Multi-objective Environment Research Articles

Phased Method for Solving Multi-Objective MPM Job Shop Scheduling Problem

The project portfolio scheduling problem has become very popular in recent years since many modern organizations operate in multi-project and multi-objective environment. Current project oriented organizations have to design a plan in order to execute a set of projects sharing common resources such as personnel teams. This problem can be seen as an extension of the job shop scheduling problem; the multi-purpose job shop scheduling problem. In this paper, the authors propose a hybrid approach to deal with a bi-objective optimisation problem; Makespan and Total Weighted Tardiness. The approach consists of three phases; in the first phase they utilise a Genetic Algorithm (GA) to generate a set of initial solutions, which are used as inputs to recurrent neural networks (RNNs) in the second phase. In the third phase the authors apply adaptive learning rate and a Tabu Search like algorithm with the view to improve the solutions returned by the RNNs. The proposed hybrid approach is evaluated on some well-known benchmarks and the experimental results are very promising.

Solving a multi-objective transportation problem with nonlinear cost and multi-choice demand

This study develops a mathematical model for a transportation problem consisting of a multi-objective environment with nonlinear cost and multi-choice demand. The objective functions of the proposed transportation problem are non-commensurable and conflict with each other. The focus of the paper is on objective functions of nonlinear type, which occur due to the extra cost of supplying goods remaining at their points of origin to various destinations, and on demand parameters that are considered to be of multi-choice type. Thus, the mathematical model is formulated by considering nonlinear cost and multi-choice demand. Multi-choice programming models cannot be solved directly. A general transformation technique is developed to make multi-choice demand tractable with the help of binary variables. Therefore, an equivalent multi-objective decision making model is established in order to find the optimal solution of the problem. The outcome from a numerical example demonstrates the feasibility of the proposed method.

Volume Preserved Mass-Spring Model with Novel Constraints for Soft Tissue Deformation.

An interactive surgical simulation system needs to meet three main requirements, speed, accuracy, and stability. In this paper, we present a stable and accurate method for animating mass-spring systems in real time. An integration scheme derived from explicit integration is used to obtain interactive realistic animation for a multiobject environment. We explore a predictor-corrector approach by correcting the estimation of the explicit integration in a poststep process. We introduce novel constraints on positions into the mass-spring model (MSM) to model the nonlinearity and preserve volume for the realistic simulation of the incompressibility. We verify the proposed MSM by comparing its deformations with the reference deformations of the nonlinear finite-element method. Moreover, experiments on porcine organs are designed for the evaluation of the multiobject deformation. Using a pair of freshly harvested porcine liver and gallbladder, the real organ deformations are acquired by computed tomography and used as the reference ground truth. Compared to the porcine model, our model achieves a 1.502 mm mean absolute error measured at landmark locations for cases with small deformation (the largest deformation is 49.109 mm) and a 3.639 mm mean absolute error for cases with large deformation (the largest deformation is 83.137 mm). The changes of volume for the two deformations are limited to 0.030% and 0.057%, respectively. Finally, an implementation in a virtual reality environment for laparoscopic cholecystectomy demonstrates that our model is capable to simulate large deformation and preserve volume in real-time calculations.

Heuristically accelerated reinforcement learning modularization for multi-agent multi-objective problems

This article presents two new algorithms for finding the optimal solution of a Multi-agent Multi-objective Reinforcement Learning problem. Both algorithms make use of the concepts of modularization and acceleration by a heuristic function applied in standard Reinforcement Learning algorithms to simplify and speed up the learning process of an agent that learns in a multi-agent multi-objective environment. In order to verify performance of the proposed algorithms, we considered a predator-prey environment in which the learning agent plays the role of prey that must escape the pursuing predator while reaching for food in a fixed location. The results show that combining modularization and acceleration using a heuristics function indeed produced simplification and speeding up of the learning process in a complex problem when comparing with algorithms that do not make use of acceleration or modularization techniques, such as Q-Learning and Minimax-Q.

Multi-objective fuzzy job shop scheduling

The job shop scheduling problem (JSSP) deals with determining schedule for each resource/machine under job and machine flow restrictions such that the selected objective function is satisfied. Multi-objective scheduling is widely used to obtain desirable results in the existence of more than one performance measure in scheduling problems. The paper focuses on multi-objective scheduling in a job shop environment. One of the useful methods in multi-objective environment is the use of fuzzy operators in modelling the system. Fuzzy operators provide the model with characteristics where user can input desired bounds for all of the performance measures with suitable membership functions. In this study, three mathematical models are presented and combined as a multiple objective scheduling model where the considered three objectives are minimising number of tardy jobs, total tardiness and maximum tardiness, respectively. For the solution of the model, fuzzy programming is utilised by using min operator and augmented max-min operator. The model with augmented max min operator found only non-dominated solutions. Then mathematical model optimising two performance measure is also discussed. The quality between solutions obtained from math model optimising two performance measures model versus three performance measures is also discussed.

