能力与资源双重约束下的启发式组合生产计划研究

企业面向市场的产品交付能力,一般受约束于各制造环节的生产能力,而各制造环节的生产能力又受约束于其可得的制造资源。研究了在局部生产能力具有上限,且总体制造资源受约束的情况下,如何制定生产计划,以使总体利润最大的问题。建立了基于能力和资源双重约束下的生产计划组合优化模型,设计了一种"双线退火"的模拟退火算法,来启发式求解此类连续变量组合优化问题,并以大量算例计算验证了双线模拟退火算法的效率和正确性。最后结合某耐火材料企业进行了应用分析研究,展示了模型的应用原理和算法的求解效果。     

基于遗传算法的虚拟企业协同资源优化问题研究

面向复杂零件的协同制造,以工艺流程为核心将协同制造任务进行分解,并有效利用"逻辑制造单元"和"逻辑加工路线"等概念描述复杂零件的协同制造任务,最终目标是形成基于复杂零件工艺流程的、可支撑异地协同生产的加工路线。对复杂零件协同制造的制造资源优化配置问题进行了数学分析和描述,建立了问题的目标函数与约束条件。本文以加工时间、运输费用和加工质量作为目标,约束条件包括顺序约束、释放期约束、时间约束、交货期约束、成本约束和质量约束,最终将资源优化配置问题归结为多目标优化问题,并利用遗传算法进行求解,得到了较为满意的结果。通过实例分析,将模型应用于某型号发动机叶片协同制造,说明采用本论文的模型可以有效解决复杂零件协同制造的资源优化配置问题。     

A method combining genetic algorithm with simultaneous perturbation stochastic approximation for linearly constrained stochastic optimization problems

This paper considers the optimization of linearly constrained stochastic problem which only noisy measurements of the loss function are available. We propose a method which combines genetic algorithm (GA) with simultaneous perturbation stochastic approximation (SPSA) to solve linearly constrained stochastic problems. The hybrid method uses GA to search for optimum over the whole feasible region, and SPSA to search for optimum at local region. During the GA and SPSA search process, the hybrid method generates new solutions according to gradient projection direction, which is calculated based on active constraints. Because the gradient projection method projects the search direction into the subspace at a tangent to the active constraints, it ensures new solutions satisfy all constraints strictly. This paper applies the hybrid method to nine typical constrained optimization problems and the results coincide with the ideal solutions cited in the references. The numerical results reveal that the hybrid method is suitable for multimodal constrained stochastic optimization problem. Moreover, each solution generated by the hybrid method satisfies all linear constraints strictly.     

Managerial Involvement and Perceptions of Strategy Process

Prior academic research attests to both positive and negative effects of involvement on the process of developing strategy. On the one hand, it has been argued that involvement strengthens shared vision, increases rationality and improves adaptiveness in strategy-making. On the other hand, involvement is said to lead to intense political behaviour, increased cultural inertia and more constraints in the strategy process. The purpose of this study is to investigate the relative importance of these effects. In a survey of over 6,000 managers, we find that their reported levels of involvement are positively associated with perceptions of strategy development processes that are more rational, more focused by a shared vision, and more adaptive. In addition, involvement is negatively associated with statements describing the process as top-down, influenced by politics and slowed by internal culture. Moreover, those who are more involved tend to see business and non-business constraints as less important in determining strategy. We argue that these associations between involvement and desirable features of strategy process are important because perceptions are the basis of managerial behaviour. Thus, managers who are more involved in strategy not only see the process in a more favourable light but also act in ways that make the process more effective. The main implication of these findings is that for most organisations increasing involvement improves the strategy process.     

A LABORATORY STUDY OF INDIVIDUAL SEARCH PATTERNS IN A DECISION-MAKING SITUATION

This study investigates the effects of time and probability constraints on the search process in a decision-making situation. The setting was a behavioral laboratory in which subjects were asked to choose one art print from a selection of 260 prints. The prints were in sets of five for convenience in showing. In the nonconstraint condition, subjects were able to set their own pace in viewing prints and could obtain any print in the whole set that was desired. Under the constraint condition, the time and probability constraints were both operative. Under the time constraint, viewing additional sets required increasing amounts of time. Under the probability constraint, there was a constantly reducing probability—down to a minimum of ten percent—of obtaining an earlier print as more prints were viewed. In comparison to unconstrained subjects, constrained subjects viewed fewer prints and did more rechecking in total while the search was in process.     

