A multi-objective robust optimization model for multi-product multi-site aggregate production planning in a supply chain under uncertainty

Manufacturers need to satisfy consumer demands in order to compete in the real world. This requires the efficient operation of a supply chain planning. In this research we consider a supply chain including multiple suppliers, multiple manufacturers and multiple customers, addressing a multi-site, multi-period, multi-product aggregate production planning (APP) problem under uncertainty. First a new robust multi-objective mixed integer nonlinear programming model is proposed to deal with APP considering two conflicting objectives simultaneously, as well as the uncertain nature of the supply chain. Cost parameters of the supply chain and demand fluctuations are subject to uncertainty. Then the problem transformed into a multi-objective linear one. The first objective function aims to minimize total losses of supply chain including production cost, hiring, firing and training cost, raw material and end product inventory holding cost, transportation and shortage cost. The second objective function considers customer satisfaction through minimizing sum of the maximum amount of shortages among the customers’ zones in all periods. Working levels, workers productivity, overtime, subcontracting, storage capacity and lead time are also considered. Finally, the proposed model is solved as a single-objective mixed integer programming model applying the LP-metrics method. The practicability of the proposed model is demonstrated through its application in solving an APP problem in an industrial case study. The results indicate that the proposed model can provide a promising approach to fulfill an efficient production planning in a supply chain.     

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.     

Integrated multi-period cell formation and subcontracting production planning in dynamic cellular manufacturing systems

In this paper, an integrated mathematical model of the multi-period cell formation and production planning in a dynamic cellular manufacturing system (DCMS) is proposed with the aim of minimizing machine, inter/intra-cell movement, reconfiguration, partial subcontracting, and inventory carrying costs. This paper puts emphasis on the effect of the trade-off between production and outsourcing costs on the re-configuration of the cells in cellular manufacturing systems (CMSs) under a dynamic environment, in which the product mix is different from a period to another resulting in the operational dynamism in the cells. The proposed model is verified by a number of numerical examples and related sensitivity analysis.     

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.     

论提高采油工程规划方案的质量

阐述了在采油工程规划方案编制中,采用"PDCA"质量环运行模式使质量管理体系贯穿于采油工程规划质量形成的全过程。对采油工程规划系统过程中单个过程及其之间的联系和相互作用进行连续的控制,保证了采油工程规划方案编制工作的顺利实施,提高了采油工程规划方案编制质量。     

Operational model for planning the harvest and distribution of perishable agricultural products

This paper presents an operational model that generates short term planning decisions for the fresh produce industry. In particular, the application developed helps the grower to maximize his revenues by making production and distribution decisions during the harvest season. The main motivation for this model comes from the fact that the profitability of producers is highly dependent on the handling of short term planning in the harvest season. Some of the factors affecting profitability include the management of labor costs, the preservation of the value of perishable crops, and the use transportation modes that provide the best trade-off between time (quality of products) and cost. These issues are interrelated, and their judicious management is fundamental for attaining good financial results. The results of the proposed planning model indicate that significant savings can be obtained by managing the trade-off of the freshness at the delivery of the product with the added labor and transportation cost at the grower's side. Moreover the results also show that dynamic, information based, management practices might be preferred over traditional practices based in fixed labor allocation and distribution practices.     

A hierarchical process industry production–distribution planning model

Global and multinational companies are subject to government regulations in addition to other international uncertainties due to operation in diverse geographic locations. Such government regulations often affect the cost of raw materials adversely which in turn creates adverse impact on product cost and forces the decision makers to re-evaluate current production–distribution plan. This paper presents an integrated supply chain model for simultaneous strategic and operational planning of a strategic business unit (SBU) in a global supply chain affected by government regulations. The model incorporates impact of changes in cost of inputs on expected product cost and solves for an optimal strategic and operational plan for the entire supply chain. In addition, the model includes exchange rates, border crossing costs and solves a multi-period model with due consideration of uncertainties in demand and transportation time.     

