General Quadratic Assignment Problem Research Articles

An efficient adaptive large neighborhood search algorithm based on heuristics and reformulations for the generalized quadratic assignment problem

Operations Research (OR) analytics play a crucial role in optimizing decision-making processes for real-world problems. The assignment problem, with applications in supply chains, healthcare logistics, and production scheduling, represents a prominent optimization challenge. This paper focuses on addressing the Generalized Quadratic Assignment Problem (GQAP), a well-known NP-hard combinatorial optimization problem. To tackle the GQAP, we propose an OR analytical approach that incorporates efficient relaxations, reformulations, heuristics, and a metaheuristic algorithm. Initially, we employ the Reformulation Linearization Technique (RLT) to generate various linear relaxation models, carefully selecting the most efficient ones. Building upon this foundation, we introduce a robust reformulation based on Benders Decomposition (BD), which serves as the basis for an iterative optimization algorithm applied to the GQAP. Furthermore, we develop a constructive heuristic algorithm to identify near-optimal solutions, followed by an enhancement utilizing an Adaptive Large Neighborhood Search (ALNS) metaheuristic algorithm. This ALNS algorithm is enhanced through the integration of a tabu list derived from Tabu Search (TS) and a decision rule inspired by Simulated Annealing (SA). To validate our approach and evaluate its performance, we conduct a comparative analysis against state-of-the-art algorithms documented in the literature. This comparison confirms the significant improvements achieved in terms of solution quality and computational efficiency through our proposed methodology. These advancements contribute to the state of the art in solving the GQAP and hold the potential to enhance decision-making processes in a wide range of domains. Our methodology demonstrates remarkable improvements in solution quality and computational efficiency when compared to existing approaches, as evidenced by the comparative results with state-of-the-art algorithms. The potential implications of our research extend to optimizing decision-making processes in diverse fields, rendering it highly relevant and impactful.

Hybrid algorithms for placement of virtual machines across geo-separated data centers

Cloud computing has emerged as a new paradigm for hosting and supplying services over the Internet. This technology has brought many benefits, such as eliminating the need for maintaining expensive computing hardware. With an increasing demand for cloud computing, providing performance guarantees for applications that run over cloud become important. Applications can be abstracted into a set of virtual machines with certain guarantees depicting the quality of service of the application. In this paper, we consider the placement of these virtual machines across multiple data centers (VMPlacement), meeting the quality of service requirements while minimizing the bandwidth cost of the data centers. This problem is a generalization of the NP-hard generalized quadratic assignment problem (GQAP). In this paper, we present a greedy randomized adaptive search procedure and a biased random-key genetic algorithm, both hybridized with a path-relinking strategy and a local search based on variable neighborhood descent for solving this problem. The hybrid heuristics are also tested on instances of the GQAP. We show that both algorithms are effective in quickly solving small and large instances of VMPlacement problem, especially when the path-relinking is used. For GQAP, the results outperform the previous state-of-the-art algorithms.

A tabu search heuristic for a generalized quadratic assignment problem

The generalized quadratic assignment problem (GQAP) is the task of assigning a set of facilities to a set of locations such that the sum of the assignment and transportation costs is minimized. The facilities may have different space requirements, and the locations may have varying space capacities. Also, multiple facilities may be assigned to each location such that space capacity is not exceeded. In this paper, an application of the GQAP is presented for assigning a set of machines to a set of locations on the plant floor. Construction algorithms and a simple tabu search heuristic are developed for the GQAP. A set of test problems available in the literature was used to evaluate the performances of the TS heuristic using different construction algorithms. The results show that the simple TS heuristic is effective for solving the GQAP.