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.     

Big-data analytics framework for incorporating smallholders in sustainable palm oil production

Abstract

This paper aims to address the constraints faced in incorporating smallholders in sustainable palm oil production. There exists literature that acknowledges the need for incorporating smallholders in the production of sustainable palm oil but none has proposed a solution beyond ‘Roundtable on Sustainable Palm Oil’ (RSPO) certification. In the current business scenario, several organizations are struggling to procure RSPO certified palm oil even after committing huge resources. RSPO, though a good first step, has a major process and capacity constraints resulting in long processing times, delays, and lack of traceability for the customers. This paper proposes a Big Data Analytics framework enabled by cutting-edge technologies to incorporate smallholders in the RSPO certification process. The data used was collected through farm visits, stakeholder meetings, key stakeholder interviews, and, secondary sources. The proposed framework not only addresses the limitation of the current certification process but also converts it from being punitive to preventive. The outcomes of this research will be extremely useful for all the stakeholders in the palm oil supply chain.     

Frugality-based advantage

We analyze frugality-based advantage and explain its types and implications. Frugality-based advantage is an advantage that a firm achieves over competitors in its ability to develop and use frugal innovations, that is, innovations that overcome external resource constraints. We differentiate among three types of frugality-based advantages based on the external constraints they solve: (1) Input frugality-based advantage, which is the result of addressing restrictions in the provision of inputs needed for the production process; (2) Income frugality-based advantage, which is driven by solving limitations in the income of consumers; and (3) Infrastructure frugality-based advantage, which is the outcome of resolving constraints in the hard and soft infrastructure of the country. We explain how these three types of frugality-based advantages differ in their transferability across locations and their sustainability across time. Frugality-based advantage complements the resource-based view by explaining how the scarcity of external resources, rather than their abundance, can support the advantage of some firms.     

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.     

不完备市场中再保险-投资的M-V及M-VaR最优策略

本文研究不完备市场中的保险公司再保险-投资问题.在保险公司盈余过程服从扩散过程的假设及不完备市场条件下,通过求解带约束的二次优化问题和二次优化对偶问题,分别得到均值-方差(M-V)模型和均值-在险价值(M-VaR)模型下保险公司再保险-投资问题的最优常数再调整策略及其有效前沿,对两种模型下的结论进行比较发现:两种模型下的最优常数再投资策略都表现为特定"共同基金"的倍数,但对最优倍数的选择不一定相同;两种模型下的再保险-投资有效前沿都表现为射线,但射线的起始点及斜率(风险价格)不一定相同.     

Exact solution method to solve large scale integer quadratic multidimensional knapsack problems

In this paper we develop a branch-and-bound algorithm for solving a particular integer quadratic multi-knapsack problem. The problem we study is defined as the maximization of a concave separable quadratic objective function over a convex set of linear constraints and bounded integer variables. Our exact solution method is based on the computation of an upper bound and also includes pre-procedure techniques in order to reduce the problem size before starting the branch-and-bound process. We lead a numerical comparison between our method and three other existing algorithms. The approach we propose outperforms other procedures for large-scaled instances (up to 2000 variables and constraints). A extended abstract of this paper appeared in LNCS 4362, pp. 456–464, 2007.     

考虑交货因素的热轧无缝钢管订单排程模型与算法

无缝钢管的市场需求具有多品种、小批量的特点，为了在满足客户需求的同时保证高效连续化生产，文章在满足生产工艺特征的基础上将配送地址和交货期等合同因素引入热轧无缝钢管订单排程问题中，建立了以适期交货、订单集中生产配送和最小化机器设备调整为优化目标的订单排程优化模型，并设计了两阶段求解算法：首先，以订单交货期与配送地址差异最小为目标，基于凝聚策略设计了订单聚类算法，将具有相同工艺约束、相似合同要求的订单进行聚类，并形成初始轧制计划；然后，以设备调整和提前/拖期最小为目标，设计混合变邻域搜索算法，对初始轧制批次进行排程优化。基于实际订单数据的实验结果表明，模型和算法对问题的描述和求解是可行有效的。     

Stochastic simulation based genetic algorithm for chance constrained data envelopment analysis problems

Genetic algorithm (GA) approach is developed for solving the P-model of chance constrained data envelopment analysis (CCDEA) problems, which include the concept of “Satisficing”. Problems here include cases in which inputs and outputs are stochastic, as well as cases in which only the outputs are stochastic. The basic solution technique for the above has so far been deriving “deterministic equivalents”, which is difficult for all stochastic parameters as there are no compact methods available. In the proposed approach, the stochastic objective function and chance constraints are directly used within the genetic process. The feasibility of chance constraints are checked by stochastic simulation techniques. A case of Indian banking sector has been presented to illustrate the above approach.     

