Analysis of Some Problems Arising From the Current Domestic Clothing Product Planning

At present,the importance of product planning is valued unceasingly by the domestic garment enterprises. But there are a number of garment enterprises not fully understanding the significance of product planning, let alone mastering how to make a successful product planning. Therefore, this paper is based on the related theory of product planning, analyses the basic content of garment product planning, explains the main types of product planning in China and the current situation and existing problems of the product plalming in our country, and puts forward the corresponding solutions. There are a certain guiding significance and reference value to garment product planning in the future.

Remanufacturing production planning considering quality cost and demand substitution

Aiming to minimize the total production costs in a single planning period, a nonlinear integer programming model for remanufacturing production plans is established considering the influence of different qualities of returns acting on production cost. Three different remanufacturing and discarding strategies are adopted to analyze the change rules of the total production costs. The results returns is greater than indicate that when the number of remanufacturing returns of high the demand, preferentially quality and discarding those of low quality can bring better economic benefits due to manufacturing cost reduction. However, when the number of returns is smaller than the demand, there is no need to consider grading of returns, whereas new demand of remanufacturing. parts are required to satisfy the

A Hybrid Programming Model for Optimal Production Planning under Demand Uncertainty in Refinery

Abstract Production planning under uncertainty is considered as one of the most important problems in plant-wide optimization. In this article, first, a stochastic programming model with uniform distribution assumption is developed for refinery production planning under demand uncertainty, and then a hybrid programming model incorporating the linear programming model with the stochastic programming one by a weight factor is proposed. Subsequently, piecewise linear approximation functions are derived and applied to solve the hybrid programming model-under uniform distribution assumption. Case studies show that the linear approximation algorithm is effective to solve.the hybrid programming model, along with an error≤0.5% when the deviatiorgmean≤20%. The simulation results indicate that the hybrid programming model with an appropriate weight factor （0.1-0.2） can effectively improve the optimal operational strategies under demand uncertainty, achieving higher profit than the linear programming model and the stochastic programming one with about 1.3% and 0.4% enhancement, respectavely.

The Customer Requirements Analysis Method of Engineering Products Based on Multiple Preference Information

To effectively evaluate the fuzziness of the market environment in product planning,a customer requirements analysis method based on multiple preference information is proposed.Firstly,decision-makers use a preferred information form to evaluate the importance of each customer requirement.Secondly,a transfer function is employed to unify various forms of preference information into a fuzzy complementary judgment matrix.The ranking vector is then calculated using row and normalization methods,and the initial importance of customer requirements is obtained by aggregating the weights of decision members.Finally,the correction coefficients of initial importance and each demand are synthesized,and the importance of customer requirements is determined through normalization.The development example of the PE jaw crusher demonstrates the effectiveness and feasibility of the proposed method.

A Novel Close-loop Strategy for Integrating Process Operations of Fluidized Catalytic Cracking Unit with Production Planning Optimization

Production planning models generated by common modeling systems do not involve constraints for process operations, and a solution optimized by these models is called a quasi-optimal plan. The quasi-optimal plan cannot be executed in practice some time for no corresponding operating conditions. In order to determine a practi- cally feasible optimal plan and corresponding operating conditions of fluidized catalytic cracking unit （FCCU）, a novel close-loop integrated strategy, including determination of a quasi-optimal plan, search of operating conditions of FCCU and revision of the production planning model, was proposed in this article. In the strategy, a generalized genetic algorithm （GA） coupled with a sequential process simulator of FCCU was applied to search operating conditions implementing the quasi-optimal plan of FCCU and output the optimal individual in the GA search as a final genetic individual. When no corresponding operating conditions were found, the final genetic individual based correction （FGIC） method was presented to revise the production planning model, and then a new quasi-optimal production plan was determined. The above steps were repeated until a practically feasible optimal plan and corresponding operating conditions of FCCU were obtained. The close-loop integrated strategy was validated by two cases, and it was indicated that the strategy was efficient in determining a practically executed optimal plan and corresponding operating conditions of FCCU.

