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.

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.

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.

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.

PRODUCTION PLANNING PROBLEM WITH PRICING UNDER RANDOM YIELD:CVAR CRITERION

In this paper, we address a basic production planning problem with price dependent demand and stochastic yield of production. We use price and target quantity as decision variables to lower the risk of low yield. The value of risk control becomes more important especially for products with short life cycle. This is because, the profit implications of low yield might be unbearable in the short run. We apply Conditional Value at Risk （CVaR） to model the, risk. CVaR measure is a coherent risk measure and thereby having nice conceptual and mathematical underpinnings. It is also widely used in practice. We consider the problem under general demand function and general distribution function of yield and find sufficient conditions under which the problem has a unique local maximum. We also both analytically and numerically analyze the impact of parameter change on the optimal solution. Among our results, we analytically show that with increasing risk aversion, the optimal price increases. This relation is opposite to that of in Newsvendor problem where the uncertainty lies in demand side.

A framework for ICT-enabled real-time production planning and control

This paper aims to develop a conceptual framework for real-time production planning and control（PPC）.Firstly,we discuss the most prominently applied contemporary information and communication technologies for PPC.Enterprise resource planning（ERP） systems that integrate the value chain in an enterprise,manufacturing execution systems that manage and control the production on shopfloor,and advanced planning and scheduling（APS）systems that develop solutions for complex planning problems are the planning and control systems that have been analyzed.We emphasize the application of radio frequency identification as the most advanced and promising emerging real-time data capture technology that is currently available to manufacturers.Having analyzed the features and shortcomings of the individual systems perse,and by considering the advantages that may be realized through effective integration of these otherwise discrete systems,we propose a framework for real-time PPC.

Production Planning System for the Whole Steelmaking Process of Panzhihua Iron and Steel Corporation

A production planning system is designed for the whole steelmaking process, which includes desulfurization, vanadium extraction, steelmaking and continuous casting processes of Panzhihua Iron and Steel Corporation. The system is composed of modeling module, data interface module, data analysis module, static planning module, re-planning module, plan evaluation module, plan simulation module and human-computer interaction module. The system is connected with the L3 system of steel plant by using the data interface module, which can receive the real time scheduling information to make the static plan and re-plan under production disturbances. The simulation and plan evaluation function of the system can assist decision-maker to check and modify the production plan. The simulation experiments with real process data show that the system can make a feasible, executable, and effective production plan, and deal with primary random disturbances during production process timely.

A NOVEL HYBRID GENETIC ALGORITHMS AND PATTERN SEARCH TECHNIQUES FOR INDUSTRIAL PRODUCTION PLANNING

Soft computing has attracted many research scientists,decision makers and practicing researchers in recent years as powerful computational intelligent techniques,for solving unlimited number of complex real-world problems particularly related to research area of optimization.Under the uncertain and turbulence environment,classical and traditional approaches are unable to obtain a complete solution with satisfaction for the real-world problems on optimization.Therefore,new global optimization methods are required to handle these issues seriously.One such method is hybrid Genetic algorithms and Pattern search,a generic,flexible,robust,and versatile framework for solving complex problems of global optimization and search in real-world applications.

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.

Apply neural network for improving production planning at Samarang petrol mine

Purpose-Artificial neural network(ANN)is considered a good solution for building non-linear relationship between input and output parameters,which is suitable for solving production back allocation,which is the most important step for production planning of petroleum mine.The purpose of this paper is to suggest a solution for solving production back allocation problem at Samarang petrol mine based on ANN approach.Design/methodology/approach-In this study,well operational parameters’surveillance was conducted and ANN was used to build relationships between operation parameters and production rates.Experimental method is used for testing and evaluating the possibility of using ANN for supporting production planning at Samarang mine.Findings-Consequently,the proposed ANN solution can increase the accuracy of predicted values and could be used for supporting production planning at Samarang mine.Because ANN uses well test data for training and predicting(without adding new devices),it could be a feasible and cheap solution.Research limitations/implications-There is a need for applying other methods,such as:support machine vector,non-linear autoregressive models,etc.for better evaluation of ANN solution.Practical implications-The ANN models helped operation engineers to understand well production performance and make decision to improve production plan in timely manner.This solution could be generalized for the whole mine or to similar petroleum mines in practice.Originality/value-This paper aims to propose a solution based on ANN for solving production back allocation problem of petroleum industry.The solution is tested at Samarang mine.

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

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

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.

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.

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.

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.