Sharif Digital Repository

Incremental learning is a learning algorithm that can get new information from new training sets without forgetting the acquired knowledge from the previously used training sets. In this paper, an incremental learning algorithm based on ensemble learning is proposed. Then, an application of the proposed algorithm for spam filtering is discussed. The proposed algorithm called incremental RotBoost, assumes the environment is stationary. It trains new weak classifiers for newly arriving data, which are added to the ensemble of classifiers. To evaluate the performance of the proposed algorithm, several computer experiments are conducted. The results of computer experiments show the ability of... 

An effective facility layout implies that departments with high flow are laid adjacent. However, in the case of a very narrow boundary length between the neighbouring departments, the adjacency would actually be useless. In traditional layout design methods, a score is generally assigned independent of the department’s boundary length. This may result in a layout design with a restricted material flow. This article proposes a new concept of adjacency in which the department pairs are laid adjacent with a wider path. To apply this concept, a shop with unequal rectangular departments is contemplated and a mathematical programming model with the objective of maximizing the sum of the adjacency... 

Traditionally, the model of a resource-constrained project-scheduling problem (RCPSP) contains a single objective function of either minimizing project makespan or maximizing project net present value (NPV). In order to be more realistic, in this paper, two multi-objective meta-heuristic algorithms of multi-population and two-phase sub-population genetic algorithms are proposed to find Pareto front solutions that minimize the project makespan and maximize the project NPV of a RCPSP, simultaneously. Based on standard test problems constructed by the RanGen project generator, a comprehensive computational experiment is performed, where response surface methodology is employed to tune the... 

Multi-agent systems can be seen as an apparatus for testing the performance of real distributed systems. One problem encountered in multi-agent systems with the learning capability is credit assignment. This paper presents two methods for solving this problem. The first method assigns credit to the agents according to the history of the interaction while the second method assigns credit to the agents according to the knowledge of agents, and thus the shares of the agents are extracted from the feedback of the environment. The computer experiments show that critic learning has a positive impact in credit assignment problem  

In this manuscript a new phenomenon of adjacency and closeness rating is proposed for constructing the optimal facility layout design. In the proposed approach, the closeness rating is applied to every pair of neighboring departments in a range of their attached point up to a predefined distance between them. Also, while in the traditional approach the closeness rating is applied for determining either the proximity or farness of two departments, the proposed approach considers both the degree of closeness as the economical purposes, and farness as the safety purposes concurrently. A mathematical model is proposed for constructing the optimal layout design. For evaluating the efficiency and... 

Given a triangulation of a point set on the plane, dilation of any pair of the points is the ratio of their shortest path length to their Euclidean distance. The maximum dilation over all pairs of points is called the dilation of this triangulation. Minimum dilation triangulation problem seeks a triangulation with the least possible dilation of an input point set. For this problem no polynomial time algorithm is known. We present an exact algorithm based on a branch and bound method for finding minimum dilation triangulations. This deterministic algorithm after generating an initial solution, iteratively computes a lower bound for the answer and then applies a branch and bound method to find... 

In this paper, the problem of scheduling commercial messages during the peak of viewing time of a TV channel is formulated as a combinatorial auction-based mathematical programming model. Through this model, a profitable and efficient mechanism for allocating the advertising time to advertisers is developed by which the revenue of TV channels is maximised while the effectiveness of advertising is increased. We developed a steady-state genetic algorithm to find an optimal or a near optimal solution for the proposed problem. A computational experiment was conducted for evaluating the efficiency of the proposed algorithm. A set of test problems with different sizes were generated, using the... 

Bus network design is an important problem in public transportation. In practice, some parameters of this problem are uncertain. We propose two models for the bus terminal location problem with fuzzy parameters. In the first formulation, the number of passengers corresponding to each node is a fuzzy number. In the second formulation, an additional assumption of fuzzy neighborhood is considered. These problems being NP-hard, we use a genetic algorithm (GA) and a simulated annealing (SA) algorithm for solving them. We also propose an idea to hybridize these algorithms. In our hybrid algorithm, SA is applied as a neighborhood search procedure of GA on the best individual of the population,... 

This paper presents a special case of the general shop called partial job shop problem. The partial job shop is a more realistic generalization of the mixed shop problem. The problem is formulated as a mixed integer programming model. A scatter search algorithm combined with tabu search and path relinking is used to tackle this problem with makespan criterion. The computational experiments are performed on some problem instances. The results are compared with a lower bound and the effectiveness of the algorithm is shown  

In this manuscript, a vehicle allocation problem involving a heterogeneous fleet of vehicles for delivering products from a manufacturing firm to a set of depots is considered. Each depot has a specific order quantity and transportation costs consist of fixed and variable transportation cost. The objective is to assign the proper type and number of vehicle to each depot route to minimize the total transportation costs. It is assumed that the number of chartering vehicle types is limited. It is also assumed that a discount mechanism is applied to the vehicles renting cost. The discount mechanism is applied to the fixed cost, based on the number of vehicles to be rented. A mathematical... 

