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algorithms for Smart Grid management

SUSTAINABLE CITIES AND SOCIETY 2018年 38卷 627-635页

作者： Marah, Rim El Hibaoui, Abdelaaziz FS Abdelmalek Essaadi Univ POB 2121 Mhannech 2 Tetouan 93030 Morocco

In this paper, we will consider a modelization of Smart Grid on three sub-components: local level, microgrid level and T&D level. Thus and on one hand, we will propose an algorithm that manages its local level. Our algorithm is paired with branch and bound algorithm to solve knapsack problem. The main goal of this algorithm is to regulate consumption peaks and manage the priority of domestic appliances, by spreading at best the energy depending on the priority and consumption without exceeding the total energy received. Furthermore, we will introduce an asynchronous distributed Max-Flow algorithm to resolve the routing problem in order to optimize the T&D level. This algorithm uses a local computation to compute max-flow. Nodes communicate only by exchanging messages and no global information is needed. Our algorithm uses more than one augmenting path at each iteration allowing optimization of the execution time required for computing the max-flow.

关键词： Smart Grid modelization Knapsack problem branch and bound algorithm Routing problem Distributed max-flow algorithm Optimization

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Workload smoothing in simple assembly line balancing

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COMPUTERS & OPERATIONS RESEARCH 2018年 89卷 51-57页

作者： Azizoglu, Meral Imat, Sadullah Middle East Tech Univ Dept Ind Engn TR-06800 Ankara Turkey Roketsan Ankara Turkey

This paper considers a simple assembly line balancing problem with fixed number of workstations and prespecified cycle time. Our objective is to minimize the sum of the squared deviations of the workstation loads around the cycle time, hence maintain workload smoothing. We develop several optimality properties and bounding mechanisms, and use them in our branch and bound algorithm. The results of our computational study reveal that our branch and bound algorithm is capable of solving medium sized problem instances in reasonable times. (C) 2017 Published by Elsevier Ltd.

关键词： Assembly lines Workload smoothing branch and bound algorithm

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A Genetic algorithm with New Local Operators for Multiple Traveling Salesman Problems

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2018年第1期11卷 692-705页

作者： Lo, Kin-Ming Yi, Wei-Ying Wong, Pak-Kan Leung, Kwong-Sak Leung, Yee Mak, Sui-Tung Chinese Univ Hong Kong Dept Comp Sci & Engn Hong Kong Hong Kong Peoples R China Chinese Univ Hong Kong Inst Future Cities Hong Kong Hong Kong Peoples R China Univ Southampton Dept Elect & Comp Sci Univ Rd Southampton SO17 1BJ Hants England

Multiple Traveling Salesman Problem (MTSP) is able to model and solve various real-life applications such as multiple scheduling, multiple vehicle routing and multiple path planning problems, etc. While Traveling Salesman Problem (TSP) focuses on searching a path of minimum traveling distance to visit all cities exactly once by one salesman, the objective of the MTSP is to find m paths for m salesmen with a minimized total cost - the sum of traveling distances of all salesmen through all of the respective cities covered. They have to start from a designated depot which is the departing and returning location of all salesmen. Since the MTSP is a NP-hard problem, a new effective Genetic algorithm with Local operators (GAL) is proposed in this paper to solve the MTSP and generate high quality solution within a reasonable amount of time for real-life applications. Two new local operators, branch and bound (BaB) and Cross Elimination (CE), are designed to speed up the convergence of the search process and improve the solution quality. Results demonstrate that GAL finds a better set of paths with a 9.62% saving on average in cost comparing to two existing MTSP algorithms.

关键词： Multiple Traveling Salesman Problem Genetic algorithm branch and bound algorithm Local operators

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Weapon-Target Assignment algorithm for Simultaneous and Sequenced Arrival

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2018年第11期41卷 2361-2373页

作者： Volle, Kyle Rogers, Jonathan Georgia Inst Technol Sch Mech Engn Atlanta GA 30332 USA

Weapon-target assignment is a multi-agent control problem in which each weapon is assigned to a target to minimize the expected survival value of the targets. In this work, a multi-objective version of the weapon-target assignment problem is considered in which the quality of an assignment is dependent on both the total effectiveness of the weapons assigned to each target and the relative timing of agents' arrival. Such timing constraints may be important in real-world scenarios in which a mission planner wishes to enforce an element of surprise on each target. Building on previous work, a new modified cost function is presented that couples weapon effectiveness and timing metrics into a combined cost. In cases where weapon-target closing speeds are limited to a certain range, this combined cost allows the inclusion of arrival time constraints in the assignment decision process. The performance of this new cost function is demonstrated through theoretical analysis and simulation. Results show that the proposed cost function balances the dual goals of optimizing effectiveness and arrival time considerations under closing speed limitations and that a user-defined tuning parameter can be used to adjust the priority of the dual goals of sequenced arrival and achieving the desired probability of kill.