多项目人力资源调度实证研究

针对某公寓大修项目,建立有关人力资源约束下的多项目进度管理问题混合整数规划模型。其中考虑了多种约束,如项目对人员能力、水平的不同要求,而人员又具有多种能力及水平;目标为满足约束的条件下总成本最小化,其中包含按时间计费的工资,和福利等的固定费用。为了简化计算,采用列生成法把复杂的多项目模型分解为一个主问题和多个子问题并协调主问题和子问题求解。同时由于子问题的复杂性难以精确求解,采用启发式算法求解:首先由基于优先原则的启发式方法给出问题的初始解,再由遗传算法寻优。最后通过该实际案例的应用,表明此方法能够快速有效的解决实际问题,给决策者提供信息,帮助指导实践。     

Competitive procurement strategies: Building strength and reducing vulnerability

Current economic conditions and resource constraints call into question many of the implicit assumptions to which traditional strategic planning models subscribe. An attempt is made to bridge the gap between product/market decisions and concerns for present and future resource availability. The author develops the notion of strategic procurement planning as an approach to make more salient the potential competitive ramifications of procurement related decisions and for better integrating purchasing into the corporate planning process.     

基于混合蚁群算法的生产系统设施规划问题研究

设施规划问题主要研究生产设备的布局规划,从而减小厂区内的物料搬运成本。一个有效的设施规划有利于生产过程中整体运作效率的提高。随着市场竞争的日趋激烈,市场环境处于不断的变化之中,制造企业需不断对设施布局进行重新规划来适应不断变化的市场环境对产品需求量的影响,并达到降低成本的目的。这一问题便需要用多阶段设施规划（MFLP）的方法来解决。本文提出了一种改进的混和蚁群算法（HACO）来解决带有财务预算约束的多阶段设施规划问题,并将此方法与其他一些典型的启发式算法进行了对比分析。结果表明,本文提出的HACO算法是求解带有财务预算约束的MFLP问题的一种有效的方法。     

Calibrated Incentive Contracts

This paper studies a dynamic agency problem which includes limited liability, moral hazard, and adverse selection. The paper develops a robust approach to dynamic contracting based on calibrating the incentive properties of simple benchmark contracts that are attractive but infeasible, due to limited liability constraints. The resulting dynamic contracts are detail‐free and satisfy robust performance bounds independently of the underlying process for returns, which need not be i.i.d. or even ergodic.     

学习智障对组织知识转化影响研究

在剖析了学习智障的构成要素(结构智障、管理智障、文化智障、心智智障、执行智障)基础上,分析了学习智障对知识转化的削弱作用,构建了学习智障对知识转化影响的结构模型,提出了相关研究假设;运用实证研究方法,对259份有效问卷的综合分析结果表明:组织学习智障的5个构成要素对知识转化过程的削弱作用总体比较明显,但管理智障对社会化过程、结构智障和执行智障对外显化过程以及文化智障对组合化过程的制约作用不明显,但组织整体学习智障对组织知识转化过程具有显著的负向作用。     

Information for more effective strategic planning

This paper illustrates how an entity—called a ‘strategic data base’ (SDB)—can be developed to provide important information in a form which makes it directly useful in various phases of a strategic planning process. The strategic data bases are concise statements of the organizational and environmental situations which define the organization's most salient problems, opportunities, and constraints. These SDBs may be developed through a participative process involving tasks forces which are made up of managers representing the diverse interests of the organization. The strategic data bases thereby become important informational inputs to planning which can directly serve to enhance the quality of planning decisions. Moreover, the process of developing SDBs can be an important learning device for those middle managers who can become involved in such a process at a much earlier point in their career than that at which they might normally engage substantively in the organization's overall strategic choice process.