Electricity Time‐of‐Use Tariff with Stochastic Demand

In this article, we study the electricity time‐of‐use (TOU) tariff for an electricity company with stochastic demand. The electricity company offers the flat rate (FR) and TOU tariffs to customers. Under the FR tariff, the customer pays a flat price for electricity consumption in both the peak and non‐peak periods. Under the TOU tariff, the customer pays a high price for electricity consumption in the peak period and a low price for electricity consumption in the non‐peak period. The electricity company uses two technologies, namely the base‐load and peak‐load technologies, to generate electricity. We derive the optimal capacity investment and pricing decisions for the electricity company. Furthermore, we use real data from a case study to validate the results and derive insights for implementing the TOU tariff. We show that in almost all the cases, the electricity company needs less capacity for both technologies under the TOU tariff than under the FR tariff, even though the expected demand in the non‐peak period increases. In addition, except for some extreme cases, there is essentially no signicant reduction in the total demand of the two periods, although the TOU tariff can reduce the demand in the peak period. Under the price‐cap regulation, the customer may pay a lower price on average under the TOU tariff than under the FR tariff. We conduct an extensive numerical study to assess the impacts of the model parameters on the optimal solutions and the robustness of the analytical results, and generate managerial implications of the research findings.     

Managing Demand Risk in Tactical Supply Chain Planning for a Global Consumer Electronics Company

We consider the problem of managing demand risk in tactical supply chain planning for a particular global consumer electronics company. The company follows a deterministic replenishment‐and‐planning process despite considerable demand uncertainty. As a possible way to formally address uncertainty, we provide two risk measures, “demand‐at‐risk” (DaR) and “inventory‐at‐risk” (IaR) and two linear programming models to help manage demand uncertainty. The first model is deterministic and can be used to allocate the replenishment schedule from the plants among the customers as per the existing process. The other model is stochastic and can be used to determine the “ideal” replenishment request from the plants under demand uncertainty. The gap between the output of the two models as regards requested replenishment and the values of the risk measures can be used by the company to reallocate capacity among different products and to thus manage demand/inventory risk.     

基于智能电网技术的能源网络供应链买电策略研究

智能电网的提出不仅是电网技术本身发展的需要，更是各国能源和经济发展战略层面的需要。可再生能源作为清洁能源的突出代表，其分布式发电系统是智能电网发展的必然趋势。但是，由于天气等因素导致的可再生能源高度不确定性和间歇性却给智能电网系统的稳定带来很大挑战。为了应对这一挑战，并发挥可再生能源的成本优势，本文从微观运营的角度，研究带有可再生能源供给的"多对多"能源网络供应链的买电决策问题，求得能源集成商的最优买电量，以及能源短缺成本、发电厂产能、可再生能源不确定性等参数对最优解的影响。本文发现，发电厂产能对能源集成商使用可再生能源的策略有直接影响。并且，可再生能源产能较大的能源集成商因为规避不确定性风险反而会提高买电量。本文的结论能帮助智能电网节省用电成本，为实现节约型、可靠型和稳定型电网系统提供一定参考。     

考虑随机需求与收入共享的风险规避型V2G备用决策模型

在CVaR风险度量准则下,构建了考虑随机需求与收入共享的风险规避型V2G备用决策模型,推导了集中和分散两种决策下渠道成员最优决策行为的解析解,并进一步比较分析了随机需求变量服从均匀分布时的均衡策略。研究发现,集中决策下的最优V2G备用预留因子与渠道整体的风险规避度正相关,而均衡时的V2G备用销售价格与渠道整体的风险规避度的相关性不确定,且受到随机需求变量的分布函数影响;分散决策下的最优V2G备用预留因子仅与电网公司的风险规避度有关,而均衡时的V2G备用销售价格受到电网公司的风险规避度、购电价格以及电动汽车用户收入共享系数等的共同影响;电动汽车用户的最优V2G备用收入共享系数与其风险规避度正相关,而与电网公司的风险规避度负相关。数值仿真结果表明,在绝大多数情形下收入共享合约并不能完美协调此类V2G备用渠道的分散决策行为。     

基于逆向供应的V2G市场电价策略研究

本文讨论了电动汽车普及后电力公司借助V2G技术建立V2G市场的市场运行模式,对电力公司在不同价格策略下,电动汽车保有者参与V2G市场向电力公司反向供电的响应度进行对比、分析.结果表明:电力公司采取分时电价策略时,可以以较不采用分时电价情景低的反向购电价格获得更高的V2G市场响应度,在不损失电动汽车保有者效益的情况下,使电力公司获得更高的总收益.     

