Exact and Heuristic Algorithms for the Job Shop Scheduling Problem with Earliness and Tardiness Over a Common Due Date

Scheduling has turned out to be a fundamental activity for both production and service organizations. As competitive markets emerge, Just-In-Time (JIT) production has obtained more importance as a way of rapidly responding to continuously changing market forces. Due to their realistic assumptions, job shop production environments have gained much research effort among scheduling researchers. This research develops exact and heuristic methods and algorithms to solve the job shop scheduling problem when the objective is to minimize both earliness and tardiness costs over a common due date. The objective function of minimizing earliness and tardiness costs captures the essence of the JIT approach in job shops. A dynamic programming procedure is developed to solve smaller instances of the problem, and a Multi-Agent Systems approach is developed and implemented to solve the problem for larger instances since this problem is known to be NP-Hard in a strong sense. A combinational auction-based approach using a Mixed-Integer Linear Programming (MILP) model to construct and evaluate the bids is proposed. The results showed that the proposed combinational auction-based algorithm is able to find optimal solutions for problems that are balanced in processing times across machines. A price discrimination process is successfully implemented to deal with unbalanced problems. The exact and heuristic procedures developed in this research are the first steps to create a structured approach to handle this problem and as a result, a set of benchmark problems will be available to the scheduling research community

Mathematics in the Supply Chain

[no abstract available

A Case for Cooperative and Incentive-Based Coupling of Distributed Clusters

Research interest in Grid computing has grown significantly over the past five years. Management of distributed resources is one of the key issues in Grid computing. Central to management of resources is the effectiveness of resource allocation as it determines the overall utility of the system. The current approaches to superscheduling in a grid environment are non-coordinated since application level schedulers or brokers make scheduling decisions independently of the others in the system. Clearly, this can exacerbate the load sharing and utilization problems of distributed resources due to suboptimal schedules that are likely to occur. To overcome these limitations, we propose a mechanism for coordinated sharing of distributed clusters based on computational economy. The resulting environment, called \emph{Grid-Federation}, allows the transparent use of resources from the federation when local resources are insufficient to meet its users' requirements. The use of computational economy methodology in coordinating resource allocation not only facilitates the QoS based scheduling, but also enhances utility delivered by resources.Comment: 22 pages, extended version of the conference paper published at IEEE Cluster'05, Boston, M

Review on Radio Resource Allocation Optimization in LTE/LTE-Advanced using Game Theory

Recently, there has been a growing trend toward ap-plying game theory (GT) to various engineering fields in order to solve optimization problems with different competing entities/con-tributors/players. Researches in the fourth generation (4G) wireless network field also exploited this advanced theory to overcome long term evolution (LTE) challenges such as resource allocation, which is one of the most important research topics. In fact, an efficient de-sign of resource allocation schemes is the key to higher performance. However, the standard does not specify the optimization approach to execute the radio resource management and therefore it was left open for studies. This paper presents a survey of the existing game theory based solution for 4G-LTE radio resource allocation problem and its optimization

Financial Derivatives Market for Grid Computing

This Master thesis studies the feasibility and properties of a financial derivatives market on Grid computing, a service for sharing computing resources over a network such as the Internet. For the European Organization for Nuclear Research (CERN) to perform research with the world's largest and most complex machine, the Large Hadron Collider (LHC), Grid computing was developed to handle the information created. In accordance with the mandate of CERN Technology Transfer (TT) group, this thesis is a part of CERN's dissemination of the Grid technology. The thesis gives a brief overview of the use of the Grid technology and where it is heading. IT trend analysts and large-scale IT vendors see this technology as key in transforming the world of IT. They predict that in a matter of years, IT will be bought as a service, instead of a good. Commoditization of IT, delivered as a service, is a paradigm shift that will have a broad impact on all parts of the IT market, as well as on the society as a whole. Political, economic and physical factors advocate a market for standardized computing resources supplied by multiple professional providers, benefiting from economies of scale. We argue for the trade of Virtual Servers as the standardized bundle of computer resources. Continuous trade of homogeneous resources allows for scheduling market efficiency and liquidity, but may entail a risk of erratic, unpredictable prices. We therefor e construct a complete, coherent Grid economy, consisting of both a spot market and a derivatives market. While the spot market is the trading place for the computer resources, the derivatives market aims to disperse the risk among those who are willing to invest in it. Because the Virtual Servers are non-storable assets, normal arbitrage theory cannot be used to price derivatives contracts. We propose to solve this issue by creating storable swap contracts priced by an auction-based market, where we argue that the price process follows a geometric Brownian motion. Taking into account the absence of arbitrage in the swap market and the requirement for a complete market, we offer a theoretical framework for martingale pricing and hedging of derivatives written on swaps