Statistical multi-objective optimization of electrical discharge machining parameters in machining titanium grade 5 alloy using graphite electrode

In industrial applications, particularly in aero, marine and medical industries, titanium has received great attention as a useful material and electrical discharge machining as its machining process. Selection of optimal machining parameters in a multi-objective environment is essential for specific workpiece and tool material combination, which is the concern of industries to improve the overall productivity at less cost. In this article, optimization of critical electrical discharge machining parameters such as pulse current, on time of pulse, off time of pulse and tool geometry depending on the responses such as titanium machining rate, graphite wear rate, surface roughness and deviation between entry and exit while machining titanium grade 5 alloy with graphite tool electrode at negative polarity is presented. Taguchi’s L27 orthogonal array was used to design the experiment with interaction between factors. The weighing method was used to integrate different objectives into one performance. The optimal combination of process parameters was found statistically using signal-to-noise ratios. Significance was checked by analysis of variance. Optimum parameters were found to be pulse current 15 A, on time of pulse 50 µs, off time of pulse 200 µs and cylindrical tool geometry. Resultant percentage improvements in different responses were presented.

Base station identification in single-frequency network positioning system under mixed line-of-sight/non-line-of-sight condition

A base station (BS) identification algorithm which does not modify the transmitter hardware and digital terrestrial television broadcasting signal framework is proposed for single-frequency networks positioning system. Similar to the outlier detection in multi-object tracking environments, the BS identification is formulated as a data validation problem which is solved with gate construction. Compared with traditional gate algorithm, an additional mode probability parameter is introduced to describe the wireless channel condition and overcome its drawback in non-stationary wireless channel environments by the interacting multiple model approach. In the proposed algorithm, two parallel innovation statistical distribution deduction modes with line-of-sight and non-line-of-sight condition are employed to estimate the corresponding parameters, respectively. Then a combination scheme is utilised to mix the two estimated statistical parameters by the predicted mode probabilities and obtains the final innovation distribution. At last, gate parameter is calculated like traditional gate algorithm and the BS identification is achieved. Simulation results demonstrate its robustness and high performance in different scenarios.

Optimal irrigation planning using multi-objective fuzzy linear programming models

Crisp linear programming approach has been used to get the individual solutions of three conflicting objectives: maximisation of net benefit, maximisation of employment generation and minimisation of cost of cultivation. These crisp solutions were, then, solved together to obtain the solution under fuzzy multi-objective environment with maximum–minimum operators. Also, an effort has been made to improve the solution from maximum-minimum operator approach using two-phase as well as compromised fuzzy approaches. The applicability of the aforesaid algorithms were demonstrated through a case study of Kakrapar Right Bank Main Canal (KRBMC) under Ukai command area in India.

Multi-objective differential evolution application to irrigation planning

In the present study, applicability of Multi-objective Differential Evolution (MODE) in irrigation planning perspective is demonstrated through a case study of Mahi Bajaj Sagar Project, Rajasthan, India. Three objectives, namely, net benefits, agricultural production and labour employment are analysed in the multi-objective environment. Non-dominated alternatives generated by MODE are reduced to a manageable subset with the help of K-means cluster analysis for effective decision making. Optimal number of groups is determined based on the cluster validation indices, namely, Davies–Bouldin and Dunn's. It is concluded that selection of suitable parameters is necessary for effective implementation of above methodologies in real-world planning situations.

Interactive fuzzy programming for multi-level programming problems: a review

Decision-making problems in decentralised organisations are often modelled as Stackelberg games, and they are formulated as two-level mathematical programming problems with two decision-makers. If they do not have any motivation to cooperate mutually and behave rationally, outcomes of the problems can be explained by Stackelberg equilibrium which is not always Pareto optimal. From computational aspects, it is known that solving two-level programming problems is NP-hard even if the objective functions and the constraint functions are linear. In contrast, if the decision-makers can select strategies cooperatively, the most important aspect is to derive a Pareto optimal solution favourable to the decision-makers. As a method of this line of approach interactive fuzzy programming has been developed, taking into account fuzziness of human judgements. In this paper, after reviewing the development of solution methods for two- and multi-level programming problems, we focus on cooperative decision-making in decentralised organisations and give interactive fuzzy programming for two-level linear programming problems, which provides satisfactory solutions in accordance with the preference of the decision-makers. Moreover, we present extensions of interactive fuzzy programming for two-level linear programming problems under multi-objective environments and under uncertainty.

