Integrating noncyclical preventive maintenance scheduling and production planning for a single machine

This paper deals with the problem of integrating noncyclical preventive maintenance and tactical production planning for a single machine. We are given a set of products that must be produced in lots during a specified finite planning horizon. The maintenance policy suggests possible preventive replacements at the beginning of each production planning period, and minimal repair at machine failure. The proposed model determines simultaneously the optimal production plan and the instants of preventive maintenance actions. The objective is to minimize the sum of preventive and corrective maintenance costs, setup costs, holding costs, backorder costs and production costs, while satisfying the demand for all products over the entire horizon. The problem is solved by comparing the results of several multi-product capacitated lot-sizing problems. The value of the integration and that of using noncyclical preventive maintenance when the demand varies from one period to another are illustrated through a numerical example and validated by a design of experiment. The later has shown that the integration of maintenance and production planning can reduce the total maintenance and production cost and the removal of periodicity constraint is directly affected by the demand fluctuation and can also reduce the total maintenance and production cost.     

供应链可靠度管理中的模糊QFD应用

在供应链管理（SCM）中,人们更关注于寻找一种系统的方法,能够在计划阶段对可靠度进行评估。提出了一种模糊QFD模型,使其能够在供应链管理的过程中,传达用户需求与设计参数间的模糊关系;同时提出一套模糊的决策程序：模糊多准则决策．用来寻找一组CRT设计中可靠度需求性能的最优解。主要目的是找出合适的可靠性测试等级,以便使每一条供应链中模糊需求与产品生产参数之间的关系更加合理,以满足用户的需求。     

投资决策中基本折现率的确定及其调整

探讨了公司在项目投资决策中折现率的确定问题。提出了在公司项目投资决策中确定折现率应该采取的步骤:运用资本资产定价模型得到基本折现率,在此基础上考虑特定风险、股东要求的最低收益率、融资限额等因素,对基本折现率进行调整和改进,从而得到进行项目投资决策需要的财务折现率。     

Robust optimal policies of production and inventory with uncertain returns and demand

We consider an inventory and production planning problem with uncertain demand and returns, in which the product return process is integrated into the manufacturing process over a finite planning horizon. We first propose an inventory control model for the return and remanufacturing processes with consideration of the uncertainty of the demand and returns. Then a robust optimization approach is applied to deal with the uncertainty of the problem through formulating a robust linear programming model. Moreover, properties on the robust optimization model are studied, and an equivalent robust optimization model based on duality theory is obtained which allows the solutions to be derived more efficiently. Finally, we provide a set of numerical examples to verify the effectiveness of the approach and analyze the effects of the key parameters on the solutions.     

Competition under capacitated dynamic lot-sizing with capacity acquisition

Lot-sizing and capacity planning are important supply chain decisions, and competition and cooperation affect the performance of these decisions. In this paper, we look into the dynamic lot-sizing and resource competition problem of an industry consisting of multiple firms. A capacity competition model combining the complexity of time-varying demand with cost functions and economies of scale arising from dynamic lot-sizing costs is developed. Each firm can replenish inventory at the beginning of each period in a finite planning horizon. Fixed as well as variable production costs incur for each production setup, along with inventory carrying costs. The individual production lots of each firm are limited by a constant capacity restriction, which is purchased up front for the planning horizon. The capacity can be purchased from a spot market, and the capacity acquisition cost fluctuates with the total capacity demand of all the competing firms. We solve the competition model and establish the existence of a capacity equilibrium over the firms and the associated optimal dynamic lot-sizing plan for each firm under mild conditions.     

Modeling and simulation of order-driven planning policies in build-to-order automobile production

In adopting build-to-order order fulfillment systems, automotive companies strive to better synchronize their production output with market demand. This essentially gives rise to a new paradigm in production planning. Since all business is linked to customer orders, the operational performance is substantially determined by order-driven planning. Therefore, a clear understanding of the associated planning tasks, order promising and master production scheduling, as well as their dynamic interaction is essential. Based on the analysis of the decision situation of order-driven planning in build-to-order settings, we provide a framework comprising separate interlinked quantitative models for order promising and master production scheduling. The focus of the contribution is on the modeling and evaluation of both models in a dynamic setting. The approach is evaluated by means of a simulative analysis using empirical data from the automotive industry. Conclusions regarding the potentials of such systems with respect to customer service, the leveling of resource utilization, and holding are presented.     

