Determining Lot Sizes Research Articles

EVALUASI PRODUKSI DAN PERSEDIAAN PADA PRODUK PIPA DI CV.XYZ

CV. XYZ is a distributor of building materials but focuses on PVC pipe products under the Target brand. CV. XYZ was founded in 2013. At CV. XYZ is very necessary for inventory scheduling which needs to be carried out by CV. XYZ in order to always meet consumer demand. The author suggests inventory calculations and scheduling to meet consumer demand using the Distribution Requirement Planning (DRP) method. In the DRP method there are several stages of data needed, namely demand data, inventory, lead time, holding cost data. Data processing in this DRP method is demand forecasting, determining Lead Time, determining lot size, calculating safety stock, and creating DRP tables. In the calculation results, results were obtained for distribution costs for 5 pipe products which initially had excess stock and could not meet demand. For Target Pipe AW 1.0 x 4m products savings of 33%, Target Pipe AW 2 ½ x 4m savings of 67%, Target Pipe C 5/8 x 4m savings of 67%, Target Pipe D 1 ½ savings of 50%, Target Pipe D 6.0 x 4m savings of 67%.

A cooperative coevolutionary hyper-heuristic approach to solve lot-sizing and job shop scheduling problems using genetic programming

Lot-sizing and scheduling in a job shop environment is a fundamental problem that appears in many industrial settings. The problem is very complex, and solutions are often needed fast. Although many solution methods have been proposed, with increasingly better results, their computational times are not suitable for decision-makers who want solutions instantly. Therefore, we propose a novel greedy heuristic to efficiently generate production plans and schedules of good quality. The main innovation of our approach represents the incorporation of a simulation-based technique, which directly generates schedules while simultaneously determining lot sizes. By utilising priority rules, this unique feature enables us to address the complexity of job shop scheduling environments and ensures the feasibility of the resulting schedules. Using a selection of well-known rules from the literature, experiments on a variety of shop configurations and complexities showed that the proposed heuristic is able to obtain solutions with an average gap to Cplex of 4.12%. To further improve the proposed heuristic, a cooperative coevolutionary genetic programming-based hyper-heuristic has been developed. The average gap to Cplex was reduced up to 1.92%. These solutions are generated in a small fraction of a second, regardless of the size of the instance.

Joint determination of purchasing and production lot sizes in an unreliable production system

This paper discusses a production-inventory system under random machine breakdown. Holding safety stock is the common way to mitigate the effect of random machine breakdown on shortages that may occur during machine repair time. Since holding safety stock can be costly, especially for expensive products, this paper investigates an alternative strategy in which it is assumed that the production manager can purchase the same products from a supplier in order to meet the demands that may be lost due to the depletion of the inventory after the machine breakdown. The supplier has known lead-time and reliability with the quality assured products. Despite holding safety stock, purchasing occurs only when the machine breakdown happens. The question is about the optimal amount of production and purchasing lot sizes to minimize the total expected costs. The optimality of the model is investigated when failure and repair time follow an exponential distribution, and a computational algorithm for finding the optimal lot sizes is presented. A comparison between the purchasing strategy and holding safety stock is performed through a sensitivity analysis regarding some effective parameters. This study shows using the purchasing strategy when holding or production cost rises is more beneficial than holding safety stock.

Inventory control analysis of frozen processed shrimp using silver meal heuristic method (case study at PT. X Malang, East Java, Indonesia)

Shrimp is the leading commodity in fishery exports in Indonesia. PT. X is one of the companies producing frozen processed shrimp for export. To fulfill export needs, the availability of shrimp raw materials becomes important for the company to maintain. But on the other hand, the supply of shrimp also needs to be controlled at an appropriate amount to avoid costly waste. The purpose of this study was to compare the performance of raw material inventory control applied by the company and the Silver Meal Heuristic method. Silver Meal Heuristic is a method used to determine lot size by considering inventory costs. The results of this study indicate that the company controls inventory by ordering raw materials which are constant every 1 week for shrimp products and once every 2 weeks for others. The total cost of inventories incurred by the company was IDR 263,730,918.63 in the period June 2020 to May 2021. Inventory control using the Silver Meal Heuristic method has different frequencies where the frequency produced is smaller than the company ordering frequency. The total inventory cost using the Silver Meal Heuristic method is IDR 223,622,213. By applying the Silver Meal Heuristic method, total inventory costs were saved by 15% or IDR 40,108,706 in the period June 2020 to May 2021.

Apoptosis-based method for determining lot sizes in the filling of human-induced pluripotent stem cells.