Multi-objective environment chamber system for studying plant responses to climate change

This paper describes the technical information and performance of a new multi-objective chamber system enabling the control of environmental variables (e.g., temperature, CO2, air humidity, wind speed, and UV-B radiation) for understanding plant responses to climate change. Over a whole growing season, four different climate scenarios were evenly programmed into the system's 16 chambers as ambient environment (AMB), elevated temperature (ET), elevated CO2 concentration (EC) and elevated temperature and CO2 concentration (ETC). Simultaneously, the chamber effects were assessed regarding the physiological responses and growth of a boreal perennial grass (reed canary grass, Phalaris arundinacea L.). During the growing season, the chamber system provided a wide variety of climatic conditions for air temperature (T a), relative humidity (RH) and CO2 concentration (C a) in the AMB chambers following outside conditions. The target temperature (+3.5°C) was achieved to a good degree in the ET and ETC chambers, being on average 3.3°C and 3.7°C higher than ambient conditions, respectively. The target concentration of CO2 (700 ppm) was also well achieved in the EC and ETC chambers, being on average 704 ppm and 703 ppm, respectively. The stable airflow condition inside all of the chambers provided a homogeneous distribution of gases and temperature. The decreases in RH and increases in vapour pressure deficit (VPD) in the elevated temperature chambers were also maintained at a low level. Chamber effects were observed, with some physiological and growth parameters of plants being significantly lower in the AMB chambers, compared to outside conditions. The plant growth was negatively affected by the reduced radiation inside the chambers.

CADAC: Multi-use Architecture for Constructive Aerospace Simulations

In today’s network-centric world, aerospace vehicles interact with many objects. They navigate by overhead satellites, synchronize their flight paths with other vehicles, swarm over hostile territory and attack multiple targets. Studying these engagements with high-fidelity constructive simulations has become an important task of modeling and simulation (M&S). The simulation framework Computer Aided Design of Aerospace Concepts (CADAC) has its roots in FORTRAN code that dates back to the 1960s and was used by industry and the U.S. Air Force to simulate aerospace vehicles in all flight environments. To adapt CADAC to the new environment, a complete rewrite was carried out in C++, taking advantage of object-oriented programming techniques. The architecture of CADAC++ is based on the hierarchical structure of inherited classes. The vehicles (aircraft, missiles, satellites or ground targets), inherit the six-degree-of-freedom (6-DoF) equations of motion from the classes ‘Flat6’ or ‘Round6’, conveying either the flat or elliptical Earth model. In turn, these classes inherit the communication structure from the base class ‘Cadac’. The components of the vehicle, e.g., aerodynamics, propulsion and autopilot, are represented by modules, which are member functions of the vehicle class. Communication among the modules occurs by protected module-variable arrays. Every instantiated vehicle object is encapsulated with its methods and data. To communicate between vehicles, data packets are loaded onto a global data bus for recall by other vehicles. Input occurs by ASCII file and output is compatible with CADAC Studio, a plotting and data processing package. CADAC++ is chiefly an engineering tool for refining the components of the primary vehicle and exploring its performance as it interacts (possibly repeatedly instantiated) with the multi-object environment. Its modular structure enables reuse of component models across simulations. In the 10 years of development, CADAC++ based constructive simulations have been built for many types of aerospace vehicles and integrated with mission-level simulations.

Stakeholder Consensus Building in Multiobjective Environments

This paper introduces a method that assists in the evaluation of project and policy alternatives by identifying stakeholder reactions not only to the predicted economic, environmental, and social impacts but also to the distribution and balance of those impacts among all stakeholders. Gross impact (GI) and degree of impact imbalance (IB) evaluation criteria are adopted for multiobjective analyses in two water resources planning studies. In both studies, stakeholders converted various quantitative and qualitative indicators of economic, environmental, social, financial, institutional, and technical project objectives into GI and IB measures for the evaluation and ranking of alternative projects or policies. In these two studies, having both the GI and IB measures helped the stakeholders during the project formulation and screening, ranking, and consensus-building phases of the studies. Although applied to water resources planning projects, this approach to multiobjective stakeholder-driven planning or poli...

Dynamic clothing collision resolution using PSO

This paper presents a novel stochastic method for dynamic clothing simulation and collision detection by using a Particle Swarm Optimisation (PSO) algorithm. Firstly, we convert the collision detection problem to a multi-objective dynamic environment optimisation problem. Then, we use a density-based clustering PSO to find optimal or near-optimal solutions. During the searching process, we also put forward a restrictive fly algorithm to prevent particles from flying out of the solution space. Experimental results demonstrate that PSO has good performances in our optimisation problem. Besides, with active pair searching and Bounding Volume Hierarchies (BVHs), our method can achieve high detection ratio and balanced performances at interactive rates.

