Sharif Digital Repository

In recent years, a number of new heuristic search methods have been developed in the field of automated planning. Enforced hill climbing (EHC) is one such method which has been frequently used in a number of AI planning systems. Despite certain weaknesses, such as getting trapped in dead-ends in some domains, this method is more competitive than several other methods in many planning domains. In order to enhance the efficiency of ordinary enforced hill climbing, a new form of enforced hill climbing, called guided enforced hill climbing, is introduced in this paper. An adaptive branch ordering function is the main feature that guided enforced hill climbing has added to EHC. Guided enforced... 

The cutting stock problem is an important problem in the field of combinational optimization. In classic versions of this problem, the aim is to find a solution for cutting some equal-width mother rolls into smaller parts with given width so that the amount of trim-loss become as small as possible. There are plenty of works which addressed the classic cutting stock problem. However in real world applications there are usually some constraints which make the problem different form classical version and make it harder to solve. In this paper we investigate a special cutting stock problem in film industry and show that it can not be solved by using previous methods. We propose a new hybrid... 

The three-dimensional multiple bin-size bin packing problem (3D-MBSBPP) has many practical applications in the logistic problems such as warehouse management, transportation planning and container loading. An efficient solution to the problem can have significant effects on reducing the transportation costs, improving the status and increasing the productivity and profitability of the companies. The mathematical models proposed for this problem are few in number and can, therefore, be expanded and improved much further. In this paper, based on the needs of a spare-part company in Iran, we developed a mathematical model for 3D-MBSBPP. This model takes into account the rotation of the boxes... 

We study the well-known problem of approximating a polygonal path P by a coarse one, whose vertices are a subset of the vertices of P. In this problem, for a given error, the goal is to find a path with the minimum number of vertices while preserving the homotopy in presence of a given set of extra points in the plane. We present a heuristic method for homotopy-preserving simplification under any desired measure for general paths. Our algorithm for finding homotopic shortcuts runs in O( mlog(n + m) + nlogn log(nm) + k) time, where k is the number of homotopic shortcuts. Using this method, we obtain an O(n 2 + mlog(n + m) + nlogn log(nm)) time algorithm for simplification under the Hausdorff... 

Enforced hill climbing (EHC), a heuristicaa search method, has been frequently used in a number of AI planning systems. This paper presents a new form of EHC, guided enforced hill climbing (GEHC), to enhance EHC efficiency. Main feature in GEHC is an adaptive ordering function. GEHC has shown a significant improvement in EHC efficiency, especially when applied to larger problems  

In this paper we claim that local optimization can produce proper start point for genetic search. We completely test this claim on partitioning problem and on the performance of genetic search in a real problem that is finding aggregation tree in the sensor networks. The presented method (named Tendency algorithm) increases the performance of heuristic searches, and can be used in parallel with other tuning methods. The paper justifies the logic behind tendency algorithm by measuring the "entropy" of solution (in regard to optimal solution), and by numerous empirical tests. © 2008 IEEE  

The major application of stochastic intelligent methods in optimisation, control and management of complex systems is transparent. Many researchers try to develop collective intelligent techniques and hybrid meta-heuristic models for improving the reliability of such optimisation algorithms. In this paper, a new optimisation method that is the simulation of 'the great salmon run' (TGSR) is developed. This simulation provides a powerful tool for optimising complex multi-dimensional and multi-modal problems. For demonstrating the high robustness and acceptable quality of TGSR, it is compared with most of the well-known proposed optimisation techniques such as parallel migrating genetic... 

Transportation network design problem (TNDP) aims to choose from among a set of alternatives (e.g., set of new arcs) which minimizes an objective (e.g., total travel time), while keeping consumption of resources (e.g., budget) within their limits. TNDP is formulated as a bilevel programming problem, which is difficult to solve on account of its combinatorial nature. Following a recent, heuristic by ant colony optimization (ACO), a hybridized ACO (HACO) has been devised and tested on the network of Sioux Falls, showing that the hybrid is more effective to solve the problem. In this paper, employing the heuristic of particle swarm optimization (PSO), an algorithm is designed to solve the TNDP.... 

Multi-Facility Weber Problem (MFWP), also known as continuous location–allocation problem, entails determining the locations of a predefined number of facilities in a planar space and their related customer allocations. In this paper, we focus on a new variant of the problem known as Single-Source Capacitated MFWP (SSCMFWP). To tackle the problem efficiently and effectively, an iterative two-phase heuristic algorithm is put forward. At the phase I, we aim to determine proper locations for facilities, and during the phase II, assignment of customers to these facilities is pursued. As an alternative solution method, a simulated annealing (SA) algorithm is also proposed for carrying out the... 

