Determination of an Optimal Forecast Model for Ambulance Demand Using Goal Programming

A multistep approach to determining the optimal parameters of an exponential smoothing model was used to forecast emergency medical service (E.M.S.) demand for four counties of South Carolina. Daily emergency and routine (non-emergency) demand data were obtained and forecast statistics generated for each county sampled, using Winters' exponential smoothing model. A goal programme was formulated to combine forecast results for emergency calls with routine call forecasts. The goal programme gave a higher priority to accurate forecasting of emergency demand. The forecast model generated implicitly weights demand by severity and provides a reliable estimate of demand overall. The optimal parameter values for the smoothing model were obtained by minimizing the objective function value of the goal programming problem. The parameter values obtained were used to forecast demand for E.M.S. in the selected counties. The results of the model were compared to those using a multiple linear regression model and a single-objective-based exponential smoothing model for 2 months of data. When compared with two single-objective forecast models, the multiple-objective approach yielded more accurate forecasts and, therefore, was more cost-effective for the planner. The model presents and demonstrates a theoretical approach to improving the accuracy of ambulance demand forecasts. The possible impact of this approach on planning efficiency is discussed.     

A semi-parametric approach for estimating critical fractiles under autocorrelated demand

Forecasting critical fractiles of the lead time demand distribution is an important problem for operations managers making newsvendor-type inventory decisions. In this paper, we propose a semi-parametric approach to forecasting the critical fractile when demand is serially correlated. Starting from a user-defined but potentially misspecified forecasting model, we use historical demand data to generate empirical forecast errors of this model. These errors are then used to (1) parametrically correct for any bias in the point forecast conditional on the recent demand history and (2) non-parametrically estimate the critical fractile of the demand distribution without imposing distributional assumptions. We present conditions under which this semi-parametric approach provides a consistent estimate of the critical fractile and evaluate its finite sample properties using simulation and real data for retail inventory planning.     

基于灰色马尔可夫模型的物流配送需求量预测研究

研究可以较准确地预测物流配送需求量的数学模型,以服务于政府及企业的物流规划及其决策.将灰色理论和离散状态的马尔可夫链相结合,用灰色马尔可夫链对物流配送需求量进行实证研究.针对灰色数据系列首先用GM模型进行趋势预测,然后利用马尔可夫状态转移概率矩阵预报方法对其预测值进行二次拟合,预测精度明显高于GM模型预测.     

Spare parts logistics and installed base information

Many of the challenges in spare parts logistics emerge due to the combination of large service networks, and sporadic/slow-moving demand. Customer heterogeneity and stringent service deadlines entail further challenges. Meanwhile, high revenue rates in service operations motivate companies to invest and optimize the service logistics function. An important aspect of the spare parts logistics function is its ability to support customer-specific requirements with respect to service deadlines. To support customer specific operations, many companies are actively maintaining and utilizing installed base data during forecasting, planning and execution stages. In this paper, we highlight the potential economic value of installed base data for spare parts logistics. We also discuss various data quality issues that are associated with the use of installed base data and show that planning performance depends on the quality dimensions.     

供应链协同需求预测机制研究

如何实现供应链需求预测的有效协同是一个需要解决的难点问题。论文基于博弈论分析了协同需求预测的利益分配机制,定量分析了供应链协同需求预测的整体收益系数,提出了考虑收益和成本的动态激励机制,还定性分析了核心企业主导、信息共享以及信任等机制。提出的机制对提高供应链整体的预测能力,提升竞争力和整体绩效具有很好的价值。     

Furniture supply chain tactical planning optimization using a time decomposition approach

We study the supply chain tactical planning problem of an integrated furniture company located in the Province of Québec, Canada. The paper presents a mathematical model for tactical planning of a subset of the supply chain. The decisions concern procurement, inventory, outsourcing and demand allocation policies. The goal is to define manufacturing and logistics policies that will allow the furniture company to have a competitive level of service at minimum cost. We consider planning horizon of 1 year and the time periods are based on weeks. We assume that customer’s demand is known and dynamic over the planning horizon. Supply chain planning is formulated as a large mixed integer programming model. We developed a heuristic using a time decomposition approach in order to obtain good solutions within reasonable time limit for large size problems. Computational results of the heuristic are reported. We also present the quantitative and qualitative results of the application of the mathematical model to a real industrial case.     

