面向使用寿命的工业包装回收组合成本模型研究

目的定量化分析工业包装回收的成本组成及其经济效益。方法通过构建工业包装回收组合成本模型,考虑设计、采购、运输、库存和回收处理等成本,分析工业包装在使用寿命周期内回收的经济效益,并通过实际案例分析验证了模型。结果通过企业的实例研究,在寿命周期6 a里,不回收时木制托盘总成本是21 228万元,实施回收后塑料托盘的总成本是12 048.8万元,节省成本9179.2万元。结论在工业包装的使用寿命周期内,良好的回收处理可以给企业带来经济效益,同时产生很好的生态和社会效益。     

谈作业成本法对成本计算方法的影响

结合变动成本法,分析了作业成本计算法的理论依据、作业成本习性,指出了作业成本计算法的现实意义。     

基于作业成本法的机械制造企业成本控制问题探讨

机械制造企业现行的成本控制方法着眼于在生产过程中降低成本，是一种“生产型导向”的成本控制，已经越来越不能适应科学技术的发展和经济环境变化提出的新要求。本文针对传统成本控制方法的局限性，构建基于作业成本法的机械制造企业成本控制体系，以期为机械制造企业准确核算和控制产品成本提供参考。     

作业成本制下的成本函数结构

介绍企业生产系统的决策对象和相关成本信息的特性，给出在作业成本制下的成本函数表达式，并在此基础上分析作业成本法的应用条件。     

CIPS中基于ABC原理的能源成本管理方法研究

在分析CIPS环境下现代能源成本管理需求基础上，提出了基于ABC原理的流程企业能源成本管理要求与原理，并基于ABC原理应用需要和数字化实现角度，对企业能源结构体系建立及约束关系、流程企业的工艺特性等基础问题进行深入探讨。最后还探析了CIPS环境下能源成本管理中的作业／考核控制点划分原则和深化处理问题。     

作业成本法在物流成本控制中的应用

以作业成本法为工具,通过将物流活动按职能划分为运输、装卸、包装、存储、流通加工和信息处理六类作业,建立了系统的物流成本控制方法。利用该方法不仅可以准确计算企业整体的物流成本,而且可以为企业降低成本提供有效信息。     

基于成本作业法的回收包装系统的研究及应用

随着人们经济与环保意识的逐步提高,回收包装开始受到企业青睐.而回收包装的成本核算受到了企业的特别关注,成为回收包装实行与否的关键问题.作业成本法(简称ABC)以"产品耗费作业,作业耗费资源"为主线,将成本计算深入作业层次.     

论风机检修的方法和技巧

机组电机断电，润滑油供油切断，机组已从生产系统切除，交付检修，施工作业票办理完毕检修前准备，办理施工工作票，确认机组已经具备安全拆卸的条件，拆卸烟气轮机各部零部件；检查润滑油系统，拆卸附属设施并对润滑油系统进行检查，拆卸联轴器、进气锥及静叶体、轴承箱并吊出转子。     

基于作业成本法的一个库存成本控制模型

作业成本法在物流系统中运行,将可以更好地帮助决策者进行物流决策.论文通过对传统库存模型分析,基于作业成本原理深入分析了库存系统中补货、库存持有和缺货作业的成本动因,并根据这些成本动因建立了基于作业成本法的库存模型来改进传统库存模型,同时数值算例分析与比较也被提供来分析作业成本法对传统库存模型的影响.研究结果显示基于作业成本法的库存模型比传统库存模型在成本控制方面更有效,如果采用传统库存模型可能导致决策错误.     

议如何搞好工程项目成本管理

分析了成本预测和成本控制的关系，介绍了成本预测的具体内容。从采取组织措施和经济措施、加强质量成本管理和工期成本管理等方面，论述了控制成本的具体措施。     

Flexible disassembly planning considering product conditions

To increase the economic benefit of product recovery at the end of life of a consumer product, the profit margins should be augmented. This can be realised by utilising the existing flexibility of today’s mostly manually-conducted disassembly processes. Moreover, with increased economic benefits, the recovery becomes even more attractive, which is also beneficial to the environment. A key component of product recovery is disassembly. Allowing different disassembly states (or levels) per core (i.e. recovered product) increases flexibility in planning. This should result in higher profits, as long as the flexibility is still manageable for the companies. This study focuses on flexible disassembly planning, i.e. the integration of sequencing aspects into disassembly (process) planning. In addition, we further incorporate the condition of items in the core, item damaging, purity requirements, special treatment of hazardous items and several limitations like core availability, item, module and material distribution, disposal and labour time limit. We base our developed mixed integer linear programme on graphs, such as the disassembly state graph for sequencing, and a hypergraph to model the core condition. Lastly, our considerations are illustrated by a numerical example.     

