Institution
SolveIT Software
About: SolveIT Software is a based out in . It is known for research contribution in the topics: Evolutionary algorithm & Supply chain. The organization has 15 authors who have published 25 publications receiving 343 citations.
Topics: Evolutionary algorithm, Supply chain, Evolutionary computation, Supply chain management, Optimization problem
Papers
20 Jun 2013
TL;DR: It is argued that real-world problems usually consist of two or more sub-problems that are interdependent (to each other), and this interdependence is responsible for the complexity of the real- world problems, while the type of complexity in current benchmark problems is missing.
Abstract: There are some questions concerning the applicability of meta-heuristic methods for real-world problems; further, some researchers claim there is a growing gap between research and practice in this area. The reason is that the complexity of real-world problems is growing very fast (e.g. due to globalisation), while researchers experiment with benchmark problems that are fundamentally the same as those of 50 years ago. Thus there is a need for a new class of benchmark problems that reflect the characteristics of real-world problems. In this paper, two main characteristics of real-world problems are introduced: combination and interdependence. We argue that real-world problems usually consist of two or more sub-problems that are interdependent (to each other). This interdependence is responsible for the complexity of the real-world problems, while the type of complexity in current benchmark problems is missing. A new problem, called the travelling thief problem, is introduced; it is a combination of two well-known problems, the knapsack problem and the travelling salesman problem. Some parameters which are responsible for the interdependence of these two sub-problems are defined. Two sets of parameters are introduced that result in generating two instances of the travelling thief problem. The complexities that are raised by interdependences for these two instances are discussed in detail. Finally, a procedure for generating these two instances is given.
166 citations
1 Jan 2007
TL;DR: This chapter contains a general discussion on the prediction and optimization issues present in dynamic environments, and explains the ideas behind Adaptive Business Intelligence.
Abstract: This chapter contains a general discussion on the prediction and optimization issues present in dynamic environments, and explains the ideas behind Adaptive Business Intelligence. The chapter also presents three diverse case studies. The first deals with pollution control, the second one with ship navigation, and the third one with car distribution. All these problems are set in dynamic environments; all three problems require some level of prediction (prediction of weather for pollution optimization, prediction of paths for unidentified ships at sea, and prediction of prices for cars sold at different auction sites). All these problems also require optimization for recommending the best course of action.
53 citations
TL;DR: A software system that combines prediction, optimization, and adaptation techniques has generated impressive profits for a large auto manufacturer.
Abstract: Information technology applications that support decision-making processes and problem-solving activities have proliferated and evolved Distributing used cars to various automobile auctions is a complicated problem with multiple variables We developed a software system to address these complexities and implemented it on a real distribution problem for a large car manufacturer The system detects data trends in a dynamic environment, incorporates optimization modules to recommend a near-optimum decision, and includes self-learning modules to improve future recommendations A software system that combines prediction, optimization, and adaptation techniques has generated impressive profits for a large auto manufacturer
43 citations
TL;DR: A decision-support system that optimises multi-component operational problems is of great importance for an organisation and supports what-if analysis for operational and strategic decisions and trade-off analysis to handle multi-objective optimisation problems.
Abstract: This article describes a decision-support system that was developed in 2011 and is currently in production use. The purpose of the system is to assist planners in constructing delivery schedules of water tanks to often remote areas in Australia. A delivery schedule consists of a number of delivery trips by trucks. An optimal delivery schedule minimises cost to deliver a given total sales value of delivered products. To construct an optimal delivery schedule, trucks need to be optimally packed with water tanks and accessories to be delivered to a set of delivery locations. This packing problem, which involves many packing and loading constraints, is intertwined with the transport problem of minimising distance travelled by road. Such a decision-support system that optimises multi-component operational problems is of great importance for an organisation; it supports what-if analysis for operational and strategic decisions and trade-off analysis to handle multi-objective optimisation problems; it is capable of handling and analysing variances; it is easy to modify – constraints, business rules, and various assumptions can be re-configured by a client. Construction of such decision-support systems requires the use of heuristic methods rather than linear/integer programming.
30 citations
TL;DR: The results presented in this paper show that CMA-ES is able to generate maps that fulfil the desired conditions, and any optimal route for these maps should differ distinctively from: 1) the optimal distance-based TSP route and 2) the route that corresponds to always approaching the nearest waypoint first.
Abstract: This paper presents a method for generating complex problems that allow multiple nonobvious solutions for the physical traveling salesman problem (PTSP). PTSP is a single-player game adaptation of the classical traveling salesman problem that makes use of a simple physics model: the player has to visit a number of waypoints as quickly as possible by navigating a ship in real time across an obstacle-filled 2-D map. The difficulty of this game depends on the distribution of waypoints and obstacles across the 2-D plane. Due to the physics of the game, the shortest route is not necessarily the fastest, as the ship's momentum makes it difficult to turn sharply at high speed. This paper proposes an evolutionary approach to obtaining maps where the optimal solution is not immediately obvious. In particular, any optimal route for these maps should differ distinctively from: 1) the optimal distance-based TSP route and 2) the route that corresponds to always approaching the nearest waypoint first. To achieve this, the evolutionary algorithm covariance matrix adaptation-evolutionary strategy (CMA-ES) is employed, where maps, indirectly represented as vectors of real numbers, are evolved to differentiate maximally between a game-playing agent that follows two or more different routes. The results presented in this paper show that CMA-ES is able to generate maps that fulfil the desired conditions.
27 citations
Authors
Showing all 15 results
| Name | H-index | Papers | Citations |
|---|---|---|---|
| Zbigniew Michalewicz | 63 | 265 | 39013 |
| Ralf Zurbruegg | 26 | 106 | 2453 |
| Luigi Barone | 21 | 63 | 3495 |
| Neal Wagner | 11 | 41 | 534 |
| Philipp Rohlfshagen | 11 | 14 | 2475 |
| Thuy-Duong To | 10 | 32 | 317 |
| Martin Schmidt | 9 | 18 | 367 |
| Arvind Mohais | 6 | 16 | 126 |
| Sven Schellenberg | 6 | 16 | 109 |
| Constantin Chiriac | 4 | 7 | 233 |
| Matthew Michalewicz | 4 | 6 | 253 |
| Jacob Stolk | 1 | 2 | 25 |
| G. Smith | 1 | 1 | 2 |
| Isaac Mann | 1 | 2 | 25 |
| Maris Ozols | 0 | 1 | 0 |
Related Institutes (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2014 | 2 |
| 2013 | 7 |
| 2012 | 3 |
| 2011 | 2 |
| 2010 | 3 |
| 2009 | 2 |