An improvement on “Integrated production strategy and reuse scenario: A CoFAQ model and case study of mail server system development”

The authors (Tang et al. (2013) [1] developed a CoFAQ model to formulate a solution for the problem of production strategy decision and reuse scenario selection for a software product family. In the previous research, we stated that the CoFAQ model was a 0–1 mixed integer nonlinear program, where only a local optimal solution might be found. In a recent study, we found that the CoFAQ could be transformed into a 0–1 mixed integer linear programming model. By solving the model, a global optimal solution can be obtained. In this paper, we present the improved formulation and the optimal solution for the case study.     

Global supplier development considering risk factors using fuzzy extended AHP-based approach

Global supplier development is a multi-criterion decision problem which includes both qualitative and quantitative factors. The global supplier selection problem is more complex than domestic one and it needs more critical analysis. The aim of this paper is to identify and discuss some of the important and critical decision criteria including risk factors for the development of an efficient system for global supplier selection. Fuzzy extended analytic hierarchy process (FEAHP) based methodology will be discussed to tackle the different decision criteria like cost, quality, service performance and supplier's profile including the risk factors involved in the selection of global supplier in the current business scenario. FEAHP is an efficient tool to handle the fuzziness of the data involved in deciding the preferences of different decision variables. The linguistic level of comparisons produced by the customers and experts for each comparison are tapped in the form triangular fuzzy numbers to construct fuzzy pair-wise comparison matrices. The implementation of the system is demonstrated by a problem having four stages of hierarchy which contains different criteria and attributes at wider perspective. The proposed model can provide not only a framework for the organization to select the global supplier but also has the capability to deploy the organization's strategy to its supplier.     

On the product selection and plant dimensioning problem under uncertainty

We present a two-stage full recourse model for strategic production planning under uncertainty, whose aim consists of determining product selection and plant dimensioning. The main uncertain parameters are the product price, demand and production cost. The benefit is given by the product net profit over the time horizon minus the investment depreciation and operation costs. The Value-at-Risk and the reaching probability are considered as risk measures in the objective function to be optimized as alternatives to the maximization of the expected benefit over the scenarios. The uncertainty is represented by a set of scenarios. The problem is formulated as a mixed 0–1 Deterministic Equivalent Model. The strategic decisions to be made in the first stage are represented by 0–1 variables. The tactical decisions to be made in the second stage are represented by continuous variables. An approach for problem solving based on a splitting variable mathematical representation via scenario is considered. The problem uses the Twin Node Family concept within the algorithmic framework known as Branch-and-Fix Coordination for satisfying the nonanticipativity constraints. Some computational experience is reported.     

消费者短视和以旧换新情形下设计架构与定价策略研究

耐用品的耐用性会抑制了新一代耐用品的销售,企业通常会采用以旧换新政策来激励新一代耐用品的销售。企业有两种产品设计架构策略:一体化架构策略与模块化架构策略,同时企业也有两种定价策略:静态定价策略和动态定价策略。在这种情况下,企业该如何确定产品设计架构和定价策略?在假定两期内消费者是短视情形下,论文建立了消费者与企业博弈模型分析和比较了三种情况。研究发现,无论是静态定价还是动态定价,无论是模块化架构还是一体化架构,购买了第一代产品的消费者都会整体更换成第二代产品;随着第二代核心系统的质量提升,以旧换新促使企业产品架构选择从一体化架构转变为选择模块化架构;当采用动态定价、第二代核心系统质量提升适中和折扣因子高时,企业会选择模块化架构;当企业采用模块化架构时,以旧换新政策会降低模块化产品第二代核心子系统和基础子系统之间的兼容性;如果两代产品之间的质量差距比较大,企业将会选择动态定价策略,反之会选择静态定价策略。     

PRODUCTION SCHEDULING OF CONTINUOUS FLOW LINES: MULTIPLE PRODUCTS WITH SETUP TIMES AND COSTS

The problem of production planning and setup scheduling of multiple products on a single facility is studied in this paper. The facility can only produce one product at a time. A setup is required when the production switches from one type of product to another. Both setup times and setup costs are considered. The objective is to determine the setup schedule and production rate for each product that minimize the average total costs, which include the inventory, backlog, and setup costs. Under the assumption of a constant production rate, we obtain the optimal cyclic rotation schedule for the multiple products system. Besides the decision variables studied in the classical economic lot scheduling problem (ELSP), the production rate is also a decision variable in our model. We prove that our solutions improve the results of the classical ELSP.     

