for different planning schemes and their impact on the economic performance of the GTL network is discussed.     

Generic mathematical programming formulation and solution for computer-aided molecular design

This short communication presents a generic mathematical programming formulation for computer-aided molecular design (CAMD). A given CAMD problem, based on target properties, is formulated as a mixed integer linear/non-linear program (MILP/MINLP). The mathematical programming model presented here, which is formulated as an MILP/MINLP problem, considers first-order and second-order molecular groups for molecular structure representation and property estimation. It is shown that various CAMD problems can be formulated and solved through this model.     

A hybrid hub-and-spoke postal logistics network with realistic restrictions: A case study of Korea Post

Postal logistics has a complex transportation network for efficient mail delivery. Therefore, a postal logistics network consists of various functional sites with a hybrid hub-and-spoke structure. More specifically, there are multiple Delivery & Pickup Stations (D&PSs), multiple Mail Processing Centers (MPCs), and one Exchange Center (EC). In this paper, we develop two mathematical models with realistic restrictions for Korea Post for the current postal logistics network by simultaneously considering locations and allocations. We propose an Integer Linear Programming (ILP) model for transportation network organization and vehicle operation and a Mixed Integer Linear Programming (MILP) model that considers potential ECs for decision making while simultaneously regarding the EC location, transportation network organization, and vehicle operation. We use modified real data from Korea Post. Additionally, we consider several scenarios for supporting EC decision makers. The proposed models and scenarios are very useful in decision making for postal logistics network designers and operators.     

A solution approach to the design of multi-period,multipurpose batch plants

A deterministic model for multipurpose, multiperiod batch plants was presented in a linearized form to predict the future design according to the change of demand by using a modified Benders’ Decomposition. The OSL code offered by the IBM corporation as optimizer was employed for solving several example problems. The decomposition method was successful, showing remarkable reduction in the computing times as compared with those of the direct solution method. Also the heuristic used as a solution approach for the multiperiod model provided an efficient methodology to the block-structured problem by dividing the large overall problem into the manageable single period blocks.     

Strategic mining options optimization:Open pit mining,underground mining or both

Near-surface deposits that extend to considerable depths are often amenable to both open pit mining and/or underground mining. This paper investigates the strategy of mining options for an orebody using a Mixed Integer Linear Programming(MILP) optimization framework. The MILP formulation maximizes the Net Present Value(NPV) of the reserve when extracted with(i) open pit mining,(ii) underground mining, and(iii) concurrent open pit and underground mining. Comparatively, implementing open pit mining generates a higher NPV than underground mining. However considering the investment required for these mining options, underground mining generates a better return on investment than open pit mining. Also, in the concurrent open pit and underground mining scenario, the optimizer prefers extracting blocks using open pit mining. Although the underground mine could access ore sooner, the mining cost differential for open pit mining is more than compensated for by the discounting benefits associated with earlier underground mining.     

复杂约束下梯级水电站短期厂网协调多目标MILP模型

随着电力系统负荷的增长及峰谷差的加大,电力消费结构不断优化.电网侧期望水电利用灵活启停特性充分参与电网调峰,以减小系统的峰谷差.电厂侧则在调度过程中追求梯级电量最大,以实现效益最大化.为协调厂网两侧对梯级出力过程的矛盾需求,同时满足厂网两侧复杂的约束条件,以梯级水电站的机组为调度单元,选取梯级发电量最大和电网剩余负荷峰谷差最小为目标函数.充分考虑电网侧联络线约束及电站侧尾水顶托、限制出力曲线、不规则振动区等与水头相关的复杂约束并线性化处理,构建了复杂约束条件下厂网协调多目标短期混合整数线性规划模型,对多目标函数进行归一化处理并采用权重法转化为单目标函数,利用Gurobi商业求解器进行模型求解.中国红水河梯级水电站的实际应用表明所提模型可获得兼顾厂网两侧目标期望的梯级发电计划.     

炼油企业蒸汽动力系统改造优化研究

针对当前形势下炼油企业蒸汽动力系统存在的巨大改造潜力,提出了蒸汽动力系统改造的优化方法;该方法首先通过运行优化方案分析和系统设备诊断找出系统存在问题,然后通过专家知识系统提出一系列解决的方案,建立系统改造方案的超结构和考虑柔性调节的优化改造混合整数线性规划(MILP)模型,求解模型并确定最优的改造方案;文章最后部分给出了详细的案例分析,结果表明应用本文提出的改造优化策略方法确定的最佳改造方案可以为案例背景炼油企业蒸汽动力系统节约大约6%的运行费用,投资回收期约0.38年;本文提出的优化改造方法为推动炼油企业蒸汽动力系统的节能降耗提供了定的理论指导和技术支持.     

基于智能软开关的三相不平衡配电网动态重构策略

针对单相分布式电源（DG）的接入会加剧配电网不平衡程度、增大网络损耗,且在严重不平衡时影响系统安全运行的问题,提出计及智能软开关（SOP）的三相不平衡配电网动态重构策略。首先,构建考虑SOP和DG电流不平衡度约束的三相不平衡配电网动态重构模型;然后,针对模型的非凸性将原模型转化为混合整数线性规划模型;最后,对改进的IEEE34节点配电网和某地78节点实际配电网进行算例分析,结果表明所提模型和策略可在保证配电网的安全运行的同时提升配电网的经济效益。     

An enumeration algorithm for integer‐valued data envelopment analysis

Data envelopment analysis (DEA) models assume real‐valued inputs and outputs, but on many occasions, some inputs and/or outputs can only take integer values. In these cases, using DEA models can result in misleading efficiency assessments and inaccurate performance targets. In this paper, we propose an enumeration algorithm for computing efficiency scores and performance targets of decision‐making units with integer value inputs/outputs. In the presented algorithm, we do not use any of the mixed integer linear programming (MILP) models that are used in previous studies. We show that the result of our algorithm and that of the MILP model presented in this context is the same. We also generalize our algorithm for different types of returns to scale as well as for the hybrid setting with real‐valued data.     

Combining a new data classification technique and regression analysis to predict the Cost-To-Serve new customers

Identifying the Cost-To-Serve (CTS) of customers is one of the most challenging problems in Supply Chain Management because of the diversity in their business activities. For the particular case of the industrial gas business, we are interested in predicting the cost to deliver bulk (liquefied) gas to new customers using a multifactor linear regression model. Developing a single model, i.e. analyzing the observations all at once, produces poor prediction results. Therefore prior to the regression analysis, a new supervised learning technique is used to group customers who are similar in some sense. Classes of customers are represented by hyper-boxes and a linear regression model is subsequently built within each class. The combination of data classification and regression is proven to increase the accuracy of the prediction.Two Mixed-Integer-Linear Programming (MILP) models are developed for data classification purposes. Although we are dealing with a supervised learning method, classes are not predefined in our case. Rather, we input a continuous “classification” attribute that is optimally discretized by the MILP’s in order to minimize the number of misclassifications. Therefore our data classification model offers a broader range of applications. A number of illustrative examples are used to prove the effectiveness of the proposed approach.