Constructive procedures to solve 2-dimensional bin packing problems with irregular pieces and guillotine cuts

This paper presents an approach for solving a new real problem in cutting and packing. At its core is an innovative mixed integer programme model that places irregular pieces and defines guillotine cuts. The two-dimensional irregular shape bin packing problem with guillotine constraints arises in the glass cutting industry, for example, the cutting of glass for conservatories. Almost all cutting and packing problems that include guillotine cuts deal with rectangles only, where all cuts are orthogonal to the edges of the stock sheet and a maximum of two angles of rotation are permitted. The literature tackling packing problems with irregular shapes largely focuses on strip packing i.e. minimizing the length of a single fixed width stock sheet, and does not consider guillotine cuts. Hence, this problem combines the challenges of tackling the complexity of packing irregular pieces with free rotation, guaranteeing guillotine cuts that are not always orthogonal to the edges of the stock sheet, and allocating pieces to bins. To our knowledge only one other recent paper tackles this problem. We present a hybrid algorithm that is a constructive heuristic that determines the relative position of pieces in the bin and guillotine constraints via a mixed integer programme model. We investigate two approaches for allocating guillotine cuts at the same time as determining the placement of the piece, and a two phase approach that delays the allocation of cuts to provide flexibility in space usage. Finally we describe an improvement procedure that is applied to each bin before it is closed. This approach improves on the results of the only other publication on this problem, and gives competitive results for the classic rectangle bin packing problem with guillotine constraints.     

Bin packing under linear constraints

In this paper, we study a bin packing problem in which the sizes of items are determined by k linear constraints, and the goal is to decide the sizes of items and pack them into a minimal number of unit sized bins. We first provide two scenarios that motivate this research. We show that this problem is NP-hard in general, and propose several algorithms in terms of the number of constraints. If the number of constraints is arbitrary, we propose a 2-approximation algorithm, which is based on the analysis of the Next Fit algorithm for the bin packing problem. If the number of linear constraints is a fixed constant, then we obtain a PTAS by combining linear programming and enumeration techniques, and show that it is an optimal algorithm in polynomial time when the number of constraints is one or two. It is well known that the bin packing problem is strongly NP-hard and cannot be approximated within a factor 3 / 2 unless P = NP. This result implies that the bin packing problem with a fixed number of constraints may be simper than the original bin packing problem. Finally, we discuss the case when the sizes of items are bounded.     

Selfish bin coloring

The bin packing problem, a classical problem in combinatorial optimization, has recently been studied from the viewpoint of algorithmic game theory. In this bin packing game each item is controlled by a selfish player minimizing its personal cost, which in this context is defined as the relative contribution of the size of the item to the total load in the bin.     

Approximability of the subset sum reconfiguration problem

The subset sum problem is a well-known NP-complete problem in which we wish to find a packing (subset) of items (integers) into a knapsack with capacity so that the sum of the integers in the packing is at most the capacity of the knapsack and at least a given integer threshold. In this paper, we study the problem of reconfiguring one packing into another packing by moving only one item at a time, while at all times maintaining the feasibility of packings. First we show that this decision problem is strongly NP-hard, and is PSPACE-complete if we are given a conflict graph for the set of items in which each vertex corresponds to an item and each edge represents a pair of items that are not allowed to be packed together into the knapsack. We then study an optimization version of the problem: we wish to maximize the minimum sum among all packings in a reconfiguration. We show that this maximization problem admits a polynomial-time approximation scheme, while the problem is APX-hard if we are given a conflict graph.     

Packing of Unequal Spheres and Automated Radiosurgical Treatment Planning

We study an optimization problem of packing unequal spheres into a three-dimensional (3D) bounded region in connection with radiosurgical treatment planning. Given an input (R, V, S, L), where R is a 3D bounded region, V a positive integer, S a multiset of spheres, and L a location constraint on spheres, we want to find a packing of R using the minimum number of spheres in S such that the covered volume is at least V; the location constraint L is satisfied; and the number of points on the boundary of R that are touched by spheres is maximized. Such a packing arrangement corresponds to an optimal radiosurgical treatment planning. Finding an optimal solution to the problem, however, is computationally intractable. In particular, we show that this optimization problem and several related problems are NP-hard. Hence, some form of approximations is needed. One approach is to consider a simplified problem under the assumption that spheres of arbitrary (integral) diameters are available with unlimited supply, and there are no location constraints. This approach has met with certain success in medical applications using a dynamic programming algorithm (Bourland and Wu, 1996; Wu, 1996). We propose in this paper an improvement to the algorithm that can greatly reduce its computation cost.     

