Sharif Digital Repository

Nowadays pull system is widely used in many industries. In the recent decade, many researchers adopt pull system to supply chain and present preference of this system. In this paper, a general Pull system for a stochastic supply chain process will be adapted along with optimizing a Pull Stochastic Supply chain. This Supply Chain, SC, is used with combining CONWIP and KANBAN, the two famous pull systems, for controlling the SC. Under assumptions of stochastic demand rate, stochastic production and transportation times, and stochastic distributions for backlog cost, a simulation modeling is used. In optimization process, concerning supply chain complexity, a simulation optimization procedure... 

Integration of wind power generation creates new concerns for operation engineers in a power system. Unlike conventional power generation sources, wind power generators supply intermittent power due to uncertainty in parameters of wind such as its velocity. This paper presents probabilistic model for load and wind power uncertainty which can be used in operation planning (with durations up to one or two years). A stochastic model is proposed to simulate the status of units that are directly affected by the load and wind power generation uncertainties. This paper develops a solution method for generation scheduling of power system, while taking into account the stochastic behavior of the load... 

In this paper, a stochastic framework including two hierarchical stages is presented for the operation of distribution systems with high penetrations of wind power. In the first stage, termed Day Ahead Market Stage (DAMS), power purchases from day-ahead (DA) market and commitment of distributed generations (DGs) are determined. The DAMS model is formulated as a mixed integer linear programming (MILP) optimization problem. The uncertainty in predictions of wind generation, real time prices, and load profile are included in the optimization problem according to a scenario-based stochastic programming approach. The risk encountered due to the uncertainties is also taken into account. The... 

This paper deals with energy management of microgrids during unscheduled islanding events, initiated by disturbances in the main grid. In these situations, the main challenge is uncertainty about duration of disconnection from the main grid. In order to tackle this issue, a stochastic framework is proposed for optimal scheduling of microgrid resources over this period. The presented framework addresses the prevailing uncertainties of islanding duration as well as prediction errors of demand and renewable power generation. According to this framework, the probability distribution of islanding duration needs to be estimated, instead of predicting its exact value. The objective is to minimize... 

The railroad blocking problem is classified in the tactical level of freight rail transportation problems. The objective of this problem is to determine the optimal paths for each shipment such that the railway limitations are satisfied. In this problem, the quantities of both demand and supply resource indicators are often assumed to be certain and known, but because a blocking solution is designed for a relatively long period of time, this assumption is not reasonable. In this paper, we have developed a two-stage stochastic program for this problem to consider the uncertainty inherent in demand and supply resource indicators. Due to the size and complexity of the stochastic program and the... 

This paper develops a stochastic multi-period mixed-integer model for collection, production, storage, and distribution of platelet in Blood Transfusion Organizations ranging from blood collection centers to clinical points. In this model, the age of platelet and ABO-Rh priority matching rules are incorporated based on the type of patient to raise the quality and safety of platelet transfusion services. At first, a discrete Markov Chain Process is applied to predict the number of donors. Afterwards, the uncertain demand is captured using a two-stage stochastic programming. A challenging aspect of applying stochastic programming in a dynamic setting is to construct an appropriate set of... 

In this paper, we use both stochastic dynamic programming and Bayesian inference concepts to design an optimum-acceptance-sampling-plan policy in quality control environments. To determine the optimum policy, we employ a combination of costs and risk functions in the objective function. Unlike previous studies, accepting or rejecting a batch are directly included in the action space of the proposed dynamic programming model. Using the posterior probability of the batch being in state p (the probability of non-conforming products), first, we formulate the problem into a stochastic dynamic programming model. Then, we derive some properties for the optimal value of the objective function, which... 

This work proposes comprehensive empirical predictive equations for generating stochastic synthetic 3-dimensional accelerograms for the Iranian plateau based on the existing database. First, the databank of Iranian accelerograms is collected, sorted, processed, declustered and categorized into the pulse-like and non-pulse-like data. To simulate the artificial accelerograms, a stochastic model capable of handling both the temporal and spectral non-stationarity of accelerograms is adopted. By implementing nonlinear curve fitting, parameters of the stochastic model are estimated. Then, the recorded events are categorized into eight distinct groups based on the existence of pulse-like... 

Consistent growth in the volume and dynamic behavior of traffic mandates new requirements for fast and adaptive resource allocation in metro networks. We propose a dynamic resource allocation technique for adaptive minimization of spectrum usage in metro elastic optical networks. We consider optical transmission as a service specified by its bandwidth profile parameters, which are minimum, average, and maximum required transmission rates. To consider random traffic events, we use a stochastic optimization technique to develop a novel formulation for dynamic resource allocation in which service level specifications and network stability constraints are addressed. Next, we employ the elegant... 

A comprehensive optimal bidding strategy model has been developed for renewable micro-grids to take part in day-ahead (energy and reserve) and real-time markets considering uncertainties. A two-stage stochastic programming method has been employed to integrate the uncertainties into the problem. Moreover, the Latin hypercube sampling method has been proposed to generate the wind speed, solar irradiance, and load realizations via Weibull, Beta, and normal probability density functions, respectively. In addition, a hybrid fast forward/backward scenario reduction technique has been applied to reduce the large number of scenarios. Furthermore, the risk of participation in the markets has been... 

