三级供应链中信息共享的价值研究

通过建立模型研究三级供应链中信息共享的价值.研究建立简单的三级供应链模型,在Lee H L,So KC,Tang C S模型的基础上重点研究并量化了第三级即供应商共享需求信息所带来的价值,并对影响参数进行matlab仿真实验.研究表明信息共享可以降低供应商库存水平和期望费用,给供应商带来价值;供应商在制造商参与信息共享的条件下要比不参与时更优,制造商在三级供应链需求信息共享中地位至关重要.     

JIT环境下三级供应链供需协调模型研究

模型以整个供应链成本最低为目标,在JIT环境下通过对原材料供应商、制造商和零售商等各阶段库存模型以及其他相关成本的研究,得到供应链的最优物料补充策略,制造商的最优生产策略以及产成品运输策略。供应链结构包括一个制造商,多个原材料供应商和零售商。研究表明,通过有效的加强整条供应链的协作,能够有效降低供应链的总成本。     

基于数量弹性契约的供应商数量选择问题研究

供应商数量对供应链的风险和成本有重要的影响,确定供应商数量必须考二方面因素。在引入数量弹性契约的基础上,考虑供应商的供应风险和市场需求的随机性,通过最大化供应链企业的收益,建立了最优供应商数量的整数规划模型;然后分析了数量弹性契约的可行性,衡量了最优供应商数量下存在的风险以及风险的影响程度;算例和仿真证明了模型的有效性。该模型在考虑供应风险、需求风险和供应成本的情况下选择最优的供应商数量,可以为供应链企业的供应商选择决策提供重要依据。     

考虑供应链风险和绩效的供应商选择的模糊动态非线性多目标规划模型

基于供应链风险和供应链绩效的模糊性和供应商选择问题的动态性,本文考虑供应链风险和供应链绩效作为模糊变量,讨论如何给生产商一个满意的动态多目标供应商选择方案,确定供应链风险和总成本最小,以及供应链绩效最大。然后对该问题提出了一个动态多目标多产品供应商选择模型,该模型是首次同时考虑供应商选择,订单分配,供应链风险和供应链绩效的一个模糊动态非线性多目标规划模型。为了去模糊化和求解该模型,给出了一个风险和绩效的模糊评估法。最后给出一个数值算例验证了该模型的可行性,为决策者选择供应商提供了理论依据。     

随机需求下的价格折扣策略研究

供需关系贯穿供应链的整个过程。因此，供需双方订货批量研究是供应链管理的一个最重要也最基本的内容之一。本以订单方式为背景，讨论了供应链环境下一个供应商和一个订货商在非合作情况下的订货模型，并在此基础上引进了一个价格折扣策略，该策略同时考虑了增量折扣和减量折扣两个情况，并给出了供需双方订货的Stackelberg博弈模型和考虑完全合作的Pareto优化模型。最后以算例加以说明。     

供应链视角下零售商内部股权融资决策研究

供应链下游的中小型零售商在发展过程中通常面临着资金短缺的问题。作为供应链的核心企业，供应商可以通过提供供应链内部的股权投资来帮助下游企业扩大市场份额，并从中获得更高的利润回报。本文构建了一个两级供应链Stackelberg博弈模型，考虑了资金有限的零售商和资金充裕的供应商之间的博弈情况。在不同的内部股权融资情况下，包括无股权融资、零售商具有开拓市场决策权(R模式)和供应商具有开拓市场决策权(S模式),研究了供应链各成员的最优决策和利润。我们发现：首先，作为中小企业的零售商引入供应链内部股权融资后不一定会增加利润。当零售商的成长性较低时，盲目引入股权融资可能会降低其利润。其次，供应商为下游中小企业提供股权融资可以提高自身的利润。最后，当零售商的成长性较高时，投融双方通常会采用零售商具有开拓市场控制权(R模式)的股权融资方式来达成合作。     

随机需求下有产能约束的技术创新策略研究

在随机需求和技术变革的环境下,基于有产能约束的单供应商-单零售商的供应链结构,研究供应商分销价格决策和技术创新策略以及零售商订货决策。建立了三阶段Stackelberg博弈模型,通过逆推方法求得了供应商最优分销价格和技术创新策略以及零售商最优订货量,深入探讨了供应商产能、新技术出现概率以及市场需求期望与波动分别对供应商、零售商和供应链整体利润的影响。结果表明当供应商产能不足时进行技术创新会提高供应商和供应链的利润,但零售商因间接承担供应商技术创新的投资成本而利润下降;当供应商产能过剩时进行技术创新则会降低供应商及供应链的利润,而零售商的利润增加。新技术出现概率增加会提高供应链各成员的利润;提高市场需求期望并减小市场波动对供应商及供应链有利,但可能会降低零售商的利润。     