Standardization in process design and operation is needed in the commercial production of human-induced pluripotent stem (hiPS) cells. Lot sizing in the filling of hiPS cells into containers, a part of the preservation process, also needs to be standardized because of the temporal changes in cell quality during the process. Here, we present an apoptosis-based method that can determine lot sizes in the filling of hiPS cells considering temporal changes in cell quality. Two indicators were developed for (i) the cell quality change using reactive oxygen species (ROS) measurement and (ii) the cell survival and probability of filling success, which are parts of the lot-sizing problem. Using computational simulation, a map out of the optimal lot size was produced that minimized the expected production costs at a given cell demand and an acceptable change in cell quality. At a filling temperature of 4°C, the largest possible lot size was calculated as 6 L (corresponding to a filling time of 125 min). The results of a sensitivity analysis recommended cold filling or the addition of an antioxidant. The presented method is effective to determine the lot size considering the change in cell quality during filling. The study uniquely combines the experimental results with mathematical modeling and computational simulation techniques. The map out of the optimal lot size could guide the development of industrial filling processes of hiPS cells.

Determination of Joint Lot Size using the supply chain approach with Vendor Managed Inventory (VMI) Method at PT. XYZ

PT. XYZ is a company producing animal feed products which is located in the Medan Region. This company is a branch of Z Group company based in Surabaya. In the process company business activities, both from the process of ordering raw materials to suppliers up to the sale of products to buyers, often happens planning mismatches in determining production lots so often cause overstock and stockout. This is based on the lack of integration each component in the supply chain. Each component is still using the traditional way that is by making each plan without coordinating with each other. This manufacturing company still needs to do improvements in the production planning system. The absence of the PPIC Department make planning in the production in the company becomes out of control. This study aims to improve the supply chain system by method VMI and determine the optimum combined lot size by the JELS method. The JELS calculation results show an optimal Q of 8,692 bags, with QM for 437,082 bags with a frequency of product shipments 50 times and the frequency of replenishment of raw materials 13 times.

MATERIAL REQUIREMENT PLANNING UNTUK MEMENUHI PRODUKSI PADA CV. BANGUN CIPTA ARTHA DI BADUNG

Inventory management without Material Requirement systems in CV. Bangun Cipta Artha resulted in the lot size for each order of raw materials not optimal. One concept that can be used to plan and control raw materials is the Material Requirement Planning. This study is to determine the number of sizes of raw material orders, the exact order time, the method that produces the lowest cost for each raw material, and the effect of using MRP on inventory costs. This research conducted on 160x200cm spring bed products. Data was analyzed by making production master schedules, determining net requirements, determining lot size, and making MRP tables. Based on the results, the determination the best lot sizing is the order quantiy period which results in a total inventory cost of Rp. 26,475,220 where the total cost is lower, compared to lot for lot method which Rp. 43,464,000. part period balancing Rp. 33,106,576, and conventional method Rp.49,472,912.
 Keywords: Material Requirement Planning (MRP), Sizing Lot, Lot For Lot, Balancing Part Period, Period Order Quantiy

Managing unreliability in automotive supply networks - an extension of the joint economic lot size model

Within assembly network supply chains, supply disruptions can occur on every supplier-buyer link. Managing this network unreliability can help to reduce schedule instability and increases the overall efficiency of the supply chain accordingly. In this line, a stylised assembly network supply chain model is proposed with two suppliers and a single buyer using the joint economic lot sizing approach. This supply network can be disrupted by a shortage occurring at one of the two suppliers due to random machine breakdowns, which consequently creates dependent requirements variations affecting both the buyer and the entire network. First, the basic joint economic lot sizing model is extended by the said schedule instability. Second, a solution approach is presented concerning the determination of optimal lot sizes, the investment into the reliability of the supply network as well as the determination of safety stocks. Furthermore, the sensitivity of relevant model parameters is investigated by means of a numerical example. Managerial implications are accordingly derived focusing on the reliability of the supply network members and internal incentive structures.

Incorporating carbon emissions in queuing models to determine lot sizes and inventory buffers in a supply chain

In this paper, we present a supply chain model that considers both inventory-related costs and emissions. We used the queueing-based performance model wherein emissions in three stages of supply chain activities are captured. The model was solved by the decomposition approach. For model validation, we have used a discrete event simulation. The computation results show that the two results, i.e., the decomposition approach and the simulation, are very close, indicating the accuracy the approach that we used. Experiments were conducted to test the applicability of the model. The numerical examples show that the change in parameter values is not always responded the same way by the total inventory-related costs and the emission costs, indicating the importance of including these two response variables in the model.

Incorporating carbon emissions in queuing models to determine lot sizes and inventory buffers in a supply chain

In this paper, we present a supply chain model that considers both inventory-related costs and emissions. We used the queueing-based performance model wherein emissions in three stages of supply chain activities are captured. The model was solved by the decomposition approach. For model validation, we have used a discrete event simulation. The computation results show that the two results, i.e., the decomposition approach and the simulation, are very close, indicating the accuracy the approach that we used. Experiments were conducted to test the applicability of the model. The numerical examples show that the change in parameter values is not always responded the same way by the total inventory-related costs and the emission costs, indicating the importance of including these two response variables in the model.

