Using biased randomization for solving the two-dimensional loading vehicle routing problem with heterogeneous fleet

This paper discusses the two-dimensional loading capacitated vehicle routing problem (2L-CVRP) with heterogeneous fleet (2L-HFVRP). The 2L-CVRP can be found in many real-life situations related to the transportation of voluminous items where two-dimensional packing restrictions have to be considered, e.g.: transportation of heavy machinery, forklifts, professional cleaning equipment, etc. Here, we also consider a heterogeneous fleet of vehicles, comprising units of different capacities, sizes and fixed/variable costs. Despite the fact that heterogeneous fleets are quite ubiquitous in real-life scenarios, there is a lack of publications in the literature discussing the 2L-HFVRP. In particular, to the best of our knowledge no previous work discusses the non-oriented 2L-HFVRP, in which items are allowed to be rotated during the truck-loading process. After describing and motivating the problem, a literature review on related work is performed. Then, a multi-start algorithm based on biased randomization of routing and packing heuristics is proposed. A set of computational experiments contribute to illustrate the scope of our approach, as well as to show its efficiency.     

An exact algorithm for the knapsack sharing problem with common items

We are concerned with a variation of the knapsack problem as well as of the knapsack sharing problem, where we are given a set of n items and a knapsack of a fixed capacity. As usual, each item is associated with its profit and weight, and the problem is to determine the subset of items to be packed into the knapsack. However, in the problem there are s players and the items are divided into s + 1 disjoint groups, N_k (k = 0, 1, … , s). The player k is concerned only with the items in N₀ ∪ N_k, where N₀ is the set of ‘common’ items, while N_k represents the set of his own items. The problem is to maximize the minimum of the profits of all the players. An algorithm is developed to solve this problem to optimality, and through a series of computational experiments, we evaluate the performance of the developed algorithm.     

An algorithm for the two-dimensional assortment problem

In this paper we consider the two-dimensional assortment problem. This is the problem of choosing from a set of stock rectangles a subset which can be used for cutting into a number of smaller rectangular pieces. Constraints are imposed upon the number of such pieces which result from the cutting.A heuristic algorithm for the guillotine cutting version of the problem is developed based on a greedy procedure for generating two-dimensional cutting patterns, a linear program for choosing the cutting patterns to use and an interchange procedure to decide the best subset of stock rectangles to cut.Computational results are presented for a number of test problems which indicate that the algorithm developed produces good quality results both for assortment problems and for two-dimensional cutting problems.     

Heuristics and memetic algorithm for the two-dimensional loading capacitated vehicle routing problem with time windows

This work deals with a new combinatorial optimization problem, the two-dimensional loading capacitated vehicle routing problem with time windows which is a realistic extension of the well known vehicle routing problem. The studied problem consists in determining vehicle trips to deliver rectangular objects to a set of customers with known time windows, using a homogeneous fleet of vehicles, while ensuring a feasible loading of each vehicle used. Since it includes NP-hard routing and packing sub-problems, six heuristics are firstly designed to quickly compute good solutions for realistic instances. They are obtained by combining algorithms for the vehicle routing problem with time windows with heuristics for packing rectangles. Then, a Memetic algorithm is developed to improve the heuristic solutions. The quality and the efficiency of the proposed heuristics and metaheuristic are evaluated by adding time windows to a set of 144 instances with 15–255 customers and 15–786 items, designed by Iori et al. (Transport Sci 41:253–264, 2007) for the case without time windows.     

An inexact algorithm for the sequential ordering problem

Given the directed G = (N, A) and the penalty matrix C, the Sequential Ordering Problem (hereafter, SOP) consists of finding the permutation of the nodes from the set N, such that it minimizes a C-based function and does not violate the precedence relationships given by the set A. Strong sufficient conditions for the infeasibility of a SOP's instance are embedded in a procedure for the SOP's pre-processing. Note that it is one of the key steps in any algorithm that attempts to solve SOP. By dropping the constraints related to the precedence relationships, SOP can be converted in the classical Asymmetric Traveling Salesman Problem (hereafter, ASTP). The algorithm obtains (hopefully) satisfactory solutions by modifying the optimal solution to the related Assignment Problem (hereafter, AP) if it is not a Feasible Sequential Ordering (hereafter, FSO). The new solution ‘patches’ the subtours (if any) giving preference to the patches with zero reduced cost in the linking arcs. The AP-based lower bound on the optimal solution to ATSP is tightened by using some of the procedures given in [1]. In any case, a local search for improving the initial FSO is performed; it uses 3- and 4-changed based procedures. Computational results on a broad set of cases are reported.     