Multi-time scale Markov decision process approach to strategic network growth of reverse supply chains

This paper addresses a complex set of decisions that surround the growth over time of reverse supply chain networks that collect used products for reuse, refurbishment, and/or recycling by processors. The collection network growth problem is decomposed into strategic, tactical and operational problems. This paper focuses on the strategic problem which is to determine how to allocate capital budget resource effectively to grow the network to meet long term collection targets and collection cost constraints. We model the strategic problem as a Markov decision process which can also be posed as multi-time scale Markov decision problem. The recruitment problem in a tactical level appears as a sub-problem for the strategic model. Using dynamic programming, linear programming and Q-Learning approaches, an heuristic is implemented to solve realistically sized problems. A numerical study demonstrates that the heuristic can obtain a good solution for the large-scale problem in reasonable time which is not possible when trying to obtain the optimal solution with the exact DP approach.     

On pricing and protection of complementary products

The business literature advises firms producing complementary products to sell the core product at a low price, but to price the complementary product at a higher premium. This strategy, however, is problematic if firms face competitors in the market for complementary products as well, as observed in recent years for instance in the market for printers/ink cartridges. Motivated by several measures the firms have taken in this market, the current paper analyzes whether firms are interested in protecting their complementary product from outside competition. We find that firms protect their products only if consumers underestimate the demand for the complementary product when deciding which core product to buy. Moreover, we investigate how the decision to protect the complementary product interacts with a firm’s pricing decision. We show that the price policy proposed in the business literature should only be applied, if consumers sufficiently underestimate their demand for the complementary product so that firms strongly protect these products from outside competition.     

On the Rank-Based Selection Strategy for the Group Interview Problem*

For the so-called group interview problem in which several groups of choice alternatives are presented sequentially to the decision maker, the optimal selection strategy is derived that minimizes the expected rank of the selected choice or purchased product. For the case in which the sequence of groups can be rearranged by the decision maker, a simple heuristic procedure is proposed for obtaining a near-optimal sequence of groups, and the performance of the heuristic procedure in a Monte Carlo simulation is accessed. According to the heuristic procedure, the consumer is advised to visit smaller stores first and then move to larger stores later to increase the likelihood of finding a better product. Finally, the optimal selection strategy and the heuristic procedure are compared with those proposed by Chun, Moskowitz, and Plante (1993) and the problem of locating a new store in an area where there are several competing stores is discussed. The optimal selection strategy and the heuristic procedure can be applied to many sequential decision problems such as the consumer search and purchase process.     

基于EVA与经营风险偏好的供应链鲁棒优化模型

在由一个制造商和多个外部供应商构成的多产品、多阶段供应链中,将经济增加值（EVA）作为体现价值创造的绩效指标,并考虑决策者的经营风险偏好,采用已知概率的离散情景描述资本成本与需求的波动情况,利用鲁棒随机规划方法,建立了以价值创造为目标的供应链鲁棒优化模型。应用分析的结果表明,模型能够将供应链的绩效与风险管理结合起来,减少资本成本与需求不确定对目标值的影响,得到具有鲁棒性的最优解,而且越是风险厌恶型的决策者越会为了保持较低的经营风险而放弃较大的EVA值。同时,决策者可选择不同的权重系数侧重于解鲁棒或模型鲁棒,保证供应链运作的鲁棒性,实现价值创造的目标。     

资源约束下可打断项目组合选择模型研究

首先,本文在已有可打断项目组合选择模型的基础上,引入了消耗性资源和可更新资源约束,构建了一个更符合实际的新模型;其次,为了达到模型简化的目的,本文给出了资金约束的现值表示,并给出了理论证明;最后,利用GAMS对模型进行了算例分析。数值实验结果表明：1）资源约束下的项目打断有时可以给企业带来积极效益,这有别于已有的研究;2）在考虑资源约束的情况下,资源消耗少且同时收益高的项目应优先执行;3）当资源的供给量较少时,资源约束是决定项目选择的关键因素。此外,通过企业实际的案例对数值实验结果进行了验证。     

Distributed Development and Product Line Decisions

Distributed product development is becoming increasingly prevalent in a number of industries. We study how the global distribution of product development impacts the profit‐maximizing product line that a firm offers. Specifically, we formulate a model to understand the linkage between cost arbitrage as a driver of distributed development and consequent market implications such as customer perceived quality loss to remotely developed products. Analysis of the model reveals that a firm should expand the product line for a development‐intensive good only at intermediate values of cost advantage and quality loss. We modify the base model to include development capacity constraints as a driver of distributed development and find that the results are robust to this change. Our analysis affirms the need for product managers to incorporate the implications of distributed development in making their product line design decision.     

