基于系统柔性的MTS-MTO混合生产决策

研究生产商采用MTS、MTO混合作业的方式为不同客户提供产品和服务的策略。计划利用一组可灵活控制的动态设备处理那些不同需求的MTS和MTO生产业务,为此,我们开发了一个多服务台的排队模型,利用拟生灭过程和相位型分布得到了MTS、MTO排队系统平衡条件和稳态概率矩阵几何解。通过求解分块矩阵方程组,给出了系统队列长度、平均等待队长、顾客服务水平等绩效测度指标。建立了系统运作成本最优化的数学模型,采用搜索算法,确定了关键参数的边界值,找到了混合系统运作的最优策略。数值模拟和系统绩效比较分析结果显示：（1）动态切换策略能够更快速的帮助MTS恢复目标库存量,控制系统缺货风险,降低库存持有成本;（2）找到了满足顾客服务水平的最少的设备配置数量和库存成本最低的生产切换时间,且动态系统的平均队列长度低于静态系统;（3）混合运作策略减少了约2/3的静态系统平均队列长度,企业在队列长度减小的窗口期内可以接受更多订单和缩短MTO订单交货时间。     

Order Management in the Customization‐Responsiveness Squeeze*

Make‐to‐order (MTO) products may be either customized or standard, and customization can occur either at the configuration or component level. Consequently, MTO production processes can be divided into three customization gestalts: non‐customizers, custom assemblers, and custom producers. In this article, we examine how the multilevel nature of customization affects order management in processes that produce complex MTO products. We first empirically validate the existence of the three customization gestalts and subsequently, analyze the order management challenges and solutions in each gestalt in a sample of 163 MTO production processes embedded in seven different supply chains. In the analyses, we follow a mixed‐methods approach, combining a quantitative survey with qualitative interview data. The results show that important contingencies make different order management practices effective in different gestalts. Further qualitative inquiry reveals that some seemingly old‐fashioned practices, such as available‐to‐promise verifications, are effective but commonly neglected in many organizations. The results also challenge some of the conventional wisdom about custom assembly (and indirectly, mass customization). For example, the systematic configuration management methods—conventionally associated with project business environments—appear to be equally important in custom assembly.     

Application of interactive possibilistic linear programming to aggregate production planning with multiple imprecise objectives

Aggregate production planning (APP) addresses matching supply to forecast demand, with varying customer orders over the intermediate planning horizon. In real-world APP problems, input data and related parameters are commonly imprecise because information is incomplete or unavailable, and the decision maker (DM) must simultaneously consider conflicting objectives. This study develops an interactive possibilistic linear programming (i-PLP) approach to solve multi-product and multi-time period APP problems with multiple imprecise objectives and cost coefficients by triangular possibility distributions in uncertain environments. The imprecise multi-objective APP model designed here seeks to minimise total production costs and changes in work-force level with reference to imprecise demand, cost coefficients, available resources and capacity. Additionally, the proposed i-PLP approach provides a systematic framework that helps the decision-making process to solve fuzzy multi-objective APP problems, enabling a DM to interactively modify the imprecise data and parameters until a set of satisfactory solutions is derived. An industrial case demonstrates the feasibility of applying the proposed approach to a practical multi-objective APP problem.     

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.     

Scenario aggregation in single-resource production planning models with uncertain demand

In this paper we present an application of the scenario aggregation approach proposed by Rockafellar and Wets to a simple standard multi-product multi-period production planning problem with uncertain demand and setup cost modelled by logical zero-one variables. The uncertainty in demand is expressed by a number of demand scenarios. As compared with more traditional approaches that require distributional assumptions and/or estimates of parameters from historical demand data, the scenario approach offers greater flexibility and makes it possible to take subjective information into account. The scenario aggregation principle and the corresponding progressive hedging algorithm offer a theoretically sound basis for generating consistent solutions for production planning models with uncertain demand. Since the production planning problem studied in this paper is of mixed-integer type the original scenario aggregation approach cannot be applied directly. However, since the integer variables in the production planning model are indirectly coupled to the continuous production decisions an alternative method in which only the production quantities are used to couple the different realizations can be used. This paper is a first attempt to perform this form of coupling. We illustrate the ideas on a small example and use this example to demonstrate how the solution can be evaluated in terms of flexibility measures.     