From packing rules to cost-sharing mechanisms

Journal of Combinatorial Optimization - Bin packing is one of the most fundamental problems in resource allocation. Most research on the classical bin packing problem has focused on the design and...     

Average-Case Performance Analysis of a 2D Strip Packing Algorithm—NFDH

The two-dimensional strip packing problem is a generalization of the classic one-dimensional bin packing problem. It has many important applications such as costume clipping, material cutting, real-world planning, packing, scheduling etc. Average-case performance analysis of approximation algorithms attracts a lot of attention because it plays a crucial role in selecting an appropriate algorithm for a given application. While approximation algorithms for two-dimensional packing are frequently presented, the results of their average-case performance analyses have seldom been reported due to intractability. In this paper, we analyze the average-case performance of Next Fit Decreasing Height (NFDH) algorithm, one of the first strip packing algorithms proposed by Coffman, Jr. in 1980. We prove that the expected height of packing with NFDH algorithm, when the heights and widths of the rectangle items are independent and both obey (0, 1] uniform distribution, is about n/3, where n is the number of rectangle items. We also validate the theoretical result with experiments.This work is supported by National 973 Fundamental Research Project of China on NP Complete Problems and High Performance Software (No. G1998030403).     

Combinatorial design of a minimum cost transfer line

The problem of equipment selection for a production line is considered. Each piece of equipment, also called unit or block, performs a set of operations. All necessary operations of the line and all available blocks with their costs are known. The difficulty is to choose the most appropriate blocks and group them into (work)stations. There are some constraints that restrict the assignment of different blocks to the same station. Two combinatorial approaches for solving this problem are suggested. Both are based on a novel concept of locally feasible stations. The first approach combinatorially enumerates all feasible solutions, and the second reduces the problem to search for a maximum weight clique. A boolean linear program based on a set packing formulation is presented. Computer experiments with benchmark data are described. Their results show that the set packing model is competitive and can be used to solve real-life problems.     

A multi-faced buildup algorithm for three-dimensional packing problems

This paper provides a new approach to solving the three-dimensional packing problem. The heuristic developed uses a multi-faced buildup technique in the packing procedure for which there is no requirement for packed boxes to form flat layers. The basic algorithm is then augmented by a Look-ahead strategy. Experimental results indicate an average packing utilization of 87.8% which improve current benchmarks significantly. The new approaches given here add to heuristics currently available.     

On the fractionality of the path packing problem

Given an undirected graph G=(N,E), a subset T of its nodes and an undirected graph (T,S), G and (T,S) together are often called a network. A?collection of paths in G whose end-pairs lie in S is called an integer multiflow. When these paths are allowed to have fractional weight, under the constraint that the total weight of the paths traversing a single edge does not exceed 1, we have a fractional multiflow in G. The problems of finding the maximum weight of paths with end-pairs in S over all fractional multiflows in G is called the fractional path packing problem. In 1989, A. Karzanov had defined the fractionality of the fractional path packing problem for a class of networks {G,(T,S)} as the smallest natural D such that for any network from the class, the fractional path packing problem has a solution which becomes integer-valued when multiplied by D (see A.?Karzanov in Linear Algebra Appl. 114–115:293–328, 1989). He proved that the fractional path packing problem has infinite fractionality outside a very specific class of networks, and conjectured that within this class, the fractionality does not exceed 4. A.?Karzanov also proved that the fractionality of the path packing problem is at most 8 by studying the fractionality of the dual problem. Special cases of Karzanov’s conjecture were proved in or are implied by the works of L.R.?Ford and D.R.?Fulkerson, Y.?Dinitz, T.C.?Hu, B.V.?Cherkassky, L.?Lov?sz and H.?Hirai. We prove Karzanov’s conjecture by showing that the fractionality of the path packing problem is at most 4. Our proof is stand-alone and does not rely on Karzanov’s results.     

An Approximation Scheme for Bin Packing with Conflicts

In this paper we consider the following bin packing problem with conflicts. Given a set of items V = {1,..., n} with sizes s₁,..., s (0,1) and a conflict graph G = (V, E), we consider the problem to find a packing for the items into bins of size one such that adjacent items (j, j) E are assigned to different bins. The goal is to find an assignment with a minimum number of bins. This problem is a natural generalization of the classical bin packing problem.We propose an asymptotic approximation scheme for the bin packing problem with conflicts restricted to d-inductive graphs with constant d. This graph class contains trees, grid graphs, planar graphs and graphs with constant treewidth. The algorithm finds an assignment for the items such that the generated number of bins is within a factor of (1 + ) of optimal provided that the optimum number is sufficiently large. The running time of the algorithm is polynomial both in n and in .     