消费者反馈下企业质量改进投入决策Moran演化分析

基于消费者反馈机制，考虑生产同种产品的N个生产企业质量改进投入决策的演化轨迹。每个生产企业在权衡对消费者反馈的价值感知以及额外质量改进成本后有两种策略选择(质量优势策略或低成本策略)。基于Moran过程的固定点概率，计算出在有限的生产企业群体中，质量优势策略与低成本策略成功侵入的概率。通过对比两种策略的适应度，以及个体固定点概率与中性突变的概率大小，分别得到强选择与弱选择下策略占优的条件，并对结果进行仿真验证。研究发现：1)生产企业数量较少时，无论质量改进成本高低与否，只要消费者对质量水平有反馈并对企业收益造成影响，质量优势策略将占领整个群体；2)当生产企业对消费者反馈的感知效用远远高于额外质量改进成本时，质量优势策略为占优策略；3)对消费者反馈的感知效用略高于额外质量改进成本时，强选择下，质量优势策略占优，然而在弱选择下，占优策略取决于种群数量；4)企业对消费者反馈的重视程度取决于对自身产品的市场定位，本文将为目标定位于维持或占据不同市场份额的企业提供质量改进政策建议与指导。     

A Production–Inventory Model for a Push–Pull Manufacturing System with Capacity and Service Level Constraints

We study a hybrid push–pull production system with a two‐stage manufacturing process, which builds and stocks tested components for just‐in‐time configuration of the final product when a specific customer order is received. The first production stage (fabrication) is a push process where parts are replenished, tested, and assembled into components according to product‐level build plans. The component inventory is kept in stock ready for the final assembly of the end products. The second production stage (fulfillment) is a pull‐based assemble‐to‐order process where the final assembly process is initiated when a customer order is received and no finished goods inventory is kept for end products. One important planning issue is to find the right trade‐off between capacity utilization and inventory cost reduction that strives to meet the quarter‐end peak demand. We present a nonlinear optimization model to minimize the total inventory cost subject to the service level constraints and the production capacity constraints. This results in a convex program with linear constraints. An efficient algorithm using decomposition is developed for solving the nonlinear optimization problem. Numerical results are presented to show the performance improvements achieved by the optimized solutions along with managerial insights provided.     

Branch‐and‐Price Methods for Prescribing Profitable Upgrades of High‐Technology Products with Stochastic Demands*

This paper develops a model that can be used as a decision support aid, helping manufacturers make profitable decisions in upgrading the features of a family of high‐technology products over its life cycle. The model integrates various organizations in the enterprise: product design, marketing, manufacturing, production planning, and supply chain management. Customer demand is assumed random and this uncertainty is addressed using scenario analysis. A branch‐and‐price (B&P) solution approach is devised to optimize the stochastic problem effectively. Sets of random instances are generated to evaluate the effectiveness of our solution approach in comparison with that of commercial software on the basis of run time. Computational results indicate that our approach outperforms commercial software on all of our test problems and is capable of solving practical problems in reasonable run time. We present several examples to demonstrate how managers can use our models to answer “what if” questions.     

待遇预定制养老基金资产组合与缴费计划最优决策——基于随机波动率Heston模型及Legendre对偶变换法

待遇预定制养老金制度在中国应用非常广泛,缴费制定和资产配置是此类养老金管理的两大核心问题。由此,面对随机波动的现实市场,文章针对待遇预定制养老基金的资产组合管理问题,应用最优控制理论,选用对数效用函数,建立Heston随机波动率模型;在难以求解随机微分Bellman方程的情况下,应用Legendre变换,将原来问题转化为对偶问题,从而求得原问题的解析解。在理论上,进一步丰富了资产组合问题的随机最优控制模型的构建和随机微分方程的求解理论。在实践上,确定了养老金管理风险资产配置比例和缴费水平,给出了最优决策与总资产、发放待遇、净资产与风险溢价之间的数量关系,从而实现养老基金管理的最优资产配置和最低缴费水平的效用目标。     

Economic Lot Scheduling Problem with Returns

Motivated by a case study of a company that produces car parts, we study the multi‐product economic lot scheduling problem for a hybrid production line with manufacturing of new products and remanufacturing of returned products. For this economic lot scheduling problem with returns (ELSPR), we consider policies with a common cycle time for all products, and with one manufacturing lot and one remanufacturing lot for each product during a cycle. For a given cycle time, the problem is formulated as a mixed integer linear programming (MIP) problem, which provides the basis for an exact solution. The application of this model for one of the core products of the case study company indicates a 16% reduction in cost compared to the current lot scheduling policy.     

