Hossein Karimi

Effective heuristics and meta-heuristics for the quadratic assignment problem with tuned parameters and analytical comparisons

Quadratic assignment problem (QAP) is a well-known problem in the facility location and layout. It belongs to the NP-complete class. There are many heuristic and meta-heuristic methods, which are presented for QAP in the literature. In this paper, we applied 2-opt, greedy 2-opt, 3-opt, greedy 3-opt, and VNZ as heuristic methods and tabu search (TS), simulated annealing, and particle swarm optimization as meta-heuristic methods for the QAP. This research is dedicated to compare the relative percentage deviation of these solution qualities from the best known solution which is introduced in QAPLIB. Furthermore, a tuning method is applied for meta-heuristic parameters. Results indicate that TS is the best in 31%of QAPs, and the IFLS method, which is in the literature, is the best in 58 % of QAPs; these two methods are the same in 11 % of test problems. Also, TS has a better computational time among heuristic and meta-heuristic methods.

An analytical comparison to heuristic and meta-heuristic solution methods for Quadratic Assignment Problem

Quadratic Assignment Problem (QAP) is a well known problem in the facility location and layout. It belongs to NP-Complete class, so that the exact solution methods consume much computational time. There are some heuristic and meta-heuristic methods presented for this problem in the literature. In this paper we applied 3-Opt, greedy 3-Opt, 3-Opt, greedy 3-Opt and VNZ as the heuristic methods and TS (Tabu Search), SA (Simulated Annealing) and PSO (Particle Swarm Optimization) as the meta-heuristic methods for the QAP. This research dedicated to compare their gap with optimum solution for the selected data set from the QAPLIB. Results show that 3-Opt as a heuristic method and TS which belongs to meta-heuristic methods have a better solution in less computational time. Finally some more analysis has been illustrated in the paper.

Capacity selection for hubs and hub links in hub location problems

Hub location problems occur mainly in transportation and telecommunications networks. A hub is a transfer point at which either traffic from several origins is added up and forwarded to another hub, or disaggregated into several streams that are forwarded to their destinations. The hub location problem is concerned with locating hub facilities and allocating demand nodes to hubs in order to route the traffic between origin–destination pairs. In this research, a hub network with capacity constraints on the hubs and hub links (i.e. links which connect hubs to each other) is considered. These capacities are multi-level capacities, so hubs and hub links should take a capacity among a set of capacities. In real-world examples, such as designing a transportation network, the costs of establishing hubs and hub links are momentous in the decision making process. Consequently, as the capacity is used more, the costs of using hubs and hub links increase. In this research, a formulation is presented for the proposed model and its computational results are compared with single-level capacity problems.

Pickup and delivery supply chain network with semi soft time windows: metaheuristic approach

Abstract This paper discusses a variant of supply chain networks including simultaneous pickup and delivery, and semi soft time windows. The emphasis of this research is on the soft time windows aspect of the problem. Semi soft time windows consider penalties on late arrival. However, waiting on early arrival is allowed without cost. The objective of the problem is simultaneously to locate the depots and to design the vehicle routes at a minimal cost, without violating pickup, delivery, or time windows constraints of the customers. We represent a mixed integer linear programming formulation for the problem. The solution to this problem is obtained through a CPLEX® solver, a genetic algorithm, and a simulated annealing algorithm. Computational results are obtained on a set of randomly generated instances, and they indicate the efficiency of the proposed algorithms in terms of solution time and quality.

The capacitated hub covering location-routing problem for simultaneous pickup and delivery systems

Abstract In this study, a specific type of hub network topology, called hub location-routing, is presented in which the routes between the nodes assigned to a hub form a tour in this topology. The model minimizes the total cost of hub location and vehicle routing, subject to predefined travel time, hub capacity, vehicle capacity, and simultaneous pickups and deliveries. A polynomial-size mixed integer programming formulation is introduced for the single allocation type of the problems. In this paper, a set of valid inequalities is proposed for the formulation. In addition, a tabu-search based heuristic is suggested which determines the hub location and vehicle routes simultaneously. Series of computational tests are then executed to evaluate the performance of valid inequalities and tabu-search based heuristic. The results show that using all valid inequalities improves the solution time of the pure proposed model. Meanwhile, the proposed heuristic works efficiently in finding good-quality solutions for the proposed hub location-routing model.

The multi-route location-routing problem and zone price decision-making using a tabu and variable neighborhood search algorithm

Abstract Zone pricing is one of the most important pricing policies that are prevalently used by very companies. This paper considers integrating zone pricing and location-routing problems for profit maximization. Hence, a node-based model is proposed as mixed integer nonlinear programming to solve the problem. In order to handle the nonlinearity elements and change the model to a mixed integer linear programming, a piecewise linearization method is employed to approximate the problem. Moreover, we introduce a new heuristic variable neighborhood search (VNS) and tabu search algorithm for large-scale problems. The proposed algorithms consist of four intra-tour and seven inter-tour operators in its local search. Finally, some test instances with 20, 200, 400, and 600 nodes are solved and the results of them confirm the efficiency of the proposed algorithms and comparative superiority of the VNS algorithm over the tabu search algorithm.

Airline hub-and-spoke network design based on airport capacity envelope curve: A practical view

Abstract In this study, a new practical airline hub location model with hub capacity decision is presented based on airport capacity envelope functions. In the literature on hub location with the hub capacity decision, the capacity of hub nodes has been expressed as a constant. In real-life air network, airport capacity is expressed as some functions of arrival and departure of flights according to the functions of airport capacity envelope curve. Therefore, to make the mathematical models of Hub Location Problem (HLP) compatible with the reality, the hub capacity was considered as a function of arrival and departure of flights based on the hub capacity envelope curve. Also, this study is the first to consider the possibility of direct trip between two non-hub nodes together with hub capacity decision problem in a multiple allocation model. The presented model is solved exactly for some small-scale versions of Iranian airline network and compared to a hub capacity model with a constant capacity decision. The capacity of Iranian airports is calculated based on flight statistics reported in 2015. For each hub airport, three scenarios are considered to gain 30%, 60% and 90% airport capacity from the perspective of the airline. Also, to solve the proposed model at a large scale, an Adaptive Large Neighborhood Search (ALNS) with some new removal and insertion moves is proposed and applied to a large-scale version of Iranian airline network. The results showed that disregard for the airport capacity envelope curve can increase the airline operational costs and lead to the design of a non-optimal Hub and Spoke (HS) network. Accordingly, some recommendations based on the results are presented for this real network.

Non-permutation flow shop scheduling problem with preemption-dependent processing time

Abstract In this paper, the non-permutation flow shop scheduling problem with preemption-dependent processing times is considered. A mixed integer programming formulation is proposed to tackle the problem. The optimization objective considered is the minimization of the total tardiness. The model is tested against random instances. The results allow us to identify the effect of some parameters such as coefficient of preemption-dependent processing time, number of preemptions and the selected machine for preemption on the total tardiness.