The minimum size instance of a Pallet Loading Problem equivalence class

The Pallet Loading Problem (PLP) maximizes the number of identical rectangular boxes placed within a rectangular pallet. Boxes may be rotated 90° so long as they are packed with edges parallel to the pallet’s edges, i.e., in an orthogonal packing. This paper defines the Minimum Size Instance (MSI) of an equivalence class of PLP, and shows that every class has one and only one MSI. We develop bounds on the dimensions of box and pallet for the MSI of any class. Applying our new bounds on MSI dimensions, we present an algorithm for MSI generation and use it to enumerate all 3,080,730 equivalence classes with an area ratio (pallet area divided by box area) smaller than 101 boxes. Previous work only provides bounds on the ratio of box dimensions and only considers a subset of all classes presented here.     

Solving the pallet loading problem

This paper presents new bounds, heuristics, and an exact algorithm for the Pallet Loading Problem (PLP). PLP maximizes the number of boxes placed on a rectangular pallet. All boxes have identical rectangular dimensions and, when placed, must be located completely within the pallet. Boxes may be rotated 90° so long as they are placed with edges parallel to the pallet’s edges. The set of all PLP instances with an area ratio (pallet area divided by box area) less than 101 boxes can be represented by 3,080,730 equivalent classes. Our G5-heuristic finds optimal solutions to 3,073,724 of these 3,080,730 classes and in the remaining 7006 classes only differs from the best known bound by one box. We develop three other heuristics that solve another 54 instances. Finally, we solve the 6952 remaining classes with our exact HVZ algorithm. Only a subset of these classes has been solved previously.     

The G4-Heuristic for the Pallet Loading Problem

A new heuristic for the well-known (two-dimensional orthogonal) pallet loading problem (PLP) is proposed in this paper. This heuristic, referred to as G4-heuristic, is based on the definition of the so-called G4-structure of packing patterns. The G4-structure is a generalization of the common used block structure of packing patterns which requires the same orientation of packed boxes within each block. The G4-heuristic yields in approximately 99% of the test instances an optimal solution and solves all instances exactly where at most 50 boxes are contained in an optimal packing. Although the algorithm is pseudo-polynomial the computational experiments reported show that also instances with more than 200 packed boxes in an optimal solution can be handled with a small amount of computational time. Moreover, so far there is not known any instance where the gap between optimal value and the value obtained by the G4-heuristic is larger than one box.     

An exact algorithm for the pallet loading problem

The two-dimensional packing problem of finding optimal layouts for identical rectangular boxes on a rectangular pallet has interested OR practitioners for many years. The problem is NP-complete and solution methods to date tend to be heuristic. This paper discusses the development of an exact tree search algorithm based on a graph-theoretic model of the problem.     

基于对角转轮样式的托盘装箱优化模型

针对托盘装箱问题(PLP),建立了对角转轮样式下具有托盘柔性的整数规划模型,设计了求解模型的启发式算法,并利用VB程序对模型的最优解及装箱图谱进行了讨论分析,结果表明:对角转轮样式就提高具有较大长、宽比箱子的装载效率以及解决装箱压缝问题方面具有明显的优势;而柔性也是影响托盘装载效率的重要因素之一,具有较大的回报率.     

Practical Considerations of the Pallet-Loading Problem

The pallet-loading problem, in which a number of identical boxes are to be packed orthogonally onto a rectangular pallet, has been the subject of several algorithms in recent years. These algorithms are concerned with maximizing the number of boxes fitted. In this paper, consideration is given to the development of loading patterns into pallet stacks, and criteria which might be applied to determine the suitability of these stacks for storage and transportation. A technique is developed which allows the stability and clampability of stacks to be tested, and this is applied to layouts produced by the recent algorithm of Bischoff and Dowsland. The relative importance of different criteria is illustrated and shown to have implications for the structure of algorithms that are to provide acceptable pallet stacks.     

Single machine scheduling to minimize total weighted earliness subject to minimal number of tardy jobs

Motivated by just-in-time manufacturing, we consider a single machine scheduling problem with dual criteria, i.e., the minimization of the total weighted earliness subject to minimum number of tardy jobs. We discuss several dominance properties of optimal solutions. We then develop a heuristic algorithm with time complexity O(n³) and a branch and bound algorithm to solve the problem. The computational experiments show that the heuristic algorithm is effective in terms of solution quality in many instances while the branch and bound algorithm is efficient for medium-size problems.     

Analysis of upper bounds for the Pallet Loading Problem

This paper is concerned with upper bounds for the well-known Pallet Loading Problem (PLP), which is the problem of packing identical boxes into a rectangular pallet so as to maximize the number of boxes fitted. After giving a comprehensive review of the known upper bounds in the literature, we conduct a detailed analysis to determine which bounds dominate which others. The result is a ranking of the bounds in a partial order. It turns out that two of the bounds dominate all others: a bound due to Nelissen and a bound obtained from the linear programming relaxation of a set packing formulation. Experiments show that the latter is almost always optimal and can be computed quickly.     

