Multiple System Objectives Research Articles

Topology Planning Using Q-Learning for Microwave-Based Wireless Backhaul Networks

Today, a microwave-based solution is the one commonly used for wireless backhaul networks as it has high capacity and can be easily deployed. For such a microwave-based wireless backhaul network, a well-designed topology is important for efficient capacity utilization and high-quality mobile services. In an earlier work, we presented an approach for topology planning for microwave-based wireless backhaul networks, where an integer linear programming (ILP) model was formulated and a heuristic algorithm was also developed, subject to various constraints. However, the previous heuristic algorithm may merely lead to local optima, and moreover, only a single set of weight factors was considered for the optimization. This cannot guarantee joint optimization over all the relevant performance aspects. We overcome these drawbacks here by employing the Q-learning technique for this topology planning problem. This consists of (a) re-optimizing an initial topology based on a specific set of weight factors and (b) finding an optimal set of these weight factors. To accelerate the Q-learning process, we also develop a parallel Q-learning system to find the optimal set of weight factors. Simulation results indicate that the Q-learning based approach can jointly optimize multiple system objectives and outperforms our earlier heuristic algorithm. The parallel learning system can also significantly expedite the learning process.

Spatio-Temporal Non-Intrusive Direct V2V Charge Sharing Coordination

Direct vehicle-to-vehicle (V2V) charge sharing system has the potential to provide more flexibility to electric vehicle (EV) charging without depending on the charging station infrastructure or building designated parking lots. It can also provide an opportunity to shift peak time utility load to off-peak times. However, the assignment between the EVs that demand energy and the EVs with surplus energy or existing charging stations is a challenging problem as it has to be performed in real time considering their spatio-temporal distribution, availability, and grid load. In this paper, we study this assignment problem specifically in a supplier non-intrusive scenario (without changing their mobility) and aim to understand the potential benefits of a direct V2V charge sharing system. To this end, we present two new algorithms to match the demander EVs to suppliers for charging. In the first one, the maximum system benefit is targeted under multiple system objectives with different priorities and considering the grid load. In the second one, individual EV priorities are taken into account and a stable matching process depending on predefined user preferences is provided. We conduct extensive simulations using real user commuting patterns and existing charging station locations in three different cities together with a newly developed probabilistic EV charging behavior model and EV mobility. Simulation results show that direct V2V charge sharing can reduce the energy consumption of EVs by 20%–35% by providing a closer charging facility compared to the charging station only case, offload grid charging power in peak times by 35%–55%, and help sustain up to 2.2× EV charging requests without building new charging infrastructure, while causing a marginal increase (i.e., 3%–9%) in the energy cycling of supplier EVs. The results also show that the proposed matching algorithms offer 80%–90% more energy consumption reduction compared to the intrusive V2V charging at designated parking lots.

Ridesourcing systems: A framework and review

With the rapid development and popularization of mobile and wireless communication technologies, ridesourcing companies have been able to leverage internet-based platforms to operate e-hailing services in many cities around the world. These companies connect passengers and drivers in real time and are disruptively changing the transportation industry. As pioneers in a general sharing economy context, ridesourcing shared transportation platforms consist of a typical two-sided market. On the demand side, passengers are sensitive to the price and quality of the service. On the supply side, drivers, as freelancers, make working decisions flexibly based on their income from the platform and many other factors. Diverse variables and factors in the system are strongly endogenous and interactively dependent. How to design and operate ridesourcing systems is vital—and challenging—for all stakeholders: passengers/users, drivers/service providers, platforms, policy makers, and the general public. In this paper, we propose a general framework to describe ridesourcing systems. This framework can aid understanding of the interactions between endogenous and exogenous variables, their changes in response to platforms’ operational strategies and decisions, multiple system objectives, and market equilibria in a dynamic manner. Under the proposed general framework, we summarize important research problems and the corresponding methodologies that have been and are being developed and implemented to address these problems. We conduct a comprehensive review of the literature on these problems in different areas from diverse perspectives, including (1) demand and pricing, (2) supply and incentives, (3) platform operations, and (4) competition, impacts, and regulations. The proposed framework and the review also suggest many avenues requiring future research.

