【正文】 的行為, 及時糾正。 加強(qiáng)對生產(chǎn)設(shè)備的管理加大改造力度，加快工藝設(shè)備更新?lián)Q代隨著飼料工業(yè)的發(fā)展，近幾年來，我國飼料加工設(shè)備升級換代的進(jìn)度加快，飼料加工設(shè)備的專業(yè)化程度不斷提高，飼料設(shè)備制造企業(yè)向飼料加工企業(yè)推出了節(jié)能高效的專業(yè)化新型加工設(shè)備，對于提高生產(chǎn)效率、降低生產(chǎn)成本，實(shí)現(xiàn)產(chǎn)品加工質(zhì)量的穩(wěn)定、增加產(chǎn)品的附加值有明顯的作用。同時注意對生產(chǎn)設(shè)備進(jìn)行維護(hù)，在保證產(chǎn)品生產(chǎn)質(zhì)量的前提下，注意檢修生產(chǎn)設(shè)備，盡量延長生產(chǎn)設(shè)備的使用壽命。如果利潤貢獻(xiàn)在補(bǔ)償了固定成本之后還有剩余, 企業(yè)就有利潤。它以產(chǎn)品為中心, 而整個項(xiàng)目的劃分是從產(chǎn)品設(shè)計開始, 到物料供應(yīng), 從生產(chǎn)工藝流程的各個環(huán)節(jié)、質(zhì)量檢驗(yàn)、總裝, 到發(fā)運(yùn)銷售的全過程。項(xiàng)目成本管理的函數(shù)為: 總成本函數(shù): T C =( Σ Vj Qt+ Fdir) + ( Σ Fk Yk+ Find) 其中, Vj : 作業(yè)j 的變動成本系數(shù)。Find: 服務(wù)區(qū)的其余相關(guān)間接成本。這種傳統(tǒng)的目標(biāo)成本管理在事前成本控制上存在優(yōu)勢, 做得很好。目標(biāo)成本管理主要負(fù)責(zé)產(chǎn)品成本的事前控制, 做好產(chǎn)品的研發(fā)階段的成本節(jié)約。項(xiàng)目成本管理是從最低層、具體最詳細(xì)的流程開始, 逐級向上設(shè)置的,操作比較復(fù)雜, 因而需要較為精確而高效的成本統(tǒng)計手段, 需要嚴(yán)格而科學(xué)的控制和管理體系, 而信息系統(tǒng)的高效應(yīng)用就使項(xiàng)目管理的應(yīng)用成為可能。 建立健全生產(chǎn)成本控制體系成本控制需要建立科學(xué)機(jī)制。由于成本形成于生產(chǎn)全過程，費(fèi)用發(fā)生在每一個環(huán)節(jié)、每一件事情、每一項(xiàng)活動上，因此，要把目標(biāo)成本層層分解到各個部門甚至個人。行業(yè)價值鏈: 是企業(yè)存在于某一行業(yè)價值鏈的某個點(diǎn)，包括與上、下游與渠道企業(yè)的連接點(diǎn)，如供應(yīng)商產(chǎn)品的包裝能減少企業(yè)的搬運(yùn)費(fèi)用，改善價值的縱向聯(lián)系也可以使企業(yè)與其上、下游和渠道企業(yè)共同降低成本，提高整體競爭優(yōu)勢。 成本控制四步執(zhí)行法⑴減少目標(biāo)不明確的項(xiàng)目和任務(wù)。實(shí)行“全員成本管理”的方法。沒有數(shù)字進(jìn)行標(biāo)準(zhǔn)量化，就無從談及節(jié)儉和控制。當(dāng)然，有效的激勵也是成本控制的好辦法，所以，成本控制獎勵也成為員工工資的一部分。當(dāng)今的市場競爭，是實(shí)力的競爭，人才的競爭，產(chǎn)品以及服務(wù)質(zhì)量的競爭，也是成本的競爭。 總的來說，山東泉道農(nóng)業(yè)科技集團(tuán)在生產(chǎn)成本控制中存在的問題在我國的大多數(shù)中小型企業(yè)中都普遍存在。而成本控制是企業(yè)管理活動中永恒的主題，成本控制的直接結(jié)果是降低成本，增加利潤，從而提升企業(yè)的管理水平，增強(qiáng)企業(yè)核心競爭力。這次畢業(yè)論文設(shè)計我得到了很多老師和同學(xué)的幫助，其中我的論文指導(dǎo)老師崔樹德老師對我的關(guān)心和支持尤為重要。同時，本篇畢業(yè)論文的寫作也得到了很多同學(xué)的熱情幫助，感謝每一位在我畢業(yè)論文設(shè)計過程中給予我?guī)椭娜藗?。s expenses within the planning horizon. A stochastic optimization problem is formulated, followed by a heuristic solution via simulation. A numerical example is given. Extensive experimentation has been undertaken to illustrate the efficiency of the presented algorithm. The algorithm has been used in practice on a real man177。 Cost objective。s good name.Thus, given: the target amount to be acplished on time, the due date,several production speeds defined by their probability density functions, the average processing cost of realizing the manufacturing process per time unit for each production speed separately,the average cost of carrying out an inspection at the control point, andthe chance constraint to meet the deadline on time,the problem is to determine both, control points and production speeds to be introduced at each control point, to minimize the average manufacturing expenses within the planning horizon subject to the chance constraint. This is a plicated stochastic optimization problem with a random number of decision variables,Since an optimal algorithm to solve the problem cannot be found, a heuristic one is suggested and developed. The algorithm is based on simulation and pares, one by one, sorted couples of production speeds in order to find an optimal couple that results in minimizing the average expenses. The algorithm has to be realized at any control point to choose both, the speed to be introduced and the next control point.Note