Construction of integrated manufacturing optimization system in material industry

In manufacturing industries, production planning and scheduling strategy usually flow in a hierarchical direction. In this direction, the production planning problem is solved first; then the scheduling problem is solved to meet the production targets. This often generates an infeasible production plan because of not considering the details of scheduling. Therefore, it is necessary to develop models that can integrate production planning and scheduling. For manufacturing companies that have identical units of single products and are often grouped in production batches (lots), lot streaming can be used as a scheduling technique. However, there has been no model to integrate production planning (lot sizing) and scheduling, using lot streaming technique which can accelerate production. The main objective of this research is to develop mixedinteger mathematical models for integration of lot sizing and flowshop lot streaming problems such as variable sublots, consistent and equal sublots, scheduling with learning effects and the possibility of preventive maintenance tasks. The objective of these mathematical models is minimization of total costs and also, five goals of problem can simultaneously be solved, namely: determining the sequence among sublots, optimal number of sublots for each lot, size of each lot, inventory levels and size of individual sublots. The second objective of this research is to propose a solution procedure for problems when data are fuzzy. Finally, the third research objective is to validate the proposed model through a case study. Three software are used to extract the results of mathematical models and validate the solution procedure. The name of these software are: LINGO, MINITAB and MATLAB. In this research, the author proposes the first mixed-integer mathematical models for integration of lot sizing and lot streaming problems. By these proposed models, not only sequencing and timing decisions of multiple products are calculated but also lot size of each product, work-in-process and inventory levels of finished products are calculated when lots can be split into smaller sublots. To get the results of mathematical models, in three examples, 70 randomlygenerated problems are solved by LINGO solver. Moreover, a two-way ANOVA test as a statistical method is applied to validate the mathematical model, using four examples consisting sixteen problems, by MINITAB software. The NDM Company is used as a case study. The mathematical models are used to solve NDM company’s problems by LINGO solver. The results showed 32 percent (77.66 hours) reduction in makespan compared to non-integrated mathematical model. Validation of the proposed solution procedure is achieved by comparison of results with max-min method results,using Shahab Shishe company data. The results of using the proposed mathematical models in this research are first to reduce cost by using these proposed models. Secondly is greater marginal benefits were obtained by intermingled sublot cases than non-intermingled sublot cases. It is concluded that better makespans were obtained by intermingled sublot cases than non-intermingled sublot case.

Aiming at the design and development of integrated production planning and scheduling system(PPSS) of rare metal manufacturing, the goals and main functions of the integrated production planning and scheduling system were analyzed and designed based on the analysis of rare metal manufacturing enterprise’s main characteristics and management status. Through analysis of the present situation and trend of production planning and scheduling, an integrated system architecture was proposed. Key technologies of system integration and system developing were delivered, and some key functions of production planning and scheduling system which include annual plans management, monthly plans management, MRP management, production scheduling management and production Kanban management were developed based on JSP technology and WEB platform. The practical applications in some rare metal manufacturing enterprises were proved that the integrated production planning and scheduling system could enhance the manufacturing management level effectively.

The formation of realistic implementable medium-range production plans requires explicit recognition of the multiple conflicting objectives of production planning. However, suggested applications of multiobjective optimization to production planning have been limited to goal programming procedures which fail to capitalize on the intrinsic flexibility of a multiobjective model. Alternatively, interactive multiobjective solution techniques could be used to allow planners to enhance decision making without excessive computational effort. This study describes an interactive multiple objective decision framework and evaluates its effectiveness via a multiobjective capacitated lot sizing model based on a real manufacturing facility. The results suggest that this approach is an effective solution strategy and useful decision aid for complex production planning problems.

Conventional approach for solving replenishment lot size problem is by using the differential calculus with the need of applying the first-order and second-order differentiations to the long-run average production-inventory cost. A recent published paper used conventional method to determine optimal lot size for an integrated production-delivery system with scrap and cost lessening distribution policy. This paper uses an alternative algebraic approach to reexamine the aforementioned problem without referring to derivatives. As a result, optimal lot size and a simpler expression for the long-run average cost are derived, and they are identical to what were obtained by using the conventional method. Key words: Lot size, optimization, scrap, multiple distribution policy, algebraic approach, production planning.

Analysis of propagation of stochastic errors through an MRP system, and an industrial case study

SYNOPTIC ABSTRACTThis paper deals with the effect on the {optimal lot sizes, cycle length, and cost} of the system for several products produced on several machines with repetitive common cycle length when the solution is constrained by limited storage space or capital. In order to solve the constrained production lot size problem it is necessary to determine the maximum volume or value of the inventory (MV) required for a given production plan. One simple measure of the storage space or capital required is the sum of the peak values of the individual product inventory levels (SMV). Since the peak inventory levels for all products do not occur simultaneously, SMV is usually much larger than the exact MV and, therefore, would be expected to yield a higher cost solution.In this paper efficient computational procedures are developed to find the exact MV and the corresponding constrained optimal lot sizes and cycle length when the products are made on a single machine. Techniques to determine the MV are explo...