Risk-aware decision support with constrained goal models

PurposeThe selection of security configurations for complex information systems is a cumbersome process. Decision-making regarding the choice of security countermeasures has to take into consideration a multitude of, often conflicting, functional and non-functional system goals. Therefore, a structured method to support crucial security decisions during a system’s design that can take account of risk whilst providing feedback on the optimal decisions within specific scenarios would be valuable.Design/methodology/approachSecure Tropos is a well-established security requirements engineering methodology, but it has no concepts of Risk, whilst Constrained Goal Models are an existing method to support relevant automated reasoning tasks. Hence we bridge these methods, by extending Secure Tropos to incorporate the concept of Risk, so that the elicitation and analysis of security requirements can be complimented by a systematic risk assessment process during a system’s design time and supporting the reasoning regarding the selection of optimal security configurations with respect to multiple system objectives and constraints, via constrained goal models.FindingsAs a means of conceptual evaluation, to give an idea of the applicability of the approach and to check if alterations may be desirable, a case study of its application to an e-government information system is presented. The proposed approach is able to generate security mechanism configurations for multiple optimisation scenarios that are provided, whilst there are limitations in terms of a natural trade-off of information levels of risk assessment that are required to be elicited.Originality/valueThe proposed approach adds additional value via its flexibility in permitting the consideration of different optimisation scenarios by prioritising different system goals and the automated reasoning support.

Multi-objective versus single-objective optimization of batch bioethanol production based on a time-dependent fermentation model

Traditional process simulators, such as Aspen Plus, are inadequate for optimizing multiple-objective systems in fermentation-based processes. This work uses a novel integrated platform of the robust genetic algorithm optimization in MATLAB linked with an Aspen Plus unsteady-state batch fermentation simulation to optimize the batch ethanolic fermentation process with respect to initial substrate concentration, fermentation time, and in situ product removal. A time-dependent fermentation model that utilizes both glucose and xylose, the major sugars present in lignocellulosic hydrolysate, with Monod cell growth kinetics, substrate and product inhibitions, is used as a model system. The optimized design variables from the multi-objective optimization (MOO) and single-objective optimization (SOO) suggest the typical concentrations of sugars from lignocellulosic hydrolysate must be concentrated to optimize the performance of the batch fermentation process. Furthermore, time-dependent information from an unsteady-state simulation was used to design an integrated batch fermentation with in situ product recovery, allowing higher initial sugars concentrations to be used in the fermentation process (about 50%, for the best optimal solution in the MOO). This resulted in 15% ethanol productivity, 143% total ethanol produced, and 67% fraction of sugar converted improvements relative to the batch fermentation without product recovery. Unlike the single optimal solution from the SOO, MOO presents many equally optimal solutions that can be used as a decision-support tool to guide the choice of design variables for optimum process performance. This study creates a platform that can be used to optimize integrated biorefinery and refinery processes.

Integrated bio-economic models as tools to support land-use decision making: a review of potential and limitations

Abstract Bio-economic modelling has become a useful tool for anticipating the outcomes of policies and technologies before their implementation. Advances in mathematical programming have made it possible to build more comprehensive models. In an overview of recent studies about bio-economic models applied to land-use problems in agriculture and forestry, we evaluated how aspects such as uncertainty, multiple objective functions, system dynamics and time have been incorporated into models. We found that single objective models were more frequently applied at the farm level, while multiple objective modelling has been applied to meet concerns at the landscape level. Among the objectives, social aspects are seldom represented in all models, when being compared to economic and environmental aspects. The integration of uncertainty is occasionally a topic, while stochastic approaches are more frequently applied than non-stochastic robust methods. Most multiple-objective models do not integrate uncertainty or sequential decision making. Static approaches continue to be more recurrent than truly dynamic models. Even though integrating multiple aspects may enhance our understanding of a system; it involves a tradeoff between complexity and robustness of the results obtained. Land-use models have to address this balance between complexity and robustness in order to evolve towards robust multiple-objective spatial optimization as a prerequisite to achieve sustainability goals.

