A survey of dual-feasible and superadditive functions
---
paper_title: A cutting-plane approach for the two-dimensional orthogonal non-guillotine cutting problem

paper_content:

Abstract The two-dimensional orthogonal non-guillotine cutting problem (NGCP) appears in many industries (like wood and steel industries) and consists in cutting a rectangular master surface into a number of rectangular pieces, each with a given size and value. The pieces must be cut with their edges always parallel or orthogonal to the edges of the master surface ( orthogonal cuts ). The objective is to maximize the total value of the pieces cut. In this paper, we propose a two-level approach for solving the NGCP, where, at the first level, we select the subset of pieces to be packed into the master surface without specifying the layout, while at a second level we check only if a feasible packing layout exists. This approach has been already proposed by Fekete and Schepers [S.P. Fekete, J. Schepers, A new exact algorithm for general orthogonal d -dimensional knapsack problems, ESA 97, Springer Lecture Notes in Computer Science 1284 (1997) 144–156; S.P. Fekete, J. Schepers, On more-dimensional packing III: Exact algorithms, Tech. Rep. 97.290, Universitat zu Koln, Germany, 2000; S.P. Fekete, J. Schepers, J.C. van der Veen, An exact algorithm for higher-dimensional orthogonal packing, Tech. Rep. Under revision on Operations Research, Braunschweig University of Technology, Germany, 2004] and Caprara and Monaci [A. Caprara, M. Monaci, On the two-dimensional knapsack problem, Operations Research Letters 32 (2004) 2–14]. We propose improved reduction tests for the NGCP and a cutting-plane approach to be used in the first level of the tree search to compute effective upper bounds. Computational tests on problems derived from the literature show the effectiveness of the proposed approach, that is able to reduce the number of nodes generated at the first level of the tree search and the number of times the existence of a feasible packing layout is tested.
---
paper_title: New reduction procedures and lower bounds for the two-dimensional bin packing problem with fixed orientation

paper_content:

The two-dimensional bin-packing problem (2BP) consists of minimizing the number of identical rectangles used to pack a set of smaller rectangles. In this paper, we propose new lower bounds for 2BP in the discrete case. They are based on the total area of the items after application of dual feasible functions (DFF). We also propose the new concept of data-dependent dual feasible functions (DDFF), which can also be applied to a 2BP instance. We propose two families of Discrete DFF and DDFF and show that they lead to bounds which strictly dominate those obtained previously. We also introduce two new reduction procedures and report computational experiments on our lower bounds. Our bounds improve on the previous best results and close 22 additional instances of a well-known established benchmark derived from literature.
---
paper_title: Integer and Combinatorial Optimization

paper_content:

FOUNDATIONS. The Scope of Integer and Combinatorial Optimization. Linear Programming. Graphs and Networks. Polyhedral Theory. Computational Complexity. Polynomial-Time Algorithms for Linear Programming. Integer Lattices. GENERAL INTEGER PROGRAMMING. The Theory of Valid Inequalities. Strong Valid Inequalities and Facets for Structured Integer Programs. Duality and Relaxation. General Algorithms. Special-Purpose Algorithms. Applications of Special- Purpose Algorithms. COMBINATORIAL OPTIMIZATION. Integral Polyhedra. Matching. Matroid and Submodular Function Optimization. References. Indexes.
---
paper_title: A branch-and-price-and-cut algorithm for the pattern minimization problem

paper_content:

In cutting stock problems, after an optimal (minimal stock usage) cutting plan has been devised, one might want to further reduce the operational costs by minimizing the number of setups. A setup operation occurs each time a different cutting pattern begins to be produced. The related optimization problem is known as the Pattern Minimization Problem, and it is particularly hard to solve exactly. In this paper, we present different techniques to strengthen a formulation proposed in the literature. Dual feasible functions are used for the first time to derive valid inequalities from different constraints of the model, and from linear combinations of constraints. A new arc flow formulation is also proposed. This formulation is used to define the branching scheme of our branch-and-price-and-cut algorithm, and it allows the generation of even stronger cuts by combining the branching constraints with other constraints of the model. The computational experiments conducted on instances from the literature show that our algorithm finds optimal integer solutions faster than other approaches. A set of computational results on random instances is also reported.
---
paper_title: Bin packing with items uniformly distributed over intervals [a,b]

paper_content:

We consider the problem of packing n items which are drawn uniformly from intervals of the form [a,b], where 0 ≪ a ≪ b ≪ 1. For a fairly large class of a and b, we determine a lower bound on the asymptotic expected number of bins used in an optimum packing. The method of proof is related to the dual of the linear programming problem corresponding to the bin packing problem. We demonstrate that these bounds are tight by providing simple packing strategies which achieve them.
---
paper_title: A General Framework for Bounds for Higher-Dimensional Orthogonal Packing Problems

paper_content:

Higher-dimensional orthogonal packing problems have a wide range of practical applications, including packing, cutting, and scheduling. In the context of a branch-and-bound framework for solving these packing problems to optimality, it is of crucial importance to have good and easy bounds for an optimal solution. Previous efforts have produced a number of special classes of such bounds. Unfortunately, some of these bounds are somewhat complicated and hard to generalize. We present a new approach for obtaining classes of lower bounds for higher-dimensional packing problems; our bounds improve and simplify several well-known bounds from previous literature. In addition, our approach provides an easy framework for proving correctness of new bounds.
---
paper_title: Computing redundant resources for the resource constrained project scheduling problem

paper_content:

Several efficient lower bounds and time-bound adjustment methods for the resource constrained project scheduling problem (RCPSP) have recently been proposed. Some of them are based on redundant resources. In this paper we define redundant functions which are very useful for computing redundant resources. We also describe an algorithm for computing all maximal redundant functions. Once all these redundant functions have been determined, we have to identify those that are useful for bounding. Surprisingly, their number is reasonable even for large resource capacities, so a representative subset of them can be tabulated to be used efficiently. Computational results on classical RCPSP instances confirm their usefulness.
---
paper_title: A Subadditive Approach to Solve Linear Integer Programs

paper_content:

