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.

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.

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.

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.

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...

Multi-period mine planning with multi-process routes

This paper attempts to optimize optimal capacities, block routing and mine sequencing problems in a mining system. The solution approach is based on a heuristics and the mixed integer programming (MIP). Unlike previous sequential solution approaches, the problems are herein solved at the same time. Furthermore, the proposed approach guarantees practical solutions because it considers ore material distribution within orebody. The paper has two main contributions: (a) the proposed approach generates production rates in a manner that the capacities are satisfied; (b) the proposed approach does not use pre-defined marginal cut-off grades. Thus, idle capacity problem is eliminated and different scheduling combinations are allowed. To see the performance of the approach proposed, a case study is carried out using a gold data. The schedule generated shows that the approach can determine optimal production rates, block destination and sequencing effectively.

Fuzzy linear model for production optimization of mining systems with multiple entities

Planning and production optimization within multiple mines or several work sites （entities） mining systems by using fuzzy linear programming （LP） was studied. LP is the most commonly used operations research methods in mining engineering. After the introductory review of properties and limitations of applying LP, short reviews of the general settings of deterministic and fuzzy LP models are presented. With the purpose of comparative analysis, the application of both LP models is presented using the example of the Bauxite Basin Niksic with five mines. After the assessment, LP is an efficient mathematical modeling tool in production planning and solving many other single-criteria optimization problems of mining engineering. After the comparison of advantages and deficiencies of both deterministic and fuzzy LP models, the conclusion presents benefits of the fuzzy LP model but is also stating that seeking the optimal plan of production means to accomplish the overall analysis that will encompass the LP model approaches.

Research and development of production planing management system in 2150 mm product line of Ansteel

Production planning management is the core function of manufacturing execution system(MES).It plays an important role in modern industry production.Using the MES of 2 150 mm hot milling product line in Ansteel as a background,this paper discusses the function and dynamic management strategy of Production planning management system,and the whole process of design,development and realization.For the production needs of 2 150 mm product line,the system uses the relationship model between planning items and the slab dimension,steel grade,weight and quality scale.Through a long time running,it has made the good progress,the feasibility and reliability of the system is fully proved,it can adequately meet the needs of hot milling,and and has achieved the anticipated target.

MIP Formulations and Metaheuristics for Multi-Item Capacitated Lot-Sizing Problem with Non-Customer Specific Production Time Windows and Setup Times

Our research focuses on the development of two cooperative approaches for resolution of the multi-item capacitated lot-sizing problems with time windows and setup times (MICLSP-TW-ST). In this paper we combine variable neighborhood search and accurate mixed integer programming (VNS-MIP) to solve MICLSP-TW-ST. It concerns so a particularly important and difficult problem in production planning. This problem is NP-hard in the strong sense. Moreover, it is very difficult to solve with an exact method;it is for that reason we have made use of the approximate methods. We improved the variable neighborhood search (VNS) algorithm, which is efficient for solving hard combinatorial optimization problems. This problem can be viewed as an optimization problem with mixed variables (binary variables and real variables). The new VNS algorithm was tested against 540 benchmark problems. The performance of most of our approaches was satisfactory and performed better than the algorithms already proposed in the literature.

National Plan for Non-ferrous Metals Production Fulfilled ahead of Time

Vice president of China National Nonferrous Metals Industries Corporation Mr. WoTingshu said:"The output of 10 kinds of nonferrous metals amounted to 2.45 Mt." This factmean1s that the national plan for nonferrous metals production fulfiled earlier. The 10 kinds ofnonferrous metals are aluminium, magnesium, lead, zinc, copper, tin, nickel, antimony mercuryand titanium.

The Calculation of Material Requirements Based on BOP in Assembly Production

A calculation method for material requirements which is based on BOP（Bill of Process） in assembly production is presented in this paper. Firstly, the BOP of assembly production is constructed. Then, the calculation method Based on the BOP is brought forward for material requirements planning.