An n-tet graph approach for non-guillotine packings of n-dimensional boxes into an n-container

In this paper we propose a simple recursive uniform algorithm for the problem of packing n-dimensional boxes into an n-container. We are particularly concerned about the special case n=3 where the boxes can be packed in a given subset of their six possible positionings. Our method studies symmetries in the packings by the use of an ordered set of three directed graphs with the same edges (a 3-tet or triad) and induced smaller structures of the same kind named minors. With the method, degeneracy and symmetry issues, which curtail the implicit enumeration to practically acceptable running times, become transparent. In order to illustrate the performance of the algorithm, computational results from solving randomly generated 3-D examples are presented and compared with the ones of a layers and knapsack approach. The present study has real world applications for the problems of pallet and container loading.     

Optimal and Heuristic Procedures for Row Layout Problems in Automated Manufacturing Systems

In many automated manufacturing environments, particularly flowlines and flexible manufacturing systems (FMSs), machines are arranged along a straight material handling track with a material handling device moving jobs from one machine to aother. These layouts are referred to as row machine layouts. In this paper we study the Row Layout Problem (RLP) under the design objective of minimizing the total backtracking distance of the material handling device, which is a NP-complete problem. We propose the use of a dynamic programming algorithm for its solution. Special cases of the problem, usually encountered in flexible manufacturing cells and which can be solved with polynomial procedures, are also discussed. For the equidistant case (i.e., successive candidate locations are in equal distances), we formulate the problem as an integer linear program. The use of standard mathematical programming codes can efficiently solve this formulation. Two effective heuristic procedures, which explore simple ideas based on local optimality conditions, are also presented. Extensive computational results demonstrate the effectiveness of such heuristics.     

A simple and effective recursive procedure for the manufacturer's pallet loading problem

In this paper we present a simple and effective heuristic to solve the problem of packing the maximum number of rectangles of sizes (l,w) and (w,l) into a larger rectangle (L,W) without overlapping. This problem appears in the loading of identical boxes on pallets, namely the manufacturer's pallet loading (MPL), as well as in package design and truck or rail car loading. Although apparently easy to be optimally solved, the MPL is claimed to be NP-complete and several authors have proposed approximate methods to deal with it. The procedure described in the present paper can be seen as a refinement of Bischoff and Dowsland's heuristic and can easily be implemented on a microcomputer. Using moderate computational resources, the procedure was able to find the optimal solution of 99.9% of more than 20?000 examples analysed.     

The Three-Dimensional Pallet Chart: An Analysis of the Factors Affecting the Set of Feasible Layouts for a Class of Two-Dimensional Packing Problems

The two-dimensional orthogonal packing problem of packing identical rectangles into a large containing rectangle is important in pallet loading and has recently received much attention in O.R. publications. In this paper, we examine the conditions under which the set of feasible layouts remains unchanged and show that these conditions can be represented by a series of planes in three-dimensional space. We call this representation the three-dimensional pallet chart because it is an extension of the two-dimensional pallet charts presently used in many practical situations. The strength of this result is demonstrated by three examples of its use. Accurate two-dimensional charts are produced from the three-dimensional version with a minimum of calculation, and a complete sensitivity analysis to changes in box and pallet dimensions can be carried out visually by viewing the chart at different angles. Finally, the result is used to generate a new procedure for determining the maximum number of rectangles which can be fitted. This method is shown to be accurate for over 90% of observations in a random sample of 5000—an improvement of 20% over previous methods.     

Minmax earliness–tardiness costs with unit processing time jobs

The problem addressed in this paper is defined by M parallel identical machines, N jobs with identical (unit) processing time, job-dependent weights, and a common due-date for all jobs. The objective is of a minmax type, i.e. we are interested in minimizing the cost of the worst scheduled job. In the case of a non-restrictive (i.e., sufficiently large) common due-date, the problem is shown to have a solution that is polynomial in the number of jobs. The solution in the case of a restrictive due-date remains polynomial in the number of jobs, but is exponential in the number of machines. We introduce a lower bound on the optimal cost and an efficient heuristic. We show that the worst case relative error of the heuristic is bounded by 2 and that this bound is tight. We also prove that the heuristic is asymptotically optimal under very general assumptions. Finally, we provide an extensive numerical study demonstrating that in most cases the heuristic performs extremely well.     

An iterated-tabu-search heuristic for a variant of the partial set covering problem

In this paper, we propose a heuristic algorithm to solve a new variant of the partial set covering problem. In this variant, each element $e_i$ has a gain $g_i$ (i.e., a positive profit), each set $s_j$ has a cost $c_j$ (i.e., a negative profit), and each set $s_j$ is part of a unique group $G_k$ that has a fixed cost $f_k$ (i.e., a negative profit). The objective is to maximize profit and it is not necessary to cover all of the elements. We present an industrial application of the model and propose a hybrid heuristic algorithm to solve it; the proposed algorithm is an iterated-local-search algorithm that uses two levels of perturbations and a tabu-search heuristic. Whereas the first level of perturbation diversifies the search around the current local optimum, the second level of perturbation performs long jumps in the search space to help escape from local optima with large basins of attraction. The proposed algorithm is evaluated on thirty real-world problems and compared to a memetic algorithm. Computational results show that most of the solutions found by ITS are either optimal or very close to optimality.     