基于模糊推理的不确定环境下拆卸工艺规划

回收产品因磨损等原因其结构和质量会发生改变,拆卸序列规划存在不确定性,拆卸前很难确定产品最优拆卸序列。首先建立一个模糊拆卸Petri网模型表示产品可行拆卸序列和拆卸过程存在的不确定信息,为降低产品质量和拆卸能力的不确定性对拆卸序列优化的影响,建立一个自适应的模糊推理系统,利用模糊推理和反馈学习的方法对产品各拆卸步骤的成本进行预测,然后通过计算不同拆卸序列下拆卸的收益来得到产品最优拆卸序列,最后通过算例证明了方法的有效性。     

Dynamic disassembly planning for remanufacturing of multiple types of products

With the increased need for remanufacturing of end-of-life products, achieving economic efficiency in remanufacturing is urgently needed. The purpose of this study was to devise a cost-minimisation plan for disassembly and remanufacturing of end-of-life products returned by consumers. A returned end-of-life product is disassembled into remanufacturable parts, which are supposed to be used for new products after being remanufactured. Each end-of-life product is disassembled into parts at variable levels and through variable sequences as needed, taking into account not only disassembly but also manufacturing, remanufacturing, and holding inventory of remanufacturable parts. This study proposes a mixed integer linear programming (MILP) model for derivation of the optimal disassembly plan for each returned product, under deterministically known demand and return flows. For the purposes of an illustrative example, the proposed model was applied to the formulation of an optimal disassembly and remanufacturing plan of ‘fuser assembly’ of laser printers. The solution reveals that variable-level disassembly of products saves a significant remanufacturing cost compared with full disassembly.     

An improved gravitational search algorithm for profit-oriented partial disassembly line balancing problem

Disassembly is indispensable to recycle and remanufacture end-of-life products, and a disassembly line-balancing problem (DLBP) is studied frequently. Recent research on disassembly lines has focused on a complete disassembly for optimising the balancing ability of lines. However, a partial disassembly process is widely applied in the current industry practice, which aims at reusing valuable components and maximising the profit (or minimising the cost). In this paper, we consider a profit-oriented partial disassembly line-balancing problem (PPDLBP), and a mathematical model of this problem is established, which is to achieve the maximisation of profit for dismantling a product in DLBP. The PPDLBP is NP-complete since DLBP is proven to be a NP-complete problem, which is usually handled by a metaheuristics. Therefore, a novel efficient approach based on gravitational search algorithm (GSA) is proposed to solve the PPDLBP. GSA is an optimisation technique that is inspired by the Newtonian gravity and the laws of motion. Also, two different scale cases are used to test on the proposed algorithm, and some comparisons with the CPLEX method, particle swarm optimisation, differential evolution and artificial bee colony algorithms are presented to demonstrate the excellence of the proposed approach.     

An approach for estimating the end-of-life product disassembly effort and cost

Disassembly is the process of physically separating a product into its parts or subassembly pieces. The overall economics of the disassembly process, and in particular the cost to disassemble, is still not well understood. In this paper our goal is to introduce a methodology that will support and facilitate the economic analysis of the disassembly activity. We present a multi-factor model to compute the disassembly effort index (DEI) score, which is representative of the total operating cost to disassemble a product. The DEI score can then be compared against the projected market value of the disassembled parts and subassemblies to get an economic measure. To develop the DEI model we surveyed a variety of commercial disassembly facilities. Based on these surveys we propose a multifactor weighted estimation scheme. The seven factors are (i) time, (ii) tools, (iii) fixture, (iv) access, (v) instruct, (vi) hazard, and (vii) force requirements. The DEI scale is defined in the 0 to 100 range. This range is assigned on a weighted basis to each of the seven factors. For each factor, an independent utility scale is formulated, using the assigned range as anchors. Using a conversion scale the DEI score is used to derive an estimate of disassembly cost and the disassembly return on investment. An example is presented.     