Time, cost and quality are among the major objectives of any project. In recent years, the demands of project stakeholders regarding reductions in the total cost and time of a project along with achieving the desirable quality of the project have risen significantly. This leads researchers to developing models that incorporate the quality factor to previously existing time-cost trade-off models. This study presents a model for the discrete time-cost-quality trade-off problem. In this model there are a number of execution modes to select for each activity, and the best execution mode (t, c, q) of the activities should be determined to optimize total cost and time objectives, subjected to... 

This paper proposes a new approach for solving generation scheduling and ramp rate constrained unit commitment with considering spinning reserve probability. In order to simulate the probability of reserve in the formulation, the estimated probability that spinning reserve is called and generated has been considered. A hybrid heuristic method between genetic algorithm and lambdad iteration method is applied to solve the problem. The proposed approach is applied to two different cases such as a 10-unit base problem without considering probability of reserve and 10-unit problem with considering probability of reserve. The results are compared with other approaches results to exhibit the... 

Graph sum colouring problem is a special class of graph vertex colouring problem. Because of its various applications in practical areas, especially in scheduling, many researchers have been focused on it during the past decade. In recent years, several heuristic and meta-heuristic algorithms have been developed to solve sum colouring problem. In this research, a hybrid algorithm based on mini-max ant system and simulated annealing is applied for the problem. This algorithm is tested on benchmark random graphs and compared to the previous algorithms. Results show that in many cases, the best known results can be obtained or improved by the proposed algorithm. Copyright © 2019 Inderscience... 

Graph sum colouring problem is a special class of graph vertex colouring problem. Because of its various applications in practical areas, especially in scheduling, many researchers have been focused on it during the past decade. In recent years, several heuristic and meta-heuristic algorithms have been developed to solve sum colouring problem. In this research, a hybrid algorithm based on mini-max ant system and simulated annealing is applied for the problem. This algorithm is tested on benchmark random graphs and compared to the previous algorithms. Results show that in many cases, the best known results can be obtained or improved by the proposed algorithm. Copyright © 2019 Inderscience... 

In this paper, we introduce DiFF, a novel extension of the Fast Forward (FF) planning system. FF is a domain independent planner that employs a forward heuristic search. Its search strategy is an enforced form of hill climbing. In order to move to a more promising state, FF evaluates successor states without any particular order. In this paper, we introduce a new form of the enforced hill climbing, which we call directed enforced hill climbing, to enhance the efficiency of Fast Forward planning. This strategy evaluates successor states in the order recommended by an adaptive heuristic function. Our experimental results in several planning domains show a significant improvement in the... 

Design and construction of multi-agent systems is a challenging but an intriguing problem. It is because of the intrinsic distribution of the intelligent components. In such environments the interaction and communication between the constituent parts extends the complexity since appropriate coordination methods need to be designated and employed. In this paper a successful experiment in designing and implementing such an environment is presented1. The test bed for this research is the rescue simulation environment. The architecture of the implemented heterogeneous agents takes advantage of various algorithms. These algorithms make the agents act intelligently by themselves albeit they happen... 

This paper deals with the problem of scheduling bus maintenance activities. The scheduling of maintenance activities is an important component in bus transit operations planning process. The other components include network route design, setting timetables, scheduling vehicles, and assignment of drivers. This paper presents a mathematical programming approach to the problem. This approach takes as input a given daily operating schedule for all buses assigned to a depot along with available maintenance resources. It, then, attempts to design daily inspection and maintenance schedules for the buses that are due for inspection so as to minimize the interruptions in the daily bus operating... 

Ant Colony Optimization (ACO) is a well-known metaheuristic in which a colony of artificial ants cooperate in exploring good solutions to a combinatorial optimization problem. In this paper, an ACO algorithm is presented for the graph coloring problem. This ACO algorithm conforms to Max-Min Ant System structure and exploits a local search heuristic to improve its performance. Experimental results on DIMACS test instances show improvements over existing ACO algorithms of the graph coloring problem  

This paper presents a new solution framework to locate the workstations in the tandem automated guided vehicle (AGV) systems. So far, the research has focused on minimizing the total flow or minimizing the total AGV transitions in each zone. In this paper, we focus on minimizing total cumulative flow among workstations. This objective allocates workstations to an AGV route such that total waiting time of workstations to be supplied by the AGV is minimized. We develop a property which simplifies the available mathematical formulation of the problem. We also develop a heuristic algorithm for the problem. Computational results show that our heuristic could yield very high-quality solutions and... 

Photovoltaic (PV) panel accurate modeling is essential for operational studies and generation estimation under actual conditions and power electronic control device designs in grid connected or isolated power systems. Inaccuracy in modeling parameters such as photon's current, diode's saturation current, series and parallel resistances and diodes ideality coefficients causes errors in modeling and simulating photovoltaic panel's operation. In this paper, photovoltaic panel's parameters are estimated according to characteristic curves obtained from experiments. Heuristic optimization methods are utilized to calculate parameters for both singlediode and double-diode models. Results indicate...