The one-dimensional cutting stock problem with due dates

The one-dimensional cutting stock problem is the problem of cutting stock material into shorter lengths, in order to meet demand for these shorter lengths while minimizing waste. In industrial cutting operations, it may also be necessary to fill the orders for these shorter lengths before a given due date. We propose new optimization models and solution procedures which solve the cutting stock problem when orders have due dates. We evaluate our approach using data from a large manufacturer of reinforcement steel and show that we are able to solve industrial-size problems, while also addressing common cutting considerations such as aggregation of orders, multiple stock lengths and cutting different types of material on the same machine. In addition, we evaluate operational performance in terms of resulting waste and tardiness of orders using our model in a rolling horizon framework.     

A two-objective mathematical model without cutting patterns for one-dimensional assortment problems

This paper considers a one-dimensional cutting stock and assortment problem. One of the main difficulties in formulating and solving these kinds of problems is the use of the set of cutting patterns as a parameter set in the mathematical model. Since the total number of cutting patterns to be generated may be very huge, both the generation and the use of such a set lead to computational difficulties in solution process. The purpose of this paper is therefore to develop a mathematical model without the use of cutting patterns as model parameters. We propose a new, two-objective linear integer programming model in the form of simultaneous minimization of two contradicting objectives related to the total trim loss amount and the total number of different lengths of stock rolls to be maintained as inventory, in order to fulfill a given set of cutting orders. The model does not require pre-specification of cutting patterns. We suggest a special heuristic algorithm for solving the presented model. The superiority of both the mathematical model and the solution approach is demonstrated on test problems.     

A multi-objective programming approach to 1.5-dimensional assortment problem

In this paper we study a 1.5-dimensional cutting stock and assortment problem which includes determination of the number of different widths of roll stocks to be maintained as inventory and determination of how these roll stocks should be cut by choosing the optimal cutting pattern combinations. We propose a new multi-objective mixed integer linear programming (MILP) model in the form of simultaneously minimization two contradicting objectives related to the trim loss cost and the combined inventory cost in order to fulfill a given set of cutting orders. An equivalent nonlinear version and a particular case related to the situation when a producer is interested in choosing only a few number of types among all possible roll sizes, have also been considered. A new method called the conic scalarization is proposed for scalarizing non-convex multi-objective problems and several experimental tests are reported in order to demonstrate the validity of the developed modeling and solving approaches.     

A mixed integer programming model for scheduling orders in a steel mill

The problem of scheduling orders at each facility of a large integrated steel mill is considered. Orders are received randomly, and delivery dates are established immediately. Each order is filled by converting raw materials into a finished saleable steel product by a fixed sequence of processes. The application of a deterministic mixed integer linear programming model to the order scheduling problem is given. One important criterion permitted by the model is to process the orders in a sequence which minimizes the total tardiness from promised delivery for all orders; alternative criteria are also possible. Most practical constraints which arise in steelmaking can be considered within the formulation. In particular, sequencing and resource availability constraints are handled easily. The order scheduling model given here contains many variables and constraints, resulting in computational difficulties. A decomposition algorithm is devised for solving the model. The algorithm is a special case of Benders partitioning. Computational experience is reported for a large-scale problem involving scheduling 102 orders through ten facilities over a six-week period. The exact solution to the large-scale problem is compared with schedules determined by several heuristic dispatching rules. The dispatching rules took into consideration such things as due date, processing time, and tardiness penalties. None of the dispatching rules found the optimal solution.     

A matching approach for replenishing rectangular stock sizes

Consider a replenishment problem in which several different rectangular sizes of material are stocked. Customers order rectangles of the material, but the rectangles ordered have a range on specified width as well as on specified length. To satisfy the customer requirements, the stock material can be cut once longitudinally in order to satisfy two customer requirements or not cut at all, that is, an entire stock piece of material is used to satisfy one customer requirement. If an exact match is impossible in the current planning period, the unused material must be returned to stock— an expensive and undesirable situation. In this paper, a nonbipartite weighted matching problem formulation will be given for determining the replenishment requirements of rectangular stock sizes. Then, a hybrid solution approach, capable of solving real applications (typically up to 3000 nodes) efficiently, will be discussed. This solution was implemented in September 1998 and has operated successfully since then.     

Multi-job Cutting Stock Problem with Due Dates and Release Dates

The common feature of cutting stock problems is to cut some form of stock materials to produce smaller pieces of materials in quantities matching orders received. Most research on cutting stock problems focuses on either generating cutting patterns to minimize wastage or determining the required number of stock materials to meet orders. In this paper, we examine a variation of cutting stock problems that arises in some industries where meeting orders' due dates is more important than minimizing wastage of materials. We develop two two-dimensional cutting stock models with due date and release date constraints. Since adding due dates and release dates makes the traditional cutting stock problem even more difficult to solve, we develop both LP-based and non-LP-based heuristics to obtain good solutions. The computational results show that the solution procedures are easy to implement and work very well.     

