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The quadratic assignment problem is a combinatorial problem of deciding the placement of facilities in specified locations in such a way as to minimize a nonconvex objective function expressed in terms of flow between facilities, and distance between location. Due to the non-convexity nature of the problem, therefore to get a ‘good’ starting point is necessary in order to obtain a better optimal solution. In this paper we propose a combined strategy (random point strategy to get initial starting point and then use forward exchange strategy and backward exchange strategy to get ‘optimal’ solution). As a computational experience we’ve solved the problem of Esc 16b, Esc 16c and Esc 16h from QAPLIB. Finally, we present a comparative study between Combined Strategy and Data –Guided Lexisearch Algorithm. The computational study shows the effectiveness of our proposed combined strategy.The quadratic assignment problem is a combinatorial problem of deciding the placement of facilities in specified locations in such a way as to minimize a nonconvex objective function expressed in terms of flow between facilities, and distance between location. Due to the non-convexity nature of the problem, therefore to get a ‘good’ starting point is necessary in order to obtain a better optimal solution. In this paper we propose a combined strategy (random point strategy to get initial starting point and then use forward exchange strategy and backward exchange strategy to get ‘optimal’ solution). As a computational experience we’ve solved the problem of Esc 16b, Esc 16c and Esc 16h from QAPLIB. Finally, we present a comparative study between Combined Strategy and Data –Guided Lexisearch Algorithm. The computational study shows the effectiveness of our proposed combined strategy.

Since the formal introduction of metaheuristics, a huge number Quadratic Assignment Problem (QAP) instances have been introduced. Those instances however are loosely-structured, and therefore made it difficult to perform any systematic analysis. The QAPLIB for example, is a library that contains a huge number of QAP benchmark instances that consists of instances with different size and structure, but with a very limited availability for every instance type. This prevents researchers from performing organized study on those instances, such as parameter tuning and testing. In this paper, we will discuss several hard instances that have been introduced over the years, and algorithms that have been used for solving them.

The problem related to project assignment problem is one of popular practical problem that appear nowadays. The challenge of solving the problem raise whenever the complexity related to preferences, the existence of real-world constraints and problem size increased. This study focuses on solving a chambering student-case assignment problem by using a simulated annealing algorithm where this problem is classified under project assignment problem. The project assignment problem is considered as hard combinatorial optimization problem and solving it using a metaheuristic approach is an advantage because it could return a good solution in a reasonable time. The problem of assigning chambering students to cases has never been addressed in the literature before. For the proposed problem, it is essential for law graduates to peruse in chambers before they are qualified to become legal counselor. Thus, assigning the chambering students to cases is a critically needed especially when involving many preferences. He...

Quadratic Assignment Problem (QAP), like Vehicle Routing Problem, is one of those optimization problems that interests many researchers in the last decades. The Quay Management Problem is a specific problem which could be presented as a QAP which involves a double assignment of customers and products toward loading positions using lifting trucks. This study focuses on the routing problem while delivering the customers' demands. In this problem, lifting trucks will route around the storage sections to collect the products then deliver to the customers who are assigned to specific loading positions. The objective of minimizing the residence time for each customer is sought. This paper presents the problem and the proposed model.

Quadratic Assignment Problems (QAP) is classified as the NP hard problem. It has been used to model a lot of problem in several areas such as operational research, combinatorial data analysis and also parallel and distributed computing, optimization problem such as graph portioning and Travel Salesman Problem (TSP). In the literature, researcher use exact algorithm, heuristics algorithm and metaheuristic approaches to solve QAP problem. QAP is largely applied in facility layout problem (FLP). In this paper we used QAP to model university facility layout problem. There are 8 facilities that need to be assigned to 8 locations. Hence we have modeled a QAP problem with n ≤ 10 and developed an Ant Colony Optimization (ACO) algorithm to solve the university facility layout problem. The objective is to assign n facilities to n locations such that the minimum product of flows and distances is obtained. Flow is the movement from one to another facility, whereas distance is the distance between one locations of a facility to other facilities locations. The objective of the QAP is to obtain minimum total walking (flow) of lecturers from one destination to another (distance).

An approach of population dynamics and clustering for permutative problems is presented in this paper. Diversity indicators are created from solution ordering and its mapping is shown as an advantage for population control in metaheuristics. Self Organising Migrating Algorithm (SOMA) is modified using this approach and vetted with the Quadratic Assignment Problem (QAP). Extensive experimentation is conducted on benchmark problems in this area.