A meta-heuristic algorithm for heterogeneous fleet vehicle routing problems with two-dimensional loading constraints

The two-dimensional loading heterogeneous fleet vehicle routing problem (2L-HFVRP) is a variant of the classical vehicle routing problem in which customers are served by a heterogeneous fleet of vehicles. These vehicles have different capacities, fixed and variable operating costs, length and width in dimension, and two-dimensional loading constraints. The objective of this problem is to minimize transportation cost of designed routes, according to which vehicles are used, to satisfy the customer demand. In this study, we proposed a simulated annealing with heuristic local search (SA_HLS) to solve the problem and the search was then extended with a collection of packing heuristics to solve the loading constraints in 2L-HFVRP. To speed up the search process, a data structure was used to record the information related to loading feasibility. The effectiveness of SA_HLS was tested on benchmark instances derived from the two-dimensional loading vehicle routing problem (2L-CVRP). In addition, the performance of SA_HLS was also compared with three other 2L-CVRP models and four HFVRP methods found in the literature.     

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.     

A fast algorithm for two-dimensional pallet loading problems of large size

This paper concerns the two-dimensional pallet loading problem (PLP), which requires the determination of the orthogonal layout that loads the maximum number of identical small rectangles (i.e., boxes or products) onto a large rectangle (i.e., pallet or container) without overlapping. Although many algorithms have been developed for this problem, the large amount of time required to find efficient layouts for a large PLP presents great practical difficulties. In this paper, we develop a heuristic that finds efficient layouts with low complexity. We also propose a new algorithm, using the heuristic as a sub-algorithm, which rapidly finds complicated solutions having a 5-block structure. Finally, computational results show that the new algorithm can be successfully applied to large PLPs with sizes exceeding 6800 boxes.     

An exact depth-first algorithm for the pallet loading problem

This paper proposes a fast exact algorithm to solve the Pallet Loading Problem (PLP) using depth-first strategy. A new concept called Maximal Breadth Filling Sequence (MBFS) is introduced to bring down the size of the search tree. The algorithm makes use of two pruning rules — lower-bound pruning and state-dominance pruning. Although depth-first search, by itself, requires very little memory, the dominance pruning rule makes effective utilization of the available memory. For large problems, more the memory available, more effective is the dominance pruning. The algorithm has been tested on standard problem sets. It has been found to be quite fast in outputting optimal solutions. Empirical findings are given in detail.     

An approximate algorithm for optimal logistics of heavy and variable size items

The objective of this article is to show the improvement reached by a ceramic logistics operator using an approximate algorithm for cargo of logistics of many different products with different weights and volumes. This algorithm, which has been used successfully for efficient assignment in logistics industry, where many different products in small but heavy items have to be distributed, can improve road transport efficiency for clients’ orders in the minimum time and with the least possible costs. The paper describes how it could increase efficiency in logistics in a ceramic industry (from the initiation of activities and over several days to the end of the job cycle) and similar heavy and small items production when time and costs play the role in function criterion. The algorithm is based on several priority rules. Real life application of the algorithm developed here has been running on a time horizon of more than one week. Though the results of the first steps (initial solution) of algorithm are not as good as the results of already known algorithms for transportation assignments, the algorithm is improving the value of criterion function rapidly, during further iterations dealing with the sequences of daily assignments, which is a major improvement in applications for such types of algorithms, known up until now. The algorithm was a well accepted development and seen as very beneficial to the ceramics industry.     

An efficient sequential quadratic programming algorithm for nonlinear programming

In this paper, a new feasible sequential quadratic programming (FSQP) algorithm is proposed to solve the nonlinear programming, where a feasible descent direction is obtained by solving only one QP subproblem. In order to avoid Maratos effect, a high-order revised direction is computed by solving a linear system with involving some “active” constraints. The theoretical analysis shows that global and superlinear convergence can be deduced.     

THE FFD ALGORITHM FOR THE BIN PACKING PROBLEM WITH KERNEL ITEMS

The FFD algorithm is one of the most famous algorithms for the classical bin packing problem. In this paper,some versions of the FFD algorithm are considered in several bin packing problems. Especially,two of them applied to the bin packing problem with kernel items are analyzed. Tight worst-case performance ratios are obtained.     

An efficient numerical algorithm for simulating a two-dimensional glow discharge

A numerical algorithm of the second approximation order with respect to the space variables for simulating a two-dimensional elevated pressure glow discharge in the framework of the drift-diffusion approximation is presented. A specific feature of this algorithm is the use of the Laplace resolving operator for the solution of the system of grid equations. This makes it possible to ensure the convergence of the solution in strong grid norms. Mathematical aspects of the statement of the differential-difference and finite difference problems (solvability, nonnegativity, approximation, stability, and convergence) are discussed, and bounds on the norms of the corresponding differential and difference operators that are required for constructing an optimal iterative process are obtained.     