A multi-class multi-level capacitated lot sizing model

When demand loading is higher than available capacity, it takes a great deal of effort for a traditional MRP system to obtain a capacity-feasible production plan. Also, the separation of lot sizing decisions and capacity requirement planning makes the setup decisions more difficult. In a practical application, a production planning system should prioritize demands when allocating manufacturing resources. This study proposes a planning model that integrates all MRP computation modules. The model not only includes multi-level capacitated lot sizing problems but also considers multiple demand classes. Each demand class corresponds to a mixed integer programming (MIP) problem. By sequentially solving the MIP problems according to their demand class priorities, this proposed approach allocates finite manufacturing resources and generates feasible production plans. In this paper we experiment with three heuristic search algorithms: (1) tabu search; (2) simulated annealing, and (3) genetic algorithm, to solve the MIP problems. Experimental designs and statistical methods are used to evaluate and analyse the performance of these three algorithms. The results show that tabu search and simulated annealing perform best in the confirmed order demand class and forecast demand class, respectively.     

Opportunistic supply chain formation from qualified partners for a new market demand

This paper addresses the problem of short-term supply chain design using the idle capacities of qualified partners in order to seize a new market opportunity. The new market opportunity is characterized by a deterministic forecast over a planning horizon. The production–distribution process is assumed to be organized in stages or echelons, and each echelon may have several qualified partners willing to participate. Partners within the echelon may differ in idle production capacity, operational cost, storage cost, etc, and we assume that idle capacity may be different from one period to another period. The objective is to design a supply chain by selecting one partner from each echelon to meet the forecasted demand without backlog and best possible production and logistics costs over the given planning horizon. The overall problem is formulated as a large mixed integer linear programming problem. We develop a decomposition-based solution approach that is capable of overcoming the complexity and dimensionality associated with the problem. Numerical results are presented to support the effectiveness of this approach.     

A two-phase heuristic approach to the bike repositioning problem

An approach to overcome the bike imbalance problem is to transfer excess bikes to branches with bike shortages. This study develops a constrained mathematical model to deal with a multi-vehicle bike-repositioning problem, and aims to minimize the sum of transportation and unmet demand costs over a planning horizon through bike-transfer strategies under a minimum service requirement. A two-phase heuristic based on linear programming was proposed to solve the problem and produce compromising solutions. In the first phase, the paper developed a linear programming model to quickly develop decisions related to bike inventory, unloading, and loading for all stations for each time slot. In the second phase, this paper proposed an iterative approach through two parameter sensitive mathematical models to sequentially reduce the problem scale to develop decisions related to bike transfers. Computational results show that the proposed approach is superior to a CPLEX optimizer and a hybrid heuristic based on a genetic algorithm. The proposed approach was used to analyze the bicycle system in Taiwan. The impacts of various system parameters on the system were also investigated.     

大数据背景下的商品需求预测与分仓规划

商品需求预测对于电商企业意义重大,对阿里电商平台的交易数据进行挖掘以获取有效特征,利用特征建立模型对未来两周这些商品的需求进行动态预测,并基于预测结果和成本最小的原则提出分仓规划建议.预测模型选择随机森林做回归,然后在残差分析的基础上建立报童模型求解分仓的库存规划.对特征数量众多的电商交易数据挖掘所建立的模型有助于电商企业进行有效的商品需求预测并据此制定成本更低的分仓规划.     

Short-term forecasting of electricity demand by decomposition analysis

A model for short-term forecasting of electricity demand is developed, which consists of an annual base load augmented by demand variations on a weekly, daily, and hourly or half-hourly basis. Each of these components is individually modelled and forecast, and the aggregate forecast is refined by inclusion of a stochastic component for characterization of variations not attributable to elements of time. The approach is consistent with the operational concept of power system management, and can be readily adopted for on-line forecasting and control.     

Pricing and lot-sizing for a deteriorating item in a periodic review inventory system with shortages

We consider a single product that is, subject to continuous decay, a multivariate demand function of price and time, shortages allowed and completely backlogged in a periodic review inventory system in which the selling price is allowed to adjust upward or downward periodically. The objective of this paper is to determine the periodic selling price and lot-size over multiperiod planning horizon so that the total discount profit is maximized. The proposed model can be used as an add-in optimizer like an advanced planning system in an enterprise resource planning system that coordinates distinct functions within a firm. Particular attention is placed on investigating the effect of periodic pricing jointly with shortages on the total discount profit. The problem is formulated as a bivariate optimization model solved by dynamic programming techniques coupled with an iterative search process. An intensive numerical study shows that the periodic pricing is superior to the fixed pricing in profit maximization. It also clarifies that shortages strategy can be an effective cost control mechanism for managing deterioration inventory.     

A bi-level programming model and solution algorithm for the location of logistics distribution centers

In this paper, by considering benefits of customers and logistics planning departments, a bi-level programming model is presented to seek the optimal location for logistics distribution centers. The upper-level model is to determine the optimal location by minimizing the planners’ cost, and the lower gives an equilibrium demand distribution by minimizing the customers’ cost. Based on the special form of constraints, a simple heuristic algorithm is proposed. Finally, a numerical example is used to illustrate the application of the method, which shows that the algorithm is feasible and advantageous.     