A harmony search-based memetic optimization model for integrated production and transportation scheduling in MTO manufacturing

This paper investigates an integrated production and transportation scheduling problem in an MTO supply chain. A harmony search-based memetic optimization model is developed to handle this problem, in which certain heuristic procedures are proposed to convert the investigated problem into an order assignment problem. A novel improvisation process is also proposed to improve the optimum-seeking performance. The effectiveness of the proposed model is validated by numerical experiments. The experimental results show that (1) the proposed model can solve the investigated problem effectively and that (2) the proposed memetic optimization process exhibits better optimum-seeking performance than genetic algorithm-based and traditional memetic optimization processes.     

多品种集约生产计划问题的模糊方法

提出具有模糊需求量和模糊能力约束以及资本水平约束的多品种类集约生产计划问题 的模糊优化模型及模糊解方法. 通过对模糊需求量和模糊等式的描述,提出了模糊需求环境下 生产- 库存平衡方程的两种等价的描述方法, 并给出了模糊等式的实用解释. 建立了具有模 糊需求量和模糊能力约束集约生产计划问题的优化模型FMAPP ,并给出了求解模型的参数规 划方法.     

2MTO,a new mapping tool to achieve lean benefits in high-variety low-volume job shops

This paper proposes the ‘Mapping Tool for Make-To-Order companies’ (2MTO), a new approach to map, analyse and achieve lean benefits in high-variety-low-volume job shops. The focus is, mainly, on the transitional phase from push to pull, when there is a compelling need to acquire a deep understanding of the current state of the system under analysis. Sometimes, in this initial phase, a pure pull approach cannot be developed and a job shop configuration must be maintained. If so, 2MTO allows selecting a suitable hybrid Production Planning and Control system to control Work-In-Process and to limit lead times, so as to quickly achieve lean benefits, without significantly altering the original layout. 2MTO is fully explained making reference to an Italian precision mechanic company, where the application of the method led to a successful implementation of a Workload Control system.     

A robust optimization model for stochastic aggregate production planning

The aggregate production planning (APP) problem considers the medium-term production loading plans subject to certain restrictions such as production capacity and workforce level. It is not uncommon for management to often encounter uncertainty and noisy data, in which the variables or parameters are stochastic. In this paper, a robust optimization model is developed to solve the aggregate production planning problems in an environment of uncertainty in which the production cost, labour cost, inventory cost, and hiring and layoff cost are minimized. By adjusting penalty parameters, decision-makers can determine an optimal medium-term production strategy including production loading plan and workforce level while considering different economic growth scenarios. Numerical results demonstrate the robustness and effectiveness of the proposed model. The proposed model is realistic for dealing with uncertain economic conditions. The analysis of the tradeoff between solution robustness and model robustness is also presented.     

Adjusting MRP for dynamic differentiation of identical items for process customisation

In the make-to-order (MTO) environment, different customer orders often require similar products that share identical basic features but require different customisation processes. To achieve efficiency, these products are often manufactured in a group at the initial stages. A significant issue then emerges as to how to differentiate identical items at the intermediate stages so that they can be dispatched to different kinds of work centres for process customisation. This article adjusts the current material requirements planning (MRP) approach for the MTO mode. A yield-allocating approach is then proposed for adjusting the material demand plans according to real-time process quality information. An approach for dynamically differentiating identical customised items at intermediate stages is then presented. How to implement the proposed methodology in a computer-based environment is also briefly introduced. A case study demonstrates that the proposed methodology can fulfil the dynamical differentiation task and can result in more reasonable differentiation decisions than the traditional MRP approach.     

