Optimal scheduling

In this work, a novel optimal scheduling approach is proposed for isolated microgrids (MGs) with renewable generations by incorporating demand response of electric vehicles (EVs). First, a bi-level programming-based MG scheduling model is proposed under real-time pricing environments, where the upper- and lower- levels seek to minimize the MG net operating cost and the EV charging cost. Second, a hybrid solution algorithm called JAYA-interior point method is put forward to solve the model. And finally, the simulation results demonstrate that incorporating demand response of electric vehicles is able to guide EV users to actively participate in MG scheduling and achieve the peak load shaving, which offers a fundamental way to balance the interests between MG and EV users.

By modeling the uncertainty of spinning reserves provided by energy storage with probabilistic constraints, a new optimal scheduling mode is proposed for minimizing the operating costs of an isolated microgrid (MG) by using chance-constrained programming. The model is transformed into a readily solvable mixed integer linear programming (MILP) formulation in GAMS via a proposed discretized step transformation (DST) approach and finally solved by applying the CPLEX solver. By properly setting the confidence levels of the spinning reserve probability constraints, the MG operation can be achieved a trade-off between reliability and economy. The test results on the modified ORNL DECC lab MG test system reveal that the proposal significantly exceeds the commonly used hybrid intelligent algorithm with much better and more stable optimization results and significantly reduced calculation times.

A new dynamic optimization (DO) approach to solve large scale optimal scheduling problems for water distribution networks is presented. The main motivation of this research is to formulate an algorithm which is significantly faster than existing approaches. Optimal scheduling is a complex task as it includes the extended period hydraulic model represented by differential algebraic equations and mixed-integer decision variables. Obtaining a strictly optimal solution involves excessive computational effort; however, a near optimal solution can be found at significantly reduced effort using a simple heuristic assumption. The proposed method progresses in two stages—initially a relaxed continuous problem is solved and in the second stage, a mixed-integer solution is found which tracks the optimal reservoir trajectories by time decomposition and application of a local branch and bound method. This paper describes the first stage of the method. The state and algebraic variables are numerically resolved using a ...

The local optimality (LO) property for discrete event dynamic systems (DEDS) modeled using Petri nets (PNs) is presented. If a system is divided into independent subsystems and if the optimal behavior of these subsystems guarantees the optimal behavior of the whole system, then this system possesses the LO property. The main advantage of this approach is that it is possible to find an optimal schedule for this class of systems in less time. A characterization of systems exhibiting the LO property, as well as a ...

Abstract—The Progressive Edge Growth (PEG) algorithm is one of the most widely-used method for constructing finite length LDPC codes. In this paper we consider the PEG algorithm together with a scheduling distribution, which specifies the order in which edges are established in ...

The problem of multiprocessor scheduling can be stated as scheduling a general task graph on a multiprocessor system such that a set of performance criteria will be optimized. This study investigates the use of near optimal scheduling strategies in multiprocessor scheduling problem. The multiprocessor scheduling problem is modeled and simulated using five different simulated annealing algorithms and a genetic algorithm. In this paper, the comparison of the simulation results of the simulated annealing algorithm, the modified versions of simulated annealing algorithms, and the genetic algorithm is presented. In addition, results of sensitivity analysis on the simulated annealing algorithm, the modified simulated annealing algorithms, and the genetic algorithm are included.  1999 AAAI. All rights reserved. (http://www.aaai.org)

In this paper, we consider joint scheduling of jobs and preventive maintenance operations on a single machine with an objective to minimize the total earliness and tardiness of jobs about a common due date. The properties of an optimal schedule are identified and utilized to develop a constructive heuristic and a lower bound estimate. The properties are also utilized to hybridize Tabu search and Simulated Annealing algorithms. A numerical study with over 3200 randomly generated problems is reported to demonstrate the performance of the proposed solution methods. The study shows that the effectiveness of the proposed lower bound and constructive heuristic is sensitive to maintenance related parameters. We also show that hybridized Tabu search and Simulated Annealing algorithms are efficient approaches to solve the problem.

