Interactive Optimization Method Research Articles

Multi-Objective Optimization of Customer-Centered Intermodal Freight Routing Problem Based on the Combination of DRSA and NSGA-III

The satisfaction of requirements and preferences of shippers is critical to enable the practicability of solutions that are derived from intermodal transportation routing problems. This study aims to propose a decision process to help shippers participate better in routing decisions. First, we considered shippers’ requests on transportation cost, timeliness, reliability, and flexibility to construct a multi-objective optimization model. Then, to solve the interactive optimization method that was proposed, NSGA-III was applied to obtain the Pareto front and dominance-based rough set approach to model the preference information. Finally, a case study was conducted and an expert was invited as decision-maker to demonstrate the applicability of the proposed model and the effectiveness of the interactive method for shippers. The results are expected to provide shippers with more rational transportation schemes and insights for the sustainable development of intermodal transportation.

Intelligent Optimization of Diversified Community Prevention of COVID-19 Using Traditional Chinese Medicine

Traditional Chinese medicine (TCM) has played an important role in the prevention and control of the novel coronavirus pneumonia (COVID-19), and community prevention has become the most essential part in reducing the risk of spread and protecting public health. However, most communities use a unified TCM prevention program for all residents, which violates the "treatment based on syndrome differentiation" principle of TCM and limits the effectiveness of prevention. In this paper, we propose an intelligent optimization method to develop diversified TCM prevention programs for community residents. First, we use a fuzzy clustering method to divide the population based on both modern medicine and TCM health characteristics; we then use an interactive optimization method, in which TCM experts develop different TCM prevention programs for different clusters, and a heuristic algorithm is used to optimize the programs under the resource constraints. We demonstrate the computational efficiency of the proposed method, and report the application results of the method in TCM-based prevention of COVID-19 in 12 communities in Zhejiang province, China, during the peak of the pandemic.

Interactive Optimization With Parallel Coordinates: Exploring Multidimensional Spaces for Decision Support

Interactive optimization methods are particularly suited for letting human decision makers learn about a problem, while a computer learns about their preferences to generate relevant solutions. For interactive optimization methods to be adopted in practice, computational frameworks are required, which can handle and visualize many objectives simultaneously, provide optimal solutions quickly and representatively, all while remaining simple and intuitive to use and understand by practitioners. Addressing these issues, this work introduces SAGESSE (Systematic Analysis, Generation, Exploration, Steering and Synthesis Experience), a decision support methodology, which relies on interactive multiobjective optimization. Its innovative aspects reside in the combination of (i) parallel coordinates as a means to simultaneously explore and steer the underlying alternative generation process, (ii) a Sobol sequence to efficiently sample the points to explore in the objective space, and (iii) on-the-fly application of multiattribute decision analysis, cluster analysis and other data visualization techniques linked to the parallel coordinates. An illustrative example demonstrates the applicability of the methodology to a large, complex urban planning problem.

Coherent Semantic-Visual Indexing for Large-Scale Image Retrieval in the Cloud.

The rapidly increasing number of images on the internet has further increased the need for efficient indexing for digital image searching of large databases. The design of a cloud service that provides high efficiency but compact image indexing remains challenging, partly due to the well-known semantic gap between user queries and the rich semantics of large-scale data sets. In this paper, we construct a novel joint semantic-visual space by leveraging visual descriptors and semantic attributes, which narrows the semantic gap by combining both attributes and indexing into a single framework. Such a joint space embraces the flexibility of coherent semantic-visual indexing, which employs binary codes to boost retrieval speed while maintaining accuracy. To solve the proposed model, we make the following contributions. First, we propose an interactive optimization method to find the joint semantic and visual descriptor space. Second, we prove convergence of our optimization algorithm, which guarantees a good solution after a certain number of iterations. Third, we integrate the semantic-visual joint space system with spectral hashing, which finds an efficient solution to search up to billion-scale data sets. Finally, we design an online cloud service to provide a more efficient online multimedia service. Experiments on two standard retrieval datasets (i.e., Holidays1M, Oxford5K) show that the proposed method is promising compared with the current state-of-the-art and that the cloud system significantly improves performance.