An existence and uniqueness theorem for an optimal inventory problem with forecasting

An existence and uniqueness theorem is proved for an optimal inventory problem with forecasting. The model assumes costs are fixed and that unsatisfied demand is lost. At each stage a forecast is obtained on the basis of which the decisionmaker has a known conditional probability distribution of demand. The theorem is a generalization of a result stated but not proved by White.     

ARMS: An automated resource management system for British Telecommunications plc

Accurate demand forecasting combined with resource planning is critical to a company’s performance and profitability. This paper describes ARMS (automated resource management system), an integrated system developed for the customer service operations of British Telecommunications plc to help with the operational/tactical planning and deployment of the company’s 20,000-strong field engineer workforce. ARMS integrates a forecasting tool with a resource planning tool and a resource balancing tool providing an end-to-end automated resource management solution for the organisation. OR techniques are used throughout the system, including ARIMA for forecasting, constraint satisfaction for problem modelling, heuristic search for problem solving thus demonstrating the value and relevance of OR in solving today’s business problems.     

Incorporating demand uncertainty and forecast error in supply chain planning models

This paper develops a framework for examining the effect of demand uncertainty and forecast error on unit costs and customer service levels in the supply chain, including Material Requirements Planning (MRP) type manufacturing systems. The aim is to overcome the methodological limitations and confusion that has arisen in much earlier research. To illustrate the issues, the problem of estimating the value of improving forecasting accuracy for a manufacturer was simulated. The topic is of practical importance because manufacturers spend large sums of money in purchasing and staffing forecasting support systems to achieve more accurate forecasts. In order to estimate the value a two-level MRP system with lot sizing where the product is manufactured for stock was simulated. Final product demand was generated by two commonly occurring stochastic processes and with different variances. Different levels of forecasting error were then introduced to arrive at corresponding values for improving forecasting accuracy. The quantitative estimates of improved accuracy were found to depend on both the demand generating process and the forecasting method. Within this more complete framework, the substantive results confirm earlier research that the best lot sizing rules for the deterministic situation are the worst whenever there is uncertainty in demand. However, size matters, both in the demand uncertainty and forecasting errors. The quantitative differences depend on service level and also the form of demand uncertainty. Unit costs for a given service level increase exponentially as the uncertainty in the demand data increases. The paper also estimates the effects of mis-specification of different sizes of forecast error in addition to demand uncertainty. In those manufacturing problems with high demand uncertainty and high forecast error, improved forecast accuracy should lead to substantial percentage improvements in unit costs. Methodologically, the results demonstrate the need to simulate demand uncertainty and the forecasting process separately.     

在预报更新的环境下具有两种配送方式的供应链模型

本文研究在预报更新环境下具有快、慢两种配送方式和需求预报更新的库存系统，为了得到更多关于费用参数和预报改进对最优定货量以及最优的平均费用的影响，我们考虑两个周期的情形．以动态规划为工具我们得到了系统的最优策略．对于需求预报服从均匀分布情形，本文得到了最优定货量和最优的平均总费用的精确表达式．我们通过一些数值例子来说明库存费用、罚金、需求的预报改进和预报误差对最优定货量和最优的     

The value of combining forecasts in inventory management – a case study in banking

Managing inventories in the face of uncertain stochastic demand requires an investment in safety stocks. These are related to the accuracy in forecasting future demands and the noise in the demand generation process. Reducing the demand forecasting error can free up capital and space, and reduce the operating costs of managing the inventories. A leading bank in Hong Kong consumes more than three hundred kinds of printed forms for its daily operations. A major problem of its inventory control system for the forms management is to forecast the monthly demand of these forms. In this study the idea of combining forecasts is introduced and its practical application is addressed. The individual forecasts come from well established time series models and the weights for combination are estimated with Quadratic Programming. The combined forecast is found to perform better than any of the individual forecasts.     

融合Bass模型和三阶段过程模型的续集电影需求扩散研究

续集电影与母电影之间存在品牌溢出效应但不存在替代效应。现有多代扩散模型主要考虑替代效应,难以适用于续集电影。构建了一个融合Bass模型和三阶段过程模型的续集电影需求扩散模型,以预测续集电影的市场潜量和上映期间每日需求量。以2011年至2016年国内上映的续集电影相关数据对模型进行了验证,并与Marshall模型和SBM进行了比较。结果表明1)母电影品牌溢出效应、两代电影特征差距和市场扩张显著影响续集电影的市场潜量;2)电影需求过程存在显著的季节性波动;3)所建模型在拟合优度和预测精度方面均优于两个对比模型。所建模型一方面更适用于预测续集电影的需求扩散,另一方面研究对象拓展至不存在替代效应的多代短生命周期体验品,考虑了多代产品品牌溢出效应,是对已有多代扩散模型的补充。