关键词： Weapon Target Assignment algorithm Weapon Target Assignment branch and bound algorithm Monte Carlo Simulation algorithm Performance Optimization algorithm Mobile Robotic System Damage Assessment Annealing Computing

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Optimization-Based Scheduling Method for Agile Earth-Observing Satellite Constellation

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JOURNAL OF AEROSPACE INFORMATION SYSTEMS 2018年第11期15卷 611-626页

作者： Cho, Doo-Hyun Kim, Jun-Hong Choi, Han-Lim Ahn, Jaemyung Korea Adv Inst Sci & Technol Dept Aerosp Engn Daejeon 34141 South Korea

This Paper proposes a two-step binary linear programming formulation for task scheduling of a constellation of low-Earth-orbit satellites and demonstrates its applicability and scalability to obtain high-quality solutions using a standard mixed-integer linear programming solver. In this instance, the goal of satellite constellation task scheduling is to allocate each task for the satellites and to determine the task starting times in order to maximize the overall mission performance metric. The scheduling problem is formulated to find the solution by first finding a set of candidate communication time intervals for each satellite/ground-station pair as one of the key constraints and time tabling the observation task to acquire the user-requested data, with the incorporation of key constraints for satellite constellation operation. Numerical experiments are designed for investigating the trends, sensitivity, and characteristics of scheduling outputs based on multiple representative instances. The performance of the scheduling solutions by the proposed two-step binary linear programming method exhibits significant improvement of up to 35% in the number of assignments and the sum of profits over the general greedy algorithm.

关键词： Low Earth Orbit Satellites Earth branch and bound algorithm Constellations Ant Colony Optimization algorithms Mixed Integer Linear Programming Satellite Ground Station Ground Station EOS Data Store

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Optimal asset management strategies for mixed transit fleet

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TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE 2018年第Nov.期117卷 103-116页

作者： Ngo, Huan Hoang Shah, Rohan Mishra, Sabyasachee Univ Memphis Dept Civil Engn 104 Engn Sci Bldg Memphis TN 38152 USA CDM Smith Inc Transportat Serv Grp 9430 Res BlvdSuite 1-200 Austin TX 78759 USA

Transit agencies require equitable and optimal allocation of funds among transit agencies for not just regular operations and maintenance, but also for asset management including purchase of new buses and rehabilitation of aging fleet. The paper proposes a hierarchical structure of resource allocation where federal funding is routed through the state, and ultimately to local transit agencies. The framework encompasses multiple dimensions such as selection of different improvement program options (rehabilitation, remanufacturing, and replacement) of a mixed transit fleet spread over a temporally continuous planning period. It leverages optimization models for capital allocation among transit agencies in the state. Four sub-models are developed-two maximizing passenger miles traveled, and the other two maximizing the total fleet wide remaining life, all under agency-specific budget, capacity and policy constraints, and planning objectives. They are applied on real-world data from set of transit agencies spread across the state of Tennessee, containing a heterogenous fleet of 254 total buses at various levels of aging. Results indicate that by application of the framework, an average 40 percent additional mileage is generated through the planning period with the same levels of fleet size, with nearly 30 percent of the fleet receiving some form of improvement treatment per year.

关键词： Optimal resource allocation Transit asset management Rehabilitation and replacement Integer programming branch and bound algorithm

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QoS-Aware Power Management in LTE-A Networks Under Heterogeneous Traffics

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2018年第1期67卷 674-688页

作者： Ozcevik, Muge Erel Secinti, Gokhan Canberk, Berk Istanbul Tech Univ Dept Comp Engn TR-34469 Istanbul Turkey Northeastern Univ Dept Elect & Comp Engn Boston MA 02115 USA

In 3GPP LTE-A releases v.8 and beyond, the plug and play nature of the eNodeBs lets a dense deployment of eNodeBs, thus, causing over-provisioned network infrastructure. Here, while most of the recent studies aim to optimize power consumption on a single traffic type, we provide a novel framework that corresponds both power adjustment and quality of users (QoS) requirements of end-users with multitraffic types. In this manner, we propose a novel energy efficiency coefficient (EEC) considering QoS and channel utilization while reducing power consumption. Moreover, our proposed resource auction module assigns resource blocks to low-utilized cellular networks. Furthermore, the traffic types are modeled with a queuing theoretic approach. We also define an optimization problem that employs novel EEC parameters to determine new power levels (8-levels) for eNodeBs' configurations with less power consumption. To solve this optimization problem, we introduce a novel heuristic called "Multi Level branch and bound algorithm" (MLBB). MLBB algorithm contains two nested branch and bound algorithms that manipulates end-users differently. Consequently, our proposed methodology serves end-users with 67% better QoS than the conventional mechanisms.