Exploring the Environment/Energy Pareto Optimal Front of an Office Room Using Computational Fluid Dynamics-Based Interactive Optimization Method

This paper is concerned with the development of a high-resolution and control-friendly optimization framework in enclosed environments that helps improve thermal comfort, indoor air quality (IAQ), and energy costs of heating, ventilation and air conditioning (HVAC) system simultaneously. A computational fluid dynamics (CFD)-based optimization method which couples algorithms implemented in Matlab with CFD simulation is proposed. The key part of this method is a data interactive mechanism which efficiently passes parameters between CFD simulations and optimization functions. A two-person office room is modeled for the numerical optimization. The multi-objective evolutionary algorithm—non-dominated-and-crowding Sorting Genetic Algorithm II (NSGA-II)—is realized to explore the environment/energy Pareto front of the enclosed space. Performance analysis will demonstrate the effectiveness of the presented optimization method.

A Review and Taxonomy of Interactive Optimization Methods in Operations Research

This article presents a review and a classification of interactive optimization methods. These interactive methods are used for solving optimization problems. The interaction with an end user or decision maker aims at improving the efficiency of the optimization procedure, enriching the optimization model, or informing the user regarding the solutions proposed by the optimization system. First, we present the challenges of using optimization methods as a tool for supporting decision making, and we justify the integration of the user in the optimization process. This integration is generally achieved via a dynamic interaction between the user and the system. Next, the different classes of interactive optimization approaches are presented. This detailed review includes trial and error, interactive reoptimization, interactive multiobjective optimization, interactive evolutionary algorithms, human-guided search, and other approaches that are less well covered in the research literature. On the basis of this review, we propose a classification that aims to better describe and compare interaction mechanisms. This classification offers two complementary views on interactive optimization methods. The first perspective focuses on the user’s contribution to the optimization process, and the second concerns the components of interactive optimization systems. Finally, on the basis of this review and classification, we identify some open issues and potential perspectives for interactive optimization methods.

Optimal Temperature and pH Control for a Batch Simultaneous Saccharification and Co-Fermentation Process

Crisp and fuzzy optimization approaches were applied to design an optimal temperature and pH control policy for a batch process of simultaneous saccharification and co-fermentation (SSCF) for ethanol production from lignocellulose, using the enzyme and recombinant strain Zymomonas mobilis ZM (pZB5). To determine an optimal temperature and pH control policy, we applied the Arrhenius relationship to each rate constant to express the temperature and pH effects in the kinetic model for both saccharification and fermentation. The goal of the optimal design was to determine the optimal temperature, pH value, initial lignocellulosic concentration, and fermentation time for maximizing the ethanol productivity under the constraints of the follow-up separation specifications. The interactive crisp and fuzzy optimization methods were applied to solve the trade-off optimization problems for obtaining a compromised design. The fuzzy goal attainment approach obtained a compromised design more flexibly than did the crisp optimization. We also compared the performances for batch and fed-batch SSCF, and used various composition proportions for the batch SSCF to determine a series of optimal designs for the fuzzy goal attainment problem. Batch SSCF was slightly more effective than fed-batch fermentation, and spruce exhibited the maximum productivity because of its higher cellulose and lower hemicellulose contents compared with those of other sources.

Bi-objective optimization of induction machine using interval-based interactive algorithms

Purpose – Discrete highly constrained optimization of induction machine taking into consideration two objective functions: efficiency and total costs of production. The paper aims to discuss these issues. Design/methodology/approach – Interactive and semi-interactive interval-based optimization methods were used. Two concepts of multi-objective discrete optimization were proposed. Findings – Proposed methodology and algorithms allow decision maker (DM) participate in the process of optimal design and therefore decrease the total time of optimization process. The search procedure is straightforward and it does not require special skills of DM. Presented methods were successfully versified for the problem of optimal design with discrete variables. Research limitations/implications – Three interval algorithms suitable for inverse problems are researched and verified. It generally can be used for multi-objective problems. The dominance principles for interval boxes are showed in the paper. Proposed algorithms are based on the idea of hybridization of exact and evolutionary methods. Practical implications – Proposed approaches were successfully implemented within computer-aided application which is used by manufacturer of high power induction machine. Originality/value – The concept of pareto-domination using the interval boxes can be treated as original. The paper researched several elimination rules and discusses the difference between different approaches.