A distribution-based approach for determining lot sizes in the filling of human-induced pluripotent stem cells

IntroductionToward the commercial production of human-induced pluripotent stem (hiPS) cells, the process design and operation have to be standardized. Considering the change in cell quality during filling of the hiPS cells into containers, lot sizing during filling also needs to be standardized.MethodsWe present a distribution-based approach that can be used for determining the lot sizes in the filling of hiPS cells by considering change in cell quality during filling. The approach describes the “survival capability” of the cells as a continuous probability distribution, and expresses the change in quality during filling by “trimming” the distribution.ResultsA lognormal distribution was assumed as the survival capability distribution of the cells that were to be filled. The distributions after different filling times were calculated, which were compared with the distribution of the initial filling time regarding the yield of the cells and the similarity. These conditions served as strong quality constraints in determining an economically optimal lot size.ConclusionsThe presented conceptual approach would be effective in determining the lot size considering the change in cell quality during filling. For actual application, measuring the distribution information on the survival capability of hiPS cells is encouraged.

Zoning, density, and rising housing prices: A case study in Portland, Oregon

This study uses path analysis models to evaluate the associations between zoning, development density and the sales prices of new and existing single-family homes at the dwelling-unit level in Portland, Oregon. Development density is associated with the sales prices of single-family homes directly by determining land costs and indirectly by correlating with the size and construction costs of structures. A prominent trend in Portland’s and the nation’s real estate markets is that new single-family homes are getting bigger. Another trend is that single-family homes have been built on smaller lots despite their growing floor areas. Because developers tend to build smaller homes on smaller lots, the decline of lot sizes helps to contain the effect of growing home size on housing prices. However, the counter effect of smaller lot sizes is somewhat weak because home sizes have a stronger association with housing prices than lot sizes. Homebuyers in Portland are willing to pay a premium to live in neighbourhoods with higher densities, which further limits the potential of higher density development as a tool to reduce single-family home costs. In addition to its indirect associations with home prices via the determination of lot and home sizes, zoning exhibits a significant and direct association with the prices of existing single-family homes, but not with the sales prices of new single-family homes. Existing single-family homes in higher density zones tend to sell at lower prices, ceteris paribus, suggesting that the concern of future neighbourhood change prevails over the opportunity for redevelopment.

Fix-and-optimize heuristics for capacitated lot sizing with setup carryover and backordering

PurposeThe capacitated lot sizing problem (CLSP) is one of the most important production planning problems which has been widely studied in lot sizing literature. The CLSP is the extension of the Wagner-Whitin problem where there is one product and no capacity constraints. The CLSP involves determining lot sizes for multiple products on a single machine with limited capacity that may change for each planning period. Determining the right lot sizes has a critical importance on the productivity and success of organizations. The paper aims to discuss these issues.Design/methodology/approachThis study focuses on the CLSP with setup carryover and backordering. The literature focusing on this problem is rather limited. To fill this gap, a number of problem-specific heuristics have been integrated with fix-and-optimize (FOPT) heuristic in this study. The authors have compared the performances of the proposed approaches to that of the commercial solver and recent results in literature. The obtained results have stated that the proposed approaches are efficient in solving this problem.FindingsThe computational experiments have shown that the proposed approaches are efficient in solving this problem.Originality/valueTo address the solution of the CLSP with setup carryover and backordering, a number of heuristic approaches consisting of FOPT heuristic are proposed in this paper.

Determination of Economic Lot Size between Suppliers and Manufacturers for Imperfect Production System with Probabilistic Demand

This study aims to develop a combined economical lot size model between supplier and manufacturer for imperfect production processes with probabilistic demand patterns and constant lead times. The supplier side produces the product within a certain time interval then sent to the manufacturer with a certain amount of lot size. Imperfect supplier production systems are characterized by the probability of defective product (γ). The model decision variables are the lot size of the manufacturer's ordering, supplier lot size, and the reorder point of the manufacturer. The optimal decision variables are obtained by minimizing the total expected cost of the combined costs between the suppliers and the manufacturers borne by both parties. The model is built compared to the transactional partnership model, in which the supplier does not participate in the efficiency of its inventory system. A numerical example is given as an illustration of the JELS model and the transactional partnership model. Sensitivity analysis of the model is done by changing the parameters aimed at analyzing the behavior of the developed model.