A new mixed integer programming model for curriculum balancing: Application to a Turkish university

Curriculum design is a highly important activity for the academic institutions. It is discussed in literature as a balancing academic curriculum problem (BACP). The BACP schedules courses to different semesters, while balancing the total workload per period. BACP model involves precedence relations, but the related courses are not necessarily assigned to closest periods.The Relevance Based Curriculum Balancing (RBCB) models presented in this paper are the first implementation of Generalized Quadratic Assignment Problem (GQAP) to curriculum design. RBCB aims at assigning relevant courses to closest possible periods in a way that academic load per semester is balanced and prerequisite conditions are met simultaneously. RBCB models are applied to Fatih University Industrial Engineering Department undergraduate curriculum and comparative performances of different RBCB and BACP models are examined in terms of relevance and balancing efficiency.

On semidefinite programming relaxations of maximum $$k$$ -section

We derive a new semidefinite programming bound for the maximum $$k$$ -section problem. For $$k=2$$ (i.e. for maximum bisection), the new bound is at least as strong as a well-known bound by Poljak and Rendl (SIAM J Optim 5(3):467–487, 1995). For $$k \ge 3$$ the new bound dominates a bound of Karisch and Rendl (Topics in semidefinite and interior-point methods, 1998). The new bound is derived from a recent semidefinite programming bound by De Klerk and Sotirov for the more general quadratic assignment problem, but only requires the solution of a much smaller semidefinite program.

To lay out or not to lay out?

The Quadratic Assignment Problem (QAP) is known as one of the most difficult problems within combinatorial optimization. It is used to model many practical problems including different layout problems. The main topic of this paper is to provide methods to check whether a particular instance of the QAP is a layout problem. An instance is a layout problem if the distances of the objects can be reconstructed on the plane and/or in the 3-dimensional space. A new mixed integer programming model is suggested for the case if the distances of the objects are supposed to be rectilinear distances. If the distances are Euclidean distances then the use of the well-known Multi-Dimensional Scaling (MDS) method of statistics is suggested for reconstruction purposes. The well-known difficulty of QAP makes it a popular and suitable experimental field for many algorithmic ideas including artificial intelligence methods. These types of results are published sometimes as layout problems. The methods of reconstruction can be used to decide whether the topic of a paper is layout or only general QAP. The issue what the OR community should expect from AI based algorithms, is also addressed.

Algorithms for the generalized quadratic assignment problem combining Lagrangean decomposition and the Reformulation-Linearization Technique

In this paper, we propose two exact algorithms for the GQAP (generalized quadratic assignment problem). In this problem, given M facilities and N locations, the facility space requirements, the location available space, the facility installation costs, the flows between facilities, and the distance costs between locations, one must assign each facility to exactly one location so that each location has sufficient space for all facilities assigned to it and the sum of the products of the facility flows by the corresponding distance costs plus the sum of the installation costs is minimized. This problem generalizes the well-known quadratic assignment problem (QAP). Both exact algorithms combine a previously proposed branch-and-bound scheme with a new Lagrangean relaxation procedure over a known RLT (Reformulation-Linearization Technique) formulation. We also apply transformational lower bounding techniques to improve the performance of the new procedure. We report detailed experimental results where 19 out of 21 instances with up to 35 facilities are solved in up to a few days of running time. Six of these instances were open.

GRASP with path-relinking for the generalized quadratic assignment problem

The generalized quadratic assignment problem (GQAP) is a generalization of the NP-hard quadratic assignment problem (QAP) that allows multiple facilities to be assigned to a single location as long as the capacity of the location allows. The GQAP has numerous applications, including facility design, scheduling, and network design. In this paper, we propose several GRASP with path-relinking heuristics for the GQAP using different construction, local search, and path-relinking procedures. We introduce a novel approximate local search scheme, as well as a new variant of path-relinking that deals with infeasibilities. Extensive experiments on a large set of test instances show that the best of the proposed variants is both effective and efficient.

On Semidefinite Programming Relaxations of the Traveling Salesman Problem

We consider a new semidefinite programming (SDP) relaxation of the symmetric traveling salesman problem (TSP), that may be obtained via an SDP relaxation of the more general quadratic assignment problem (QAP). We show that the new relaxation dominates the one in the paper: D. Cvetkovic, M. Cangalovic and V. Kovacevic-Vujcic. Semidefinite Programming Methods for the Symmetric traveling Salesman Problem. In Proceedings of the 7th International IPCO Conference on Integer Programming and Combinatorial Optimization, 1999, 126-136, Springer-Verlag, London, UK. Unlike the bound of Cvetkovic et al., the new SDP bound is not dominated by the Held-Karp linear programming bound, or vice versa.

