A Tool for Visually Exploring Multi-objective Mixed-Integer Optimization Models

Multi-objective optimization models have been increasingly used as optimal decisions are searched in settings considering several conflicting objectives. In these cases compromises must be made and often a large number of nondominated optimal solutions exist. From these solutions decisionmakers must find the preferred one. This is a difficult task both from a computational and cognitive point of views, as it requires several solutions to be obtained and compared. An interactive visualization tool for fully understanding the best trade-offs is therefore becoming increasingly important. This paper proposes visualization solutions, implemented in a tool, for aiding decision-makers in finding the preferred solution in multiobjective optimization problems

Biomorpher: interactive evolution for parametric design

Combining graph-based parametric design with metaheuristic solvers has to date focussed solely on performance based criteria and solving clearly defined objectives. In this paper, we outline a new method for combining a parametric modelling environment with an interactive Cluster-Orientated Genetic Algorithm (COGA). In addition to performance criteria, evolutionary design exploration can be guided through choice alone, with user motivation that cannot be easily defined. As well as numeric parameters forming a genotype, the evolution of whole parametric definitions is discussed through the use of genetic programming. Visualisation techniques that enable mixing small populations for interactive evolution with large populations for performance-based optimisation are discussed, with examples from both academia and industry showing a wide range of applications

A DSS generator for multiobjective optimisation of spreadsheet-based models

Copyright © 2011 Elsevier. NOTICE: this is the author’s version of a work that was accepted for publication in Environmental Modelling & Software. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Environmental Modelling & Software Vol. 26 (2011), DOI: 10.1016/j.envsoft.2010.11.004Water management practice has benefited from the development of model-driven Decision Support Systems (DSS), and in particular those that combine simulation with single or multiple-objective optimisation tools. However, there are many performance, acceptance and adoption problems with these decision support tools caused mainly by misunderstandings between the communities of system developers and users. This paper presents a general-purpose decision-support system generator, GANetXL, for developing specific applications that require multiobjective optimisation of spreadsheet-based models. The system is developed as an Excel add-in that provides easy access to evolutionary multiobjective optimisation algorithms to non-specialists by incorporating an intuitive interactive graphical user interface that allows easy creation of specific decision-support applications. GANetXL’s utility is demonstrated on two examples from water engineering practice, a simple water supply reservoir operation model with two objectives and a large combinatorial optimisation problem of pump scheduling in water distribution systems. The two examples show how GANetXL goes a long way toward closing the gap between the achievements in optimisation technology and the successful use of DSS in practice.Engineering and Physical Sciences Research Council (EPSRC

Modeling order guidelines to improve truckload utilization

Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 36-37).Freight vehicle capacity, whether it be road, ocean or air transport, is highly underutilized. This under-utilization presents an opportunity for companies to reduce their vehicular traffic and reduce their carbon footprint through greater supply chain integration. This thesis describes the impact of ordering guidelines on the transport efficiency of a large firm and how those guidelines and associated practices can be changed in order to gain better efficiency. To that end, we present three recommendations on improving the guidelines based on the shipment data analysis. First, we discuss the redundancy of one of the company's fill metrics based on a scatter plot analysis and a chi-square independence test. Second, we explore the impact of using linear programming to allocate SKUs to different shipment, highlighting the reduction in the number of shipments through better truck mixing. Finally, we divide the SKUs into three groups: cube-constrained, neutral, and weight-constrained. Based on this segmentation, we present a basic model that mixes different SKUs and helps a shipment to achieve a much higher utilization rate. The application of the last two findings can be further explored to address under-utilization in freight carriers across different industries.by Jaya Banik and Kyle Rinehart.M.Eng.in Logistic

Sustainable waste solutions: Optimizing location-allocation of 3R waste management sites in Gondokusuman, Yogyakarta, Indonesia through multi-maximal covering location approach

Developing a Multi-Maximal Covering Location Model (MMCLM) for waste management in Gondokusuman Sub-district, Yogyakarta, Indonesia, is urgently needed. The closure of the Piyungan landfill has resulted in the need for additional Reduce, Reuse, and Recycle Waste Management Sites (3R-WMSs) to reduce waste that the landfill cannot accommodate. The primary objective of this model is to optimize the location and allocation of demand volume nodes, representing the resident population, to a specific set of 3R-WMS. These demand nodes are located at different distances from 3R-WMSs, including high and low coverage areas. The research in the Gondokusuman Sub-district employed MMCLM with facility capacity constraints and was developed using mixed integer linear programming methodology. The study identified five optimal locations for a 3R-WMS establishment that comprehensively cover all demand nodes (15301) and population clusters (45903) in the sub-district, including both high (5085) and low coverage areas (10216). This research represents a significant step forward in developing a sustainable environment by ensuring easy access to reducing, reusing, and recycling-based waste management facilities for residents

A methodology for designing flexible multi-generation systems

An FMG (flexible multi-generation system) consists of integrated and flexibly operated facilities that provide multiple links between the various layers of the energy system. FMGs may facilitate integration and balancing of fluctuating renewable energy sources in the energy system in a cost- and energy efficient way, thereby playing an important part in smart energy systems. The development of efficient FMGs requires systematic optimization approaches. This study presents a novel, generic methodology for designing FMGs that facilitates quick and reliable pre-feasibility analyses. The methodology is based on consideration of the following points: Selection, location and dimensioning of processes; systematic heat and mass integration; flexible operation optimization with respect to both short-term market fluctuations and long-term energy system development; global sensitivity and uncertainty analysis; biomass supply chains; variable part-load performance; and multi-objective optimization considering economic and environmental performance. Tested in a case study, the methodology is proved effective in screening the solution space for efficient FMG designs, in assessing the importance of parameter uncertainties and in estimating the likely performance variability for promising designs. The results of the case study emphasize the importance of considering systematic process integration when developing smart energy systems. (C) 2016 Elsevier Ltd. All rights reserved