关键词： branch and bound algorithm energy efficiency heterogeneous traffic LTE-A power consumption

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Order acceptance and scheduling in a parallel machine environment with weighted completion time

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EUROPEAN JOURNAL OF INDUSTRIAL ENGINEERING 2018年第4期12卷 535-557页

作者： Palakiti, Venkata Prasad Mohan, Usha Ganesan, Viswanath Kumar Indian Inst Technol Madras Dept Management Studies Madras Tamil Nadu India Tata Consultancy Syst Ltd Decis Sci & Algorithm Lab Madras 600036 Tamil Nadu India

This paper studies the order acceptance and scheduling (OAS) problem in an identical parallel machine environment where orders are characterised by their revenues, processing times and weights. A mixed integer linear programming (MILP) model is presented for the problem with the objective of maximising the revenue minus the scheduling cost. The problem is NP-hard and a branch and bound (B&B) algorithm is developed to solve the problem. An extension of the B&B algorithm is proposed to solve very large problem instances to obtain 6-optimal solutions. The B&B algorithm and the extended B&B are evaluated for their performances against the solutions obtained by solving the MILP problem formulation using CPLEX solver through computational experiments.

关键词： order acceptance scheduling parallel machines branch and bound algorithm B&B

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A Petri Net-based Modeling and Scheduling with a branch and bound algorithm

A Petri Net-based Modeling and Scheduling with a Branch and ...

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IEEE International Conference on Systems, Man, and Cybernetics

作者： Hyun-Jung Kim Jun-Ho Lee Tae-Eog Lee Department of Industrial and Systems Engineering Korea Advanced Institute of Science and Technology (KAIST)

ISBN: (纸本)9781467317139

We examine a non-cyclic scheduling problem of a timed Petri net (TPN) with a branch and bound (B & B) algorithm. There have been many approaches and algorithms for conventional scheduling problems such as job shops, resource-constrained project scheduling problems (RCPSPs), and robotized system scheduling problems. Most of these methods have focused on their effectiveness or efficiency in solving their own problems. However, they tend to ignore the issue of compatibility with other scheduling problems and the solution methods are ad hoc and hard to be used for other scheduling problems with even small changes. Petri nets have been widely used for modeling and analyzing complex discrete event dynamic systems, such as robotized manufacturing cells or other automated manufacturing systems. There are studies on scheduling cyclic Petri net models and some non-cyclic Petri net models for specific applications. In this paper, we examine a scheduling problem for non-cyclic TPNs, where there is the starting and end transitions, and the transitions do not repeat an identical firing cycle. We also allow multiple arc weights in TPNs so as to model batch processing of tasks at a resource and multiple units of a resource required for a task. We briefly explain how various scheduling constraints and objectives can be modeled by TPNs. Then, we develop an efficient B & B procedure that utilizes a dynamic branching strategy and a resource-based lower bound. We finally present examples of the B & B algorithm for an RCPSP and a single-armed cluster tool scheduling problem.

关键词： branch and bound algorithm Non-cyclic scheduling Timed Petri net Petri nets branches cluster tools branch and bound algorithms network model multiple unit Intravenous Hyperalimentation Boron Discrete event dynamic systems

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Scheduling a Wet Station Using a branch and bound algorithm

Scheduling a Wet Station Using a Branch and Bound Algorithm

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IEEE International Conference on Systems, Man, and Cybernetics

作者： Hyun-Jung Kim Jun-Ho Lee Tae-Eog Lee Department of Industrial and Systems Engineering Korea Advanced Institute of Science and Technology (KAIST)

ISBN: (纸本)9781467317139

We examine a scheduling problem of a wet station with multiple job flows. The wet station performs cleaning processes for removing residual contaminants after wafer fabrication processes. It consists of several chemical and rinse baths, and multiple transport robots. Most studies on scheduling robotized systems including a wet station assume identical jobs and deal with cyclic scheduling that repeats a predefined work cycle. However, jobs arrive dynamically and many different jobs are processed concurrently at a wet station. We therefore examine a non-cyclic scheduling problem of the wet station to minimize the makespan. We first develop a Petri net model and solve the problem using a branch and bound (B & B) algorithm. We also propose a dynamic branching method and evaluate a lower bound based on a bottleneck process. During searching the nodes, we analyze deadlocks and add places to the Petri net model for precedence relations among the robot tasks by applying the deadlock prevention conditions. We finally show that the proposed B & B algorithm is sufficient to solve practical problems.

关键词： branch and bound algorithm Petri net Wet station network model branches STATIONS wetting branch and bound algorithms jobs cyclic scheduling

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