Alternative configurations for cutting machines in a tube cutting mill

In a steel tube mill where an endless stream of steel tube is supplied from a manufacturing facility, trim waste is never made regardless of cutting patterns used and the standard cutting stock problem seems meaningless. Therefore, the continuous stock cutting problem with setup is introduced to minimize the sum of cutting time and pattern changing time to meet the given demand. We propose a new configuration of cutting machines to achieve higher production efficiency, namely the open-ended configuration as opposed to the traditional closed-ended configuration, thereby two variants of the problem are defined. We propose linear formulations for both problems using binary expansion of the number of pieces of different types in a pattern. Furthermore, we define the time for pattern change as a linear function of the number of knives used in the pattern to be more realistic. Computational studies suggest that the open-ended cutting machine may improve the production time by up to 44% and that our linear formulations are more efficient than the existing ones.     

An Improved Algorithm for the Non-Guillotine-Constrained Cutting-Stock Problem

A personal-computer-based algorithm to solve the non-guillotine-constrained two-dimensional cutting-stock problem is developed. The problem is constrained to single-sized rectangles placed orthogonally on a larger containing rectangle. The algorithm uses the linear combination of box lengths and widths that minimizes waste along the cutting stock's length and width to determine an optimal layout. The algorithm's performance is evaluated using two sets of test cases and compared to the results of other algorithms.     

Infinite split scheduling: a new lower bound of total weighted completion time on parallel machines with job release dates and unavailability periods

This paper addresses an identical parallel machine scheduling problem with job release dates and unavailability periods to minimize total weighted completion time. This problem is known to be NP-hard in the strong sense. We propose a new lower bound that can be computed in polynomial time. The test on more than 8 400 randomly generated instances shows a very significant improvement with respect to existing results for previously studied special cases: without unavailability constraints, unweighted version, or identical job release dates. For instance, the average improvement for the unweighted problem is as much as 20.43% for 2 machines, 53.03% for 7 machines and 66.70% for 15 machines. For some instances, the improvement can be even as much as 93%.     

Minimizing the number of machines for scheduling jobs with equal processing times

In this paper, we consider a parallel machine environment when all jobs have the same processing time and arbitrary release dates and deadlines of the jobs are given. We suppose that the available number of machines, which can be used simultaneously, may vary over time. The aim is to construct a feasible schedule in such a way that the maximal number of simultaneously used machines is minimal. We give a polynomial algorithm for this problem.     

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.     

Parallel machine scheduling with a common due window

In this paper, we consider a machine scheduling problem where jobs should be completed at times as close as possible to their respective due dates, and hence both earliness and tardiness should be penalized. Specifically, we consider the problem with a set of independent jobs to be processed on several identical parallel machines. All the jobs have a given common due window. If a job is completed within the due window, then there is no penalty. Otherwise, there is either a job-dependent earliness penalty or a job-dependent tardiness penalty depending on whether the job is completed before or after the due window. The objective is to find an optimal schedule with minimum total earliness–tardiness penalty. The problem is known to be NP-hard. We propose a branch and bound algorithm for finding an optimal schedule of the problem. The algorithm is based on the column generation approach in which the problem is first formulated as a set partitioning type formulation and then in each branch and bound iteration the linear relaxation of this formulation is solved by the standard column generation procedure. Our computational experiments show that this algorithm is capable of solving problems with up to 40 jobs and any number of machines within a reasonable computational time.     

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.     

Optimization of column width in website layout for advertisement fit

Advertising is the basic source of income for many web businesses. Preparing website layout for the advertisements is a problem faced by many web designers. The ads and other content are placed in several columns. Usually column widths are chosen ad hoc to fit the widest advertising unit. To make a more informed decisions website column width selection is formulated in this paper as optimization problem. A method of selecting column widths for a given set of advertisement units is proposed. Ad unit combinations that fit the given column widths are generated by the improved Wang algorithm for two-dimensional stock cutting problem. Column widths are evaluated for several objective functions. Two approaches are proposed. The first constructs a Pareto frontier of column width combinations. The second calculates the optimum column widths with respect to a weighted linear function of the objectives. To justify the weights expert survey was conducted. Both approaches are examined on datasets of internet advertising units.     

Customer Service vs Trim Waste in Corrugated Box Manufacture

Many approaches to the problem of arranging customer orders for cutting or corrugation have focused on the minimization of trim waste. This views the corrugator more or less in isolation. When downstream machines or customer due-dates exist, however, customer service may suffer from the desire to keep scrap at a low level. Thus, if slightly higher levels of waste were accepted, the production scheduler might be able to improve performance regarding due dates.We developed a simulation model, for Domtar Packaging Ltd, of a corrugated cardboard box factory, which included the corrugation process and four finishing machines. Customer orders were generated via empirical and theoretical probability distributions, then sent through the model according to one of several scheduling rules. This allowed the relationship between various levels of trim waste and customer service to be viewed. Results of the simulation experiments, as well as a discussion of the model itself, are given. Comments and conclusions regarding both our model and corrugator algorithms in general are presented in the light of the role of the human scheduler in plants of this type.     

Notes on the complexity of sorting in abstract machines

The complexity of sorting with pointer machines and successor-predecessor random access machines is studied. The size of the problem is defined as the length of the problem string. A linear time algorithm is achieved for sorting by pointer machines. For successor-predecessor random access machines linear time is sufficient in a special case.     