A Dynamic Disaggregation Approach to Approximate Linear Programs for Network Revenue Management

The linear programming approach to approximate dynamic programming has received considerable attention in the recent network revenue management (RM) literature. A major challenge of the approach lies in solving the resulting approximate linear programs (ALPs), which often have a huge number of constraints and/or variables. Starting from a recently developed compact affine ALP for network RM, we develop a novel dynamic disaggregation algorithm to solve the problem, which combines column and constraint generation and exploits the structure of the underlying problem. We show that the formulation can be further tightened by considering structural properties satisfied by an optimal solution. We prove that the sum of dynamic bid‐prices across resources is concave over time. We also give a counterexample to demonstrate that the dynamic bid‐prices of individual resources are not concave in general. Numerical experiments demonstrate that dynamic disaggregation is often orders of magnitude faster than existing algorithms in the literature for problem instances with and without choice. In addition, adding the concavity constraints can further speed up the algorithm, often by an order of magnitude, for problem instances with choice.     

Production and pricing problems in make-to-order supply chain with cap-and-trade regulation

This paper studies the production and pricing problems in MTO (make-to-order) supply chain containing an upstream manufacturer who produces two products based on MTO production and a downstream retailer. The manufacturer is regulated by cap-and-trade regulation and determines the wholesale prices of the two products. To comply with the regulation, the manufacturer can buy or sell emission permits through an outside market. The retailer determines its order quantities to meet the price-sensitive demands. We derive the optimal total emissions and production quantities of the two products, and based on them, we analyze the impact of emission trading price on the optimal production decisions and the two firms’ optimal profits. The emission trading decisions follow a two-threshold policy and the optimal total emissions are increasing in the cap. However, contrary to intuition, the optimal production quantities of the two products may be decreasing in the cap. The manufacturer׳s optimal profit is decreasing (increasing) in the buying (selling) price of emission permits, and that the retailer׳s optimal profit is decreasing in the buying (selling) price of emission permits. The optimal total emissions are decreasing in buying or selling price of emission permits, however, the optimal production quantities of the two products may be increasing (decreasing) in the buying (selling) price of emission permits. Numerical examples are conducted to illustrate our findings and some managerial insights are presented.     

Genetic algorithms for optimization of a mine system under uncertainty

The production planning of a mine system associated with mining, processing and refining stages dictates to determine optimal system parameters such as optimal production rates, location of refining facility and the best reconstruction time of production rates. This paper proposes a combination of the chance constrained programming (CCP) and the genetic algorithms (GA) to find the optimal system parameters simultaneously. In generic form the problem is expressed as the maximization of net present value of future cash flows such that the capacity constraint and predefined specifications are satisfied. The blending requirements expressed in the CCP are transformed into deterministic equivalents. A new form of the problem is solved by the GA. The approach was demonstrated on extraction, processing and refining of four iron ore mines with varying reserves, ore qualities, geological and topographic conditions, four mineral processing units and one refining facility. The results showed that the proposed algorithm could be used to determine optimal production rates, the facility location and the best reconstruction time.     

The impact of manufacturing and supply chain improvement initiatives: A survey comparing make-to-order and make-to-stock firms

Manufacturing firms aim at improving both internal and external processes to improve the competitive advantage. Such initiatives include lean practices as well as supplier rationalization and integration. In this paper, we analyze these improvement initiatives and their impact on business performance. In particular, we explore potential differences between make-to-order (MTO) and make-to-stock (MTS) firms. We use data from 216 Australian manufacturing firms. We find a clear difference of improvement focus between MTO and MTS firms. MTO firms exhibit a significant impact of supplier integration on business performance, but not for lean practices and supplier rationalization. The situation is completely reversed for MTS firms, since they have significant effects for internal lean practices and supplier rationalization, but not for logistics integration with supplier. The results show that the distinction between MTO and MTS firms is important when analyzing manufacturing and supply chain improvement initiatives.     