The quickest path reliability problem evaluates the probability of transmitting some given units of flow from a source node to a sink node through a single minimal path in a stochastic-flow network within some specified units of time. This problem has been recently extended to the case of transmitting flow through q separate minimal paths (SMPs) simultaneously within a budget constraint. Here, we propose a new algorithm to solve such problems. The algorithm finds all the minimal vectors for which a given demand level d units of flow can be transmitted through q SMPs from a source node to a sink node satisfying some given time and budget limits. In our proposed algorithm, a special... 

The problem of allocating different types of vehicles for transporting a set of products in an existing transportation network, to minimize the total transportation costs, is considered. The distribution network involves a heterogeneous fleet of vehicles each with the given capacity and with a variable transportation cost and a fixed cost with a discounting mechanism. Due to nonlinearity of the discounting mechanism, a nonlinear mathematical programming model is developed. A prioritized K-mean clustering encoding is introduced to designate the distribution depots distances, their demands, and the vehicles’ capacity. Using this priority clustering, a heuristic routine is developed by which... 

The learning effects of the workers and machine deterioration in an aggregate production planning (APP) problem have not been taken into account in the literature yet. These factors affect the performance of any real-world production system and require attention. In this paper, a bi-objective optimization model is developed for an APP problem with labor learning effect and machine deterioration. The first objective of this model maximizes the profit by improving learning and reducing the failure cost of the system. The second objective function minimizes the costs associated with repairs and deterioration, which depend on the failure rate of the machines in the production periods. The aim of... 

In this paper, we study the problem of finding the shortest path in stochastic graphs and propose an iterative algorithm for solving it. This algorithm is based on distributed learning automata (DLA), and its objective is to use a DLA for finding the shortest path from the given source node to the given destination node whose weight is minimal in expected sense. At each stage of this algorithm, DLA specifies edges needed to be sampled. We show that the given algorithm finds the shortest path with minimum expected weight in stochastic graphs with high probability which can be close to unity as much as possible. We compare the given algorithm with some distributed learning automata-based... 

In this study, a new mathematical programming approach for solving the joint timetabling and train stop planning problem in a railway line with double-track segments is proposed. This research aims to design an optimized train timetable subject to the station- capacity and time-dependent dwell time constraints. The objective function is to maximize the railway infrastructure capacity by minimizing the schedule makespan. The problem is formulated as a particular case of blocking permutation flexible flow shop scheduling model with dynamic time window constraints. Due to the combinatorial nature of the problem, heuristic algorithm and bound tightening methods are proposed that generate... 

Scheduling under nonrenewable resources is one of the challenging issues in project scheduling problems. There are many cases where the projects are subject to some nonrenewable resources. In most of the literature, nonrenewable resources are assumed to be available in full amount at the beginning of the project. However, in practice, it is prevalent that these resources are procured along the project horizon. This paper studies the generalized resource-constrained project scheduling problem (RCPSP) where, in addition to renewable resources, nonrenewable resources are considered, such as budget or consuming materials by the project activities. As the problem is NP-hard, some sub-algorithm... 

The traffic assignment problem (TAP) is traditionally defined without side constraints. Although introducing side constraints into the TAP is used as a way of refining applied assignment models, it brings some computational difficulties that prevent the refined model from being applicable in real-case problems. In this paper an iterative solution algorithm is developed for TAP with linear side constraints. This method is based on implicitly considering the side constraints by means of adding a dynamic penalty function (DPF) to the link travel times. Each of the iterations of the algorithm is reduced to an unconstrained assignment followed by updating the penalty functions. The unconstrained... 

Traffic assignment problem (TAP) is traditionally defined without side constraints. Although introducing the side constraints into the TAP is used as a way of refining the applied assignment models, it brings some computational difficulties preventing the refined model to be applicable in the real-case problems. In this paper an iterative solution algorithm is developed for TAP with linear side constraints. This method is based on implicitly considering the side constraints by means of adding a dynamic penalty function (DPF) to the link travel times. Each of the iterations of the algorithm is reduced to an unconstrained assignment followed by updating the penalty functions. The unconstraint... 

Outsourcing as a useful strategy in the industry can be integrated into scheduling problems. Moreover, batch outsourcing is a practical assumption owing to the logistics issues for transferring the parts between the manufacturer and the subcontractors. However, this assumption is rarely addressed in the scheduling literature. In this paper, a novel single machine scheduling problem with the option of batch outsourcing is studied. The objective is to minimize the sum of the total completion time of the jobs and the total outsourcing cost. To solve the problem, first, two mixed-integer linear programming (MILP) models, named MP1 and MP2, are developed, which respectively use a straightforward...