考虑供应商努力水平和风险规避行为的供应链协调机制

利益冲突是影响供应链绩效的重要原因,如何消除供应链冲突、设置合理的协调机制对供应链稳定和可持续发展具有重要的意义.考虑到供应商努力水平和风险规避行为是影响供应链绩效的重要因素,利用博弈论和"均值-方差法",构建一种基于供应商努力水平的主体效用模型,设计了基于增值利润共享的供应链协调机制.结果表明,所设计的机制能够有效地保障供应链主体在利润不受损情形下调动成员合作的积极性与能动性,实现零售商与供应商长期合作和供应链的可持续发展.     

基于可靠性的企业优化模型

供应链中的企业连接着供应商和销售商,它的运作存在很多风险,如原材料供应的风险,生产过程的风险,用户需求的风险,这些风险直接影响企业运作的可靠性.本文通过分析供应链中企业的状态转移,利用增补变量法,建立了偏微分方程组刻画的供应链中企业的优化模型.     

竞争环境下随机产出供应商的融资策略研究

考虑由一个制造商和两个竞争性供应商组成的供应链系统,其中一个供应商存在资金短缺的问题,自有资金不足以完成正常生产任务,需要通过融资获得资金完成生产。针对供应链内部和外部两种融资模式,分别构建了供应链成员的收益模型,计算了不同融资方式下的决策结果,并在此基础上对两种融资模式进行了比较分析。结果表明,资金短缺的供应商生产不确定性较低时,供应链成员所能获得的利润较高,并且内部融资下资金短缺的供应商生产积极性高于外部融资。同时发现,对融资方式偏好的不一致将导致制造商利润损失,此时制造商可以采取一定的激励措施引导供应商选择合适的融资方式以提高整体收益。     

Tactical level planning in float glass manufacturing with co-production,random yields and substitutable products

We investigate tactical level planning problems in float glass manufacturing. Float glass manufacturing is a process that has some unique properties such as uninterruptible production, random yields, partially controllable co-production compositions, complex relationships in sequencing of products, and substitutable products. Furthermore, changeover times and costs are very high, and production speed depends significantly on the product mix. These characteristics render measurement and management of the production capacity difficult. The motivation for this study is a real life problem faced at Trakya Cam in Turkey. Trakya Cam has multiple geographically separated production facilities. Since transportation of glass is expensive, logistics costs are high. In this paper, we consider multi-site aggregate planning, and color campaign duration and product mix planning. We develop a decision support system based on several mixed integer linear programming models in which production and transportation decisions are made simultaneously. The system has been fully implemented, and has been in use at Trakya Cam since 2005.     

Multi-period aggregate production planning in a news-vendor framework

The paper focuses on the control decisions in the area of multi-period, aggregate production planning. The plan consists of a number of sequential planning periods stretching out to a particular time horizon. Owing to machine downtime, quality, supply, and maintenance problems, true productivity differs from the expected. Therefore, at the end of each planning period, the planner must have immediate feedback about production outcome. The results are analysed for variations from the plan (shortfalls or surpluses) and any differences are used to modify subsequent plans. The goal is to minimize the expected total costs including productivity, overtime as well as over- and under-production costs. Although the presented solution is generally not of base-stock type, a correspondence between it and the solution of the classical newsboy problem is revealed. This paper was developed during a consultation assignment in the food industry where the planning results were tested, and the case is presented.     

Integrated markdown pricing and aggregate production planning in a two echelon supply chain: A hybrid fuzzy multiple objective approach

Given high variability of demands for short life cycle products, a retailer has to decide about the products’ prices and order quantities from a manufacturer. In the meantime, the manufacturer has to determine an aggregate production plan involving for example, production, inventory and work force levels in a multi period, multi product environment. Due to imprecise and fuzzy nature of products’ parameters such as unit production and replenishment costs, a hybrid fuzzy multi-objective programming model including both quantative and qualitative constraints and objectives is proposed to determine the optimalprice markdown policy and aggregate production planning in a two echelon supply chain. The model aims to maximize the total profit of manufacturer, the total profit of retailer and improving service aspects of retailing simultaneously. After applying appropriate strategies to defuzzify the original model, the equivalent multi-objective crisp model is then solved by a fuzzy goal programming method. An illustrative example is also provided to show the applicability and usefulness of the proposed model and solution method.     

The incorporation of learning in production planning models

Production managers employ numerous aggregate planning models to smooth work loads and minimize labor and inventory costs. Some of the more recently developed models incorporate the learning that occurs during repetitive work. This article discusses the history of both aggregate planning and learning models, the various combined models, and their appropriateness to a given environment.     