THE USE OF INDUSTRIAL SOFTWARE TO CREATE EXPERIENTIAL LEARNING ACTIVITIES IN OPERATIONS MANAGEMENT COURSES

Computer-based manufacturing planning and control (MPC) systems are widely used in industry to gain competitive advantage through integration and coordination of managerial activities. In collegiate business schools, important operations management activities are taught and studied, often by sequential examination of discrete topics such as aggregate production planning, master production scheduling, capacity planning, material planning, and production activity control. This paper explores the potential use of industrial MPC software in the classroom to create experiential learning activities that address the dynamic and integrative nature of operations management. Experiences with this pedagogical approach over the past decade are reported.     

Simultaneous production and logistics operations planning in semicontinuous food industries

The production and logistics operations planning in real-life single- or multi-site semicontinuous food industries is addressed in this work. A discrete/continuous-time mixed integer programming model, based on the definition of families of products, is developed for the problem in question. A remarkable feature of the proposed approach is that in the production planning problem timing and sequencing decisions are taken for product families rather than for products. However, material balances are realized for every specific product, thus permitting the detailed optimization of production, inventory, and transportation costs. Changeovers are also explicitly taken into account and optimized. Moreover, alternative transportation modes are considered for the delivery of final products from production sites to distribution centers. The efficiency and the applicability of the proposed approach is demonstrated by solving to optimality two industrial-size case studies, for an emerging real-life Greek dairy industry.     

Joint production and subcontracting planning of unreliable multi-facility multi-product production systems

This article addresses the problem of joint optimization of production and subcontracting of unreliable production systems. The production system considered presents a common problem in the pharmaceutical industry. It is composed of multiple production facilities with different capacities, each of which is capable of producing two different classes of medications (brand name and generic). The resort to subcontracting is double: first, it involves the quantity of products received on a regular basis in order to compensate for insufficient production capacity in existing facilities, second, when needed, urgent orders are also launched in order to reduce the risk of shortages caused by breakdowns of manufacturing facilities. Failures, repairs and urgent delivery times may be represented by any probability distributions.The objective is to propose a general control policy for the system under consideration, and to obtain, in the case of two facilities, optimal control parameters that minimize the total incurred cost for a specific level of the customer service provided. Given the complexity of the problem considered, an experimental optimization approach is chosen in order to determine the optimal control parameters. This approach includes experimental design, analysis of variance, response surface methodology and simulation modeling. It allows the accurate representation of the dynamic and stochastic behaviors of the production system and the assessment of optimal control parameters. Other control parameters which represent the subcontracting are introduced and three joint production/subcontracting control policies (general, urgent, regular) are compared to one another. The proposed joint production/regular subcontracting control policy involves a cost decrease of up to 20%, as compared to results obtained by Dror et al. [1], who used a simplified control policy in addition to a heuristic solution approach for a real case study. This policy offers not only cost savings, but is also easier to manage, as compared to that proposed by Dror et al. [1]. Numerical examples and a sensitivity analysis are also performed to illustrate the robustness of the proposed control policy and the solution approach.     

Ho-Lee利率模型下带有零息票债券的投资-消费模型

在随机利率环境下研究一类带有零息票债券的投资-消费问题,其中假设无风险利率是服从Ho-Lee利率模型的随机过程,且金融市场由一种无风险资产、一种风险资产和一种零息票债券所构成。投资人希望选择一种最优投资-消费策略来最大化其有限时间段内终端财富和累积消费的期望效用。文章应用动态规划原理和变量替换方法得到了幂效用和对数效用下最优投资-消费策略的显示解。算例分析演示了最优投资-消费策略随市场参数的变化而变化的动态行为,并给出了一些经济学涵义。     

A stochastic programming model for production planning of perishable products with postponement

This study addresses the production planning problem for perishable products, in which the cost and shortage of products are minimised subject to a set of constraints such as warehouse space, labour working time and machine time. Using the concept of postponement, the production process for perishable products is differentiated into two phases to better utilise the resources. A two-stage stochastic programming with recourse model is developed to determine the production loading plan with uncertain demand and parameters. A set of data from a toy company shows the benefits of the postponement strategy: these include lower total cost and higher utilisation of resources. The impact of unit shortage cost under different probability distribution of economic scenarios on the total cost is analyzed. Comparative analysis of solutions with and without postponement strategies is also performed.