A master production scheduling procedure for stochastic demand and rolling planning horizons

The problem of interest is a one product, uncapacitated master production schedule (MPS) in which decisions are made under rolling planning horizons. Demand is stochastic and time varying, and effectiveness is measured by inventory holding, production setup, and backorder costs.Typically, in both the research literature and the business practice the stochastic nature of the problem is modeled in an ad hoc fashion. The stochastic MPS problem is usually solved by adding safety stock to production quantities obtained from a deterministic lot-sizing algorithm. Here, the stochastic nature of the problem is explicitly considered, as an optimal algorithm for solving the static probabilistic dynamic lot-sizing problem is adapted to rolling planning horizons. The resulting algorithm is found to dominate traditional approaches over a wide variety of experimental factors, reducing total costs by an average of 16% over traditional methods.     

电网规划研究平台的框架设计

电网规划研究平台是实现电网规划业务统一组织、电网规划数据统一管理的重要平台，能有效提高我国电网规划工作的合理性、科学性。基于空间数据库技术，采用工程时态的思路进行平台数据模型设计。以地理信息系统为实现手段，采用B／S和C／S结合的网络架构，开发电网网架分析功能，构建平台的原型系统。研究平台具有信息化、可视化、交互性和智能化的特点，能够将规划工作管理和功能技术实现进行有效结合，有助于建设统一规划体系。     

谈项目质量策划

介绍了项目质量策划的主要内容和实施步骤。项目质量策划的主要内容包括产品实现的策划、设计和开发的策划、采购策划、生产和服务的策划、质量改进的策划。项目质量策划的主要步骤包括项目总体策划和细部策划两部分，总体策划包括确定组织机构与职责、制定项目总体目标、确定项目所需的资源及项目的过程和子过程（包括分包过程）、确定项目控制要求等；项目总体策划完成后在每个过程实施前还要作进一步的详细策划工作，主要涉及质量目标的分解、质量职责的分解、确定每一过程的具体控制方法和所需要的控制文件和记录等等。     

A JIT lot-splitting model for supply chain management: Enhancing buyer–supplier linkage

This study develops a buyer–supplier coordination model to facilitate frequent deliveries in small lot sizes in a manufacturing supply chain. The proposed model, based on the integrated total relevant costs of both buyer and supplier, determines optimal order quantity, the number of deliveries/setups, and shipping quantity over a finite planning horizon in a relatively simple JIT single buyer single supplier scenario. Under deterministic conditions for a single product, we show that the optimal delivery policy adopted by both buyer and supplier in a cooperative manner can be economically beneficial to both parties. It is shown that the optimal delivery size can be unique, regardless of the order quantity and the number of deliveries. Numerical results are also presented.     

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.     

An integrated product planning model for pricing and bundle selection using Markov decision processes and data envelope analysis

When bundling products during the product planning stage, there are a number of possible combinations that can be offered to customers. Consider a firm that offers N distinctive products, then there are 2^N−(N+1) possible bundling combinations. Now, if we wish to make pricing and bundle selection decisions, keeping in my mind limited inventory and a finite time horizon, then the size of the state space could be very large and finding an optimal solution could be impossible. To tackle this issue, we formulate an integrated model that utilizes a Markov decision process and data envelope analysis. Bundle selections are made using data envelope analysis in each decision epoch. Once the efficient bundles are selected they are priced by solving a Markov decision process using dynamic programming. Numerical examples are solved to exhibit the model's potential in solving real-world problems.     