that all previous publications [25,10] are based on the risk averse principle, which is very efficient for noncost objectives, but cannot be applied to the newly formulated costoptimization model. It is therefore substituted for another one, namely the chance constraint principle, which is embedded in the heuristic algorithm and fits the cost structure of the paper is as follows. In Section 2, the description of the production system is outlined. Section 3 considers Notations and presents the stochastic optimization problem. In Section 4, the chance constraint principle is described. In Section 5, the heuristic algorithm is outlined. Section 6describes an illustrative numerical example, while in Section 7 extensive experimentation to verify the efficiency of the heuristic algorithm is presented. Practical applications of the algorithm on a real manmachine plant are outlined in Section 8. Conclusions and future research are presented in Section 9.2. Description of the systemThe system under consideration produces a single product or a production program that can be measured by a single value, just like the system described in [2], . in percentages of the planned total volume. Such an approach is often used in Ramp。_N the average number of inspection points (without t0)._J the average index of the speed to be introduced within one simulation run.8. Practical applicationsIn 19971998, extensive experimentations were undertaken in several industrial plants in Serbia to test the fitness of the developed costoptimization production control model. The best results were achieved in a Serbian pany`Hidrogradnja39。s good name and to pete with other panies, the projects39。 is difficult and costly, so periodic inspections are preferred. The actual output observed at a current inspection point is measured in percentages of the total project (target amount). When realizing a certain project in `Hidrogradnja39。D projects, etc. The system39。 and(b) a new railway bridge over river West Morava.In the course of the control model39。 can work with one, two and three shifts, the number of possible speeds has been taken to be three for all experiments.Evaluating the project39。 through a mountain, various power stations, etc. `Hidrogradnja39。s actual output, can be carried out only via timely inspections at pregiven control points. At every inspection (control) point, the decisionmaker observes the amount produced and has to determine both, the proper speed and the next control point. Assume that it is prohibited to use unnecessarily high speeds (especially at the beginning of manufacturing the products), unless there is an emergency situation, . a tendency to deviate from the target which maycause delay of the pletion time. This is because lengthy work at higher speeds when utilizing restricted resources (. manpower working in two or three shifts, etc.) can prematurely wear out the system. Assume, further, that the inspection and the speedreset times are zero. The costs of all processing speeds per time unit, as well the cost of performing a single inspection at the control point are pregiven.5. The heuristic algorithmReferring to Refs. [3,10], the heuristic control algorithm at each routine control point, ti, enablesminimization of the manufacturing expenses (Eq. (2)) during the remaining time D 255。