A Framework for Implementing Predictive-CFD Capability in a Design-by-Simulation Environment

Computational fluid dynamics (CFD) tools are becoming more widely used in thermal-hydraulic (T/H) and plant analyses due to advances in computational capability, data storage, and speed. However, to date, most CFD studies are ad hoc in nature with little emphasis on building links between and among CFD studies and CFD users. Thus, CFD codes have not yet been effectively leveraged as design tools within the T/H and nuclear applications communities due to lack of a comprehensive and rigorous approach to both verification and validation and uncertainty propagation. Consequentially, CFD is generally relegated to limited diagnostic use or as an adjunct to conventional lumped-parameter codes that often are based on limited testing and use conservative bounding factors applied to the needed design calculations.Because significant technical progress and development of CFD have occurred over the last decade, the potential now exists to move the use of CFD into the mainstream of analysis tools to address design, operational, and regulatory issues for complex hydraulic systems. This potential can serve as a basis upon which to develop CFD for use in an integrated design-by-simulation (IDS) environment. The CFD methodology to provide this rigor is identified as predictive-CFD (P-CFD) in this technical note.In the P-CFD/IDS methodology, synergy and consensus will be obtained through more rigorous validation of the underlying physics phenomena of each analysis objective through use of an extensive database of validation-level tests (VLTs) by many universities and laboratories. This approach logically suggests the creation of a national P-CFD database to contain these VLT data sets for general practitioner access. Thus, the underlying physics is a building block for multiple system objectives whose phenomena require those physics behaviors for the needed assessments. By using the P-CFD/IDS methodology, CFD methods can be made consistent, credible, and reproducible.Extensive references have been included to provide the status of the underlying background that supports P-CFD/IDS development. The path outlined is fully practical but difficult. This technical note is written to show a framework by which a validated CFD study for a given hydraulic objective can be prepared and used for the analyses of complex hydraulic systems to support design conclusions.

Concurrent Optimization of Rail Transit Alignments and Station Locations

Designing urban rail transit systems is a complex problem, which involves the determination of station locations, track geometry, and various other system characteristics. Most of the previous rail transit route optimization studies have focused on the alignment design between predetermined stations, whereas a practical design process has to account for the complex interactions among railway alignments and station locations. This paper proposes a methodology for concurrently optimizing station locations and the rail transit alignment connecting those stations, by accommodating multiple system objectives, satisfying various design constraints, and integrating the analysis models with a geographical information system database. The methodology incorporates demand and station costs in the evaluation framework and employs a genetic algorithm for optimizing the decision variables for station locations, station types, and track alignments. It is expected that transit planners may greatly benefit from the proposed methodology, with which they can conveniently and efficiently optimize candidate alternatives. The Baltimore Red Line is used as a case study to demonstrate how the model can find very good solutions in regions with complex geography.

Multi-Objective Topology Planning for Microwave-Based Wireless Backhaul Networks

Wireless backhaul networks provide vital infrastructure support for large public and private networks, and their efficient design is essential for smoothly handling the rapid growth of the Internet traffic. For its efficient capacity utilization, a well-planned topology would be crucial for such networks. Existing studies on planning for wireless backhaul network topologies have mostly focused on a single parameter or on a few performance aspects, e.g., to minimize the network cost or to maximize the network reliability. It would, however, be more realistic to consider multiple performance aspects jointly, subject to a variety of system constraints for a microwave-based wireless backhaul network, as proposed in this paper. For the optimization, we formulate a general cost that incorporates the various performance aspects considered, based on different weight factors. We develop an integer linear programming (ILP) optimization model and also propose an efficient heuristic algorithm to plan cost-minimized tree topologies for both single stage and multi-stage design scenarios. This paper shows that the proposed heuristic algorithm is efficient to optimize multiple system objectives jointly and performs close to the ILP model. The performances of the topologies planned with the periodic constraint and the single stage scenario are also close to each other further confirming the efficiency of the proposed heuristic algorithm.