大数据成为现实生产要素的企业实现机制:产品创新视角

数字经济的崛起,使数据成为新的生产要素,但现有研究对数据成为现实生产要素的企业实现过程缺乏理论剖析与实证研究。鉴于产品创新是企业活动中各种生产要素最具集成特征的一环,本文从产品创新视角,构建“大数据资源—企业能力—产品创新绩效”链式中介模型开展实证研究,探讨大数据从可能的生产要素成为企业现实生产要素的实现机制。基于特征映射逻辑,将大数据分析、组织学习、惯例更新、行业竞争压力、行业信息技术使用强度分别视为劳动、知识、管理、资本、技术的替代变量,将企业的产品创新实现机制映射为数据与其他五个生产要素相结合成为现实生产要素的实现路径模型,提出数据是将现有生产要素进一步联系起来的桥梁型生产要素的观点,形成对数据作为生产要素的新认知。本文结论为数据作为新生产要素的思想提供了企业产品创新视角的理论解释,也为企业大数据创新实践提供了管理启示。     

A practical approach to sensitivity analysis in linear programming under degeneracy for management decision making

Linear programming (LP) is a widely used tool in management decision making. Theoretically, sensitivity analysis of LP problems provides useful information for the decision maker. In practice, however, most LP software provides misleading sensitivity information if the optimal solution is degenerate. The paper shows how sensitivity analysis of LP problems can be done correctly when the optimal solution is degenerate. A production planning example is presented to illustrate the incorrect sensitivity analysis results automatically provided by most LP solvers. The general characteristics of the misleading results and the possible effects of this incorrect information on management decisions are also discussed.     

电机生产计划计算机优化系统

本文介绍了电机生产计划计算机优化系统.该系统主要由模型优化和数据处理两大部分组成,采用结构化方法进行系统分析与设计.该系统解决了合理、充分地利用有限原材料的问题,为工厂制订更合理、更科学的生产计划提供了必要的信息。     

Designing a reverse logistics network for optimal collection, recovery and quality-based product-mix planning

This study proposes an integrated, reverse logistics supply chain planning process with modular product design that produces and markets products at different quality levels. A mixed integer programming (MIP) model formulates the overall planning process required to maximize profit by considering the collection of returned products, the recovery of modules and the proportion of the product mix at different quality levels. This paper proposes the collection of returnables (end-of life, defective, product under warranty) through retail outlets combined with the recovery of modules from the collected products using a network of recovery service providers. The proposed modular product design approach would create a design criterion that provides an improved recovery process at a lower cost. This study uses a total supply chain view that considers the production, transportation and distribution of products to customers, while a numerical problem illustrates the applicability of the models.     

On the present worth of multivariate quality loss

Quality loss function has been introduced, by Taguchi, to be a quality performance measure for products since the 1980s. In this paper, we extend the work of Teran et al. [The Engineering Economist 42 (1) (1996) 39–52] and incorporate the concept of time value of money into the multivariate loss function. First, the model for the present worth of the expected multivariate quality loss (PWML) is established and its solution procedure is developed. Then, an example is provided to illustrate how the model can be applied. Some sensitivity analyses are conducted to study the effects of planning horizon, customer discount rate and coefficients of parameter drift on the optimal means at production time and the associated quality loss. From the results of analysis, the longer the planning horizon of the product is, the farther the means should be set relative to the targets at production time. Also, as the customer discount rate increases, the mean should be set closer to the target at production time.     

An optimization approach for managing fresh food quality throughout the supply chain

One of the most challenging tasks in today's food industry is controlling the product quality throughout the food supply chain. In this paper, we integrate food quality in decision-making on production and distribution in a food supply chain. We provide a methodology to model food quality degradation in such a way that it can be integrated in a mixed-integer linear programming model used for production and distribution planning. The resulting model is applied in an illustrative case study, and can be used to design and operate food distribution systems, using both food quality and cost criteria.     

The MPS parameterization under lead time uncertainty

The supply planning of assembly systems under lead times uncertainty is studied. The used criteria is the sum of the average holding cost for the components, the average backlogging cost for the finished product, and the setup cost. The decision variables are the planned lead times of components and the periodic ordering quantity. A new generalized Newsboy model gives the optimal solution under the assumption that the lead times of the different types of components follow the same distribution probability, and that the holding costs per period of the ordered quantities are the same.