The Transportation Security Administration (TSA) staffs and operates over 450 airports in the US. TSA has been using simulation to determine staffing requirements since 2005 and has recently completed a refresh of their manpower planning and scheduling system. The objectives of the effort were to replace the GPSS simulation engine, optimizer and user-interface (UI) to take advantage of current network-based

This article is devoted to the run-time redistribution of arrays that are distributed in a blockcyclic fashion over a multidimensional processor grid. While previous studies have concentrated on efficiently generating the communication messages to be exchanged by the processors involved in the redistribution, we focus on the scheduling of those messages: how to organize the message exchanges into "structured" communication steps that minimize contention. We build upon results of Walker and Otto, who solved a particular instance of the problem, and we derive an optimal scheduling for the most general case, namely, moving from a CYCLIC(r) distribution on a P -processor grid to a CYCLIC(s) distribution on a Q-processor grid, for arbitrary values of the redistribution parameters P , Q, r, and s. This work was supported in part by the National Science Foundation Grant No. ASC-9005933; by the Defense Advanced Research Projects Agency under contract DAAH04-95-1-0077, administered...

-This paper addresses the design and scheduling problem of biodiesel plants in conjunction with typical oil refineries via blending of biodiesel and petro-diesel. The feedstocks are often seasonal and their availability and cost usually vary with time. A multi-period scheduling framework is formulated as an optimization problem to determine the optimal feedstock utilization and blending of biodiesel with petro-diesel using a property-integration framework. A case study is solved to illustrate the applicability of the devised approach.

The Progressive Edge Growth (PEG) algorithm is one of the most widely-used methods for constructing finite length LDPC codes. In this paper we consider the PEG algorithm together with a scheduling distribution, which specifies the order in which edges are established in the graph. The goal is to find a scheduling distribution that yields “the best ” performance in terms of decoding overhead, performance metric specific to erasure codes and widely used for upper-layer forward error correction (UL-FEC). We rigorously formulate this optimization problem, and we show that it can be addressed by using genetic optimization algorithms. We also exhibit PEG codes with optimized scheduling distribution, whose decoding overhead is less than half of the decoding overhead of their classical-PEG counterparts. Keywords: LDPC codes, bipartite graph, PEG, UL-FEC, decoding overhead/inefficiency.

Summary. Scheduling problems vary widely in the nature of their constraints and optimiza-tion criteria. Most scheduling algorithms make restrictive assumptions about the constraints and criteria and hence are applicable to only a limited set of scheduling problems. A recon-figurable ...

... An Object-Oriented Implementation of Behavior-Based Control Architecture Yunho Jeon JungminPark Insub Song Young-Jo Cho Sang-Rok Oh Division of Electronics and Information Technology, KIST PO Box 131, Cheongryang, Seoul 130-650, Korea Abstract ...

This paper proposes several schemes for optimal scheduling of power producers in a shipboard power system for a typical offshore supply vessel, having a multiple number of possibly varying capacity gensets. The proposed scheduling methods are illustrated using four alternative power system configurations, ranging from a few large gensets to many gensets of smaller ratings. Mixed integer linear programming is used to formulate the optimization problems. Three formulations are presented: one for minimizing online capacity without further objectives, one that includes a redundancy constraint for loosing a group of gensets (to account for a worst-case failure scenario), and one to also balance running hours and minimizing connect/disconnect of the gensets. These different objectives can be combined and weighted based on importance, with or without redundancy constraints. Simulations are carried out to demonstrate the properties of the three different scheduling methods. The first method, minimizing online capacity only, is also used to illustrate the differences between the four genset configurations. This shows, for instance, that using more small gensets ensures generally a lower online available power (the connected capacity matches better the prevailing load) and near optimal loading of each genset around 80\% (assuming equal loadsharing), at the same time as resilience to genset failures is preserved.

This letter establishes the similarity between the sensor scheduling problem and the handoff (i.e., base station assignment) problem in cellular networks. A mobile user behavior is then modelled by a Hidden Markov Model (HMM). The handoff problem is formulated as an optimization problem of base station scheduling that minimizes a cost function that involves the HMM state estimation error and base station measurement costs. The optimization problem can be solved using algorithms known as partially observed Markov decision processes.