Scheduling orders for multiple product types to minimize total weighted completion time

We consider the problem of scheduling orders for multiple different product types in an environment with m dedicated machines in parallel. The objective is to minimize the total weighted completion time. Each product type is produced by one and only one of the m dedicated machines; that is, each machine is dedicated to a specific product type. Each order has a weight and may also have a release date. Each order asks for certain amounts of various different product types. The different products for an order can be produced concurrently. Preemptions are not allowed. Even when all orders are available at time 0, the problem has been shown to be strongly NP-hard for any fixed number (?2) of machines. This paper focuses on the design and analysis of efficient heuristics for the case without release dates. Occasionally, however, we extend our results to the case with release dates. The heuristics considered include some that have already been proposed in the literature as well as several new ones. They include various static and dynamic priority rules as well as two more sophisticated LP-based algorithms. We analyze the performance bounds of the priority rules and of the algorithms and present also an in-depth comparative analysis of the various rules and algorithms. The conclusions from this empirical analysis provide insights into the trade-offs with regard to solution quality, speed, and memory space.     

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.     

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.     

A branch-and-price algorithm for scheduling parallel machines with sequence dependent setup times

We consider the problem of scheduling n independent jobs on m unrelated parallel machines with sequence-dependent setup times and availability dates for the machines and release dates for the jobs to minimize a regular additive cost function. In this work, we develop a new branch-and-price optimization algorithm for the solution of this general class of parallel machines scheduling problems. A new column generation accelerating method, termed “primal box”, and a specific branching variable selection rule that significantly reduces the number of explored nodes are proposed. The computational results show that the approach solves problems of large size to optimality within reasonable computational time.     

A graph coloring approach to scheduling of multiprocessor tasks on dedicated machines with availability constraints

We address a generalization of the classical 1- and 2-processor unit execution time scheduling problem on dedicated machines. In our chromatic model of scheduling machines have non-simultaneous availability times and tasks have arbitrary release times and due dates. Also, the versatility of our approach makes it possible to generalize all known classical criteria of optimality. Under these stipulations we show that the problem of optimal scheduling of sparse tree-like instances can be solved in polynomial time. However, if we admit dense instances then the problem becomes NP-hard, even if there are only two machines.     

On the representation of the one machine sequencing problem in the shifting bottleneck heuristic

The Shifting Bottleneck heuristic decomposes the Job Shop problem into a series of One Machine Sequencing Problems (OMSPs) with release and due dates, precedence constraints and the minimization of maximum lateness objective. It is well known that delayed precedence constraints may exist between two operations to be performed on the same machine. We identify a new type of precedence relationship that may exist in an OMSP between the predecessor of an operation and the successor of another. The premise that an OMSP captures the sequencing relationships on other machines in the release and due date information is not valid when such precedence relationships exist. A modification of the OMSP representation is proposed based on a generalized lateness objective defined on a due window. The implications of such a representation for the OMSP solution procedure have been explored.     

Sugar cane transportation in Cuba,a case study

In this study the authors present a mixed integer linear programming model to solve the problem of cost minimization of sugar cane removal and its transport from the fields to the sugar mill at operational level. The complexity of the problem is basically determined by the system approach which results in the generation of a great number of variables and constraints that refer to the following operational dimensions: (a) need for continuous supply to the sugar mill; (b) cutting means used in cane harvesting; (c) transportation vehicles and (d) providing routes, which are characterized by the existence of storage facilities at the beginning of the railroads. The results demonstrate the model is not only useful to minimize transportation cost, but also for scheduling daily cane road transport and harvesting quotas of cutting means.     

Parallel machine scheduling problems with a single server

In this paper, we consider the problem of scheduling jobs on parallel machines with setup times. The setup has to be performed by a single server. The objective is to minimize the schedule length (makespan), as well as the forced idle time. The makespan problem is known to be NP-hard even for the case of two identical parallel machines. This paper presents a pseudopolynomial algorithm for the case of two machines when all setup times are equal to one. We also show that the more general problem with an arbitrary number of machines is unary NP-hard and analyze some list scheduling heuristics for this problem. The problem of minimizing the forced idle time is known to be unary NP-hard for the case of two machines and arbitrary setup and processing times. We prove unary NP-hardness of this problem even for the case of constant setup times. Moreover, some polynomially solvable cases are given.     

The Optimization of Paper Machine Scheduling

A method for determining schedules in a paper mill is described and applied to real production data. The problem is to allocate products to machines and determine their sequence so as to minimize total costs. The costs include production losses through set-ups, stock holding and penalties for delayed delivery. The method is based on a permutation representation of the production schedule and the determination of a locally optimal solution. A permutation is locally optimal if it cannot be improved by transferring groups of products to a different place in the schedule. The method has been programmed and the results show a considerable improvement over alternative manual procedures. The paper has been written in two parts, the first dealing with the development of the method and the second discussing its application to a specific problem.     

Scheduling with a minimum number of machines

We investigate the scheduling problem with release dates and deadlines on a minimum number of machines. In the case of equal release dates, we present a 2-approximation algorithm. We also show that Greedy Best-Fit (GBF) is a 6-approximation algorithm for the case of equal processing times.