Parallel machine replacement under horizon uncertainty

Construction projects usually get delayed for several time periods. When the planning horizon of a project is extended, projections for purchase and salvage of machinery within the planning horizon become inaccurate and less beneficial and often lead to unexpected costs. In this article, we formulate a parallel machine replacement (PMR) problem as a two-stage stochastic program with an uncertain planning horizon. We consider renting as an alternative to purchasing and maintaining the machinery. We show the application of the model through a case study in construction projects. Through numerical analysis, we derive managerial implications and show the value of the stochastic model.     

浅谈炼化企业如何开展生产经营优化工作

炼化企业生产经营优化的特点是炼化一体化优化,将炼油和化工业务作为一个整体,统筹进行资源配置.构建炼化一体化生产经营优化机制是一项复杂的系统工程,需要建立起相应的组织机制、运行机制、评估和考核机制,并依靠科学的管理理念、成熟的管理手段和先进的工具软件,对供应、生产、销售、运输等各环节进行全面优化.优化工作的重点是资源配置优化、产品结构优化、原料和产品库存优化、检修时间优化.文章以某国家大型炼化企业为例,详细介绍了炼化企业如何开展生产经营优化工作.     

An inventory reservation problem with nesting and fill rate-based performance measures

In many inventory settings companies wish to provide customer-differentiated service levels. These may, for example, be motivated by differences in the perceived customer lifetime value or by specific contractual agreements. One approach to provide differentiated service levels is to reserve some portion of the available inventory exclusively for specific customer classes. Existing approaches to inventory reservation are typically based on the assumption that a company can assign a customer specific revenue or penalty cost to any order or unit of demand filled or unfulfilled. In practice, however, it is usually extremely difficult to accurately estimate (especially long term) monetary implications of meeting or not meeting customer demand and corresponding service level requirements. The research presented in this paper addresses the problem of setting appropriate inventory reservations for different customer classes based on fill rate-based performance measures. We model a single period inventory reservation problem with two customer classes and nesting. We develop exact expressions for two conflicting performance measures: (1) the expected fill rate of high priority customers and (2) the expected loss in the system fill rate induced by inventory reservation. With these expressions a decision maker can analyze the tradeoff between the loss in overall system performance and the higher expected fill rates for prioritized customers. We provide analytical insights into the effects of nesting and the impact of relevant problem parameters on these two performance measures. The analytical insights are illustrated and highlighted through a set of numerical examples. Although we limit our analysis to a single period inventory reservation problem, we expect that our results can be utilized in a wide range of problem settings in which a decision maker has to ration a perishable resource among different classes of customers.     

Turning New Product Development into a Continuous Learning Process

Product developers understand the difficulties of trying to hit a moving target from atop a runaway train. Competitors come and go, technological change occurs at an ever-increasing rate, customer wants and needs are constantly shifting, and a product's life cycle may be shorter than its development time. We can't meet these challenges with a methodical, step-by-step approach to product development. In such a fast-paced environment, product development must be transformed into a continuous, iterative, learning process focused on customer value. G. David Hughes and Don C. Chafin describe one means for making this transformation: the value proposition process (VPP). The objectives of this development approach are continuous learning, identifying the certainty of knowledge used for decision-making, building consensus, and focusing on adding value. The VPP consists of a framework of continuous planning cycles, called the value proposition cycle (VPC), and an integrated screening methodology, called the value proposition readiness assessment (VPRA). The VPC comprises four iterative loops, addressing the following activities: Capturing the market value of the proposition (Does the customer care?); Developing the business value (Do we care?); Delivering a winning solution (Can we beat the competition?); and, Applying project and process planning (Can we do it?). Somewhat akin to stage-gate methods (but with the added dimension of continuous cycling), the VPRA involves screens along each loop in the VPC. This screening methodology summarizes the company's critical success factors, allowing the project team to assess the success potential of a new product idea. Each screen involves a structured set of questions. For each question, respondents provide three measures: their evaluation of that success factor's favorability to the company's position, the certainty of their evaluation, and their estimate of the relative importance of that success factor. Rather than building a model that computes a weighted-sum score for predicting the success of a new idea, the VPRA serves as a consensus facilitator, allowing team members to see how closely they agree. The certainty measures help the team identify knowledge gaps, and the importance measures help the team set priorities for successive iterations of the VPRA.     