Adaptive Job-shop Control Based on Permanent Order Sequencing

A century of evolution from Harris׳s basic lot size model: Survey and research agenda

The planning of purchasing and production lot-size for multi-item multi-period multi-workstation capacitated production (MLCLSP-M) is discussed. Since the model is a NP-hard problem, this research proposes a heuristic method (A-LS), which decomposes the model into two sub-problems and iteratively solves them. The two sub-problems consisting of an assignment sub-problem with given lot size and a lot sizing sub-problem with given assignment. Solution method was developed, tested and compared to illustrate the solution quality and computational time. Mathematical models for both sub-problems were solved with AMPL/CPLEX 8.0.0 solver. The computational results indicate that the proposed heuristic (A-LS) gives satisfactory solution with faster solving time comparing with the original mathematical model.

In this paper, a cutting plane model is presented for solving a problem in a cast polypropylene (CPP) plastic film manufacturer. The company produces plastic rolls from plastic pellets with widths ranging from 3 050 mm to 3 250 mm. The plastic rolls are trimmed according to customer’s orders. In prior to the trimming process, the production planning and inventory control (PPIC) department scheduled the machines and arranged the plastic trim compositions manually. In this work, the plastic trimming problem is solved by applying the trim loss model. Since trimmed loss problem is an NP-hard problem. In this case, the permutations are selected in advance so that the total length is feasible to the machine length. The computation is carried out using visual basic for application (VBA). The model outcomes are then used for optimizing the machine scheduling process. Modified earliest due date is proposed to schedule in which machines customer’s orders should be done. The machines scheduling represents the company conditions and the cutting production can be scheduled for daily basis. Keywords: cutting plane; cutting stock; earliest due date; machine scheduling; non-polinomial-hard problem.

Business competitiveness today is influenced by factors both internally and externally. For this reason, an effective and efficient operation and production strategy is required. These activities require demand estimation to be one of the keys in production planning and control. This means that forecasting will drive business decisions on the importance of quickly and accurately capturing information about demand. The output of the forecasting value can be the basis of production, capacity, and scheduling systems, financial planning, marketing and labor. Thus, the implementation of a forecasting method that is suitable for the data pattern and the level of accuracy or accuracy becomes an important instrument.

The following investigation shows the problems in “Industria Granero del Centro EIRL”, it is a company that is dedicated to the production of grains such as split bean, split wheat, american morón and corn flour. The goal is to improve the production planning and scheduling system, and eliminate overproduction and economic losses. VSM (Value Stream Mapping) was used to identify the improvement in the production process. The root causes of the problem were then determined using the Ishikawa diagram. The activities to be carried out are: identification of times of each process of the evaluated product, carry out the implementation of the master production plan, application of Kanban and application of Heijunka for load balancing [1]. The results of this implementation will be visualized through a simulation making use of efficiency, effectiveness and productivity indicators to meet the objective.

Thin‐film transistor liquid‐crystal displays (TFT‐LCDs) have gradually replaced cathode‐ray tubes because of their compact size and efficient use of power. Since product lifecycles are shortening, consumers require instant order delivery, which affects fluctuations in supply and demand. As various product types in the LCD industry exist, manufacturing time has to be reduced to satisfy consumer demand. This study investigates the dispatching of parallel machines for TFT‐LCD manufacturing. Since customers’ orders and suppliers’ deliveries vary significantly and the manual adjustment of production planning is time consuming, an appropriate production planning and scheduling system is necessary. Consequently, this study develops a genetic algorithm to solve the scheduling problem of unrelated parallel machines, taking account of multiple performance measurement indicators. We also adopt LINGO to solve the problem and compare the obtained solution quality and computation time of the two approaches to justify the effectiveness of the proposed genetic algorithm.

Seasonality is a function of a time series in which the data experiences regular and predictable changes that repeat each calendar year. Two-stage stochastic programming model for real industrial systems at the case of a seasonal demand is presented. Sampling average approximation (SAA) method was applied to solve a stochastic model which gave a productive structure for distinguishing and statistically testing a different production plan. Lingo tool is developed to obtain the optimal solution for the proposed model which is validated by Math works Matlab. The actual data of the industrial system; from the General Manufacturing Company, was applied to examine the proposed model. Seasonal future demand is then estimated using the multiplicative seasonal method, the effect of seasonality was presented and discussed. One might say that the proposed model is viewed as a moderately accurate tool for industrial systems in case of seasonal demand. The current research may be considered a significant tool in case of seasonal demand. To illustrate the applicability of the proposed model a numerical example is solved using the proposed technique. ANOVA analysis is applied using MINITAB 17 statistical software to validate the obtained results.

Most deterministic and stochastic inventory models assume that the lead time is a given parameter, and determine the optimal operating policy on the basis of this unrealistic assumption. In this paper, we incorporate a common inter-relationship between lot size and lead time in the stochastic continuous review inventory control (Q,r) model. We present an efficient iterative procedure that determines the optimal or near-optimal lot size and reorder point that minimize the total annual inventory cost for the extended (Q,r) model. We further propose another analytical procedure that generates the lot size and safety stock simultaneously. We also show that our model will result in smaller production lot size and shorter lead time than an earlier model reported in the literature.