Showing 6 results for Location Problem
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Volume 13, Issue 2 (8-2016)
Abstract
In this paper we consider the problem of finding a core of weighted interval trees. A core of an interval graph is a path contains some intervals of graph so that the sum of distances from all intervals to this path is minimized. We show that intervals on core of a tree should be maximal, then a linear time algorithm is presented to find the core of interval trees
S.s. Sajadi, F. Moeen Moghadas, H. Karimi,
Volume 15, Issue 1 (4-2018)
Abstract
The present study evaluates two problems of single allocation hub-covering problem with star structure including two problems of maximal p-hub covering and hub covering by considering the flow transfer costs. The star structure is as there is a central hub with definite location and other hubs are connected directly to the central hub. In the first problem, the goal is selection of p-hub locations and allocation of each customer to at most one hub as total transferred demand between customers is maximum. The purpose of the second problem is minimizing the sum of constant costs of construction of hubs and flow transfer costs between the network nodes as complete covering is created in the network. In two problems, connection of customers to hub centers and connection of hubs to the central hub is as the source to destination distance by considering discount factor to connect hub and central hub is lower or equal to the predefined value. After presenting the math model in two problems, linearization is performed, and then Lagrangian relaxation is applied to find suitable bounds. In addition, in the second problem, valid inequalities equal to two constraints of problem are presented. Finally, the results of solution of linear, non-linear models and using Lagrangian relaxation are evaluated and compared. The evaluation of these results on CAB data set shows that the linear models are better than non-linear models in terms of optimal value of objective function and implementation time. Based on the results, the bounds of Lagrangian relaxation are closer to the optimal solution of problems.
F. Moeen Moghadas, S. Roobin,
Volume 15, Issue 4 (1-2019)
Abstract
P-hub maximal covering location problem is one of the most commonly used location- allocation problems. In this problem, the goal is to determine the best location for the hubs such that the covered demand is maximized by considering the predefined coverage radius. In classical hub problems, if the distance between the origin and destination is less than this radius, coverage is possible; otherwise the demand between the two points will not be covered. In this paper, the problem of p-hub maximal covering is investigated with gradual coverage. First, the concept of gradual coverage and its developed functions is examined and then, a new mathematical model is presented for the problem. Also, in order to calculate the appropriate upper bound for the problem, the Lagrangian relaxation method is used and a heuristic method and a genetic algorithm are used to solve it. Finally, the results of using these methods are compared with the results of GAMS software. This comparison shows that the new model presented for gradual coverage and the new covering parameter have more suitable results in comparison with the coverage model and function in the literature of the subject. Also, applying Lagrangian relaxation will provide a suitable upper bound for the problem. The heuristic method yields better computational results in less time, and the genetic algorithm provides more coverage with less computational time compared to solving examples with the GAMS software, especially for larger test instances.
R. Khanduzi, H. R. Maleki,
Volume 18, Issue 1 (3-2021)
Abstract
This paper seeks to present a new multi-objective defensive location problem (MDLP) with the manufacturing cost and considering different capacities for facilities in a network. In the novel MDLP, to prevent the attacker from reaching strategic sites (cores), the defender locates various facilities in the vertices of the network. In this regard, a mixed integer programming is formulated to find the Stackelberg solutions that defender and attacker are the upper and lower-level decision makers, respectively. In this study, the goal of defensive strategy is to maximize the distance between the cores and the attacker, which aims at reaching the closest possible vertices to the cores. On the other hand, since the problem is an NP-hard problem, to find a satisfactory solution, an interactive fuzzy method based on the cuckoo search algorithm has been introduced. The application of the method is illustrated solving two random instances of MDLP.
M. Omrani, Z. Naji Azimi, A. Pooya, M. Salari,
Volume 19, Issue 1 (3-2022)
Abstract
Using mathematical modeling of the location problem is effective in optimization of the location of electronic charity boxes. This paper investigates the effective criteria of amount of donation in each district and each type of location, following the determination of study districts, identifying types of locations and extracting conventional charity boxes information. The objectives of the model are to maximize the total amount of benefit and amount of donation motivation. In order to extract the donation motivation at any potential location, a number of criteria using the evaluation based on distance from average solution (EDAS) method are used, including district impact factor, location type impact factor, and human traffic factor. The constraints of the problem are defined as building the culture and prevention of accumulation of e-charity boxes at any district and any type of location. Due to the multi-objective model and the randomness of the amount of benefit and the human traffic factor at any potential location, two objectives have been used as a chance-constrained goal programming model, which after changing from a stochastic multi-objective model to a deterministic single-objective one and setting the parameters, 60 locations are extracted as the final locations.
P. Mohammadi Alashti, M. Nojavan, D. Mohammaditabar,
Volume 19, Issue 3 (9-2022)
Abstract
The main objective of reliability redundancy allocation problems (RRAP) is to maximize the overall system reliability through adding additional parallel components or by improving the reliability of the system components. In this paper, second objective of minimizing the overall system failure risk is also considered in the RRAP problem, and this bi-objective problem is developed considering fuzzy constraints including cost, weight and reliability. In order to obtain the failure risk function of the system, is used the Failure Mode and Effect Analysis (FMEA) method and the Risk Priority Numbers (RPN) of components are determined by multiplying coefficients occurrence probability, intensity and detectability of each risk that the functions of risks occurrence probability define with considering reliability and number of parallel components.Regarding the fact that the RRAP problem is NP-hard, the modified Cuckoo search algorithm is applied to produce efficient solutions. The proposed model and its solving method is applied in the unrepairable connections of a flying object as case study and the validity and efficiency of proposed model is verified.