Infeasibility Diagnosis on the Linear Programming Model of Production Planning in Refinery

In order to effectively diagnose the infeasible linear programming (LP) model of production planning in refinery, the article proposed three stages strategy based on constraints’ classification and infeasibility analysis. Generally, infeasibility sources involve structural inconsistencies and data errors, and the data errors are further classified intoⅠ, Ⅱ and Ⅲ. The three stages strategy are: (1) Check data when they are inputted to detect data error Ⅰ and repair them; (2) Inspect data whether they are accorded with material balance before solving the LP model to identify data error Ⅱ and repair them; (3) Find irreducible inconsistent system of infeasible LP model and give diagnosis information priority-ranked to recognize data error Ⅲ and structural inconsistencies. These stages could be automatically executed by computer, and the approach has been applied to diagnose the infeasible model well in our graphic I/O petro-chemical industry modeling system.

Optimized algorithm in mine production planning, mined material destination, and ultimate pit limit

An integral connection exists among the mine production planning, the mined material destination, and the ultimate pit limit （UPL） in the mining engineering economy. This relation is reinforced by real information and the benefits it engenders in the mining economy. Hence, it is important to create optimizing algorithms to reduce the errors of economic calculations. In this work, a logical mathematical algorithm that considers the important designing parameters and the mining economy is proposed. This algorithm creates an optimizing repetitive process among different designing constituents and directs them into the maximum amount of the mine economical parameters. This process will produce the highest amount of ores and the highest degree of safety. The modeling produces a new relation between the concept of the cutoff grade, mine designing, and mine planning, and it provides the maximum benefit by calculating the destination of the ores. The proposed algorithm is evaluated in a real case study. The results show that the net present value of the mine production is increased by 3% compared to previous methods of production design and UPL.

HEURISTIC MODELING FOR A DYNAMIC AND GOAL PROGRAMMING IN PRODUCTION PLANNING OF CONTINUOUS MANUFACTURING SYSTEMS

At the first sight it seems that advanced operation research is not used enough in continuous production systems as comparison with mass production, batch production and job shop systems, but really in a comprehensive evaluation the advanced operation research techniques can be used in continuous production systems in developing countries very widely, because of initial inadequate plant layout, stage by stage development of production lines, the purchase of second hand machineries from various countries, plurality of customers. A case of production system planning is proposed for a chemical company in which the above mentioned conditions are almost presented. The goals and constraints in this issue are as follows： （1） Minimizing deviation of customer＇s requirements. （2） Maximizing the profit. （3） Minimizing the frequencies of changes in formula production. （4） Minimizing the inventory of final products. （5） Balancing the production sections with regard to rate in production. （6） Limitation in inventory of raw material. The present situation is in such a way that various techniques such as goal programming, linear programming and dynamic programming can be used. But dynamic production programming issues are divided into two categories, at first one with limitation in production capacity and another with unlimited production capacity. For the first category, a systematic and acceptable solution has not been presented yet. Therefore an innovative method is used to convert the dynamic situation to a zero- one model. At last this issue is changed to a goal programming model with non-linear limitations with the use of GRG algorithm and that＇s how it is solved.

Modeling and optimization methods of integrated production planning for steel plate mill with flexible customization

With diversified requirements and varying manufacturing environments, the optimal production planning for a steel mill becomes more flexible and complicated. The flexibility provides operators with auxiliary requirements through an implementable integrated production planning. In this paper, a mixed-integer nonlinear programming(MINLP) model is proposed for the optimal planning that incorporates various manufacturing constraints and flexibility in a steel plate mill. Furthermore, two solution strategies are developed to overcome the weakness in solving the MINLP problem directly. The first one is to transform the original MINLP formulation to an approximate mixed integer linear programming using a classic linearization method. The second one is to decompose the original model using a branch-and-bound based iterative method. Computational experiments on various instances are presented in terms of the effectiveness and applicability. The result shows that the second method performs better in computational efforts and solution accuracy.

