Multilevel Planning with Conflicting Objectives

Decision problems are usually complex and involve evaluation of several conflicting criteria (parameters). Multi Criteria Decision Making (MCDM) is a promising field that considers the parallel influence of all criteria and aims at helping decision makers in expressing their preferences, over a set of predefined alternatives, on the basis of criteria (parameters) that are contradictory in nature. The Analytic Hierarchy Process (AHP) is a useful and widespread MCDM tool for solving such type of problems, as it allows the incorporation of conflicting objectives and decision makers preferences in the decision making. The AHP utilizes the concept of pair wise comparison to find the order of criteria (parameters) and alternatives. The comparison in a pairwise manner becomes quite tedious and complex for problems having eight alternatives or more, thereby, limiting the application of AHP. This paper presents a soft hierarchical process approach based on soft set decision making which eliminates the least promising candidate alternatives and selects the optimum(potential) ones that results in the significant reduction in the number of pairwise comparisons necessary for the selection of the best alternative using AHP, giving the approach a more realistic view. A supplier selection problem is used to illustrate the proposed approach.

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Dealing with multiple experts and non-stationarity in inverse reinforcement learning: an application to real-life problems

Machine Learning ◽

10.1007/s10994-020-05939-8 ◽

2021 ◽

Author(s):

Amarildo Likmeta ◽

Alberto Maria Metelli ◽

Giorgia Ramponi ◽

Andrea Tirinzoni ◽

Matteo Giuliani ◽

...

Keyword(s):

Reinforcement Learning ◽

Real World ◽

Real Life ◽

User Preferences ◽

Inverse Reinforcement Learning ◽

Water Release ◽

Reward Function ◽

Model Free ◽

Conflicting Objectives ◽

Multiple Experts

AbstractIn real-world applications, inferring the intentions of expert agents (e.g., human operators) can be fundamental to understand how possibly conflicting objectives are managed, helping to interpret the demonstrated behavior. In this paper, we discuss how inverse reinforcement learning (IRL) can be employed to retrieve the reward function implicitly optimized by expert agents acting in real applications. Scaling IRL to real-world cases has proved challenging as typically only a fixed dataset of demonstrations is available and further interactions with the environment are not allowed. For this reason, we resort to a class of truly batch model-free IRL algorithms and we present three application scenarios: (1) the high-level decision-making problem in the highway driving scenario, and (2) inferring the user preferences in a social network (Twitter), and (3) the management of the water release in the Como Lake. For each of these scenarios, we provide formalization, experiments and a discussion to interpret the obtained results.

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A Two-Stage Mono- and Multi-Objective Method for the Optimization of General UPS Parallel Manipulators

Mathematics ◽

10.3390/math9050543 ◽

2021 ◽

Vol 9 (5) ◽

pp. 543

Author(s):

Alejandra Ríos ◽

Eusebio E. Hernández ◽

S. Ivvan Valdez

Keyword(s):

Genetic Algorithm ◽

Performance Metrics ◽

Tracking Error ◽

Parallel Manipulators ◽

Optimization Method ◽

Statistical Hypothesis ◽

Two Stage ◽

Multi Objective ◽

Second Stage ◽

Conflicting Objectives

This paper introduces a two-stage method based on bio-inspired algorithms for the design optimization of a class of general Stewart platforms. The first stage performs a mono-objective optimization in order to reach, with sufficient dexterity, a regular target workspace while minimizing the elements’ lengths. For this optimization problem, we compare three bio-inspired algorithms: the Genetic Algorithm (GA), the Particle Swarm Optimization (PSO), and the Boltzman Univariate Marginal Distribution Algorithm (BUMDA). The second stage looks for the most suitable gains of a Proportional Integral Derivative (PID) control via the minimization of two conflicting objectives: one based on energy consumption and the tracking error of a target trajectory. To this effect, we compare two multi-objective algorithms: the Multiobjective Evolutionary Algorithm based on Decomposition (MOEA/D) and Non-dominated Sorting Genetic Algorithm-III (NSGA-III). The main contributions lie in the optimization model, the proposal of a two-stage optimization method, and the findings of the performance of different bio-inspired algorithms for each stage. Furthermore, we show optimized designs delivered by the proposed method and provide directions for the best-performing algorithms through performance metrics and statistical hypothesis tests.