Case Study: Solving a Rental Fleet Sizing Model with a Large Time–Space Network

This article addresses a rental fleet sizing problem (RFS) in the context of the truck rental industry, subject to uncertain customer travel time and nonstationary customer demand that is dependent on geographical location, time, and the economic cycle of the industry. We integrate tactical (asset purchases and sales) and operational (empty truck movement and vehicle assignment) decisions, with the explicit incorporation of an asset age factor, to achieve lower cost solutions. Typically, the length of time horizon and number of locations under consideration are quite large, which makes the RFS model computationally challenging to solve. Aggregation procedures are employed for location clustering and end-of-horizon effects are examined through demand scenario-based analyses. For the reduced time–space networks, decision analyses are conducted for the RFS model to provide insights into the truck rental business regarding asset movement decisions and asset procurement/disposal decisions over time and locations.     

An empirical analysis of multiple services and choices of consumer in the Swedish telecommunications market

Received analyses state that firms can use a multiple services offering strategy to retain customers and capture new customers. Factors that determine the multiple services strategy include product discount, service provider and customer characteristics. Consequently, this study addresses the fundamental question: what are the key determining factors that explain the probability that a consumer buys multiple services? A generalized Poisson regression model is employed to examine whether the product discount, service provider, socio-economic variables and geographical location impact consumer decisions. Data from a national survey in 2009 commissioned by Post-och Telestyrelsen, the Swedish telecommunications regulator, are analyzed. The results clearly show that the discount, service provider and income of the consumer affect the consumer׳s buying decision. A consumer who receives a discount or has a high income is more likely to buy multiple services or select more services from the current service provider into his basket than a consumer who does not receive discount or has lower income. Service providers, cable TV operators and telecommunications carriers can also lock-in their consumer and expand their market position from one particular service to another using bundling service. Thus, this may be the time for the telecommunications regulator to consider the market definition.     

Letter From the Editor

This paper Identifies situations in which the widely recommended procedures for evaluating mutually exclusive projects with unequal lives may result in incorrect project rankings. These situations arise from the failure of conventional techniques to place alternatives on an appropriate equivalent-risk basis and may occur whether a net present value or equivalent annual annuity approach is utilized. We develop alternative discounted cash flow procedures which correctly reflect the nature of risk. In so doing, we address the following important issues: 1) the choice of the discount rate to be used in calculating the net present value of a series of replications of a project: 2) the choice of a discount rate for calculating the equivalent annual annuity for a series of replications: and 3) the selection of an appropriate common-life horizon for comparison of alternative projects.     

Optimal Interest Rate-Discount Points Combination: Strategy for Mortgage Contract Terms

This paper is distinguished from previous papers by its focus on income-producing properties, rather than owner-occupied single-family residential properties. The real estate investor's strategy, in terms of choosing an interest rate-discount points combination, is analyzed by using a discounted cash flow approach. Under this framework, the investor with a lower marginal tax rate, lower required rate of return and longer investment horizon tends to negotiate for a mortgage contract with a higher number of discount points and lower interest rate. In addition, an intermediate rate-points combination is preferred by an investor only when the lender's required interest rate is a decreasing convex function of the number of discount points.     

浅谈炼化企业提升质量管理体系运行有效性的途径

质量管理体系运行的有效性是获证企业关注的话题,提升体系运行效果的途径多种多样,关键是要结合企业实际。兰州石化公司在质量管理体系建设和实施中对提升有效性进行了尝试和探索,通过在体系策划阶段设计适宜的体系建设指导思想,充分应用以顾客为关注焦点、领导作用、全员参与、持续改进等八项质量管理原则,优化质量管理体系文件,消除体系设计中的两层皮现象,为体系高效运行奠定了基础。     

DISCOUNT RATE VALUE AND CASH FLOW DEFINITION: A NEW RELATIONSHIP AND ITS IMPLICATIONS

This article uses different standpoints to approach the question of the consistency of project valuation methods. It shows that the NPV of a project can be obtained by discounting adjusted operating cash flows at a different rate from the risk-adjusted discount rate which should normally be used. Each of the conventional project valuation methods (standard WACC, equity residual, Arditti-Levy, APV) accordingly corresponds to a specific choice of the discount rate. Thus the convergence of these methods is obvious when the risk-adjusted discount rate integrates a debt ratio equal to the one of the project. Moreover, we obtain the Modigliani-Miller relationship generalized to the case of a project of any duration.     