Optimizing Natural Gas Flows with Linear Programming and Scenarios

Recently, we developed a linear programming model to aid an electric utility company in evaluating several long-term operating decisions. Variations of this model provide insights into daily operations and valuation of supply contracts. This paper outlines the important characteristics of this class of network models including the usage of a gas storage facility, the use of market forecasts of gas prices, and the accommodation of gas contract structures. In addition, we develop a scenario optimization procedure in which demand forecasts provide scenarios. We also describe ways in which these models were automated to drive a decision support system.     

FREQUENCY OF REPLANNING IN A ROLLING HORIZON MASTER PRODUCTION SCHEDULE FOR A PROCESS INDUSTRY ENVIRONMENT: A CASE STUDY

This study addresses the problem of replanning frequency for a rolling horizon master production schedule (MPS) in a process industry environment under demand certainty. The major contribution of this paper is the demonstration of how the appropriate replanning frequency for a MPS can be determined under the conditions of minimum batch-size production restrictions in a rolling planning horizon setting. In addition, the problem environment for this study is an actual MPS operation that includes features such as multiple production lines, multiple products, capacity constraints, minimum inventory requirements, and multiple goals. Actual data from a paint company are used to determine the appropriate replanning frequency for a rolling horizon MPS. Results indicate that a 2-month replanning frequency was the best at this firm because of the significant cost savings it provided when compared to actual company performance and the other replanning intervals.     

The flow refueling location problem with load flow control

Battery electric vehicles as well as renewable energy are two key factors that can contribute significantly to sustainable development within the transportation and the energy sector. However, the market introduction of these technologies results in new challenges, especially with regard to the interaction between both sectors. So far, neither location models for charging stations nor load flow models for the electrical grid consider these interactions sufficiently. Thus, an integration of planning problems from both sectors is needed in order to exploit potential synergies and to avoid negative impacts.In this paper, we present such an integrated planning approach to locate charging infrastructure for battery electric vehicles considering interactions with the electrical grid. Herein, we combine a charging station location model and a power flow model with integrated energy stores. We aim at determining a network configuration that satisfies the charging demand of battery electric vehicles, herein maximizing the benefits and minimizing the negative impacts resulting from the interactions of the two sectors. To demonstrate the benefit of our integrated planning approach, we apply it to an illustrative case and present results of a sensitivity analysis. We derive managerial insights regarding the interdependencies of the number of sited charging stations and the installed storage capacity based on renewable energy generation and charging demand.     

模糊环境下考虑缺货和延期支付的Stackelberg均衡策略

在具有价格弹性需求的两层供应链系统中,考虑到价格弹性指数的模糊性和供应链延期支付策略,建立了含缺货的制造商-零售商协调模型.运用符号距离反模糊化方法将其转化为确定模型.基于遗传算法,设计了其求解方法,获得了制造商最优信用期和零售商最优零售价.数值结果表明:模糊环境下的延期支付策略降低了产品的市场零售价,同时增加了供应链中各成员的利润,因此实现了供应链协调.进一步分析显示当模糊价格弹性指数的上(下)界变化反映出的不确定性减弱时,制造商提供更长的信用期,且制造商和零售商的利润均增加(减少).     

Effect of forecast errors on rolling horizon master production schedule cost performance for various replanning intervals

Master production schedules are usually updated by the use of a rolling schedule. Previous studies on rolling schedules seem to form the consensus that frequent replanning of a master production schedule (MPS) can increase costs and schedule instability. Building on previous research on rolling schedules, this study addresses the impact of overestimation or underestimation of demand on the rolling horizon MPS cost performance for various replanning frequencies. The MPS model developed in this paper is based on actual data collected from a paint company. Results indicate that under both the forecast errors conditions investigated in this study, a two-replanning interval provided the best MPS cost performance for this company environment. However, results from the sensitivity analysis performed on the MPS model indicate that when the setup and inventory carrying costs are high, a 1-month replanning frequency (frequent replanning) seems more appropriate for both of the above forecast error scenarios.     

Real-time schedule adjustment decisions: a case study

Service capacity management has been extensively studied and successfully applied in many industries, with an emphasis on tour/shift scheduling and assignment decisions. However, few studies have addressed real-time work schedule adjustment decisions made necessary by demand uncertainty and/or labor supply disruption. This case study deals with the real-time work schedule adjustment decision and investigates the association of managerial experience, workforce mix (full- and part-time staff), and information accuracy with managers’ adjustment decisions. We designed an experiment that involved practicing service managers from a McDonald's franchise who provided their adjustment decisions for a given set of test scenarios. Their decisions were analyzed and evaluated via a controlled experiment. Using profitability as the primary performance measure, the study identifies the following outcomes: First, senior managers of a store with a higher proportion of part-time staff made more adjustments and attained slightly higher profitability than junior managers, when all employees accepted adjusted schedules. Second, managers of a store with a higher proportion of part-time staff were able to make slightly higher profits, particularly when capacity shortages occurred. And third, to achieve the majority of the benefits from schedule adjustments, it is sufficient to search for information that correctly identifies the direction of demand changes, rather than identifying the exact magnitude of the changes.     