Advanced Mechanism Design for Electric Vehicle Charging Scheduling in the Smart Infrastructure

Electric vehicle (EV) continues to grow rapidly due to low emission and high intelligence. This thesis considers a smart infrastructure (SI) as an EV-centered ecosystem, which is an integrated and connected multi-modal network involving interacting intelligent agents, such as EVs, charging facilities, electric power grids, distributed energy resources, etc. The system modeling paradigm is derived from distributed artificial intelligence and modelled as multi-agent systems (MAS), where the agents are self-interested and reacting strategically to maximize their own benefits. The integration, interaction, and coordination of EVs with SI components will raise various features and challenges on the transportation efficiency, power system stability, and user satisfaction, as well as opportunities provided by optimization, economics, and control theories, and other advanced technologies to engage more proactively and efficiently in allocating the limited charging resources and collaborative decision-making in a market environment. A core challenge in such an EV ecosystem is to trade-off the two objectives of the smart infrastructure, of system-wide efficiency and at the same time the social welfare and individual well-being against agents’ selfishness and collective behaviors. In light of this, scheduling EVs' charging activities is of great importance to ensure an efficient operation of the smart infrastructure and provide economical and satisfactory charging experiences to EV users under the support of two-way flow of information and energy of charging facilities. In this thesis, we develop an advanced mechanism design framework to optimize the charging resource allocation and automate the interaction process across the overall system. The key innovation is to design specific market-based mechanisms and interaction rules, integrated with concepts and principles of mechanism design, scheduling theory, optimization theory, and reinforcement learning, for charging scheduling and dynamic pricing problem in various market structures. Specifically, this research incorporates three synergistic areas: (1) Mathematical modelling for EV charging scheduling. We have developed various mixed-integer linear programs for single-charge with single station, single-charge with multiple stations, and multi-charge with multiple stations in urban or highway environments. (2) Market-based mechanism design. Based on the proposed mathematical models, we have developed particular market-based mechanisms from the resource provider’s prospective, including iterative bidding auction, incentive-compatible auction, and simultaneous multi-round auction. These proposed auctions contain bids, winner determination models, and bidding procedure, with which the designer can compute high quality schedules and preserve users’ privacy by progressively eliciting their preference information as necessary. (3) Reinforcement learning-based mechanism design. We also proposed a reinforcement mechanism design framework for dynamic pricing-based demand response, which determines the optimal charging prices over a sequence of time considering EV users’ private utility functions. The learning-based mechanism design has effectively improved the long-term revenue despite highly-uncertain requests and partially-known individual preferences of users. This Ph.D. dissertation presents a market prospective and unlocks economic opportunities for MAS optimization with applications to EV charging related problems; furthermore, applies AI techniques to facilitate the evolution from manual mechanism design to automated and data-driven mechanism design when gathering, distributing, storing, and mining data and state information in SI. The proposed advanced mechanism design framework will provide various collaboration opportunities with the research expertise of reinforcement learning with innovative collective intelligence and interaction rules in game theory and optimization tools, as well as offers research thrust to more complex interfaces in intelligent transportation system, smart grid, and smart city environments

Fast Scheduling of Robot Teams Performing Tasks With Temporospatial Constraints

The application of robotics to traditionally manual manufacturing processes requires careful coordination between human and robotic agents in order to support safe and efficient coordinated work. Tasks must be allocated to agents and sequenced according to temporal and spatial constraints. Also, systems must be capable of responding on-the-fly to disturbances and people working in close physical proximity to robots. In this paper, we present a centralized algorithm, named 'Tercio,' that handles tightly intercoupled temporal and spatial constraints. Our key innovation is a fast, satisficing multi-agent task sequencer inspired by real-time processor scheduling techniques and adapted to leverage a hierarchical problem structure. We use this sequencer in conjunction with a mixed-integer linear program solver and empirically demonstrate the ability to generate near-optimal schedules for real-world problems an order of magnitude larger than those reported in prior art. Finally, we demonstrate the use of our algorithm in a multirobot hardware testbed