Lot Sizing in Production Scheduling at a Personal Protection Equipment Company

This work presents an optimization model to support decisions during production planning and control in the personal protective equipment (PPE) industry (in particular, gloves). A case study was carried out at a Brazilian company with the aim of increasing productivity and improving customer service with respect to meeting deadlines. In this case study, the mixed integer linear programming model of Luche (2009) was revisited. A new model for single-stage lot sizing was applied to the production scheduling of gloves. Optimizing this scheduling was not a simple task because of the scale of the equipment setup time, the diversity of the products and the deadlines for the orders. The model was implemented in GAMS IDE and solved by CPLEX 12. The model and the associated heuristic produce better solutions than those currently used by the company.

RESEARCH ON COMBINATION AND PLANNING OF PROCESSING UNITS UNDER CIMS CONDITION

A method of the rational combinging and planning of the given processing machines into units under the condition of computer integrated manufacturing systems is presented. Here the modelling method is a kind of queuing network model with the change of productivity, which has been checked in the reality and effectivencss by a manufacturing case in China

Regularized Methods for a Two-Stage Robust Production Planning Problem and its Sample Average Approximation

In this paper,we consider a two-stage robust production planning model where the first stage problem determines the optimal production quantity upon considering the worst-case revenue generated by the uncertain future demand,and the second stage problem determines the possible demand of consumers by using a utility-based model given the production quantity and a realization of the random variable.We derive an equivalent single-stage reformulation of the two-stage problem.However,it fails the convergence analysis of the sample average approximation(SAA)approach for the reformulation directly.Thus we develop a regularized approximation of the second stage problem and derive its closed-form solution.We then present conditions under which the optimal value and the optimal solution set of the proposed SAA regularized approximation problem converge to those of the single-stage reformulation problem as the regularization parameter shrinks to zero and the sample size tends to infinity.Finally,some preliminary numerical examples are presented to illustrate our theoretical results.

Energy-efficient Approach to Minimizing the Energy Consumption in An Extended Job-shop Scheduling Problem

The traditional production planning and scheduling problems consider performance indicators like time, cost and quality as optimization objectives in manufacturing processes. However, environmentally-friendly factors like energy consumption of production have not been completely taken into consideration. Against this background, this paper addresses an approach to modify a given schedule generated by a production plarming and scheduling system in a job shop floor, where machine tools can work at different cutting speeds. It can adjust the cutting speeds of the operations while keeping the original assignment and processing sequence of operations of each job fixed in order to obtain energy savings. First, the proposed approach, based on a mixed integer programming mathematical model, changes the total idle time of the given schedule to minimize energy consumption in the job shop floor while accepting the optimal solution of the scheduling objective, makespan. Then, a genetic-simulated annealing algorithm is used to explore the optimal solution due to the fact that the problem is strongly NP-hard. Finally, the effectiveness of the approach is performed small- and large-size instances, respectively. The experimental results show that the approach can save 5%-10% of the average energy consumption while accepting the optimal solution of the makespan in small-size instances. In addition, the average maximum energy saving ratio can reach to 13%. And it can save approximately 1%-4% of the average energy consumption and approximately 2.4% of the average maximum energy while accepting the near-optimal solution of the makespan in large-size instances. The proposed research provides an interesting point to explore an energy-aware schedule optimization for a traditional production planning and scheduling problem.

Intelligent Optimization-Based Production Planning and Simulation Analysis for Steelmaking and Continuous Casting Process

Aiming at the limitations of the traditional mathematical model for production planning, a novel optimization model is proposed to improve the efficiency and performance for production planning in steelmaking and continuous casting （SCC） process. The optimization model combined with parallel-backward inferring algorithm and genetic algorithm is described. To analyze and evaluate the production plans, a simulation model based on cellular automata is presented. And then, the integrated system including the production plan optimization model and the simulation model is introduced to evaluate and adjust the production plan on-line. The test with production data in a steel plant shows that the optimization model demonstrates ability to deal with time uncertainty in production planning and to set up a conflict-free production plan, and the integrated system provides a useful tool for dynamically drawing and adjusting a production plan on-line. The average staying time of the production plan is about 5% shorter than that in a practical process.