Constrained Lot-Sizes under Conditions of Inventory Growth

When average aggregate inventory levels are constrained to equal a constant level over time, optimal lot sizes can be identified which strike a balance between holding costs and set-up costs among items which form the aggregate. However, when it is desirable to change aggregate inventory levels over time, assumptions implicit in the traditional formulation are violated. The procedure proposed generates lot sizes which are consistent not only with the current average aggregate inventory level but also with its projected growth over the planning horizon. Comparison is made to lot sizes generated by the misapplication of traditional lot sizing methods to the inventory growth situation.     

A robust optimization model for multi-product two-stage capacitated production planning under uncertainty

Production planning (PP) is one of the most important issues carried out in manufacturing environments which seeks efficient planning, scheduling and coordination of all production activities that optimizes the company’s objectives. In this paper, we studied a two-stage real world capacitated production system with lead time and setup decisions in which some parameters such as production costs and customer demand are uncertain. A robust optimization model is developed to formulate the problem in which minimization of the total costs including the setup costs, production costs, labor costs, inventory costs, and workforce changing costs is considered as performance measure. The robust approach is used to reduce the effects of fluctuations of the uncertain parameters with regards to all the possible future scenarios. A mixed-integer programming (MIP) model is developed to formulate the related robust production planning problem. In fact the robust proposed model is presented to generate an initial robust schedule. The performance of this schedule could be improved against of any possible occurrences of uncertain parameters. A case from an Iran refrigerator factory is studied and the characteristics of factory and its products are discussed. The computational results display the robustness and effectiveness of the model and highlight the importance of using robust optimization approach in generating more robust production plans in the uncertain environments. The tradeoff between solution robustness and model robustness is also analyzed.     

An aggregate production planning for a heavy manufacturing industry

This paper proposes a linear programming formulation of the aggregate production planning problem in the context of a heavy manufacturing industry. A basic model is first developed under the special requirements of the industry, to minimize the total cost of production which is assumed to be piecewise linear. The basic model is then transformed into a linear programming model to seek an optimal solution for a series of planning periods within the planning horizon. The results of the application to a real world situation are also included.     

Two-stage fuzzy production planning expected value model and its approximation method

This work develops a novel two-stage fuzzy optimization method for solving the multi-product multi-period (MPMP) production planning problem, in which the market demands and some of the inventory costs are assumed to be uncertainty and characterized by fuzzy variables with known possibility distributions. Some basic properties about the MPMP production planning problem are discussed. Since the fuzzy market demands and inventory costs usually have infinite supports, the proposed two-stage fuzzy MPMP production planning problem is an infinite-dimensional optimization problem that cannot be solved directly by conventional numerical solution methods. To overcome this difficulty, this paper adopts an approximation method (AM) to turn the original two-stage fuzzy MPMP production planning problem into a finite-dimensional optimization problem. The convergence about the AM is discussed to ensure the solution quality. After that, we design a heuristic algorithm, which combines the AM and simulated annealing (SA) algorithm, to solve the proposed two-stage fuzzy MPMP production planning problem. Finally, one real case study about a furniture manufacturing company is presented to illustrate the effectiveness and feasibility of the proposed modeling idea and designed algorithm.     

Dynamic dairy facility location and supply chain planning under traffic congestion and demand uncertainty: A case study of Tehran

In this paper, dynamic dairy facility location and supply chain planning are studied through minimizing the costs of facility location, traffic congestion and transportation of raw/processed milk and dairy products under demand uncertainty. The proposed model dynamically incorporates possible changes in transportation network, facility investment costs, monetary value of time and changes in production process. In addition, the time variation and the demand uncertainty for dairy products in each period of the planning horizon is taken into account to determine the optimal facility location and the optimal production volumes. Computational results are presented for the model on a number of test problems. Also, an empirical case study is conducted in order to investigate the dynamic effects of traffic congestion and demand uncertainty on facility location design and total system costs.     

Hierarchical production planning and scheduling with random demand and production failure

A multiple-objective hierarchical production planning and scheduling model is developed that integrates aggregate type decisions, family disaggregate decisions, lotsizing and scheduling of the jobs. It is assumed that demand and production failure are subject to uncertainties. Stochastic programming with recourse using a constraint sample approximation method is used to incorporate random demand and production failure into the model. The model evaluates final production plans, updates the demand forecasts and proceeds on a rolling horizon manner. Experimental results show that it is sufficient to generate and incorporate into the aggregate type model a small sample of the stochastic constraints from an infinite set of scenarios. A heuristic scheduling algorithm provides detailed information regarding the progress of jobs through work centers. This information is extremely useful in resolving infeasibilities during the production process. Other features of the model are also reported.