Min-Sum Bin Packing

We study min-sum bin packing (MSBP). This is a bin packing problem, where the cost of an item is the index of the bin into which it is packed. The problem is equivalent to a batch scheduling problem we define, where the total completion time is to be minimized. The problem is NP-hard in the strong sense. We show that it is not harder than this by designing a polynomial time approximation scheme for it. We also show that several natural algorithms which are based on well-known bin packing heuristics (such as First Fit Decreasing) fail to achieve an asymptotic finite approximation ratio, whereas Next Fit Increasing has an absolute approximation ratio of at most 2, and an asymptotic approximation ratio of at most 1.6188. We design a new heuristic that applies Next Fit Increasing on the relatively small items and adds the larger items using First Fit Decreasing, and show that its asymptotic approximation ratio is at most 1.5604.     

Medial Axis and Optimal Locations for Min-Max Sphere Packing

We study the following min-max sphere packing problem originated from radiosurgical treatment planning using gamma knife (Bourland and Wu, 1996; Wu, 1996). Given an input (R, V), where R is a 3-dimensional (3D) bounded region and V a positive integer, find a packing of R using the minimum number of spheres (spheres may not be identical) such that the covered volume is at least V, and the number of points on the boundary of R touched by spheres is maximized. Bourland and Wu (1996) and Wu (1996), devised a greedy algorithm to solve the problem based on medial axis analysis. In particular, the algorithm places the center of each sphere on the medial axis of each subsequent region starting from R. While this approach has met with certain success, we show that medial axis does not always provide optimal locations for min-max sphere packing.     

Packing trees in communication networks

Given an undirected edge-capacitated graph and given (possibly) different subsets of vertices, we consider the problem of selecting a maximum (weighted) set of Steiner trees, each tree spanning a subset of vertices, without violating the capacity constraints. This problem is motivated by applications in multicast communication networks. We give an integer linear programming (ILP) formulation for the problem, and observe that its linear programming (LP) relaxation is a fractional packing problem with exponentially many variables and a block (sub-)problem that cannot be solved in polynomial time. To this end, we take an r-approximate block solver (a weak block solver) to develop a (1−ε)/r-approximation algorithm for the LP relaxation. The algorithm has a polynomial coordination complexity for any ε∈(0,1). To the best of our knowledge, this is the first approximation result for fractional packing problems with only weak block solvers (with arbitrarily large approximation ratio) and a coordination complexity that is polynomial in the input size. This leads also to an approximation algorithm for the underlying tree packing problem. Finally, we extend our results to an important multicast routing and wavelength assignment problem in optical networks, where each Steiner tree is to be assigned one of a limited set of given wavelengths, so that trees crossing the same fiber are assigned different wavelengths. A preliminary version of this paper appeared in the Proceedings of the 1st Workshop on Internet and Network Economics (WINE 2005), LNCS, vol. 3828, pp. 688–697. Research supported by a MITACS grant for all the authors, an NSERC post doctoral fellowship for the first author, the NSERC Discovery Grant #5-48923 for the second and fourth author, NSERC Discovery Grant #15296 for the third author, the Canada Research Chair Program for the second author, and an NSERC industrial and development fellowship for the fourth author.     

Scheduling and packing malleable and parallel tasks with precedence constraints of bounded width

We study the problems of non-preemptively scheduling and packing malleable and parallel tasks with precedence constraints to minimize the makespan. In the scheduling variant, we allow the free choice of processors; in packing, each task must be assigned to a contiguous subset. Malleable tasks can be processed on different numbers of processors with varying processing times, while parallel tasks require a fixed number of processors. For precedence constraints of bounded width, we resolve the complexity status of the problem with any number of processors and any width bound. We present an FPTAS based on Dilworth’s decomposition theorem for the NP-hard problem variants, and exact efficient algorithms for all remaining special cases. For our positive results, we do not require the otherwise common monotonous penalty assumption on the processing times of malleable tasks, whereas our hardness results hold even when assuming this restriction. We complement our results by showing that these problems are all strongly NP-hard under precedence constraints which form a tree.     