Genetic Algorithm Based Production Planning for Alternative Process Production

Production planning under flexible job shop environment is studied.A mathematic model is formulated to help improve alternative process production.This model,in which genetic algorithm is used,is expected to result in better production planning,hence towards the aim of minimizing production cost under the constraints of delivery time and other scheduling conditions.By means of this algorithm,all planning schemes which could meet all requirements of the constraints within the whole solution space are exhaustively searched so as to find the optimal one.Also,a case study is given in the end to support and validate this model.Our results show that genetic algorithm is capable of locating feasible process routes to reduce production cost for certain tasks.

Collaborative production planning between supply chain partners by Lagrangian relaxation

A collaborative planning framework based on the Lagrangian Relaxation was developed to coordinate and optimize the production planning of independent partners in multiple tier supply chains. Linking constraints and dependent demand constraints were added to the monolithic Multi-Level, multi-item Capacitated Lot Sizing Problem (MLCLSP). MLCLSP was Lagrangian relaxed and decomposed into facility-separable subproblems. Surrogate gradient algorithm was used to update Lagrangian multipliers, which coordinate decentralized decisions of the facilities. Production planning of independent partners could be appropriately coordinated and optimized by this framework without intruding their decisionities and private information. Experimental results show that the proposed coordination mechanism and procedure come close to optimal results as obtained by central coordination.

Stochastic Programming For Order Allocation And Production Planning

Stochastic demand is an important factor that heavily affects production planning.It influences activities such as purchasing,manufacturing,and selling,and quick adaption is required.In production planning,for reasons such as reducing costs and obtaining supplier discounts,many decisions must be made in the initial stage when demand has not been realized.The effects of non-optimal decisions will propagate to later stages,which can lead to losses due to overstocks or out-of-stocks.To find the optimal solutions for the initial and later stage regarding demand realization,this study proposes a stochastic two-stage linear program-ming model for a multi-supplier,multi-material,and multi-product purchasing and production planning process.The objective function is the expected total cost after two stages,and the results include detailed plans for purchasing and production in each demand scenario.Small-scale problems are solved through a deterministic equivalent transformation technique.To solve the problems in the large scale,an algorithm combining metaheuristic and sample average approximation is suggested.This algorithm can be implemented in parallel to utilize the power of the solver.The algorithm based on the observation that if the remaining quantity of materials and number of units of products at the end of the initial stage are given,then the problems of the first and second stages can be decomposed.

AGILITY INDEXES AND APPLICATION OF PRODUCTION PLANNING

The agility of production planning is defined as two characters： synchronization and flexibility. Measurement indexes for evaluating agility are then divided into two dimensions separately. Synchronization includes two horizontal indexes： order lead time ratio and demand fulfilling rate; Two vertical indexes： instruction-reaction cycle time and resource matching rate. Flexibility includes five indexes： scope flexibility, delivery flexibility, variety flexibility, capacity buffer, inventory buffer and modification flexibility. Quantitative formulas for each measurement index are constructed. One application is discussed to illustrate the feasibility and reasonability of the indexes.

Design of Cellular Manufacturing Systems Considering Dynamic Production Planning and Worker Assignments

This article presents a comprehensive mathematical model for the design and analysis of Dynamic Cellular Manufacturing Systems （DCMS）. The proposed DCMS model considers several manufacturing attributes such as multi period production planning, dynamic system reconfiguration, duplicate machines, machine capacity, the available time for workers, worker assignments, and machine procurement. The objective is to minimize total costs; consisting of holding cost, outsourcing cost, inter-cell material handling cost, maintenance and overhead cost, machine relocation cost. While a study of published articles in the area of Cellular Manufacturing Systems （CMS） shows that workforce management issues have not sufficiently been addressed in the literature, the model presented also incorporates CMS workforce management issues such as salaries, hiring and firing costs of workers in addition to the manufacturing attributes. In-depth discussions on the results for two numerical examples are presented to illustrate applications of the proposed model. The model developed aims to raise the envelope by expanding and improving several CMS models previously presented in the literature.