Including preferences into a multiobjective evolutionary algorithm to deal with many-objective engineering optimization problems

In this paper, we introduce a new preference relation based on a reference point approach. This relation offers an easy approach to integrate decision maker’s preferences into a MOEA without modifying its basic structure. Besides finding the optimal solution of the achievement scalarizing function, the new preference relation allows the decision maker to find a set of solutions around that optimal solution. Then, a MOEA equipped with the proposed preference relation can be integrated into an interactive optimization method. One of the main advantages of the new method is that setting its parameters is an intuitive task to the decision maker. The other advantage is that, since our preference relation induces a finer order on vectors of objective space than that achieved by the Pareto dominance relation, it is appropriate to cope with problems having a high number of objectives. We evaluated the proposed preference relation an engineering problem, the optimization of an airfoil design with 6 objectives.

Analysis of a Novel Traverse Mechanism Driven by Non-Circular Gears with Fourier Pitch-Line Applied on Silk Reeling Machine

Insufficiency of traditional EB-type silk reeling machine was pointed out after analyzing its traverse mechanism. A novel traverse mechanism driven by non-circular gears with Fourier pitch-line was proposed in order to make up the insufficiency of the traditional EB-type machine. Kinematical mathematic model of this mechanism was established and an aided analysis and simulation software was compiled. A group of preferable parameters was obtained by means of human-computer interactive optimization method. New advantages of uniform roundtrip velocity and better silk winding shape were achieved by the novel traverse mechanism compared to the traditional EB-type mechanism.

A Case-Based Micro Interactive Genetic Algorithm (CBMIGA) for interactive learning and search: Methodology and application to groundwater monitoring design

Interactive optimization algorithms use real–time interaction to include decision maker preferences based on the subjective quality of evolving solutions. In water resources management problems where numerous qualitative criteria exist, use of such interactive optimization methods can facilitate in the search for comprehensive and meaningful solutions for the decision maker. The decision makers using such a system are, however, likely to go through their own learning process as they view new solutions and gain knowledge about the design space. This leads to temporal changes (nonstationarity) in their preferences that can impair the performance of interactive optimization algorithms. This paper proposes a new interactive optimization algorithm – Case-Based Micro Interactive Genetic Algorithm – that uses a case-based memory and case-based reasoning to manage the effects of nonstationarity in decision maker’s preferences within the search process without impairing the performance of the search algorithm. This paper focuses on exploring the advantages of such an approach within the domain of groundwater monitoring design, though it is applicable to many other problems. The methodology is tested under non-stationary preference conditions using simulated and real human decision makers, and it is also compared with a non-interactive genetic algorithm and a previous version of the interactive genetic algorithm.

Decision-maker's preferences modelling within the interactive imprecise goal programming model

This paper proposes an interactive optimisation method for imprecise multiple-objective decision-making situations. The aim of the proposed approach is to integrate explicitly the decision-maker's (DMs) preferences within the interactive imprecise goal programming model. The DMs preferences will be expressed through the satisfaction functions concept. The new interactive imprecise goal programming approach is solved by the taboo central memory-simulated annealing algorithm. The use of this meta-heuristic allows the proposed approach to deal with large scale decision-making situations. The proposed approach will be illustrated through some examples.

Development of a Runway Allocation Optimisation Model for Airport Strategic Planning

This paper describes the development of a runway allocation optimisation model to be used for airport strategic planning. It optimises the allocation of flights to runways on an annual basis, with respect to delay, noise and safety. The multi-objective optimisation is subject to a number of constraints, related to operational procedures, runway capacity and weather conditions. To reach a final non-dominated solution for the multi-objective problem, an interactive optimisation method has been implemented. This has resulted in a very convenient and easy-to-use optimisation procedure. Although the model has to be extended to handle more complex operational situations, the results with respect to the reduction of aircraft noise annoyance and total third-party risk are promising.

Multicriterion optimization of composite laminates for maximum failure margins with an interactive descent algorithm

An interactive multicriterion optimization method for composite laminates subjected to multiple loading conditions is introduced. Laminate margins to initial failure (First Ply Failure, FPF) with respect of the applied loading conditions are treated as criteria. The original problem is reduced to a bicriterion problem by introducing parameters to combine linearly some criteria. The problem is solved by using an interactive descent algorithm. Both the conditions required for a discrete procedure to converge towards a Pareto optimum and numerical examples are given.