Pareto‐Improving Contracts for Express Package Delivery Services

We address the problem of an express package delivery company in structuring a long‐term customer contract whose terms may include prices that differ by day‐of‐week and by speed‐of‐service. The company traditionally offered speed‐of‐service pricing to its customers, but without day‐of‐week differentiation, resulting in customer demands with considerable day‐of‐week seasonality. The package delivery company hoped that using day‐of‐week and speed‐of‐service price differentiation for contract customers would induce these customers to adjust their demands to become counter‐cyclical to the non‐contract demand. Although this usually cannot be achieved by pricing alone, we devise an approach that utilizes day‐of‐week and speed‐of‐service pricing as an element of a Pareto‐improving contract. The contract provides the lowest‐cost arrangement for the package delivery company while ensuring that the customer is at least as well off as he would have been under the existing pricing structure. The contract pricing smoothes the package delivery company's demand and reduces peak requirements for transport capacity. The latter helps to decrease capital costs, which may allow a further price reduction for the customer. We formulate the pricing problem as a biconvex optimization model, and present a methodology for designing the contract and numerical examples that illustrate the achievable savings.     

Two approaches to solving the multi-depot vehicle routing problem with time windows in a time-based logistics environment

This paper presents a two-phase heuristic method that can be used to efficiently solve the intractable multi-depot vehicle routing problem with time windows. The waiting time that was ignored by previous researchers is considered in this study. The necessity of this consideration is verified through an initial experiment. The results indicate that the waiting time has a significant impact on the total distribution time and the number of vehicles used when solving test problems with narrow time windows. In addition, to fairly evaluate the performance of the proposed heuristic method, a meta-heuristic method, which extends the unified tabu search of Cordeau et al., is proposed. The results of a second experiment reveal that the proposed heuristic method can obtain a better solution in the case of narrow time windows and a low capacity ratio, while the proposed meta-heuristic method outperforms the proposed heuristic method, provided that wide time windows and a high capacity ratio are assumed. Finally, a well-known logistics company in Taiwan is used to demonstrate the method, and a comparison is made, which shows that the proposed heuristic method is superior to the current method adopted by the case company.     

电网与天然气网络协同下配电网线路加固决策研究

燃气分布式发电机的配置与运行,对提高配电网应对自然灾害的能力具有重要作用,但同时也增加了配电网与天然气网络的相互依赖性,因此,考虑电网与天然气网络的协同运行显得尤为重要。本文对配置有燃气分布式发电机的配电网线路加固决策问题进行研究,考虑了线路受损的不确定性和电网与天然气网络协同运行,建立了具有三层结构的二阶段鲁棒优化模型。针对于天然气网络运行的非凸约束,采用二阶锥松弛将其松弛为二阶锥约束,并利用C&CG分解算法对模型进行求解。33节点-6节点的IEEE算例研究结果,表明了保证协同运行的关键配电线路具有加固的优先性,同时验证了考虑电网与天然气网络协同运行的合理性。     

海铁联运铁路集装箱定价与运营优化

铁路部门的改革加快了海铁联运的发展。本文从铁路运输经营人的角度研究铁路与公路运输竞争的情况下,铁路运输服务的定价和运营优化问题。首先利用顾客价值理论分析客户对铁路运输的需求和价格的关系。其次构建以利润最大化为目标的运营优化模型,并设计启发式算法求解。通过与遗传算法比较验证了算法的有效性。然后,得出不同价格水平下的最优班列路线、发班次数以及发班时间。结果表明对定价和运营策略的联合优化能够为铁路运输经营人带来最大的利润。     