A new dispatching rule based genetic algorithm for the multi-objective job shop problem

Hyper-heuristics or "methodologies to choose heuristics" are becoming increasingly popular given their suitability to solve hard real world combinatorial optimisation problems. Their distinguishing feature is that they operate in the space of heuristics or heuristic components rather than in the solution space. In Dispatching Rule Based Genetic Algorithms (DRGA) solutions are represented as sequences of dispatching rules which are called one at a time and used to sequence a number of operations onto machines. The number of operations that each dispatching rule in the sequence handles is a parameter to which DRGA is notoriously sensitive. This paper proposes a new hybrid DRGA which searches simultaneously for the best sequence of dispatching rules and the number of operations to be handled by each dispatching rule. The investigated DRGA uses the selection mechanism of NSGA-II when handling multi-objective problems. The proposed representation was used to solve different variants of the multi-objective job shop problem as well as the single objective problem with the sum of weighted tardiness objective. Our results, supported by the statistical analysis, confirm that DRGAs that use the proposed representation obtained better results in both the single and multi-objective environment overall and on each particular set of instances than DRGAs using the conventional dispatching rule representation and a GA that uses the more common permutation representation.

Active-Vision for the Autonomous Surveillance of Dynamic, Multi-Object Environments

This paper presents a novel method for active-vision-based sensing-system reconfiguration for the autonomous surveillance of an object-of-interest as it travels through a multi-object dynamic workspace with an a priori unknown trajectory. Several approaches have been previously proposed to address the problem of sensor selection and control. However, these have primarily relied on off-line planning methods and rarely utilized on-line planning to compensate for unexpected variations in a target's trajectory. The method proposed in this paper, on the other hand, uses a multi-agent system for on-line sensing-system reconfiguration, eliminating the need for any a priori knowledge of the target's trajectory. Thus, it is robust to unexpected variations in the environment. Simulations and experiments have shown that the use of dynamic sensors with the proposed on-line reconfiguration algorithm can tangibly improve the performance of an active-surveillance system.

IFMEP: an interval fuzzy multiobjective environmental planning model for urban systems

As a complex system, the urban environment usually presents multiobjective, uncertain, and dynamic characteristics, which leads to difficulties in urban environmental planning. In this study, an interval fuzzy multiobjective programming method is proposed for tackling such problems and the associated solution algorithm is also discussed. Based on the proposed method, an interval fuzzy multiobjective environment planning model is further developed with specific focus on urban systems. The proposed approach allows uncertainties and comprehensive interaction of system components to be effectively communicated into the optimisation process. Furthermore, using scenario analysis and interactive solution processes, stakeholder preferences are efficiently integrated, promoting feasibility and robustness of decisions. The developed model was tested by a real-world case study in Kunming City, China. An analysis of the results shows that the proposed approach is an effective means for urban environmental planning.

A fuzzy approach to the generation expansion planning problem in a multi-objective environment

In many power system problems, the use of optimization techniques has proved inductive to reducing the costs and losses of the system. A fuzzy multi-objective decision is used for solving power system problems. One of the most important issues in the field of power system engineering is the generation expansion planning problem. In this paper, we use the concepts of membership functions to define a fuzzy decision model for generating an optimal solution for this problem. Solutions obtained by the fuzzy decision theory are always efficient and constitute the best compromise. .

The assets, threats and solvability (ATS) model for setting environmental priorities

Environmental and natural resource management agencies routinely encounter the task of setting priorities. Limited time, staff and financial resources result in some projects, programmes or issues being given greater attention than others. In multi-stakeholder and multi-objective settings the decision-making process is complex. The task is often made harder by incomplete or inaccurate datasets. Decision makers will aim to adopt a procedure that is analytically robust, auditable, transparent and understandable. This paper presents the assets, threats and solvability (ATS) model for structuring an environmental priority-setting problem. A brief illustration of its use for setting priorities in the Wet Tropics of North Queensland is given. The ATS model provides a structured decision procedure applicable with limited information in a multi-objective policy environment. It helps guide the selection and application of priority-setting criteria under these conditions.

Modelling uncertainties in short-term reservoir operation using fuzzy sets and a genetic algorithm / Modélisation d’incertitudes dans la gestion de barrage à court terme grâce à des ensembles flous et à un algorithme génétique

Various uncertainties are inherent in modelling any reservoir operation problem. Two of these are addressed in this study: uncertainty involved in the expression of reservoir penalty functions, and uncertainty in determining the target release value. Fuzzy set theory was used to model these uncertainties where the preferences of the decision maker for the fuzzified parameters are expressed as membership functions. Nonlinear penalty functions are used to determine the penalties due to deviations from targets. The optimization was performed using a genetic algorithm with the objectives to minimize the total penalty and to maximize the level of satisfaction of the decision maker with fuzzified input parameters. The proposed formulation was applied to the problem of finding the optimal release and storage values, taking Green reservoir in Kentucky, USA as a case study. The approach offers more flexibility to reservoir decision-making by demonstrating an efficient way to represent subjective uncertainties, and to deal with non-commensurate objectives under a fuzzy multi-objective environment.