A new heuristic controller, called the continuous ant colony controller, is proposed for non-linear stochastic Gaussian/non-Gaussian systems. The new controller formulates the state estimation and the model predictive control problems as a single stochastic dynamic optimization problem, and utilizes a colony of virtual ants to find and track the best estimated state and the best control signal. For this purpose, an augmented state space is defined. An integrated cost function is also defined to evaluate the points of the augmented state space, explored by the ants. This function minimizes simultaneously the state estimation error, tracking error, control effort and control smoothness. Ants... 

Despite that the border between chaotic and stochastic systems is exactly defined, scientists, use high dimensional chaotic dynamics to model numerous stochastic models and sometimes use stochastic models to study chaotic systems. In this paper, we have investigated chaotic systems with a stochastic approach and proposed an estimator for the chaotic system which is used to present different algorithms for chaos control, extreme event prediction and extreme event mitigation. The stochastic estimator is constructed by meshing the phase space and applying the cell mapping method (with some considerations) which provides us with a model-free approximation of the systems. The algorithms are ideal... 

Stimulus presentation is believed to quench neural response variability as measured by fano-factor (FF). However, the relative contributions of within-trial spike irregularity and trial-to-trial rate variability to FF fluctuations have remained elusive. Here, we introduce a principled approach for accurate estimation of spiking irregularity and rate variability in time for doubly stochastic point processes. Consistent with previous evidence, analysis showed stimulus-induced reduction in rate variability across multiple cortical and subcortical areas. However, unlike what was previously thought, spiking irregularity, was not constant in time but could be enhanced due to factors such as... 

There are several exact or approximating approaches that apply {m d}-MinCuts (d-MCs) to compute multistate two-Terminal reliability. Searching for and determining d-MCs in a stochastic-flow network are important for computing system reliability. Here, by investigating the existing methods and using our new results, an efficient algorithm is proposed to find all the d-MCs. The complexity of the new algorithm illustrates its efficiency in comparison with other existing algorithms. Two examples are worked out to show how the algorithm determines all the d -MCs in a network flow with unreliable nodes, and in a network flow of moderate size. Moreover, using the d-MCs found by the algorithm, the... 

An information system network (ISN) can be modeled as a stochastic-flow network (SFN). There are several algorithms to evaluate reliability of an SFN in terms of Minimal Cuts (MCs). The existing algorithms commonly first find all the upper boundary points (called d-MCs) in an SFN, and then determine the reliability of the network using some approaches such as inclusion-exclusion method, sum of disjoint products, etc. However, most of the algorithms have been compared via complexity results or through one or two benchmark networks. Thus, comparing those algorithms through random test problems can be desired. Here, the authors first state a simple improved algorithm. Then, by generating a... 

This paper considers two different due date assignment and sequencing problems in single machine where the processing times of jobs are random variables. The first problem is to minimise the maximum due date so that all jobs are stochastically on time. It is shown that sequencing the jobs in decreasing service level (DSL) order optimally solves the problem. The results are then extended for two special cases of flow shop problem. The other problem is to minimise a total cost function which is a linear combination of three penalties: penalty on job earliness, penalty on job tardiness, and penalty associated with long due date assignment. The assignment of a common due date and distinct due... 

Clustering has become an increasingly important and highly complicated research area for targeting useful and relevant information in modern application domains such as the World Wide Web. Recent studies have shown that the most commonly used partitioning-based clustering algorithm, the K-means algorithm, is more suitable for large datasets. However, the K-means algorithm may generate a local optimal clustering. In this paper, we present novel document clustering algorithms based on the Harmony Search (HS) optimization method. By modeling clustering as an optimization problem, we first propose a pure HS based clustering algorithm that finds near-optimal clusters within a reasonable time.... 

Recently in electricity markets, a massive focus has been made on setting up opportunities for participating demand side. Such opportunities, also known as demand response (DR) options, are triggered by either a grid reliability problem or high electricity prices. Two important challenges that market operators are facing are appropriate designing and reasonable pricing of DR options.In this paper, time-of-use program (TOU) as a prevalent time-varying program is modeled linearly based on own and cross elasticity definition. In order to decide on TOU rates, a stochastic model is proposed in which the optimum TOU rates are determined based on grid reliability index set by the operator. Expected... 

If at least one out of two serial machines that produce a specific product in manufacturing environments malfunctions, there will be non conforming items produced. Determining the optimal time of the machines' maintenance is the one of major concerns. While a convenient common practice for this kind of problem is to fit a single probability distribution to the combined defect data, it does not adequately capture the fact that there are two different underlying causes of failures. A better approach is to view the defects as arising from a mixture population: one due to the first machine failures and the other due to the second one. In this article, a mixture model along with both Bayesian... 

In this paper, we study a pricing-inventory model with multiple interdependent products and stochastic demand. In the relevant literature, there are many researches that try to optimize the inventory or price independently and also optimizing joint inventory and pricing problems for single product. But joint optimization of inventory and pricing for multiple interdependent products is relatively new and still there are many potential research gaps in this area. We show that the multiplicative demand form problem, that we develop, is a convex nonlinear programming (NLP) model or is convertible to it. We also provide upper and lower bounds for the additive demand form model