有可再生能源和电力存储设施并网的智能电网优化用电策略

大量可再生能源和存储设施集中或分布接入电网,缓解了电网的供给压力,但同时也对电力系统安全造成新的威胁。合理使用新能源和可存储设施使之更好为电网服务,是现代电网亟待解决的一个问题。本文对有可存储设备和可再生能源并网的电力系统进行研究,根据可再生能源在实际生活中的情形,将其划分为两类：私人新能源发电和公共新能源发电,其中私人新能源发电可供自身直接使用,多余部分并入电网,而公共新能源发电直接并入电网,然后针对上述复杂情形,结合用户实际需求,以所有用户效用最大化、成本最小化为目标函数,建立优化模型,给出了一种既有可存储设备又有可再生能源复杂并网情况下用户优化用电策略——包括家用电器、新能源、以及存储设备充放电策略。对模型的性质进行研究,考虑到模型是凸规划,强对偶成立,用拉格朗日对偶算法给出了模型的解。求解过程中,由于目标函数是非光滑的,采用光滑化的技术将目标函数光滑化,将非光滑问题转化为光滑问题,进一步利用拟牛顿下降法求解。该策略能确保新能源得到优先、充分利用,体现用户效用最大化、成本最小化,同时可以避免由于新能源并网可能会造成电网不稳定情况的出现;光滑化的方法不但适用于本文,经过适当改进后也可适用于其他目标函数为非光滑的情况。仿真结果验证了模型的合理性和算法的可行性。     

Real‐Time Work Schedule Adjustment Decisions: An Investigation and Evaluation

Service managers often find that available worker capacity does not match with actual demand during a given day. They then must attempt to modify the planned work schedule to improve service and increase profitability. This study, which defines such a setting as the real‐time work schedule adjustment decision, pr oposes mathematical formulations of the real‐time adjustment and develops efficient heuristic approaches for this decision. The study evaluates the relative effectiveness of these heuristics versus experienced service managers, investigates the effect of the degree of schedule adjustment on profitability, and assesses the effect of demand forecast update errors on the performance of the schedule adjustment efforts. First, the results indicate that the computer based heuristics achieve higher profit improvement than experienced managers. Second, there is a trade‐off between schedule stability and profitability so that more extensive schedule revisions (efficiency first heuristics) generally result in higher profitability. However, the incremental return on schedule changes is diminishing. Third, we find that active adjustments of work schedules are beneficial as long as the direction of demand change is accurately identified.     

具有电力存储设备的实时需求响应与能量调度

实时需求响应与能量调度是智能电网中调节电力供需平衡的理想手段,其实施必然对用户的用电行为和电网的运行与管理产生深远影响。本文考虑用户具有多个可充、放电的电力存储设备,兼顾供电商发电量平稳的需求,在社会福利最大化模型的基础上,建立一个实时需求响应与能量调度的优化模型。给出模型的对偶问题,在满足强对偶性的前提下,可以通过求解对偶问题得到原问题的最优解,并确定可供用户与供电商参考的实时电价。在对偶问题中,问题可以分解为用户侧和供电侧两类子问题。进而设计分布式实时需求响应算法,并证明了算法的收敛性,供电侧与用户侧通过信息互动求得最优解。仿真结果验证了模型的合理性和算法的可行性。     

An Application of Master Schedule Smoothing and Planned Lead Time Control

Make‐to‐order (MTO) manufacturers must ensure concurrent availability of all parts required for production, as any unavailability may cause a delay in completion time. A major challenge for MTO manufacturers operating under high demand variability is to produce customized parts in time to meet internal production schedules. We present a case study of a producer of MTO offshore oil rigs that highlights the key aspects of the problem. The producer was faced with an increase in both demand and demand variability. Consequently, it had to rely heavily on subcontracting to handle production requirements that were in excess of its capacity. We focused on the manufacture of customized steel panels, which represent the main sub‐assemblies for building an oil rig. We considered two key tactical parameters: the planning window of the master production schedule and the planned lead time of each workstation. Under the constraint of a fixed internal delivery lead time, we determined the optimal planning parameters. This improvement effort reduced the subcontracting cost by implementing several actions: the creation of a master schedule for each sub‐assembly family of the steel panels, the smoothing of the master schedule over its planning window, and the controlling of production at each workstation by its planned lead time. We report our experience in applying the analytical model, the managerial insights gained, and how the application benefits the oil‐rig producer.     

Information quality deficiencies in delivery schedules and their impact on production scheduling

The purpose of this paper is to explore how different delivery schedule characteristics affect the quality of shared delivery schedule information and, in turn, how deficiencies in quality affect a supplier’s production scheduling process. It describes a case study conducted in the Swedish automotive industry involving a supplier that operates as the first-, second- and third-tier supplier to an original equipment manufacturer. The study reveals how four delivery schedule characteristics – namely, receiving frequency, planning period, frozen period and demand variation – create information quality (IQ) deficiencies in five dimensions of IQ: completeness, conciseness, reliability, timeliness and credibility. At the same time, it demonstrates how such deficiencies affect the supplier’s production scheduling process by requiring additional rescheduling, reworking and follow-up activities as well as additional capacity problems, safety time, safety stock and backlogs. In effect, the paper extends previous IQ-related research by considering IQ in delivery schedules.