MOO: An architectural framework for runtime optimization of multiple system objectives in embedded control software

Today's complex embedded systems function in varying operational conditions. The control software adapts several control variables to keep the operational state optimal with respect to multiple objectives. There exist well-known techniques for solving such optimization problems. However, current practice shows that the applied techniques, control variables, constraints and related design decisions are not documented as a part of the architecture description. Their implementation is implicit, tailored for specific characteristics of the embedded system, tightly integrated into and coupled with the control software, which hinders its reusability, analyzability and maintainability. This paper presents an architectural framework to design, document and realize multi-objective optimization in embedded control software. The framework comprises an architectural style together with its visual editor and domain-specific analysis tools, and a code generator. The code generator generates an optimizer module specific for the given architecture and it employs aspect-oriented software development techniques to seamlessly integrate this module into the control software. The effectiveness of the framework is validated in the context of an industrial case study from the printing systems domain.

A General Method for Sensor Planning in Multi-Sensor Systems: Extension to Random Occlusion

Systems utilizing multiple sensors are required in many domains. In this paper, we specifically concern ourselves with applications where dynamic objects appear randomly and the system is employed to obtain some user-specified characteristics of such objects. For such systems, we deal with the tasks of determining measures for evaluating their performance and of determining good sensor configurations that would maximize such measures for better system performance. We introduce a constraint in sensor planning that has not been addressed earlier: visibility in the presence of random occluding objects. occlusion causes random loss of object capture from certain necessitates the use of other sensors that have visibility of this object. Two techniques are developed to analyze such visibility constraints: a probabilistic approach to determine "average" visibility rates and a deterministic approach to address worst-case scenarios. Apart from this constraint, other important constraints to be considered include image resolution, field of view, capture orientation, and algorithmic constraints such as stereo matching and background appearance. Integration of such constraints is performed via the development of a probabilistic framework that allows one to reason about different occlusion events and integrates different multi-view capture and visibility constraints in a natural way. Integration of the thus obtained capture quality measure across the region of interest yields a measure for the effectiveness of a sensor configuration and maximization of such measure yields sensor configurations that are best suited for a given scenario. The approach can be customized for use in many multi-sensor applications and our contribution is especially significant for those that involve randomly occurring objects capable of occluding each other. These include security systems for surveillance in public places, industrial automation and traffic monitoring. Several examples illustrate such versatility by application of our approach to a diverse set of different and sometimes multiple system objectives.

Data Clustering of Solutions for Multiple Objective System Reliability Optimization Problems

This paper proposes a practical methodology for the solution of multi-objective system reliability optimization problems. The new method is based on the sequential combination of multi-objective evolutionary algorithms and data clustering on the prospective solutions to yield a smaller, more manageable sets of prospective solutions. Existing methods for multiple objective problems involve either the consolidation of all objectives into a single objective, or the determination of a Pareto-optimal set. In this paper, a new approach, involving post-Pareto clustering is proposed, offering a compromise between the two traditional approaches. In many real-life multi-objective optimization problems, the Pareto-optimal set can be extremely large or even contain an infinite number of solutions. Broad and detailed knowledge of the system is required during the decision making process in discriminating among the solutions contained in the Pareto-optimal set to eliminate the less satisfactory trade-offs and to select the most promising solution(s) for system implementation. The well-known reliability optimization problem, the redundancy allocation problem (RAP), was formulated as a multi-objective problem with the system reliability to be maximized, and cost and weight of the system to be minimized. A multiple stage process was performed to identify promising solutions. A Pareto-optimal set was initially obtained using the fast elitist nondominated sorting genetic algorithm (NSGA-II). The decision-making stage was then performed with the aid of data clustering techniques to prune the size of the Pareto-optimal set and obtain a smaller representation of the multi-objective design space; thereby making it easier for the decision-maker to find satisfactory and meaningful trade-offs, and to select a preferred final design solution.