Solving one-dimensional cutting stock problems exactly with a cutting plane algorithm

When solving the one-dimensional cutting stock problem (1D CSP) as an integer linear programming problem one has to overcome computational difficulties arising from the integrality condition and a huge number of variables. In the Gilmore–Gomory approach the corresponding continuous relaxation is solved via column generation techniques followed by an appropriate rounding of the in general non-integer solution. Obviously, there is no guarantee of obtaining an optimal solution in this way but it is extremely effective in practice. However, in two- and three-dimensional cutting stock problems the heuristics are not so good which necessitates the research of effective exact methods. In this paper we present an exact solution approach for the 1D CSP which is based on a combination of the cutting plane method and the column generation technique. Results of extensive computational experiments are reported.     

An algorithm for a cutting stock problem on a strip

A cutting stock problem is formulated as follows: a set of rectangular pieces must be cut from a set of sheets, so as to minimize total waste. In our problem the pieces are requested in large quantities and the set of sheets are long rolls of material. For this class of problems we have developed a fast heuristic based on partial enumeration of all feasible patterns. We then tested the effectiveness on a set of test problems ranging from practical to random instances. Finally, the algorithm has been applied to check the asymptotic behaviour of the solution when a continuous stream of pieces is requested and cutting decisions are to be made while orders are still arriving.     

An iterative sequential heuristic procedure to a real-life 1.5-dimensional cutting stock problem

This paper addresses a real-life 1.5D cutting stock problem, which arises in a make-to-order plastic company. The problem is to choose a subset from the set of stock rectangles to be used for cutting into a number of smaller rectangular pieces so as to minimize total production cost and meet orders. The total production cost includes not only material wastage, as in traditional cutting stock problems, but also production time. A variety of factors are taken into account, like cutter knife changes, machine restrictions, due dates and other work in progress limitations. These restrictions make the combinatorial structure of the problem more complex. As a result, existing algorithms and mathematical models are no longer appropriate. Thus we developed a new 1.5D cutting stock model with multiple objectives and multi-constraints and solve this problem in an incomplete enumerative way. The computational results show that the solution procedure is easy to implement and works very well.     

New model and heuristic solution approach for one-dimensional cutting stock problem with usable leftovers

In the one-dimensional cutting stock problem with usable leftovers (1DCSPUL), items of the current order are cut from stock bars to minimize material cost. Here, stock bars include both standard ones bought commercially and old leftovers generated in processing previous orders, and cutting patterns often include new leftovers that are usable in processing subsequent orders. Leftovers of the same length are considered to be of the same type. The number of types of leftovers should be limited to simplify the cutting process and reduce the storage area. This paper presents an integer programming model for the 1DCSPUL with limited leftover types and describes a heuristic algorithm based on a column-generation procedure to solve it. Computational results show that the proposed approach is more effective than several published algorithms in reducing trim loss, especially when the number of types of leftovers is limited.     

A solution procedure for a pattern sequencing problem as part of a one-dimensional cutting stock problem in the steel industry

To cut reinforcing bars for concrete buildings, machines are used which have compartments to store the cut orders until the requirement is met. Number and size of these compartments restrict kind and processing sequence of possible cutting patterns. In this paper we present the so-called “Sequencing algorithm” that tackles the problem of finding a processing sequence for the cutting patterns starting from an integer solution of the cutting stock problem and using an interpretation of relations between orders in patterns as a graph. Computational results are reported.     

二维实用下料问题的数学模型及较优解

二维下料问题是2004年首届全国部分高校研究生数学建模竞赛B题.建立了二维下料问题的数学模型,找到了用料451块,下料方式数为37的较优解,并证明了此问题总用料的下界是449块.     

New integer programming formulations and an exact algorithm for the ordered cutting stock problem

Apart from trim loss minimization, there are many other issues concerning cutting processes that arise in real production systems. One of these is related to the number of stacks that need to be opened near the cutting machines. Many researchers have worked in the last years on cutting stock problems with additional constraints on the number of open stacks. In this paper, we address a related problem: the Ordered Cutting Stock Problem (OCSP). In this case, a stack is opened for every new client's order, and it is closed only when all the items of that order are cut. The OSCP has been introduced recently in the literature. Our aim is to provide further insight into this problem. This paper describes three new integer programming formulations for solving it, and an exact algorithm based on column generation, branch-and-bound and cutting planes. We report on computational experiments on a set of random instances. The results show that good lower bounds can be computed quickly, and that optimal solutions can be found in a reasonable amount of time.     