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Transportation management in distributed enterprises

Human Systems Management ◽

10.3233/hsm-1999-183-405 ◽

1999 ◽

Vol 18 (3-4) ◽

pp. 203-212

Author(s):

Geir Hasle

Keyword(s):

Information And Communication Technologies ◽

Customer Service ◽

Management Practices ◽

Driving Forces ◽

Software Tool ◽

Transportation Management ◽

Conflicting Objectives ◽

Information And Communication ◽

Transportation Logistics ◽

Distributed Enterprises

The recent trend towards globalisation, with a tendency towards geographical distribution of manufacturing in distributed enterprises, has generally increased the complexity of transportation management. Other driving forces towards higher complexity in transportation logistics are the implementation of Just-In-Time principles, the explosion of Internet trade (including home shopping), a strengthening of environmental concerns, and the implementation of new legislation. Moreover, there is higher emphasis on customer service, timeliness, reactivity, and efficiency in the transportation function. We may safely conclude that there is a need for highly optimised transportation management practices at the strategic, tactical and operational control levels. Today, lack of planning and co-ordination is the cause of excess travel for commercial vehicles, with detrimental effects on economy and the environment. In distributed enterprises, these tasks (if supported at all) typically use isolated IT tools that cannot address the full problem, fail to address important constraints, cannot balance partially conflicting objectives, do not react to dynamics, and, cannot interact with the user in a timely and meaningful way. Recent advances in Information and Communication Technologies have enabled us to remedy these shortcomings. As a point in case, the GreenTrip Esprit project has developed a rapidly re-configurable, generic software tool for optimised transportation management. With GreenTrip as an illustration, this paper will describe state-of-the-art decision-support tools in transportation logistics, their underpinning technologies, and their possible impacts on business.

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A Comparison of Genetic Representations and Initialisation Methods for the Multi-objective Shortest Path Problem on Multigraphs

SN Computer Science ◽

10.1007/s42979-021-00512-z ◽

2021 ◽

Vol 2 (3) ◽

Author(s):

Lilla Beke ◽

Michal Weiszer ◽

Jun Chen

Keyword(s):

Shortest Path ◽

Time Budget ◽

Exact Algorithm ◽

Simple Graph ◽

Shortest Path Problem ◽

Future Application ◽

Multi Objective ◽

Trade Offs ◽

Conflicting Objectives ◽

Problem Instances

AbstractThis paper compares different solution approaches for the multi-objective shortest path problem (MSPP) on multigraphs. Multigraphs as a modelling tool are able to capture different available trade-offs between objectives for a given section of a route. For this reason, they are increasingly popular in modelling transportation problems with multiple conflicting objectives (e.g., travel time and fuel consumption), such as time-dependent vehicle routing, multi-modal transportation planning, energy-efficient driving, and airport operations. The multigraph MSPP is more complex than the NP-hard simple graph MSPP. Therefore, approximate solution methods are often needed to find a good approximation of the true Pareto front in a given time budget. Evolutionary algorithms have been successfully applied for the simple graph MSPP. However, there has been limited investigation of their applications to the multigraph MSPP. Here, we extend the most popular genetic representations to the multigraph case and compare the achieved solution qualities. Two heuristic initialisation methods are also considered to improve the convergence properties of the algorithms. The comparison is based on a diverse set of problem instances, including both bi-objective and triple objective problems. We found that the metaheuristic approach with heuristic initialisation provides good solutions in shorter running times compared to an exact algorithm. The representations were all found to be competitive. The results are encouraging for future application to the time-constrained multigraph MSPP.

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Healthcare Facility Location-Allocation Optimization for China’s Developing Cities Utilizing a Multi-Objective Decision Support Approach

Sustainability ◽

10.3390/su10124580 ◽

2018 ◽

Vol 10 (12) ◽

pp. 4580 ◽

Cited By ~ 5

Author(s):

Li Wang ◽

Huan Shi ◽

Lu Gan

Keyword(s):

Heuristic Algorithms ◽

Programming Model ◽

Rapid Development ◽

Supply And Demand ◽

Healthcare Facility ◽

Allocation Model ◽

Location Allocation ◽

Multi Objective ◽

Conflicting Objectives ◽

Allocation Problems

With rapid development of the healthcare network, the location-allocation problems of public facilities under increased integration and aggregation needs have been widely researched in China’s developing cites. Since strategic formulation involves multiple conflicting objectives and stakeholders, this paper presents a practicable hierarchical location-allocation model from the perspective of supply and demand to characterize the trade-off between social, economical and environmental factors. Due to the difficulties of rationally describing and the efficient calculation of location-allocation problems as a typical Non-deterministic Polynomial-Hard (NP-hard) problem with uncertainty, there are three crucial challenges for this study: (1) combining continuous location model with discrete potential positions; (2) introducing reasonable multiple conflicting objectives; (3) adapting and modifying appropriate meta-heuristic algorithms. First, we set up a hierarchical programming model, which incorporates four objective functions based on the actual backgrounds. Second, a bi-level multi-objective particle swarm optimization (BLMOPSO) algorithm is designed to deal with the binary location decision and capacity adjustment simultaneously. Finally, a realistic case study contains sixteen patient points with maximum of six open treatment units is tested to validate the availability and applicability of the whole approach. The results demonstrate that the proposed model is suitable to be applied as an extensive planning tool for decision makers (DMs) to generate policies and strategies in healthcare and design other facility projects.