A method is presented for solving pure integer programs by a subadditive method. This work extends to the integer linear problem a method for solving the group problem. It uses some elements of both enumeration and cutting plane theory in a unified setting. The method generates a subadditive function and solves the original integer linear program.
---
paper_title: An improved typology of cutting and packing problems

paper_content:

The number of publications in the area of Cutting and Packing (C&P) has increased considerably over the last two decades. The typology of C&P problems introduced by Dyckhoff [Dyckhoff, H., 1990. A typology of cutting and packing problems. European Journal of Operational Research 44, 145–159] initially provided an excellent instrument for the organisation and categorisation of existing and new literature. However, over the years also some deficiencies of this typology became evident, which created problems in dealing with recent developments and prevented it from being accepted more generally. In this paper, the authors present an improved typology, which is partially based on Dyckhoff’s original ideas, but introduces new categorisation criteria, which define problem categories different from those of Dyckhoff. Furthermore, a new, consistent system of names is suggested for these problem categories. Finally, the practicability of the new scheme is demonstrated by using it as a basis for a categorisation of the C&P literature from the years between 1995 and 2004. � 2006 Elsevier B.V. All rights reserved.
---
paper_title: New classes of fast lower bounds for bin packing problems

paper_content:

The bin packing problem is one of the classical NP-hard optimization problems. In this paper, we present a simple generic approach for obtaining new fast lower bounds, based on dual feasible functions. Worst-case analysis as well as computational results show that one of our classes clearly outperforms the previous best economical lower bound for the bin packing problem by Martello and Toth, which can be understood as a special case. In particular, we prove an asymptotic worst-case performance of 3/4 for a bound that can be computed in linear time for items sorted by size. In addition, our approach provides a general framework for establishing new bounds.
---
paper_title: Computing redundant resources for the resource constrained project scheduling problem

paper_content:

Several efficient lower bounds and time-bound adjustment methods for the resource constrained project scheduling problem (RCPSP) have recently been proposed. Some of them are based on redundant resources. In this paper we define redundant functions which are very useful for computing redundant resources. We also describe an algorithm for computing all maximal redundant functions. Once all these redundant functions have been determined, we have to identify those that are useful for bounding. Surprisingly, their number is reasonable even for large resource capacities, so a representative subset of them can be tabulated to be used efficiently. Computational results on classical RCPSP instances confirm their usefulness.
---
paper_title: A Linear Programming Approach to the Cutting Stock Problem---Part II

paper_content:

In this paper, the methods for stock cutting outlined in an earlier paper in this Journal [Opns Res 9, 849--859 1961] are extended and adapted to the specific full-scale paper trim problem. The paper describes a new and faster knapsack method, experiments, and formulation changes. The experiments include ones used to evaluate speed-up devices and to explore a connection with integer programming. Other experiments give waste as a function of stock length, examine the effect of multiple stock lengths on waste, and the effect of a cutting knife limitation. The formulation changes discussed are i limitation on the number of cutting knives available, n balancing of multiple machine usage when orders are being filled from more than one machine, and m introduction of a rational objective function when customers' orders are not for fixed amounts, but rather for a range of amounts. The methods developed are also applicable to a variety of cutting problems outside of the paper industry.
---
paper_title: A new LP-based lower bound for the cumulative scheduling problem

paper_content:

Abstract This paper deals with the computation of lower bounds for Cumulative Scheduling Problems. Based on a new linear programming formulation, these lower bounds take into account how resource requirements can be satisfied simultaneously for a given resource capacity. One of the main interests of this paper is that the solutions of the LP can be tabulated, for a given value of resource capacity. Thus, even if it is based on a linear programming formulation, the computation of the bounds is low time consuming as confirmed by our computational results on the Resource Constrained Project Scheduling Problem.
---
paper_title: A Linear Programming Approach to the Cutting-Stock Problem

paper_content:

The cutting-stock problem is the problem of filling an order at minimum cost for specified numbers of lengths of material to be cut from given stock lengths of given cost. When expressed as an integer programming problem the large number of variables involved generally makes computation infeasible. This same difficulty persists when only an approximate solution is being sought by linear programming. In this paper, a technique is described for overcoming the difficulty in the linear programming formulation of the problem. The technique enables one to compute always with a matrix which has no more columns than it has rows.
---
paper_title: Computing redundant resources for the resource constrained project scheduling problem

paper_content:

Several efficient lower bounds and time-bound adjustment methods for the resource constrained project scheduling problem (RCPSP) have recently been proposed. Some of them are based on redundant resources. In this paper we define redundant functions which are very useful for computing redundant resources. We also describe an algorithm for computing all maximal redundant functions. Once all these redundant functions have been determined, we have to identify those that are useful for bounding. Surprisingly, their number is reasonable even for large resource capacities, so a representative subset of them can be tabulated to be used efficiently. Computational results on classical RCPSP instances confirm their usefulness.
---
paper_title: Computing redundant resources for the resource constrained project scheduling problem

paper_content:

Several efficient lower bounds and time-bound adjustment methods for the resource constrained project scheduling problem (RCPSP) have recently been proposed. Some of them are based on redundant resources. In this paper we define redundant functions which are very useful for computing redundant resources. We also describe an algorithm for computing all maximal redundant functions. Once all these redundant functions have been determined, we have to identify those that are useful for bounding. Surprisingly, their number is reasonable even for large resource capacities, so a representative subset of them can be tabulated to be used efficiently. Computational results on classical RCPSP instances confirm their usefulness.
---
paper_title: A cutting-plane approach for the two-dimensional orthogonal non-guillotine cutting problem

paper_content:

Abstract The two-dimensional orthogonal non-guillotine cutting problem (NGCP) appears in many industries (like wood and steel industries) and consists in cutting a rectangular master surface into a number of rectangular pieces, each with a given size and value. The pieces must be cut with their edges always parallel or orthogonal to the edges of the master surface ( orthogonal cuts ). The objective is to maximize the total value of the pieces cut. In this paper, we propose a two-level approach for solving the NGCP, where, at the first level, we select the subset of pieces to be packed into the master surface without specifying the layout, while at a second level we check only if a feasible packing layout exists. This approach has been already proposed by Fekete and Schepers [S.P. Fekete, J. Schepers, A new exact algorithm for general orthogonal d -dimensional knapsack problems, ESA 97, Springer Lecture Notes in Computer Science 1284 (1997) 144–156; S.P. Fekete, J. Schepers, On more-dimensional packing III: Exact algorithms, Tech. Rep. 97.290, Universitat zu Koln, Germany, 2000; S.P. Fekete, J. Schepers, J.C. van der Veen, An exact algorithm for higher-dimensional orthogonal packing, Tech. Rep. Under revision on Operations Research, Braunschweig University of Technology, Germany, 2004] and Caprara and Monaci [A. Caprara, M. Monaci, On the two-dimensional knapsack problem, Operations Research Letters 32 (2004) 2–14]. We propose improved reduction tests for the NGCP and a cutting-plane approach to be used in the first level of the tree search to compute effective upper bounds. Computational tests on problems derived from the literature show the effectiveness of the proposed approach, that is able to reduce the number of nodes generated at the first level of the tree search and the number of times the existence of a feasible packing layout is tested.
---
paper_title: Integer and Combinatorial Optimization

paper_content:

FOUNDATIONS. The Scope of Integer and Combinatorial Optimization. Linear Programming. Graphs and Networks. Polyhedral Theory. Computational Complexity. Polynomial-Time Algorithms for Linear Programming. Integer Lattices. GENERAL INTEGER PROGRAMMING. The Theory of Valid Inequalities. Strong Valid Inequalities and Facets for Structured Integer Programs. Duality and Relaxation. General Algorithms. Special-Purpose Algorithms. Applications of Special- Purpose Algorithms. COMBINATORIAL OPTIMIZATION. Integral Polyhedra. Matching. Matroid and Submodular Function Optimization. References. Indexes.
---
paper_title: Exact Algorithm for Minimising the Number of Setups in the One-Dimensional Cutting Stock Problem

paper_content:

The cutting stock problem is that of finding a cutting of stock material to meet demands for small pieces of prescribed dimensions while minimising the amount of waste. Because changing over from one cutting pattern to another involves significant setups, an auxiliary problem is to minimise the number of different patterns that are used. The pattern minimisation problem is significantly more complex, but it is of great practical importance. In this paper, we propose an integer programming formulation for the problem that involves an exponential number of binary variables and associated columns, each of which corresponds to selecting a fixed number of copies of a specific cutting pattern. The integer program is solved using a column generation approach where the subproblem is a nonlinear integer program that can be decomposed into multiple bounded integer knapsack problems. At each node of the branch-and-bound tree, the linear programming relaxation of our formulation is made tighter by adding super-additive inequalities. Branching rules are presented that yield a balanced tree. Incumbent solutions are obtained using a rounding heuristic. The resulting branch-and-price-and-cut procedure is used to produce optimal or approximately optimal solutions for a set of real-life problems.
---
paper_title: A branch-and-price-and-cut algorithm for the pattern minimization problem

paper_content:

In cutting stock problems, after an optimal (minimal stock usage) cutting plan has been devised, one might want to further reduce the operational costs by minimizing the number of setups. A setup operation occurs each time a different cutting pattern begins to be produced. The related optimization problem is known as the Pattern Minimization Problem, and it is particularly hard to solve exactly. In this paper, we present different techniques to strengthen a formulation proposed in the literature. Dual feasible functions are used for the first time to derive valid inequalities from different constraints of the model, and from linear combinations of constraints. A new arc flow formulation is also proposed. This formulation is used to define the branching scheme of our branch-and-price-and-cut algorithm, and it allows the generation of even stronger cuts by combining the branching constraints with other constraints of the model. The computational experiments conducted on instances from the literature show that our algorithm finds optimal integer solutions faster than other approaches. A set of computational results on random instances is also reported.
---
paper_title: Integer and Combinatorial Optimization

paper_content:

FOUNDATIONS. The Scope of Integer and Combinatorial Optimization. Linear Programming. Graphs and Networks. Polyhedral Theory. Computational Complexity. Polynomial-Time Algorithms for Linear Programming. Integer Lattices. GENERAL INTEGER PROGRAMMING. The Theory of Valid Inequalities. Strong Valid Inequalities and Facets for Structured Integer Programs. Duality and Relaxation. General Algorithms. Special-Purpose Algorithms. Applications of Special- Purpose Algorithms. COMBINATORIAL OPTIMIZATION. Integral Polyhedra. Matching. Matroid and Submodular Function Optimization. References. Indexes.
---
paper_title: New classes of fast lower bounds for bin packing problems

paper_content:

The bin packing problem is one of the classical NP-hard optimization problems. In this paper, we present a simple generic approach for obtaining new fast lower bounds, based on dual feasible functions. Worst-case analysis as well as computational results show that one of our classes clearly outperforms the previous best economical lower bound for the bin packing problem by Martello and Toth, which can be understood as a special case. In particular, we prove an asymptotic worst-case performance of 3/4 for a bound that can be computed in linear time for items sorted by size. In addition, our approach provides a general framework for establishing new bounds.
---
paper_title: New reduction procedures and lower bounds for the two-dimensional bin packing problem with fixed orientation

paper_content:

