Patent Application
20060282343
This application claims priority to India Patent Application serial number 1145/DEL/2005 (entitled PAPER MANUFACTURING SYSTEM AND METHOD, filed May 5, 2005) which is incorporated herein by reference.
The invention relates generally to manufacturing of paper, and more specifically to allocating resources in production of paper.
The paperless society once predicted as a result of widespread computerization of information handling has not materialized, but has instead become a society where increasingly more information is generated and printed than ever. Paper products are also on the rise, such as an increase in production of paper packing products like cardboard influenced in part by consumers' ability to shop for products on the Internet, and to order from a cheapest or other preferred provider and have the ordered product packaged and shipped.
Personal computers have made it possible for nearly any person to generate a document, to retrieve a wide variety of information from the Internet, or to receive a document as part of a collaboration or communication with another party. These documents can now be just as easily printed, as inexpensive laser printers that produce very high quality output can be found for under one hundred dollars. As the ease and cost of producing documents has improved, the number of documents generate and printed has increased.
Further, subscriptions to magazines, newspapers, and other printed publications have not suffered the dramatic losses once predicted as a result of online publishing, but continue to provide strong demand to the paper producing industry. Information printed on paper can be carried, stored, and viewed with ease, and without concern for network access, power or battery availability, or the durability or portability of expensive electronics.
Production of paper therefore remains a strong industry, with a broad range of paper types and paper products produced. Some paper, such as inkjet printer paper, benefits from special coatings or certain weights, while other paper such as newsprint is intentionally of a lower grade to reduce cost. Geographic location of the paper production facility and the cost to transport paper to a customer also impact the profitability and efficiency of a paper manufacturing enterprise, as does inventory management and raw materials or inventory availability.
Careful management of these various paper manufacturing parameters is important to the profitability of paper production, particularly in competitive or low-margin environments. It is therefore desired to better manage paper manufacturing parameters across a large-scale paper manufacturing enterprise to ensure efficient operation.
In one example embodiment of the invention, paper is manufactured by receiving an order for paper, and allocating the paper order to a specific mill and allocating inventory to the order in a first linear programming optimization.
Production of orders within one or more mills is sequenced using a second linear programming optimization. In another example embodiment, paper is manufactured by receiving an order for paper, and allocating the paper order to a specific paper machine and allocating inventory to the order in a first linear programming optimization. Production of orders on one or more paper machines are sequenced using a second linear programming optimization.
In the following detailed description of example embodiments of the invention, reference is made to specific examples by way of drawings and illustrations. These examples are described in sufficient detail to enable those skilled in the art to practice the invention, and serve to illustrate how the invention may be applied to various purposes or embodiments. Other embodiments of the invention exist and are within the scope of the invention, and logical, mechanical, electrical, and other changes may be made without departing from the subject or scope of the present invention. Features or limitations of various embodiments of the invention described herein, however essential to the example embodiments in which they are incorporated, do not limit the invention as a whole, and any reference to the invention, its elements, operation, and application do not limit the invention as a whole but serve only to define these example embodiments. The following detailed description does not, therefore, limit the scope of the invention, which is defined only by the appended claims.
Examples of the present invention presented here seek to improve the efficiency of a paper manufacturing enterprise including multiple facilities, multiple sources of inventory, or multiple paper manufacturing machines. The paper manufacturing process is managed in one example embodiment by receiving an order for paper, and allocating the paper order to a specific mill and allocating inventory to the order in a first linear programming optimization. Production of orders within one or more mills is sequenced using a second linear programming optimization.
The paper order is in some embodiments more specifically allocated to a paper machine at 103, or in some embodiments assigned to multiple paper machines. Because each paper machine will vary somewhat from another paper machine, even between identical models, it is desired in some circumstances that an order not be split between paper machines to ensure uniformity of produced paper. In other embodiments, the order assignment may be split among multiple paper machines in the same manufacturing facility or in different manufacturing facilities to ease the challenge of scheduling production.