A hybrid GRASP/VND algorithm for two- and three-dimensional bin packing

The three-dimensional bin packing problem consists of packing a set of boxes into the minimum number of bins. In this paper we propose a new GRASP algorithm for solving three-dimensional bin packing problems which can also be directly applied to the two-dimensional case. The constructive phase is based on a maximal-space heuristic developed for the container loading problem. In the improvement phase, several new moves are designed and combined in a VND structure. The resulting hybrid GRASP/VND algorithm is simple and quite fast and the extensive computational results on test instances from the literature show that the quality of the solutions is equal to or better than that obtained by the best existing heuristic procedures.     

A hybrid algorithm for solving the economic lot and delivery scheduling problem in the common cycle case

The ELDSP problem is a combined lot sizing and sequencing problem. A supplier produces and delivers components of different types to a consumer in batches. The task is to determine the cycle time, i.e., the time between deliveries, which minimizes the total cost per time unit. This includes the determination of the production sequence of the component types within each cycle.We investigate the computational behavior of two published algorithms, a heuristic and an optimal algorithm. With large number of component types, the optimal algorithm has long running times. We devise a hybrid algorithm, which is both optimal and efficient.     

A modified variable neighborhood search for the discrete ordered median problem

This paper presents a modified Variable Neighborhood Search (VNS) heuristic algorithm for solving the Discrete Ordered Median Problem (DOMP). This heuristic is based on new neighborhoods’ structures that allow an efficient encoding of the solutions of the DOMP avoiding sorting in the evaluation of the objective function at each considered solution. The algorithm is based on a data structure, computed in preprocessing, that organizes the minimal necessary information to update and evaluate solutions in linear time without sorting. In order to investigate the performance, the new algorithm is compared with other heuristic algorithms previously available in the literature for solving DOMP. We report on some computational experiments based on the well-known N-median instances of the ORLIB with up to 900 nodes. The obtained results are comparable or superior to existing algorithms in the literature, both in running times and number of best solutions found.     

A contraction algorithm for finding small cycle cutsets

We consider the problem of finding a minimum size cutset in a directed graph G = (V, E), i.e., a vertex set that cuts all cycles in G. Since the general problem is NP-complete we concentrate on finding small cutsets. The algorithm we suggest uses contraction operations to reduce the graph size and to identify candidates for the cutset; the complexity of the algorithm is O(|E|log|V|). This contraction algorithm is compared to Shamir-Rosen algorithm. It is shown that the class of graphs for which the contraction algorithm finds a minimum cutset (completely contractible graphs) properly contains the class of graphs for which Shamir-Rosen algorithm finds a minimum cutset (quasi-reducible graphs) and thus that the contraction algorithm is more powerful. As a by-product of this analysis we construct a hierarchy of the classes of graphs for which minimum cutsets can be found efficiently. The class of quasi-reducible graphs lies, in this hierarchy, between two classes which are closely related. This result illuminates the nature of the quasi-reducible graphs. The hierarchy constructed allows us also to compare the Wang-Lloyd-Soffa algorithm to the Shamir-Rosen algorithm and to the contraction algorithm.     

An effective recursive partitioning approach for the packing of identical rectangles in a rectangle

In this work, we deal with the problem of packing (orthogonally and without overlapping) identical rectangles in a rectangle. This problem appears in different logistics settings, such as the loading of boxes on pallets, the arrangements of pallets in trucks and the stowing of cargo in ships. We present a recursive partitioning approach combining improved versions of a recursive five-block heuristic and an L-approach for packing rectangles into larger rectangles and L-shaped pieces. The combined approach is able to rapidly find the optimal solutions of all instances of the pallet loading problem sets Cover I and II (more than 50?000 instances). It is also effective for solving the instances of problem set Cover III (almost 100?000 instances) and practical examples of a woodpulp stowage problem, if compared to other methods from the literature. Some theoretical results are also discussed and, based on them, efficient computer implementations are introduced. The computer implementation and the data sets are available for benchmarking purposes.     

Fast algorithms for robust classification with Bayesian nets

We focus on a well-known classification task with expert systems based on Bayesian networks: predicting the state of a target variable given an incomplete observation of the other variables in the network, i.e., an observation of a subset of all the possible variables. To provide conclusions robust to near-ignorance about the process that prevents some of the variables from being observed, it has recently been derived a new rule, called conservative updating. With this paper we address the problem to efficiently compute the conservative updating rule for robust classification with Bayesian networks. We show first that the general problem is NP-hard, thus establishing a fundamental limit to the possibility to do robust classification efficiently. Then we define a wide subclass of Bayesian networks that does admit efficient computation. We show this by developing a new classification algorithm for such a class, which extends substantially the limits of efficient computation with respect to the previously existing algorithm. The algorithm is formulated as a variable elimination procedure, whose computation time is linear in the input size.