Integrated assembly and disassembly sequence planning using a GA approach

In a product life cycle, an assembly sequence is required to produce a new product at the start, whereas a disassembly sequence is needed at the end. In typical assembly and disassembly sequence planning approaches, the two are performed as two independent tasks. In this way, a good assembly sequence may contradict the cost considerations in the disassembly sequence, and vice versa. In this research, an integrated assembly and disassembly sequence planning model is presented. First, an assembly precedence graph (APG) and a disassembly precedence graph (DPG) are modelled. The two graphs are transformed into an assembly precedence matrix (APM) and a disassembly precedence matrix (DPM). Second, a two-loop genetic algorithm (GA) method is applied to generate and evaluate the solutions. The outer loop of the GA method performs assembly sequence planning. In the inner loop, the reverse order of the assembly sequence solution is used as the initial solution for disassembly sequence planning. A cost objective by integrating the assembly costs and disassembly costs is formulated as the fitness function. The test results show that the developed method using the GA approach is suitable and efficient for the integrated assembly and disassembly sequence planning. Example products are demonstrated and discussed.     

A precedence-constrained asymmetric traveling salesman model for disassembly optimization

In this paper, we address a product-disassembly optimization problem, which aims at minimizing the costs associated with the disassembly process (namely, the cost of breaking the joints and the sequence-dependent set-up cost associated with the disassembly operations), while maximizing the benefits resulting from the recovery of components and subassemblies that constitute the product. A method able to capture the mating relationships among the parts and joints of the product through a network representation scheme is developed. The disassembly optimization problem is formulated as a precedence-constrained asymmetric traveling salesman problem. A three-stage iterative procedure is designed to obtain optimal or near-optimal solutions to the problem. The results demonstrate that our procedure generates solutions that are within 2% of optimality for all test cases, while only requiring a reasonable computational effort.     

Analyzing the effects of inventory cost setting rules in a disassembly and recovery environment

In this study we consider a disassembly and recovery facility receiving end-of-life products and facing demand for a specific part that is disassembled from the product and then recovered. The disassembly and recovery operations can be either performed before hand, or upon customer arrival. In the latter case, a discount on the selling price is applied to compensate the customer for waiting for the completion of the disassembly and recovery operations. One of the difficulties faced in planning for such a system is the determination of the opportunity cost associated with carrying recovered parts inventory. The difficulty arises in seeking the value added to the part given the costs incurred for maintaining the product return, disassembly and recovery costs and revenue earned from the hulk, that is the remaining product after the disassembly of the part. The main objective of the study is to investigate the effect of different rules to determine this opportunity cost on the performance of the system. Six rules are considered in the study. The performance of the rules is assessed by a computational study under an approximate inventory control policy.     

Quality control of an unreliable random FMS: with Bernoulli and CSP sampling

It is widely recognized that disassembly-based product End-of-Life strategies, such as component reuse or simple fraction material recycling, are environmentally beneficial. However, current disassembly costs hinder a widespread application of these strategies. This paper quantifies the disassembly time reductions required to achieve economic feasibility of systematic product disassembly. A modelling framework, based on linear programming, is used to investigate the effect of reducing the expected disassembly time and cost on the selection of the optimal End-of-Life strategy. The problem is optimized from an End-of-Life treatment facility point of view. All findings are based on the Belgian cost and price information captured in spring 2004. The linear programming model shows that for small products from the Waste of Electric and Electrical Equipment (WEEE) category disassembly-based End-of-Life strategies will hardly become optimal, while for medium- and large-sized products, this scenario can be made optimal if a substantial disassembly time reduction is achieved. Possible strategies to realize such reduction are briefly sketched.     

Evaluation model and algorithm of product disassembly process with stochastic feature

Disassembly planning is considered as the optimization of disassembly sequences with the target of the shortest disassembly time, the lowest disassembly cost, and the minimum disassembly energy consumption. However, obsolete products suffer from the influence of a variety of uncertainties, the disassembly process of products has the strong uncertain feature. Traditionally, to account for this uncertainty, each removal operation or removal task is assumed to be an activity or event with certain probability, and the determination of the optimal path of a disassembly process is merely a probabilistic planning problem based on this assumption. In this article, based on the established stochastic disassembly network graph, combined with different disassembly decision-making criterion, typical stochastic models for disassembly time analysis are developed. In addition, a two-phase approach is proposed to solve the typical stochastic models. Initially, according to different removal probability density functions, disassembly probability density functions of feasible disassembly paths are determined by a time-domain method or frequency-domain method, and additionally, after the disassembly probability density functions have been obtained, the quantitative evaluation of a product disassembly process and stochastic optimization of feasible disassembly paths are realized by a numerical solution method. Finally, a numerical example is illustrated to test the proposed concepts and the effectiveness of the proposed approach.