The order allocation is based in some embodiments on the capabilities of each individual machine relative to the demands imposed by the customer order. The machine must be capable of producing the desired type of paper, but it may be useful to avoid using a machine with features or capacity significantly higher than needed for a specific order. Similarly, the size of the order and the production rate of the various available paper machines are considered, as is the production capacity and availability of downstream auxiliary equipment associated with the various paper machines.
Some customers may prefer or specify a particular paper manufacturing facility or machine, such as for geographic location reasons or to ensure uniform quality of the paper purchased. Assignment of jobs or orders to each mill or machine considers the workload already assigned to a specific mill or machine, including jobs that can be assigned to other machines or mills and jobs that are to be assigned to a specific mill or paper machine.
Allocation of orders to a paper machine or paper mill is also dependent in some embodiments on the location of the machine or mill, and the transportation costs involved to provide materials to complete the order and to deliver the finished product. Availability of warehouse space is also a consideration, as is the availability and age of finished paper meeting the criteria laid forth in the order.
At 104, inventory such as finished paper, paper pulp, uncut paper rolls, or other such inventory is allocated to the paper order. The inventory allocation problem seeks to minimize inventory remaining after assignment, while also minimizing loss due to age, minimizing transportation cost, minimizing storage cost, and minimizing production cost. Available inventory in one embodiment is described by its mill location, grade or specification, dimensions, and age. These criteria are used to determine whether inventory is suitable for use to fill a given order, and whether costs and efficiency will be optimized by the allocation selected.
In some embodiments, further constraints are imposed, such as restricting allocation of a job to paper machines to machines within a single mill. This enables more efficient management of the order, and simplifies transportation costs in addition to simplifying the overall process of finding a solution to the allocation problem. Further examples of constraints that may be imposed are filling an entire order from the same grade inventory, even if different grades of available inventory would meet or exceed the order requirements, and ensuring that trim loss in producing finished paper from paper roll inventory yields acceptable trim loss. Other examples of constraints exist out of necessity, such as dictating that the dimensions of the paper in a paper order should not exceed the dimensions of a paper roll used from inventory to produce the finished product. Similarly, the expected delivery date of the finished product should be before the inventory expiry date assigned to the inventory allocated to fill an order.
The costs involved with inventory allocation are minimized by considering both production plus transport costs when allocating inventory. For example, transportation costs for the finished product may be minimized by producing finished paper at one mill rather than another, but the greatest overall cost savings may result from producing the paper at another mill to minimize inventory transportation cost. Similarly, the storage costs for inventory, both that will be used and that remains unused, should be considered to ensure profitability across a production schedule of many orders, as should the cost of inventory that may go unused and expire or be reduced in value.
The inventory allocation problem and the paper order allocation problems addressed at 102, 103, and 104 are solved in some embodiments by use of a linear programming optimization. The optimization process does not necessarily produce a single optimal solution, but in some embodiments result in multiple solutions, from which a solution that is more desirable than other solutions considered is selected. The linear programming (LP) problem is a problem in which the constraints and the desired result are all linear, and is the subject of much research. The field of operations research has applied linear programming to find solutions to problems with linear constraints, and can be adapted to solve problems such as the allocation problems of 102 and 104. A further type of linear programming, known as mixed integer linear programming (or MILP), is similar to a linear programming problem but includes at least one constraint that is not continuous but must have an integer value.
In some embodiments of the invention, a solution to the paper order and the inventory allocation problems is found in a first linear programming operation, while a solution to a sequencing linear programming problem for each paper machine or mill is solved at 105. The second linear programming operation seeks to schedule those jobs or orders assigned to a specific mill at 102 or to a specific paper machine at 103, in an order that attempts to comply with several scheduling constraints.
While it may at first seem desirable to manufacture as much paper as possible as quickly as can be done, this results in a warehousing cost for the finished product before it can be shipped on the desired date. On-time delivery is important to make the customer happy, so delivery time and other constraints are considered in sequencing orders assigned to specific mills or machines. Similarly, the cost of delivery may vary, such that transportation cost can be reduced by using a cheaper transportation provider or method if a longer delivery time can be tolerated.