基于非参数估计框架的期望效用最大化最优投资组合

本文基于期望效用最大化和非参数估计框架研究了最优投资组合选择问题。和以往大多文献假定资产收益率服从某些特定分布不同资产收益率的分布类型无需作任何假设。首先在一般效用函数下,利用组合收益率密度函数的非参数核估计给出了期望效用的基本非参数估计公式,并建立了期望效用最大化投资组合选择问题的基本框架。然后,在投资者具有幂效用函数的假定下,给出了期望效用具体的非参数计算公式,并给出了求解最大期望效用的数值算法。最后,利用中国证券交易所11支股票日收益率的真实数据给出了一个数值算例。本文提出的非参数估计框架具有一般性,还可以进一步用来研究各种现实条件下（如各种现实不等式约束和具有交易成本）的投资组合管理问题。     

Resource portfolio planning of make-to-stock products using a constraint programming-based genetic algorithm

The investment on facilities for manufacturing high-tech products requires a large amount of capital. Even though the demands of such products change dramatically, a company is forced to implement some make-to-stock policies apart from a regular make-to-order production, so that the capacity of expensive resources can be highly utilized. The inherent characteristics to be considered include finite budget for investing resources, lump demands of customers, long production horizon, many types of products to mix simultaneously, time value of capital and asset, technology innovation of resources, efficient usage of multiple-function machines, and limited capacity of resources. In addition to revenue gained from products and the salvage/assets of resources, a decision maker also needs to consider costs regarding inventory, backorder, and resource acquisition-related costs through procurement, renting, and transfer. This study thus focuses on the following issues: (i) how to decide on resources portfolio regarding the way and timing of acquisting resources, and (ii) how to allocate resources to various orders in each production period. The goal is to maximize the long-term profit. This study formulates the problem as a non-linear mixed integer mathematical programming model. A constraint programming-based genetic algorithm is developed. It has been demonstrated to solve the problem efficiently.     

A strategic and global manufacturing capacity management optimisation model: A Scenario-based multi-stage stochastic programming approach

Large-scale multinational manufacturing firms often require a significant investment in production capacity and extensive management efforts in strategic planning in an uncertain business environment. In this research we first discuss what decision terms and boundary conditions a holistic capacity management model for the manufacturing industry must contain. To better understand how these decision terms and constraints have been employed by the recent model developers in the area of capacity and resource management modelling for manufacturing, 69 optimisation-based (deterministic and stochastic) models have been carefully selected from 2000 to 2018 for a brief comparative analysis. The results of this comparison shows although applying uncertainty into capacity modelling (in stochastic form) has received a greater deal of attention most recently (since 2010), the existing stochastic models are yet very simplistic, and not all the strategic terms have been employed in the current model developments in the field. This lack of a holistic approach although is evident in deterministic models too, the existing stochastic counterparts proved to include much less decision terms and inclusive constraints, which limits them to a limited applications and may cause sub-optimal solutions. Employing this set of holistic decision terms and boundary conditions, this work develops a scenario-based multi-stage stochastic capacity management model, which is capable of modelling different strategic terms such as capacity level management (slight, medium and large capacity volume adjustment to increase/decrease capacity), location/relocation decisions, merge/decomposition options, and product management (R&D, new product launch, product-to-plant and product-to-market allocation, and product phase-out management). Possibility matrix, production rates, different financial terms and international taxes, inflation rates, machinery depreciation, investment lead-time and product cycle-time are also embedded in the model in order to make it more practical, realistic and sensitive to strategic decisions and scenarios. A step-by-step open-box validation has been followed while designing the model and a holistic black-box validation plan has been designed and employed to widely validate the model. The model then has been verified by deploying a real-scaled case of Toyota Motors UK (TMUK) decision of mothballing one of their production lines in the UK after the global recession in 2010.     

A SYSTEMATIC STRATEGY FOR OPTIMIZING MANUFACTURING OPERATIONS

A manufacturing optimization strategy is developed and demonstrated, which combines an asset utilization model and a process optimization framework with multivariate statistical analysis in a systematic manner to focus and drive process improvement activities. Although this manufacturing strategy is broadly applicable, the approach is discussed with respect to a polymer sheet manufacturing operation. The asset utilization (AU) model demonstrates that efficient equipment utilization can be monitored quantitatively and improvement opportunities identified so that the greatest benefit to the operation can be obtained. The process optimization framework, comprised of three parallel activities and a designed experiment, establishes the process-product relationship. The overall strategy of predictive model development provided from the parallel activities comprising the optimization framework is to synthesize a model based on existing data, both qualitative and quantitative, using canonical discriminant analysis, to identify main effect variables affecting the principal efficiency constraints identified using AU, operator knowledge and order-of-magni-tude calculations are then employed to refine this model using designed experiments, where appropriate, to facilitate the development of a quantitative, proactive optimization strategy for eliminating the constraints. Most importantly, this overall strategy plays a significant role in demonstrating, and facilitating employee acceptance, that the manufacturing operation has evolved from an experienced-based process to one based on quantifiable science.