Myopic optimal policy for a multi-period, two-delivery-lead-times, stochastic inventory problem with minimum cumulative commitment and capacity

This paper studies a single-product, multi-period, stochastic inventory problem that imposes the lower and upper bounds on the cumulative order quantity during a planning horizon and allows two delivery lead times. This model includes three features. The first one is that a buyer purchases a fixed capacity from a supplier at the beginning of a planning horizon and the buyer’s total cumulative order quantity during the planning horizon is constrained with the capacity. The second one is that the buyer agrees to purchase the product at least a certain percentage of the purchased capacity during the planning horizon. The third one is that the supplier allows the buyer to order the product with two-delivery-lead-times. We identify conditions under which a myopic ordering policy is optimal. We also develop an algorithm to calculate the optimal capacity when the minimum cumulative order quantity commitment is a certain percentage of the capacity. We then use the algorithm to evaluate the effect of the various parameters on the buyer’s minimum expected total cost during the planning horizon. Our computation shows that the buyer would benefit from the commitments and two-delivery-lead-times.     

Optimal Pricing and Order-Up-To S Inventory Policy with Expected Utility of the Present Value Criterion

The article determines pricing and order-up-to level S inventory decisions over an infinite planning horizon from the point of view of a risk-averse decision maker. The demand is assumed to be stochastic but influenced by the selling price which is a decision variable. Shortages are allowed and backordered partially. We calculate the present value of the cash flow over the entire planning horizon and incorporate the notion of risk aversion into the model using a concave utility function. We numerically demonstrate the model and investigate the impact of different model-parameters on the optimal decisions. It is observed that the optimal selling price for a risk-averse decision maker is not less than the optimal selling price of a risk-neutral decision maker while the optimal order level for the risk-averse decision maker is always less than that of the risk-neutral decision maker.     

Service productivity and service quality: A necessary trade-off?

This paper is about service productivity and perceived service quality. Both a high service productivity and a high customer perceived quality are two main drivers of good performances in service companies and they simultaneously occur as a consequence of a synergy of several drivers such as employees' competences and motivation, organizational efficiency, technical devices availability, information technologies employment, etc. In scientific literature a trade-off between productivity and perceived service quality is asserted and several cases derived both by market observations and academic research could bear such trade-off out. But, is it possible to state that, under some circumstances, such a trade-off between productivity and perceived service quality could be avoided? And if yes, why such certified substitution should not work?In this paper, in order to trace some theoretical answer to the above questions, a managerial model based both on service science and production economics will be proposed. The model aims to find out some key causes that allow to explain the foundations of the mentioned trade-off and the potential conditions for its overcoming; moreover, such model will be employed in order to integrate services' productivity function with new variables and to provide some managerial guidelines for improving service management.     

A hierarchical planning system for energy intensive production environments

This paper describes a hierarchical production planning approach with decision support features for energy intensive industries with particular reference to a tile manufacturing factory. In the tiling industry, the facilities which contribute most to the consumption of energy (and, hence, to the production costs) are usually the kilns where the curing operation is carried out. Frequently, the kilns are also the bottleneck in terms of capacity utilization. Thus, in order to save on energy costs, a planning approach which aims at minimizing the number of active kilns throughout the year is needed besides optimizing the process design in the curing department. To achieve the latter goal, it is necessary to take into account demand fluctuations as well as detailed capacity restrictions while deciding on the lot sizes of the products and the kilns on which the products are loaded. Rather than adopting a monolithic mathematical model for developing a desirable production plan, a hierarchical approach which decomposes the problem into two sub-problems is preferred. In the first level, products and capacity are aggregated over the planning horizon to achieve an overall consideration of demand fluctuations over time. Then, the solution provided by the aggregate solution for the current planning period is disaggregated into a detailed lot sizing and loading solution. The disaggregated problem is difficult to solve and hence, a heuristic is proposed here. This planning approach is sustained by a Decision Support System which enables the elimination of possible inconsistencies in the production plan by providing an effective interaction with the decision maker.