An algorithm for the generalized quadratic assignment problem

This paper reports on a new algorithm for the Generalized Quadratic Assignment problem (GQAP). The GQAP describes a broad class of quadratic integer programming problems, wherein M pair-wise related entities are assigned to N destinations constrained by the destinations’ ability to accommodate them. This new algorithm is based on a Reformulation Linearization Technique (RLT) dual ascent procedure. Experimental results show that the runtime of this algorithm is as good or better than other known exact solution methods for problems as large as M=20 and N=15.

The service allocation problem at the Gioia Tauro Maritime Terminal

The Service Allocation Problem (SAP) is a tactical problem arising in the yard management of a container transshipment terminal. The objective is the minimization of the container rehandling operations inside the yard. This study of the SAP was undertaken for the Gioia Tauro port which is located in Italy and is the main hub terminal for container traffic in the Mediterranean Sea. The SAP can be formulated as a Generalized Quadratic Assignment Problem (GQAP) with side constraints. Two mixed integer linear programming formulations are presented. The first one exploits characteristics of the yard layout at Gioia Tauro where the berth and the corresponding yard positions extend along a line. The second formulation is an adaptation of a linearization for the GQAP. In both cases only small instances can be solved optimally. An evolutionary heuristic was therefore developed. For small size instances the heuristic always yields optimal solutions. For larger sizes it is always better than a truncated branch-and-bound algorithm applied to the exact formulations.

A Memetic Heuristic for the Generalized Quadratic Assignment Problem

In the generalized quadratic assignment problem (GQAP) we are given n weighted facilities, m capacitated sites, a traffic intensity matrix between facilities, a distance matrix between sites, unit traffic costs, and assignment costs of facilities to sites. The aim is to determine an assignment of facilities to sites so that the sum of assignment and traffic costs is minimized and the total weight of all facilities assigned to the same site does not exceed the site capacity. The GQAP is a generalization of the quadratic assignment problem (QAP) in which n = m and exactly one facility must be assigned to each site. The problem has applications in container yard management and in the assignment of equipment to manufacturing sites. This article describes a memetic heuristic for the GQAP, as well as an integer linear programming formulation that can be solved by CPLEX for small instances. For larger instances, feasible solutions can be obtained by a truncated branch-and-bound procedure. Computational experiments show that on small instances the proposed heuristic always yields an optimal solution; on larger instances it always outperforms the truncated branch-and-bound algorithm.

The quadratic assignment problem in the context of the printed circuit board assembly process

The sequencing of placement and the configuration of the feeder is among the main problems involved in printed circuit board (PCB) assembly optimization. In some machine architectures, the latter problem can be formulated as the well known NP-hard quadratic assignment problem (QAP). In this study, a search is made among those metaheuristics that have recently found widespread application in order to identify a heuristic procedure that performs well with the QAP in the PCB assembly context. To this end, specific algorithms reflecting implementations of Taboo Search, Simulated Annealing and Genetic Algorithm-type metaheuristics were tested and compared using real PCB assembly data. The same set of algorithms was also tested on general QAP problems and it was observed that algorithms which performed successfully in the PCB context could perform poorly in the general situation. In the light of this, it can be concluded that ascertaining the best performing heuristic is complicated by the fact that the performance of a heuristic depends on the context of the problem, which determines the structure and relationships of problem parameters.

A solvable case of the quadratic assignment problem

This short note investigates a restricted version of the quadratic assignment problem (QAP), where one of the coefficient matrices is a Kalmanson matrix, and where the other coefficient matrix is a symmetric circulant matrix that is generated by a decreasing function. This restricted version is called the Kalmanson-circulant QAP. We prove that – in strong contrast to the general QAP – this version can be solved easily. Our result naturally generalizes a well-known theorem of Kalmanson on the travelling salesman problem.