An evolutionary algorithm with the partial sequential forward floating search mutation for large-scale feature selection problems

Several meta-heuristic algorithms, such as evolutionary algorithms (EAs) and genetic algorithms (GAs), have been developed for solving feature selection problems due to their efficiency for searching feature subset spaces in feature selection problems. Recently, hybrid GAs have been proposed to improve the performance of conventional GAs by embedding a local search operation, or sequential forward floating search mutation, into the GA. Existing hybrid algorithms may damage individuals’ genetic information obtained from genetic operations during the local improvement procedure because of a sequential process of the mutation operation and the local improvement operation. Another issue with a local search operation used in the existing hybrid algorithms is its inappropriateness for large-scale problems. Therefore, we propose a novel approach for solving large-sized feature selection problems, namely, an EA with a partial sequential forward floating search mutation (EAwPS). The proposed approach integrates a local search technique, that is, the partial sequential forward floating search mutation into an EA method. Two algorithms, EAwPS-binary representation (EAwPS-BR) for medium-sized problems and EAwPS-integer representation (EAwPS-IR) for large-sized problems, have been developed. The adaptation of a local improvement method into the EA speeds up the search and directs the search into promising solution areas. We compare the performance of the proposed algorithms with other popular meta-heuristic algorithms using the medium- and large-sized data sets. Experimental results demonstrate that the proposed EAwPS extracts better features within reasonable computational times.     

A randomized algorithm for the min-max selecting items problem with uncertain weights

This paper deals with the min-max version of the problem of selecting p items of the minimum total weight out of a set of n items, where the item weights are uncertain. The discrete scenario representation of uncertainty is considered. The computational complexity of the problem is explored. A randomized algorithm for the problem is then proposed, which returns an O(ln K)-approximate solution with a high probability, where K is the number of scenarios. This is the first approximation algorithm with better than K worst case ratio for the class of min-max combinatorial optimization problems with unbounded scenario set.     

A Guided Tabu Search for the Vehicle Routing Problem with two-dimensional loading constraints

We present a metaheuristic methodology for the Capacitated Vehicle Routing Problem with two-dimensional loading constraints (2L-CVRP). 2L-CVRP is a generalisation of the Capacitated Vehicle Routing Problem, in which customer demand is formed by a set of two-dimensional, rectangular, weighted items. The purpose of this problem is to produce the minimum cost routes, starting and terminating at a central depot, to satisfy the customer demand. Furthermore, the transported items must be feasibly packed into the loading surfaces of the vehicles. We propose a metaheuristic algorithm which incorporates the rationale of Tabu Search and Guided Local Search. The loading aspects of the problem are tackled using a collection of packing heuristics. To accelerate the search process, we reduce the neighbourhoods explored, and employ a memory structure to record the loading feasibility information. Extensive experiments were conducted to calibrate the algorithmic parameters. The effectiveness of the proposed metaheuristic algorithm was tested on benchmark instances and led to several new best solutions.     

An improved sequential quadratic programming algorithm for solving general nonlinear programming problems

In this paper, a class of general nonlinear programming problems with inequality and equality constraints is discussed. Firstly, the original problem is transformed into an associated simpler equivalent problem with only inequality constraints. Then, inspired by the ideals of the sequential quadratic programming (SQP) method and the method of system of linear equations (SLE), a new type of SQP algorithm for solving the original problem is proposed. At each iteration, the search direction is generated by the combination of two directions, which are obtained by solving an always feasible quadratic programming (QP) subproblem and a SLE, respectively. Moreover, in order to overcome the Maratos effect, the higher-order correction direction is obtained by solving another SLE. The two SLEs have the same coefficient matrices, and we only need to solve the one of them after a finite number of iterations. By a new line search technique, the proposed algorithm possesses global and superlinear convergence under some suitable assumptions without the strict complementarity. Finally, some comparative numerical results are reported to show that the proposed algorithm is effective and promising.     

Anchoring conditions for the sequential gradient-restoration algorithm and the modified quasilinearization algorithm for optimal control problems with bounded state

In Refs. 1–2, the sequential gradient-restoration algorithm and the modified quasilinearization algorithm were developed for optimal control problems with bounded state. These algorithms have a basic property: for a subarc lying on the state boundary, the state boundary equations are satisfied at every iteration, if they are satisfied at the beginning of the computational process. Thus, the subarc remains anchored on the state boundary. In this paper, the anchoring conditions employed in Refs. 1–2 are derived.This research was supported by the Office of Scientific Research, Office of Aerospace Research, United States Air Force, Grant No. AF-AFOSR-72-2185.