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A DECOMPOSITION-COORDINATION METHOD FOR COMPLEX MULTI-OBJECTIVE SYSTEMS

Asia Pacific Journal of Operational Research ◽

10.1142/s0217595909002444 ◽

2009 ◽

Vol 26 (06) ◽

pp. 735-757 ◽

Cited By ~ 2

Author(s):

F. MIGUEL ◽

T. GÓMEZ ◽

M. LUQUE ◽

F. RUIZ ◽

R. CABALLERO

Keyword(s):

Information Exchange ◽

Exchange Process ◽

Efficient Solutions ◽

Pareto Optimal ◽

Relative Importance ◽

Pareto Optimal Solutions ◽

Optimal Solutions ◽

Conflicting Objectives ◽

Upper Level ◽

Problem Instances

The generation of Pareto optimal solutions for complex systems with multiple conflicting objectives can be easier if the problem can be decomposed and solved as a set of smaller coordinated subproblems. In this paper, a new decomposition-coordination method is proposed, where the global problem is partitioned into subsystems on the basis of the connection structure of the mathematical model, assigning a relative importance to each of them. In order to obtain Pareto optimal solutions for the global system, the aforementioned subproblems are coordinated taking into account their relative importance. The scheme that has been developed is an iterative one, and the global efficient solutions are found through a continuous information exchange process between the coordination level (upper level) and the subsystem level (lower level). Computational experiments on several randomly generated problem instances show that the suggested algorithm produces efficient solutions within reasonable computational times.

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A Privacy-Preserving Incentive Mechanism for Participatory Sensing Systems

Security and Communication Networks ◽

10.1155/2018/2593537 ◽

2018 ◽

Vol 2018 ◽

pp. 1-15 ◽

Cited By ~ 1

Author(s):

Xiaoguang Niu ◽

Jiawei Wang ◽

Qiongzan Ye ◽

Yihao Zhang

Keyword(s):

Dynamic Balance ◽

Incentive Mechanism ◽

Privacy Preserving ◽

Circulation System ◽

Participatory Sensing ◽

Sensing Systems ◽

Sensing Applications ◽

Ideal Situation ◽

Conflicting Objectives ◽

Share Information

The proliferation of mobile devices has facilitated the prevalence of participatory sensing applications in which participants collect and share information in their environments. The design of a participatory sensing application confronts two challenges: “privacy” and “incentive” which are two conflicting objectives and deserve deeper attention. Inspired by physical currency circulation system, this paper introduces the notion of E-cent, an exchangeable unit bearer currency. Participants can use the E-cent to take part in tasks anonymously. By employing E-cent, we propose an E-cent-based privacy-preserving incentive mechanism, called EPPI. As a dynamic balance regulatory mechanism, EPPI can not only protect the privacy of participant, but also adjust the whole system to the ideal situation, under which the rated tasks can be finished at minimal cost. To the best of our knowledge, EPPI is the first attempt to build an incentive mechanism while maintaining the desired privacy in participatory sensing systems. Extensive simulation and analysis results show that EPPI can achieve high anonymity level and remarkable incentive effects.

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Optimal Gas-Turbine Design for Hybrid Solar Power Plant Operation

Volume 6: Oil and Gas Applications; Concentrating Solar Power Plants; Steam Turbines; Wind Energy ◽

10.1115/gt2012-68054 ◽

2012 ◽

Author(s):

James Spelling ◽

Björn Laumert ◽

Torsten Fransson

Keyword(s):

Power Plant ◽

Gas Turbine ◽

Power Plants ◽

Solar Power ◽

Pressure Ratio ◽

Inlet Temperature ◽

Electricity Cost ◽

Conflicting Objectives ◽

Dynamic Simulation Model ◽

A Minor

A dynamic simulation model of a hybrid solar gas-turbine power plant has been developed, allowing determination of its thermodynamic and economic performance. In order to examine optimum gas-turbine designs for hybrid solar power plants, multi-objective thermoeconomic analysis has been performed, with two conflicting objectives: minimum levelized electricity costs and minimum specific CO2 emissions. Optimum cycle conditions: pressure-ratio, receiver temperature, turbine inlet temperature and flow rate, have been identified for a 15 MWe gas-turbine under different degrees of solarization. At moderate solar shares, the hybrid solar gas-turbine concept was shown to provide significant water and CO2 savings with only a minor increase in the levelized electricity cost.

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