A branch and bound method for solving the bidirectional circular layout problem

In this paper we study a special discrete layout problem, namely the Bidirectional circular layout problem (Bi-CLP) where the ‘facilities’ are arranged around a simple closed-loop path (with no shortcuts) and flows between facilities occur either in the clockwise or in the counterclockwise direction whichever is shorter along the closed-loop path. The Bi-CLP is to assign each facility to one of the predetermined sites such that the total flow times the distance over which the flows occur is minimized. The Bi-CLP is NP-hard and a special case of the quadratic assignment problem (QAP). As described in this paper, numerous existing and potential applications of the Bi-CLP can be found in modern manufacturing systems. We present an exact solution procedure for the Bi-CLP based on a new bounding scheme we used within a ‘traditional’ branching algorithm. Computational results indicate that the Bi-CLP algorithm outperforms the best algorithm available for general QAPs as the problem size increases and / or as the maximum distance between two adjacent sites increases.

Exact and heuristic algorithms for the weighted feedback arc set problem: A special case of the skew‐symmetric quadratic assignment problem

AbstractThis paper presents algorithms for finding exact and approximate solutions for the weighted feedback are set problem, determining a minimum‐cardinality set of ares that breaks all cycles in a directed graph. This is also the problem of finding a group rank ordering given the rank orders for each member of a group. The algorithms have many other applications. Mathematically, the problem is that of finding a permutation matrix P that maximizes the sum of the elements above the principal diagonal of P1 WP where W is a skew‐symmetric matrix of order n. This is a special case of the Koopmans‐Beckmann quadratic assignment problem. Computational experience is reported for a sample of randomly generated problems, including comparisons with results obtained using algorithms three other authors have developed for solving the more general quadratic assignment problem. For our sample, using randomly generated problems, the computational time required for calculating all exact solutions for each problem is approximately T(n) = c2.232n, where c = 9.8764E ‐ 6 and T(20) = 93 s for the Cray X‐MP/48 supercomputer. For our sample, the computational time required for calculating one approximate solution is approximately T(n) = an4,1, where a = 3.0361E ‐ 8 and T(250) = 206 s for the Cray.

Quadratic assignment problems on series-parallel digraphs

Christofides has shown that the quadratic assignment problem (QAP) on isomorphic trees is solvable in polynomial time by dynamic programming. We generalize the approach to minimal series-parallel digraphs and show that this problem is equivalent to the general QAP, thus NP-hard. However the restriction to the subclass of series-parallel digraphs not containing bipartite subgraphs again is solvable in polynomial time. An algorithm for this class is given.

An exact algorithm for the general quadratic assignment problem

We develop an algorithm that is based on the linearization and decomposition of a general Quadratic Assignment Problem of size n into n 2 Linear Assignment Problems of size ( n − 1). The solutions to these subproblems are used to calculate a lower bound for the original problem, and this bound is then used in an exact branch and bound procedure. These subproblems are similar to the ‘minors’ defined by Lawler [16], but permit us to calculate tighter bounds. Computational experience is given for solution to optimality of general quadratic assignment problems is sizes up to n = 10.

Asymptotic Properties of the Quadratic Assignment Problem

For the general quadratic assignment problem as well as for a planar version of this problem, we extend earlier work by Burkard and Fincke to prove that the ratio of the maximal to the minimal solution value converges to 1 almost surely. In fact, any solution value can almost surely be written asymptotically as a simple, explicitly given function of the problem size. Theoretical analysis and computational experiments reveal the convergence to be relatively fast.

Quadratic Assignment Problem Algorithms and the Location of Indivisible Facilities

This paper discusses the problem of assigning locations to indivisible facilities and its relation to the quadratic assignment problem. It also develops two suboptimal algorithms, one dealing with the general quadratic assignment problem and the other with an interesting special case. Qualitative and quantitative comparisons with existing suboptimal algorithms are presented.