Trip packing in petrol stations replenishment

This paper considers a generalized version of the trip packing problem that we encountered as a sub-problem of the petrol stations replenishment problem. In this version we have to assign a number of trips to a fleet composed of a limited number of non-identical tank-trucks. Each trip has a specific duration, working time of vehicles is limited and the net revenue of each trip depends on the truck used. The paper provides a mathematical formulation of the problem and proposes some construction, improvement and neighbourhood search solution heuristics. A set of benchmark problem instances is created in a way that reflects real-life situations and used to analyse the performance of the proposed heuristics. A real-life case is also used to further assess the proposed heuristics.     

The two-dimensional vector packing problem with piecewise linear cost function

The two-dimensional vector packing problem with piecewise linear cost function (2DVPP-PLC) is a practical problem faced by a manufacturer of children׳s apparel that ships products using courier service. The manufacturer must ship a number of items using standard-sized cartons, where the cost of a carton quoted by the courier is determined by a piecewise linear function of its weight. The cost function is not necessarily convex or concave. The objective is to pack all given items into a set of cartons such that the total delivery cost is minimized while observing both the weight limit and volume capacity constraints. This problem is commonly faced by many manufacturers that ship products using courier service. We formulate the problem as an integer programming model. Since the 2DVPP-PLC generalizes the classical bin packing problem, it is more complex and challenging. Solving it directly using CPLEX is successful only for small instances. We propose a simple heuristic that is extremely fast and produces high-quality solutions for instances of practical size. We develop an iterative local search algorithm to improve the solution quality further. We generate two categories of test data that can serve as benchmark for future research.     

On Integrality, Stability and Composition of Dicycle Packings and Covers

Given a digraph D, the minimum integral dicycle cover problem (known also as the minimum feedback arc set problem) is to find a minimum set of arcs that intersects every dicycle; the maximum integral dicycle packing problem is to find a maximum set of pairwise arc disjoint dicycles. These two problems are NP-complete.Assume D has a 2-vertex cut. We show how to derive a minimum dicycle cover (a maximum dicycle packing) for D, by composing certain covers (packings) of the corresponding pieces. The composition of the covers is simple and was partially considered in the literature before. The main contribution of this paper is to the packing problem. Let be the value of a minimum integral dicycle cover, and * () the value of a maximum (integral) dicycle packing. We show that if = then a simple composition, similar to that of the covers, is valid for the packing problem. We use these compositions to extend an O(n³) (resp., O(n⁴)) algorithm for finding a minimum integral dicycle cover (resp., packing) from planar digraphs to K_3,3-free digraphs (i.e., digraphs not containing any subdivision of K_3,3).However, if , then such a simple composition for the packing problem is not valid. We show, that if the pieces satisfy, what we call, the stability property, then a simple composition does work. We prove that if = * holds for each piece, then the stability property holds as well. Further, we use the stability property to show that if = * holds for each piece, then = * holds for D as well.     

Path packing and a related optimization problem

Let G be a supply graph, with the node set N and edge set E, and (T,S) be a demand graph, with T?N, S∩E=?. Observe paths whose end-vertices form pairs in S (called S-paths). The following path packing problem for graphs is fundamental: what is the maximal number of S-paths in G? In this paper this problem is studied under two assumptions: (a) the node degrees in N?T are even, and (b) any three distinct pairwise intersecting maximal stable sets A,B,C of (T,S) satisfy A∩B=B∩C=A∩C (this condition was defined by A. Karzanov in Linear Algebra Appl. 114–115:293–328, 1989). For any demand graph violating (b) the problem is known to be NP-hard even under (a), and only a few cases satisfying (a) and (b) have been solved. In each of the solved cases, a solution and an optimal dual object were defined by a certain auxiliary “weak” multiflow optimization problem whose solutions supply constructive elements for S-paths and concatenate them into an S-path packing by a kind of matching. In this paper the above approach is extended to all demand graphs satisfying (a) and (b), by proving existence of a common solution of the S-path packing and its weak counterpart. The weak problem is very interesting for its own sake, and has connections with such topics as Mader’s edge-disjoint path packing theorem and b-factors in graphs.     

On Split-Coloring Problems

We study a new coloring concept which generalizes the classical vertex coloring problem in a graph by extending the notion of stable sets to split graphs. First of all, we propose the packing problem of finding the split graph of maximum size where a split graph is a graph G = (V,E) in which the vertex set V can be partitioned into a clique K and a stable set S. No condition is imposed on the edges linking vertices in S to the vertices in K. This maximum split graph problem gives rise to an associated partitioning problem that we call the split-coloring problem. Given a graph, the objective is to cover all his vertices by a least number of split graphs. Definitions related to this new problem are introduced. We mention some polynomially solvable cases and describe open questions on this area. An erratum to this article is available at .