Lot size determination for finite horizon under the effect of productivity and quality learning process

ABSTRACTTraditional models for determining economic production quantity assume a constant production rate. One result of this assumption is that the number of units produced in a given period is constant. The constant production rate assumption is not valid whenever an operator begins production of a new product, changes to a new machine and restarts after a long break, or implements a new production process. In such situations, the learning effect on production planning and inventory management cannot be ignored. The conventional Wright's learning curve is a curve that solely depicts productivity increasing at a constant percentage as a cumulative number of units are produced. Thus, in addition to the productivity learning process, the quality learning process should also be considered. With the recent development of learning curves that simultaneously take into account quality and productivity, a modified production inventory model has been built to periodically determine the optimal lot size for a finite plan horizon. The productivity considered is the unit production time, while the quality concerned is the unit standard deviation of the production quality level. For the purposes of explanation and comparison, four cases are introduced in this research, and examples are provided for demonstration.

Material-handling-system-based input control policy for wafer fabrication

In this paper, a new input control strategy is proposed for wafer fabrication facilities. This strategy is an adaptation of workload regulating and CONWIP, and comprises of two sequential steps: 1) determination of operational lot sizes; 2) release of lots based on a material handling system-based CONLOAD approach. We show the superiority of our strategy over a CONWIP-based policy via simulation of a real-life wafer fab. An approach for determining target workload levels for the proposed strategy is also presented.

Material-handling-system-based input control policy for wafer fabrication

In this paper, a new input control strategy is proposed for wafer fabrication facilities. This strategy is an adaptation of workload regulating and CONWIP, and comprises of two sequential steps: 1) determination of operational lot sizes; 2) release of lots based on a material handling system-based CONLOAD approach. We show the superiority of our strategy over a CONWIP-based policy via simulation of a real-life wafer fab. An approach for determining target workload levels for the proposed strategy is also presented.

Kinetic analysis of cell decay during the filling process: Application to lot size determination in manufacturing systems for human induced pluripotent and mesenchymal stem cells

Scale-up of a cell-manufacturing system involves an extension of the filling process time because of the increased number of vials to be filled with the cells suspended in a cryopreservation solution. As the solution includes cryoprotective agents such as dimethyl sulfoxide that is toxic to cells, a longer processing time has an adverse impact on the quality of cell products. Therefore, understanding the related cell-decay kinetics is a major challenge for the development of robust and scalable cell-manufacturing systems. In this study, a new set of performance indices were defined to quantify the state and potential of cells suspended in a cryopreservation solution, i.e., the survival ratio of cells suspended in cryopreservation solution was provided for the cell state estimation, and the recovery ratio of cells after freezing and thawing, the attachment efficiency of cells after seeding, and the specific growth rate of surviving cells were provided for the cell potential estimation. Using these indices, the time-dependencies of cell viability variation in human induced pluripotent (hiPSCs) and human mesenchymal stem cells (hMSCs) suspended in a cryopreservation solution were investigated. Based on the obtained kinetic data, strategies for developing and controlling cell-manufacturing systems were discussed.

Joint decisions of machining process parameters setting and lot-size determination with environmental and quality cost consideration

In today’s highly competitive market, it is not enough to produce high quality products at lower costs but also sustainability has to be factored in the decision making process. The objective of this research is to propose an optimization model that considers both losses due to negative environmental impact and rework cost for a product-mix production planning problem manufactured through turning operation. The mathematical model solves simultaneously for the optimal production quantities and machining parameters that maximize the total expected profit. Surface roughness is used as a metric to assess the desired quality level of the finished machined part. Roughness was modeled as a random variable using normal distribution while rework cost was calculated in terms of probabilities of exceeding roughness target and upper tolerance limit. Energy consumption and CO2 emissions are estimated using lifecycle analysis (LCA) approach and used to quantify the environmental cost. A numerical example is used to illustrate the adequacy of model proposed through a turning study. Results revealed the importance of taking a system approach when solving for optimum machining parameters and lot-sizing if quality and environmental costs are considered. It was shown that a stringent quality cost target will drive process parameters such as feed and nose radius to lower values in order to minimize rework cost. Moreover, and in order for environmental cost to significantly impact decision makers, carbon selling price or cap limit needs to be stringent enough to drive emission reductions.

Optimising lot sizing with nonlinear production rates in a multi-product multi-machine environment

In a variety of discrete manufacturing environments, it is common to experience a nonlinear production rate. In particular, our interest is in the case of an increasing production rate, where learning creates efficiencies. This leads to greater output per unit time as the process continues. However, the advantages of an increasing production rate may be offset by other factors. For examples, JIT policies typically lead to smaller lot sizes, where the value of an increasing production rate is largely lost. We develop a general model that balances the impact of various competing effects. Our research focuses on determining lot sizes that satisfy demand requirements while minimising production and holding costs. We extend our prior work by developing a multi-product, multi-machine method for modelling and solving this class of production problems. The solution method is demonstrated using the production function from the PR#2 grinding process for a production plant in Carlisle, PA. The solution heuristic provides solution times that are on average only 0.22 to 0.55% above optimum as the solution parameters are varied and the ratio of heuristic solution times to optimal solution times varies from 18.16 to 14.15%.