The two-dimensional bin-packing problem (2BP) consists of minimizing the number of identical rectangles used to pack a set of smaller rectangles. In this paper, we propose new lower bounds for 2BP in the discrete case. They are based on the total area of the items after application of dual feasible functions (DFF). We also propose the new concept of data-dependent dual feasible functions (DDFF), which can also be applied to a 2BP instance. We propose two families of Discrete DFF and DDFF and show that they lead to bounds which strictly dominate those obtained previously. We also introduce two new reduction procedures and report computational experiments on our lower bounds. Our bounds improve on the previous best results and close 22 additional instances of a well-known established benchmark derived from literature.
---
paper_title: Computing redundant resources for the resource constrained project scheduling problem

paper_content:

Several efficient lower bounds and time-bound adjustment methods for the resource constrained project scheduling problem (RCPSP) have recently been proposed. Some of them are based on redundant resources. In this paper we define redundant functions which are very useful for computing redundant resources. We also describe an algorithm for computing all maximal redundant functions. Once all these redundant functions have been determined, we have to identify those that are useful for bounding. Surprisingly, their number is reasonable even for large resource capacities, so a representative subset of them can be tabulated to be used efficiently. Computational results on classical RCPSP instances confirm their usefulness.
---
paper_title: Exact Algorithm for Minimising the Number of Setups in the One-Dimensional Cutting Stock Problem

paper_content:

The cutting stock problem is that of finding a cutting of stock material to meet demands for small pieces of prescribed dimensions while minimising the amount of waste. Because changing over from one cutting pattern to another involves significant setups, an auxiliary problem is to minimise the number of different patterns that are used. The pattern minimisation problem is significantly more complex, but it is of great practical importance. In this paper, we propose an integer programming formulation for the problem that involves an exponential number of binary variables and associated columns, each of which corresponds to selecting a fixed number of copies of a specific cutting pattern. The integer program is solved using a column generation approach where the subproblem is a nonlinear integer program that can be decomposed into multiple bounded integer knapsack problems. At each node of the branch-and-bound tree, the linear programming relaxation of our formulation is made tighter by adding super-additive inequalities. Branching rules are presented that yield a balanced tree. Incumbent solutions are obtained using a rounding heuristic. The resulting branch-and-price-and-cut procedure is used to produce optimal or approximately optimal solutions for a set of real-life problems.
---
paper_title: Valid inequalities based on simple mixed-integer sets

paper_content:

In this paper we use facets of simple mixed-integer sets with three variables to derive a parametric family of valid inequalities for general mixed-integer sets. We call these inequalities two-step MIR inequalities as they can be derived by applying the simple mixed-integer rounding (MIR) principle of Wolsey (1998) twice. The two-step MIR inequalities define facets of the master cyclic group polyhedron of Gomory (1969). In addition, they dominate the strong fractional cuts of Letchford and Lodi (2002).
---
paper_title: New classes of fast lower bounds for bin packing problems

paper_content:

The bin packing problem is one of the classical NP-hard optimization problems. In this paper, we present a simple generic approach for obtaining new fast lower bounds, based on dual feasible functions. Worst-case analysis as well as computational results show that one of our classes clearly outperforms the previous best economical lower bound for the bin packing problem by Martello and Toth, which can be understood as a special case. In particular, we prove an asymptotic worst-case performance of 3/4 for a bound that can be computed in linear time for items sorted by size. In addition, our approach provides a general framework for establishing new bounds.
---
paper_title: Strengthening Chvatal-Gomory Cuts and Gomory fractional cuts

paper_content:

Chvatal-Gomory and Gomory fractional cuts are well-known cutting planes for pure integer programming problems. Various methods for strengthening them are known, for example based on subadditive functions or disjunctive techniques. We present a new and surprisingly simple strengthening procedure, discuss its properties, and present some computational results.
---
paper_title: A Subadditive Approach to Solve Linear Integer Programs

paper_content:

A method is presented for solving pure integer programs by a subadditive method. This work extends to the integer linear problem a method for solving the group problem. It uses some elements of both enumeration and cutting plane theory in a unified setting. The method generates a subadditive function and solves the original integer linear program.
---
paper_title: Strengthening Chvatal-Gomory Cuts and Gomory fractional cuts

paper_content:

Chvatal-Gomory and Gomory fractional cuts are well-known cutting planes for pure integer programming problems. Various methods for strengthening them are known, for example based on subadditive functions or disjunctive techniques. We present a new and surprisingly simple strengthening procedure, discuss its properties, and present some computational results.
---
paper_title: Exact Algorithm for Minimising the Number of Setups in the One-Dimensional Cutting Stock Problem

paper_content:

The cutting stock problem is that of finding a cutting of stock material to meet demands for small pieces of prescribed dimensions while minimising the amount of waste. Because changing over from one cutting pattern to another involves significant setups, an auxiliary problem is to minimise the number of different patterns that are used. The pattern minimisation problem is significantly more complex, but it is of great practical importance. In this paper, we propose an integer programming formulation for the problem that involves an exponential number of binary variables and associated columns, each of which corresponds to selecting a fixed number of copies of a specific cutting pattern. The integer program is solved using a column generation approach where the subproblem is a nonlinear integer program that can be decomposed into multiple bounded integer knapsack problems. At each node of the branch-and-bound tree, the linear programming relaxation of our formulation is made tighter by adding super-additive inequalities. Branching rules are presented that yield a balanced tree. Incumbent solutions are obtained using a rounding heuristic. The resulting branch-and-price-and-cut procedure is used to produce optimal or approximately optimal solutions for a set of real-life problems.
---
paper_title: New reduction procedures and lower bounds for the two-dimensional bin packing problem with fixed orientation

paper_content:

The two-dimensional bin-packing problem (2BP) consists of minimizing the number of identical rectangles used to pack a set of smaller rectangles. In this paper, we propose new lower bounds for 2BP in the discrete case. They are based on the total area of the items after application of dual feasible functions (DFF). We also propose the new concept of data-dependent dual feasible functions (DDFF), which can also be applied to a 2BP instance. We propose two families of Discrete DFF and DDFF and show that they lead to bounds which strictly dominate those obtained previously. We also introduce two new reduction procedures and report computational experiments on our lower bounds. Our bounds improve on the previous best results and close 22 additional instances of a well-known established benchmark derived from literature.
---
paper_title: A branch-and-price-and-cut algorithm for the pattern minimization problem

paper_content:

In cutting stock problems, after an optimal (minimal stock usage) cutting plan has been devised, one might want to further reduce the operational costs by minimizing the number of setups. A setup operation occurs each time a different cutting pattern begins to be produced. The related optimization problem is known as the Pattern Minimization Problem, and it is particularly hard to solve exactly. In this paper, we present different techniques to strengthen a formulation proposed in the literature. Dual feasible functions are used for the first time to derive valid inequalities from different constraints of the model, and from linear combinations of constraints. A new arc flow formulation is also proposed. This formulation is used to define the branching scheme of our branch-and-price-and-cut algorithm, and it allows the generation of even stronger cuts by combining the branching constraints with other constraints of the model. The computational experiments conducted on instances from the literature show that our algorithm finds optimal integer solutions faster than other approaches. A set of computational results on random instances is also reported.
---
paper_title: Bin packing with items uniformly distributed over intervals [a,b]

paper_content:

We consider the problem of packing n items which are drawn uniformly from intervals of the form [a,b], where 0 ≪ a ≪ b ≪ 1. For a fairly large class of a and b, we determine a lower bound on the asymptotic expected number of bins used in an optimum packing. The method of proof is related to the dual of the linear programming problem corresponding to the bin packing problem. We demonstrate that these bounds are tight by providing simple packing strategies which achieve them.
---
paper_title: The Two-Dimensional Finite Bin Packing Problem. Part II: New lower and upper bounds

paper_content:

This paper is the second of a two part series and describes new lower and upper bounds for a more general version of the Two-Dimensional Finite Bin Packing Problem (2BP) than the one considered in Part I (see Boschetti and Mingozzi 2002). With each item is associated an input parameter specifying if it has a fixed orientation or it can be rotated by \(90^{\circ}\). This problem contains as special cases the oriented and non-oriented 2BP. The new lower bound is based on the one described in Part I for the oriented 2BP. The computational results on the test problems derived from the literature show the effectiveness of the new proposed lower and upper bounds.
---
paper_title: New classes of fast lower bounds for bin packing problems

paper_content:

The bin packing problem is one of the classical NP-hard optimization problems. In this paper, we present a simple generic approach for obtaining new fast lower bounds, based on dual feasible functions. Worst-case analysis as well as computational results show that one of our classes clearly outperforms the previous best economical lower bound for the bin packing problem by Martello and Toth, which can be understood as a special case. In particular, we prove an asymptotic worst-case performance of 3/4 for a bound that can be computed in linear time for items sorted by size. In addition, our approach provides a general framework for establishing new bounds.
---
paper_title: The Two-Dimensional Finite Bin Packing Problem. Part II: New lower and upper bounds

paper_content:

This paper is the second of a two part series and describes new lower and upper bounds for a more general version of the Two-Dimensional Finite Bin Packing Problem (2BP) than the one considered in Part I (see Boschetti and Mingozzi 2002). With each item is associated an input parameter specifying if it has a fixed orientation or it can be rotated by \(90^{\circ}\). This problem contains as special cases the oriented and non-oriented 2BP. The new lower bound is based on the one described in Part I for the oriented 2BP. The computational results on the test problems derived from the literature show the effectiveness of the new proposed lower and upper bounds.
---
paper_title: New lower bounds for the three-dimensional finite bin packing problem

paper_content:

The three-dimensional finite bin packing problem (3BP) consists of determining the minimum number of large identical three-dimensional rectangular boxes, bins, that are required for allocating without overlapping a given set of three-dimensional rectangular items. The items are allocated into a bin with their edges always parallel or orthogonal to the bin edges. The problem is strongly NP-hard and finds many practical applications. We propose new lower bounds for the problem where the items have a fixed orientation and then we extend these bounds to the more general problem where for each item the subset of rotations by 90° allowed is specified. The proposed lower bounds have been evaluated on different test problems derived from the literature. Computational results show the effectiveness of the new lower bounds.
---
paper_title: New reduction procedures and lower bounds for the two-dimensional bin packing problem with fixed orientation

paper_content:

The two-dimensional bin-packing problem (2BP) consists of minimizing the number of identical rectangles used to pack a set of smaller rectangles. In this paper, we propose new lower bounds for 2BP in the discrete case. They are based on the total area of the items after application of dual feasible functions (DFF). We also propose the new concept of data-dependent dual feasible functions (DDFF), which can also be applied to a 2BP instance. We propose two families of Discrete DFF and DDFF and show that they lead to bounds which strictly dominate those obtained previously. We also introduce two new reduction procedures and report computational experiments on our lower bounds. Our bounds improve on the previous best results and close 22 additional instances of a well-known established benchmark derived from literature.
---
paper_title: Exact Algorithm for Minimising the Number of Setups in the One-Dimensional Cutting Stock Problem

paper_content:

The cutting stock problem is that of finding a cutting of stock material to meet demands for small pieces of prescribed dimensions while minimising the amount of waste. Because changing over from one cutting pattern to another involves significant setups, an auxiliary problem is to minimise the number of different patterns that are used. The pattern minimisation problem is significantly more complex, but it is of great practical importance. In this paper, we propose an integer programming formulation for the problem that involves an exponential number of binary variables and associated columns, each of which corresponds to selecting a fixed number of copies of a specific cutting pattern. The integer program is solved using a column generation approach where the subproblem is a nonlinear integer program that can be decomposed into multiple bounded integer knapsack problems. At each node of the branch-and-bound tree, the linear programming relaxation of our formulation is made tighter by adding super-additive inequalities. Branching rules are presented that yield a balanced tree. Incumbent solutions are obtained using a rounding heuristic. The resulting branch-and-price-and-cut procedure is used to produce optimal or approximately optimal solutions for a set of real-life problems.
---
paper_title: A branch-and-price-and-cut algorithm for the pattern minimization problem

paper_content:

In cutting stock problems, after an optimal (minimal stock usage) cutting plan has been devised, one might want to further reduce the operational costs by minimizing the number of setups. A setup operation occurs each time a different cutting pattern begins to be produced. The related optimization problem is known as the Pattern Minimization Problem, and it is particularly hard to solve exactly. In this paper, we present different techniques to strengthen a formulation proposed in the literature. Dual feasible functions are used for the first time to derive valid inequalities from different constraints of the model, and from linear combinations of constraints. A new arc flow formulation is also proposed. This formulation is used to define the branching scheme of our branch-and-price-and-cut algorithm, and it allows the generation of even stronger cuts by combining the branching constraints with other constraints of the model. The computational experiments conducted on instances from the literature show that our algorithm finds optimal integer solutions faster than other approaches. A set of computational results on random instances is also reported.
---
paper_title: Integer and Combinatorial Optimization

paper_content:

FOUNDATIONS. The Scope of Integer and Combinatorial Optimization. Linear Programming. Graphs and Networks. Polyhedral Theory. Computational Complexity. Polynomial-Time Algorithms for Linear Programming. Integer Lattices. GENERAL INTEGER PROGRAMMING. The Theory of Valid Inequalities. Strong Valid Inequalities and Facets for Structured Integer Programs. Duality and Relaxation. General Algorithms. Special-Purpose Algorithms. Applications of Special- Purpose Algorithms. COMBINATORIAL OPTIMIZATION. Integral Polyhedra. Matching. Matroid and Submodular Function Optimization. References. Indexes.
---
paper_title: A Subadditive Approach to Solve Linear Integer Programs

paper_content:

A method is presented for solving pure integer programs by a subadditive method. This work extends to the integer linear problem a method for solving the group problem. It uses some elements of both enumeration and cutting plane theory in a unified setting. The method generates a subadditive function and solves the original integer linear program.
---
paper_title: Strengthening Chvatal-Gomory Cuts and Gomory fractional cuts

paper_content:

Chvatal-Gomory and Gomory fractional cuts are well-known cutting planes for pure integer programming problems. Various methods for strengthening them are known, for example based on subadditive functions or disjunctive techniques. We present a new and surprisingly simple strengthening procedure, discuss its properties, and present some computational results.
---
paper_title: Strengthening Chvatal-Gomory Cuts and Gomory fractional cuts

paper_content:

Chvatal-Gomory and Gomory fractional cuts are well-known cutting planes for pure integer programming problems. Various methods for strengthening them are known, for example based on subadditive functions or disjunctive techniques. We present a new and surprisingly simple strengthening procedure, discuss its properties, and present some computational results.
---
paper_title: Strengthening Chvatal-Gomory Cuts and Gomory fractional cuts

paper_content:

Chvatal-Gomory and Gomory fractional cuts are well-known cutting planes for pure integer programming problems. Various methods for strengthening them are known, for example based on subadditive functions or disjunctive techniques. We present a new and surprisingly simple strengthening procedure, discuss its properties, and present some computational results.
---
paper_title: Valid inequalities based on simple mixed-integer sets

paper_content:

In this paper we use facets of simple mixed-integer sets with three variables to derive a parametric family of valid inequalities for general mixed-integer sets. We call these inequalities two-step MIR inequalities as they can be derived by applying the simple mixed-integer rounding (MIR) principle of Wolsey (1998) twice. The two-step MIR inequalities define facets of the master cyclic group polyhedron of Gomory (1969). In addition, they dominate the strong fractional cuts of Letchford and Lodi (2002).
---
paper_title: Valid inequalities based on simple mixed-integer sets

paper_content:

In this paper we use facets of simple mixed-integer sets with three variables to derive a parametric family of valid inequalities for general mixed-integer sets. We call these inequalities two-step MIR inequalities as they can be derived by applying the simple mixed-integer rounding (MIR) principle of Wolsey (1998) twice. The two-step MIR inequalities define facets of the master cyclic group polyhedron of Gomory (1969). In addition, they dominate the strong fractional cuts of Letchford and Lodi (2002).
---
paper_title: New reduction procedures and lower bounds for the two-dimensional bin packing problem with fixed orientation

paper_content:

The two-dimensional bin-packing problem (2BP) consists of minimizing the number of identical rectangles used to pack a set of smaller rectangles. In this paper, we propose new lower bounds for 2BP in the discrete case. They are based on the total area of the items after application of dual feasible functions (DFF). We also propose the new concept of data-dependent dual feasible functions (DDFF), which can also be applied to a 2BP instance. We propose two families of Discrete DFF and DDFF and show that they lead to bounds which strictly dominate those obtained previously. We also introduce two new reduction procedures and report computational experiments on our lower bounds. Our bounds improve on the previous best results and close 22 additional instances of a well-known established benchmark derived from literature.
---
paper_title: Strengthening Chvatal-Gomory Cuts and Gomory fractional cuts

paper_content:

Chvatal-Gomory and Gomory fractional cuts are well-known cutting planes for pure integer programming problems. Various methods for strengthening them are known, for example based on subadditive functions or disjunctive techniques. We present a new and surprisingly simple strengthening procedure, discuss its properties, and present some computational results.
---
paper_title: Exact Algorithm for Minimising the Number of Setups in the One-Dimensional Cutting Stock Problem

paper_content:

The cutting stock problem is that of finding a cutting of stock material to meet demands for small pieces of prescribed dimensions while minimising the amount of waste. Because changing over from one cutting pattern to another involves significant setups, an auxiliary problem is to minimise the number of different patterns that are used. The pattern minimisation problem is significantly more complex, but it is of great practical importance. In this paper, we propose an integer programming formulation for the problem that involves an exponential number of binary variables and associated columns, each of which corresponds to selecting a fixed number of copies of a specific cutting pattern. The integer program is solved using a column generation approach where the subproblem is a nonlinear integer program that can be decomposed into multiple bounded integer knapsack problems. At each node of the branch-and-bound tree, the linear programming relaxation of our formulation is made tighter by adding super-additive inequalities. Branching rules are presented that yield a balanced tree. Incumbent solutions are obtained using a rounding heuristic. The resulting branch-and-price-and-cut procedure is used to produce optimal or approximately optimal solutions for a set of real-life problems.
---
paper_title: The Two-Dimensional Finite Bin Packing Problem. Part II: New lower and upper bounds

paper_content:

This paper is the second of a two part series and describes new lower and upper bounds for a more general version of the Two-Dimensional Finite Bin Packing Problem (2BP) than the one considered in Part I (see Boschetti and Mingozzi 2002). With each item is associated an input parameter specifying if it has a fixed orientation or it can be rotated by \(90^{\circ}\). This problem contains as special cases the oriented and non-oriented 2BP. The new lower bound is based on the one described in Part I for the oriented 2BP. The computational results on the test problems derived from the literature show the effectiveness of the new proposed lower and upper bounds.
---
paper_title: Valid inequalities based on simple mixed-integer sets

paper_content:

In this paper we use facets of simple mixed-integer sets with three variables to derive a parametric family of valid inequalities for general mixed-integer sets. We call these inequalities two-step MIR inequalities as they can be derived by applying the simple mixed-integer rounding (MIR) principle of Wolsey (1998) twice. The two-step MIR inequalities define facets of the master cyclic group polyhedron of Gomory (1969). In addition, they dominate the strong fractional cuts of Letchford and Lodi (2002).
---
paper_title: A Subadditive Approach to Solve Linear Integer Programs

paper_content:

A method is presented for solving pure integer programs by a subadditive method. This work extends to the integer linear problem a method for solving the group problem. It uses some elements of both enumeration and cutting plane theory in a unified setting. The method generates a subadditive function and solves the original integer linear program.
---
paper_title: New classes of fast lower bounds for bin packing problems

paper_content:

The bin packing problem is one of the classical NP-hard optimization problems. In this paper, we present a simple generic approach for obtaining new fast lower bounds, based on dual feasible functions. Worst-case analysis as well as computational results show that one of our classes clearly outperforms the previous best economical lower bound for the bin packing problem by Martello and Toth, which can be understood as a special case. In particular, we prove an asymptotic worst-case performance of 3/4 for a bound that can be computed in linear time for items sorted by size. In addition, our approach provides a general framework for establishing new bounds.
---
paper_title: A Linear Programming Approach to the Cutting-Stock Problem

paper_content:

The cutting-stock problem is the problem of filling an order at minimum cost for specified numbers of lengths of material to be cut from given stock lengths of given cost. When expressed as an integer programming problem the large number of variables involved generally makes computation infeasible. This same difficulty persists when only an approximate solution is being sought by linear programming. In this paper, a technique is described for overcoming the difficulty in the linear programming formulation of the problem. The technique enables one to compute always with a matrix which has no more columns than it has rows.
---
paper_title: New classes of fast lower bounds for bin packing problems

paper_content:

The bin packing problem is one of the classical NP-hard optimization problems. In this paper, we present a simple generic approach for obtaining new fast lower bounds, based on dual feasible functions. Worst-case analysis as well as computational results show that one of our classes clearly outperforms the previous best economical lower bound for the bin packing problem by Martello and Toth, which can be understood as a special case. In particular, we prove an asymptotic worst-case performance of 3/4 for a bound that can be computed in linear time for items sorted by size. In addition, our approach provides a general framework for establishing new bounds.
---
paper_title: New reduction procedures and lower bounds for the two-dimensional bin packing problem with fixed orientation

paper_content:

The two-dimensional bin-packing problem (2BP) consists of minimizing the number of identical rectangles used to pack a set of smaller rectangles. In this paper, we propose new lower bounds for 2BP in the discrete case. They are based on the total area of the items after application of dual feasible functions (DFF). We also propose the new concept of data-dependent dual feasible functions (DDFF), which can also be applied to a 2BP instance. We propose two families of Discrete DFF and DDFF and show that they lead to bounds which strictly dominate those obtained previously. We also introduce two new reduction procedures and report computational experiments on our lower bounds. Our bounds improve on the previous best results and close 22 additional instances of a well-known established benchmark derived from literature.
---
paper_title: Exact Algorithm for Minimising the Number of Setups in the One-Dimensional Cutting Stock Problem

paper_content:

The cutting stock problem is that of finding a cutting of stock material to meet demands for small pieces of prescribed dimensions while minimising the amount of waste. Because changing over from one cutting pattern to another involves significant setups, an auxiliary problem is to minimise the number of different patterns that are used. The pattern minimisation problem is significantly more complex, but it is of great practical importance. In this paper, we propose an integer programming formulation for the problem that involves an exponential number of binary variables and associated columns, each of which corresponds to selecting a fixed number of copies of a specific cutting pattern. The integer program is solved using a column generation approach where the subproblem is a nonlinear integer program that can be decomposed into multiple bounded integer knapsack problems. At each node of the branch-and-bound tree, the linear programming relaxation of our formulation is made tighter by adding super-additive inequalities. Branching rules are presented that yield a balanced tree. Incumbent solutions are obtained using a rounding heuristic. The resulting branch-and-price-and-cut procedure is used to produce optimal or approximately optimal solutions for a set of real-life problems.
---
paper_title: A new LP-based lower bound for the cumulative scheduling problem

paper_content:

Abstract This paper deals with the computation of lower bounds for Cumulative Scheduling Problems. Based on a new linear programming formulation, these lower bounds take into account how resource requirements can be satisfied simultaneously for a given resource capacity. One of the main interests of this paper is that the solutions of the LP can be tabulated, for a given value of resource capacity. Thus, even if it is based on a linear programming formulation, the computation of the bounds is low time consuming as confirmed by our computational results on the Resource Constrained Project Scheduling Problem.
---
paper_title: A branch-and-price-and-cut algorithm for the pattern minimization problem

paper_content:

In cutting stock problems, after an optimal (minimal stock usage) cutting plan has been devised, one might want to further reduce the operational costs by minimizing the number of setups. A setup operation occurs each time a different cutting pattern begins to be produced. The related optimization problem is known as the Pattern Minimization Problem, and it is particularly hard to solve exactly. In this paper, we present different techniques to strengthen a formulation proposed in the literature. Dual feasible functions are used for the first time to derive valid inequalities from different constraints of the model, and from linear combinations of constraints. A new arc flow formulation is also proposed. This formulation is used to define the branching scheme of our branch-and-price-and-cut algorithm, and it allows the generation of even stronger cuts by combining the branching constraints with other constraints of the model. The computational experiments conducted on instances from the literature show that our algorithm finds optimal integer solutions faster than other approaches. A set of computational results on random instances is also reported.
---
paper_title: Computing redundant resources for the resource constrained project scheduling problem

paper_content:

Several efficient lower bounds and time-bound adjustment methods for the resource constrained project scheduling problem (RCPSP) have recently been proposed. Some of them are based on redundant resources. In this paper we define redundant functions which are very useful for computing redundant resources. We also describe an algorithm for computing all maximal redundant functions. Once all these redundant functions have been determined, we have to identify those that are useful for bounding. Surprisingly, their number is reasonable even for large resource capacities, so a representative subset of them can be tabulated to be used efficiently. Computational results on classical RCPSP instances confirm their usefulness.
---
paper_title: CUTGEN1: a problem generator for the standard one-dimensional cutting stock problem

paper_content:

In this paper a problem generator for the Standard One-dimensional Cutting Stock Problem (1D-CSP) is developed. The problem is defined and its parameters are identified. Then it is shown what features have been included in the program in order to allow for the generation of easily reproducible random problem instances. Finally, by applying the generator a set of benchmark problems is identified.
---

Title: A Survey of Dual-Feasible and Superadditive Functions

Section 1: Introduction
Description 1: This section introduces the concept of dual-feasible functions, their applications, and the objective of the survey.

Section 2: DFF and superadditive functions
Description 2: This section provides definitions and properties of dual-feasible and superadditive functions and explains their relationships.

Section 3: Dual-feasible functions
Description 3: This section elaborates on the concept of dual-feasible functions and presents alternative definitions and properties.

Section 4: Superadditive functions and maximal DFF
Description 4: This section discusses the role of superadditive nondecreasing functions, introduces the notion of maximal DFF, and defines their properties.

Section 5: DFF and valid inequalities
Description 5: This section highlights the use of dual-feasible functions in generating valid inequalities for integer programs.

Section 6: Frameworks for creating valid DFF
Description 6: This section presents frameworks for obtaining complex superadditive functions by combining simpler ones.

Section 7: Compositions and linear combinations
Description 7: This section explains how to combine dual-feasible functions using linear combinations and compositions.

Section 8: Finding a corresponding dominant function by symmetry
Description 8: This section demonstrates the process of creating a maximal dual-feasible function from a non-maximal one by utilizing symmetry.

Section 9: Improving a function by studying its limiting behavior
Description 9: This section describes a method for obtaining a maximal DFF from a non-maximal superadditive function by analyzing its limiting behavior.

Section 10: Using two different functions for x and r_x
Description 10: This section illustrates the approach of using two different DFFs for integer and fractional parts of values.

Section 11: Using the ceiling function
Description 11: This section discusses how to derive superadditive functions by leveraging the ceiling function.

Section 12: Comparative analysis of dual-feasible functions
Description 12: This section surveys various dual-feasible functions proposed in the literature, providing explicit definitions and determining their maximality.

Section 13: Fekete and Schepers
Description 13: This section examines the dual-feasible functions proposed by Fekete and Schepers and their properties.

Section 14: Boschetti and Mingozzi
Description 14: This section details the functions introduced by Boschetti and Mingozzi, highlighting their applications and effectiveness.

Section 15: Carlier et al.
Description 15: This section describes the contributions of Carlier et al. to dual-feasible functions and their improvements.

Section 16: Vanderbeck
Description 16: This section explores the dual-feasible functions used by Vanderbeck and their impact on pattern minimization problems.

Section 17: Burdett and Johnson
Description 17: This section outlines the function for rational values proposed by Burdett and Johnson and its application in deriving valid inequalities.

Section 18: Letchford and Lodi
Description 18: This section explains the dual-feasible function proposed by Letchford and Lodi to strengthen Chvátal-Gomory and Gomory fractional cuts.

Section 19: Dash and Günlük 
Description 19: This section introduces a maximal dual-feasible function derived by Dash and Günlük from their extended mixed-integer rounding inequalities.

Section 20: Summary
Description 20: This section summarizes key findings, dominance relations, maximality properties, and applications of dual-feasible functions discussed in the survey.

Section 21: Bin-packing instances
Description 21: This section compares different dual-feasible functions for various bin-packing problem instances and evaluates their lower-bounding effectiveness.

Section 22: Composing functions
Description 22: This section analyzes the performance improvements obtained by composing different dual-feasible functions.

Section 23: Strengthening the column generation model for the PMP with DFF
Description 23: This section evaluates the effectiveness of dual-feasible functions in strengthening valid inequalities for the pattern minimization problem.