Changing a machine from producing one type of product to another type of paper product involves at least some degree of cost, and possibly a significant cost if significant changes are made. Sequence-dependent product changeover costs are desirably minimized in sequencing order production. In addition, costs related to paper waste in trimming bulk or roll paper to finished paper are incurred, and are desirably minimized.
Production of the paper in some embodiments is subject to a minimum run length requirement, either on a machine-by-machine or mill-by-mill basis. The current status of the paper machine, including maintenance status and setup status for the previous run are considered, along with any priority that might be assigned to a particular order. For example, an order may be assigned a priority from one to five, where orders of five have the highest priority and are not permitted to be delivered late, while a priority of three may have some tolerance in delivery timing, and an order with a priority of one is a noncritical order to fill depleted finished paper inventory.
One example embodiment of a linear programming optimization operation to solve the scheduling problem will present multiple solutions, showing the tradeoffs that must be made between the various competing objectives and constraints discussed above, such as warehouse cost, transportation cost, late delivery cost, trim loss, paper machine grade change cost, and other such costs. The operator can then select a schedule that is deemed most desirable, while gaining an understanding of the tradeoffs necessary in planning a production schedule given a certain pool of jobs.
Some embodiments will schedule orders on a rolling horizon, meaning orders are added to the existing schedule on a regular basis. For example, speculative orders may be placed into the schedule, and converted to firm orders as the scheduled date of production draws closer. Large orders that are far away in time can similarly be scheduled as firm or speculative orders, irrespective of the time horizon for firm scheduling. Such a system can also be used to predict the capacity or availability of a paper mill or machine over a time horizon longer than the firm order scheduling horizon, helping plan capacity, maintenance, and recovery from unforeseen breakdowns or other such problems in the scheduling process.
In one moving or rolling horizon example, the firm orders are set only for a short time horizon, such as 10 to 15 days. After linear programming solutions are found for the short-term scheduling problem, the entire problem is solved over the long-term horizon of two to three months, considering the constraints and factors such as maintenance, large orders, and other such criteria.
The sequencing and run formation at 203 uses machine data or mill data, as well as order details and other information to sequence the paper machine or production route within the mill. The result of the sequencing operation and the constraints imposed on the scheduling operation is the schedule or schedules produced at 204, as were described in greater detail in conjunction with
Inventory allocation is performed at 304. This involves solving another linear programming problem, and the solutions in some embodiments are related to the solutions to the order allocation problem solved at 302 and so are solved in the same linear programming optimization, such as via the same computer program. The inventory allocation at 304 is performed using inventory details as shown at 305, including use of product specifications, physical specifications, expiration date information, quantity, and mill location data for the inventory. Once the inventory allocation is done, order allocation is revisited at 302 to ensure that the inventory location or other inventory factors are considered in allocating the order to a paper machine or paper mill. In alternate embodiments, order allocation and inventory allocation problems are solved concurrently in the same linear programming operation
At 306, inventory left from the inventory allocation is assigned to fill suitable pending orders, either in whole or partially, or is stored as finished product inventory to be used to fill other orders. Orders are sequenced on individual paper machines or within individual paper mills at 307, such as by using the 10-15 day firm schedule horizon discussed in conjunction with previous examples. Trim loss in cutting paper rolls or bulk paper down to finished paper is also determined at 307, and is selected in some embodiments along with derivation of an optimum or desirable pattern determined to have minimal or low paper trim loss. The cutting patterns are sequenced for individual production runs on each paper machine at 308, at which point the paper production schedule is largely complete.
The examples presented here illustrate how paper manufacturing can be planned and scheduled using multiple linear programming operations. The systems and methods presented here enable production of paper to be more efficient, more predictable, and to better utilize those resources available to the paper production operation. Although specific embodiments have been illustrated and described herein, it will be appreciated by those of ordinary skill in the art that any arrangement which is calculated to achieve the same purpose may be substituted for the specific embodiments shown. This application is intended to cover any adaptations or variations of the example embodiments of the invention described herein. It is intended that this invention be limited only by the claims, and the full scope of equivalents thereof.
| Number | Date | Country | Kind |
|---|---|---|---|
| 1145/DEL/2005 | May 2005 | IN | national |