Generation of efficient frontiers in multi-objective optimization problems by generalized data envelopment analysis

In many practical problems such as engineering design problems, criteria functions cannot be given explicitly in terms of design variables. Under this circumstance, values of criteria functions for given values of design variables are usually obtained by some analyses such as structural analysis, thermodynamical analysis or fluid mechanical analysis. These analyses require considerably much computation time. Therefore, it is not unrealistic to apply existing interactive optimization methods to those problems. On the other hand, there have been many trials using genetic algorithms (GA) for generating efficient frontiers in multi-objective optimization problems. This approach is effective in problems with two or three objective functions. However, these methods cannot usually provide a good approximation to the exact efficient frontiers within a small number of generations in spite of our time limitation. The present paper proposes a method combining generalized data envelopment analysis (GDEA) and GA for generating efficient frontiers in multi-objective optimization problems. GDEA removes dominated design alternatives faster than methods based on only GA. The proposed method can yield desirable efficient frontiers even in non-convex problems as well as convex problems. The effectiveness of the proposed method will be shown through several numerical examples.

Interactive multiobjective optimization system WWW-NIMBUS on the Internet

NIMBUS is a multiobjective optimization method capable of solving nondifferentiable and nonconvex problems. We describe the NIMBUS algorithm and its implementation WWW-NIMBUS. To our knowledge WWW-NIMBUS is the first interactive multiobjective optimization system on the Internet. The main principles of its implementation are centralized computing and a distributed interface. Typically, the delivery and update of any software is problematic. Limited computer capacity may also be a problem. Via the Internet, there is only one version of the software to be updated and any client computer has the capabilities of a server computer. Further, the World-Wide Web (WWW) provides a graphical user interface. No special tools, compilers or software besides a WWW browser are needed. Scope and purpose Interaction between the decision maker and the solution algorithm is often necessary for finding solutions to optimization problems with several conflicting criteria. The Internet provides a versatile tool in realizing such an interaction. The Internet is easily available and sets minimal requirements to the computer facilities of the user. We describe an interactive optimization method and its implementation utilizing the Internet.

APPLICATION OF COMPUTER NETWORK IN INTERACTIVE OPTIMIZATION

An interactive optimization method for multiple criteria problems by using computer network has been developed. The advantages are as follows: the problem can be solved by many computers in parallel; the researcher is active in the optimization process that makes it possible to discover a new knowledge of the problem.

PID controller tuning by an interactive multi-objective optimisation method

An interactive multiobjective optimisation method was introduced in this study to tune satisfactory modes of a PID controller. An interval process was modelled by a set of transfer functions in this study. In general, such processes could not be handled by a PID controller which was tuned by a single-objective optimisation method. A trade-off tuning rule based on multi-objective optimisation methods was introduced to overcome this drawback. The robust stability for this multi-modelling control system performed by the multi-objective optimisation approach was automatically guaranteed. Extension to cascade control systems was also developed.

Sequencing Mixed-Model Assembly Lines: A Multi-Criteria and Flexible Approach

Abstract This paper presents a flexible and multi-criteria approach to sequence Mixed-Model Assembly Lines according to two kinds of goals : smoothing the flow of work for resources and other sequencing goals (Part Usage Goal, Car Sequencing Problem.). This can be achieved thanks to several iterating and interactive optimization methods. These methods use an integrated data model which includes multi-station resources, any assembly line where products move simultaneously, and potential goals. By this way, release and due dates for jobs can be computed, and optimization criteria can be defined and dynamically linked to products or resources, according to circumstances.

Some remarks on the vector optimization method of fandel

The paper investigates properties of the normal vectors of hyperplanes, applied in the interactive vector optimization method of Fandel. This work was inspirated by a numeric example of Rietveld. In this example the corresponding hyperplane can not be used for the determination of an efficient decision. For this case we will give some conclusions concerning the properties of the vector optimization problem. Furthermore we propose two possibilities to continue the interactive method of Fandel if at the determination of the hyperplane this special case occurs.