Production Planning Based on BOM Optimization

According to a prototype enterprise, a rulebased Bill of Materials （BOM） structure is designed in order to get optimal design and management of product BOM. The constraint rules and optional objects for product data structure optimization are considered by associating customer demands with product BOM. Furthermore, the functional model of production planning system for assembling enterprise is given based on customization and BOM optimization.

Production Planning and Control of the Pipeline Assembly Enterprise under SCM

It is vital for pipeline assembly enterprise to respond to changing market needs quickly and keep its cost advantage. A production planning and control （PPC） model under supply chain management combining with operation characteristics of pipeline assembly enterprise was proposed. The present model can realize synchroni- zation in real-time response in production by establishing a win-win corporate model, classified regulation be- tween strategic parts and non-strategic parts, postponing the production of special modules and guaranteeing pro- duction at the bottleneck.

Studies on Production Planning of Dispersion Type U3Si2-Al Fuel in Plate-Type Fuel Elements for Nuclear Research Reactors

Several fuel plants that supply nuclear research reactors need to increase their production capacity in order to meet the growing demand for this kind of nuclear fuel. After the enlargement of the production capacity of such plants, there will be the need of managing the new production level. That level is usually the industrial one, which poses challenges to the managerial staff. Such challenges come from the fact that several of those plants operate today on a laboratorial basis and do not carry inventory. The change to the industrial production pace asks for new actions regarding planning and control. The production process based on the hydrolysis of UF6 is not a frequent production route for nuclear fuel. Production planning and control of the industrial level of fuel production on that production route is a new field of studies. The approach of the paper consists in the creation of a mathematical linear model for minimization of costs. We also carried out a sensitivity analysis of the model. The results help in minimizing costs in different production schemes and show the need of inventory. The mathematical model is dynamic, so that it issues better results if performed monthly. The management team will therefore have a clearer view of the costs and of the new, necessary production and inventory levels.

Liaohe Planning to Build One Million Tonsof Super-Heavy Oil Production Base

WiththepriceincreaseofPetroleumProductsintheinternationalmarketandthematurity'oftechnologyfortheproductionofsuper-heavyoil,LiaohehasestablishedaframeworkplanfortheconstructionofproductioncapacityforBlockDu-32.AproduchonbasewadanannualoutputofImillion...

PRODUCT COMPETITIVE ADVANTAGES ANALYSIS IN MARKET PERSPECTIVE

Voices of Customer” (VOC) is a driving force for new product development.The macro viewpoint of market perspective,a more global aspect of VOC,can reflect the overall situation of the state of art of market competition,by means of competitors status analysis,market opportunities seeking,and then provide a quantitative guidelines and assessment for the development of new products.In this paper,a systematic approach for the planning of new products,which is established on the macro viewpoint of market perspective,is proposed.A set of enabling techniques is also developed to overcome difficulties and to meet challenging involved in the planning.A typical engineering case study for a new vehicle program development is used to demonstrate the feasibility and effectiveness.

Stochastic optimization of mine production scheduling with uncertain ore/metal/waste supply

Optimization of long-term mine production scheduling in open pit mines deals with the management of cash flows, typically in the order of hundreds of millions of dollars. Conventional mine scheduling utilizes optimization methods that are not capable of accounting for inherent technical uncertainties such as uncertainty in the expected ore/metal supply from the underground, acknowledged to be the most critical factor. To integrate ore/metal uncertainty into the optimization of mine production scheduling a stochastic integer programming(SIP) formulation is tested at a copper deposit. The stochastic solution maximizes the economic value of a project and minimizes deviations from production targets in the presence of ore/metal uncertainty. Unlike the conventional approach, the SIP model accounts and manages risk in ore supply, leading to a mine production schedule with a 29% higher net present value than the schedule obtained from the conventional, industry-standard optimization approach, thus contributing to improving the management and sustainable utilization of mineral resources.