Sequencing Jobs Through a Multi-Stage Process in the Minimum Total Time—A Quick Method of Obtaining a Near Optimum

In the situation where a number of items must pass through a number of processes in the same order, it is suggested that the optimum (minimum total time) scheduling may be approximated by giving priority to items according to the extent to which process time increases with process number in order; this criterion is put in arithmetical form. This is confirmed by (a) a simple geometrical argument; (b) comparison with the known optimum schedules of a number of published three-process cases; (c) experiments on larger cases; it is not practical here to find the exact optimum.Methods are given for obtaining lower bounds which all scheduling times must equal or exceed; besides placing limits on the amount of effort to be expended, these bounds support the suggested approximate method.     

Model selection by sequentially normalized least squares

Model selection by means of the predictive least squares (PLS) principle has been thoroughly studied in the context of regression model selection and autoregressive (AR) model order estimation. We introduce a new criterion based on sequentially minimized squared deviations, which are smaller than both the usual least squares and the squared prediction errors used in PLS. We also prove that our criterion has a probabilistic interpretation as a model which is asymptotically optimal within the given class of distributions by reaching the lower bound on the logarithmic prediction errors, given by the so called stochastic complexity, and approximated by BIC. This holds when the regressor (design) matrix is non-random or determined by the observed data as in AR models. The advantages of the criterion include the fact that it can be evaluated efficiently and exactly, without asymptotic approximations, and importantly, there are no adjustable hyper-parameters, which makes it applicable to both small and large amounts of data.     

r-regular shape reconstruction from unorganized points

In this paper, the problem of reconstructing a surface, given a set of scattered data points is addressed. First, a precise formulation of the reconstruction problem is proposed. The solution is mathematically defined as a particular mesh of the surface called the normalized mesh. This solution has the property to be included inside the Delaunay graph. A criterion to detect faces of the normalized mesh inside the Delaunay graph is proposed. This criterion is proved to provide the exact solution in 2D for points sampling a r-regular shapes with a sampling path < sin(π/8)r. In 3D, this result cannot be extended and the criterion cannot retrieve every face. A heuristic is proposed in order to complete the surface.     

One-Dimensional Cutting Stock Problem with a Given Number of Setups: A Hybrid Approach of Metaheuristics and Linear Programming

One-dimensional cutting stock problem (1D-CSP) is one of the representative combinatorial optimization problems, which arises in many industrial applications. Since the setup costs for switching different cutting patterns become more dominant in recent cutting industry, we consider a variant of 1D-CSP, called the pattern restricted problem (PRP), to minimize the number of stock rolls while constraining the number of different cutting patterns within a bound given by users. For this problem, we propose a local search algorithm that alternately uses two types of local search processes with the 1-add neighborhood and the shift neighborhood, respectively. To improve the performance of local search, we incorporate it with linear programming (LP) techniques, to reduce the number of solutions in each neighborhood. A sensitivity analysis technique is introduced to solve a large number of associated LP problems quickly. Through computational experiments, we observe that the new algorithm obtains solutions of better quality than those obtained by other existing approaches.     

The Cutting Stock Problem in the Flat Glass Industry

The cutting stock problem occurs where large rectangles of some material require cutting into smaller rectangles, in the most appropriate way, to satisfy an order book. A linear programming approach to the problem has been suggested by P. C. Gilmore and R. E. Gomory. An application of this approach in the glass industry is described which is shown to be inadequate since it only satisfies a wastage criterion. In practice, multiple criteria must be satisfied and two alternative approaches using linear programming and heuristic scheduling are proposed.     

One-dimensional cutting stock optimization in consecutive time periods

The paper deals with the general one-dimensional cutting stock problem (G1D-CSP), where optimization is not limited to a single order. Stock cutting is treated as a permanent business process in a company in which consecutive order sets need to be fulfilled either for production needs or for its customers. Exact demand for future orders is not known in advance. The unutilized and partly utilized stock lengths left after fulfilling current order sets are stored and used later. The goal is the reduction of trim loss and costs over a broader time-span. A new approach is suggested where previously developed method for G1D-CSP is modified. Several practical examples of the cutting process for several consecutive order sets are presented. An extension to a currently used typology for cutting stock problems is proposed.     

Mathematical model and method of searching for a local extremum for the non-convex oriented polygons allocation problem

This work deals with the problem of optimal allocation of objects of a so-called irregular form. The objects are allocated on a strip of given width and with defects. This problem is insufficiently studied, but it is typical for many industries and is also interesting for developing the theory of solving cutting and packing problems. An analytical model of the problem using only continuous variables is written in terms of classical mathematical programming, and it is constructed on the basis of the original theory of φ-functions and structures of linear inequalities. The presented theory allows one to easily describe the conditions of mutual non-overlapping of objects and their allocation in the stock region. The exact method for searching a local minimum of the problem from any feasible initial point is based on the application of the active set strategy ideas. A number of examples of solving practical problems are considered.