Practical solutions for multi-objective optimization: An application to system reliability design problems

For multiple-objective optimization problems, a common solution methodology is to determine a Pareto optimal set. Unfortunately, these sets are often large and can become difficult to comprehend and consider. Two methods are presented as practical approaches to reduce the size of the Pareto optimal set for multiple-objective system reliability design problems. The first method is a pseudo-ranking scheme that helps the decision maker select solutions that reflect his/her objective function priorities. In the second approach, we used data mining clustering techniques to group the data by using the k-means algorithm to find clusters of similar solutions. This provides the decision maker with just k general solutions to choose from. With this second method, from the clustered Pareto optimal set, we attempted to find solutions which are likely to be more relevant to the decision maker. These are solutions where a small improvement in one objective would lead to a large deterioration in at least one other objective. To demonstrate how these methods work, the well-known redundancy allocation problem was solved as a multiple objective problem by using the NSGA genetic algorithm to initially find the Pareto optimal solutions, and then, the two proposed methods are applied to prune the Pareto set.

MultiGen: an integrated multiple-objective solution system

This paper presents an overview of a global multiple-objective system capable of rigourously handling a wide variety of multiple-objective and goal programming (GP) models. The system has two efficient solution engines that complement one another. These are an intelligent conventional optimisation system and a genetically driven heuristic search-based solver. The development of a linear and nonlinear sparse storage system capable of handling additional multiple-objective factors such as priority levels, goals, objectives, and upper and lower bounds is described. An appropriate input/output style for multiple-objective programmes is given. The resulting package provides an efficient and user-friendly environment for the interactive solution and analysis of a wide variety of multiple-objective programming (MOP) models.

A dynamic approach to multiple-objective resource allocation problem

A new approach using dynamic programming is developed for solving the multiple-objective resource allocation problem. There are two key issues being addressed in this approach. The first one is to develop a methodology of fuzzy evaluation and fuzzy optimization for multiple-objective systems. The procedure of getting the marginal evaluation for each objective and aggregating them synthetically into a global evaluation is presented in this paper. The second one is to design a dynamic optimization algorithm by incorporating the method of fuzzy evaluation and fuzzy optimization with the conventional dynamic programming technique. A characteristic feature of the approach presented is that various objectives are synthetically considered by the fuzzy systematic technique instead of the frequently employed weighted-average method. Numeric examples are also given to clarify the developed approach and to demonstrate its effectiveness.

Policy goals for endangered species recovery

Little attention has been given to establishing goals for the recovery of species listed under the Endangered Species Act. This article examines how alternative policy choices for recovering endangered species affect policy outcomes, in terms of which species and taxonomic groups receive priority for recovery funding. Ten priority ranking systems, designed to achieve different policy goals, were proposed and analyzed to determine how funds likely would be distributed among the various taxonomic groups. The major conclusions are: (1) single objective systems are much more selective than multiple objective systems and can be very effective in bringing about specific desired policy goals; (2) widely different multiple objective systems that use the same variables result in similar policy outcomes; and, (3) the addition of a highly selective variable is more effective for changing the outcome of multiple objective systems than changing the system structure.

IMOST: Interactive Multiple Objective System Technique

An interactive multiple objective system technique (IMOST) is investigated to improve the flexibility and robustness of multiple objective decision making (MODM) methodologies. The interactive concept provides a learning process about the system, whereby the decision maker can learn to recognize good solutions, the relative importance of factors in the system, and then design a high-productivity and zero-buffer system instead of optimizing a given system. This interactive technique provides integration-oriented, adaptation and dynamic learning features by considering all possibilities of a specific domain of MODM problems which are integrated in logical order. It encompasses the decision-making processes of formulating problems, constructing a model, solving the model, testing/examining its solution, and improving/reshaping the model and its solution in a specific problem domain. Although IMOST deals with multiple objective programming problems, it also provides some valuable orientation of integrated system methodologies.

Linear Programming in Single- and Multiple-Objective Systems

Linear Programming in Single- and Multiple-Objective Systems

Linear Programming in Single and Multiple Objective Systems

(1982). Linear Programming in Single and Multiple Objective Systems. Journal of the Operational Research Society: Vol. 33, No. 6, pp. 591-591.

Linear Programming in Single and Multiple Objective Systems

Linear Programming in Single and Multiple Objective Systems