Route Planning with Dynamic Information from the EPLOS System

The paper presents the problem of distribution route planning with dynamic information about sudden customers\u27 needs. Particular attention was paid to dynamic vehicle route planning and its influence on the distance covered by a distribution vehicle. In the article, authors assume that the quick information about customers’ sudden needs is transferred from the EPLOS tool data base. Authors analyze the available literature on transport route optimization and propose a solution to the problem of distribution among customers with sudden needs. In order to present the impact of quick information influence on the distribution route minimization, a simulation model of the vehicle routing problem was generated in the FlexSim environment

The Dynamic Multi-objective Multi-vehicle Covering Tour Problem

This work introduces a new routing problem called the Dynamic Multi-Objective Multi-vehicle Covering Tour Problem (DMOMCTP). The DMOMCTPs is a combinatorial optimization problem that represents the problem of routing multiple vehicles to survey an area in which unpredictable target nodes may appear during execution. The formulation includes multiple objectives that include minimizing the cost of the combined tour cost, minimizing the longest tour cost, minimizing the distance to nodes to be covered and maximizing the distance to hazardous nodes. This study adapts several existing algorithms to the problem with several operator and solution encoding variations. The efficacy of this set of solvers is measured against six problem instances created from existing Traveling Salesman Problem instances which represent several real countries. The results indicate that repair operators, variable length solution encodings and variable-length operators obtain a better approximation of the true Pareto front

Online Optimization with Lookahead

The main contributions of this thesis consist of the development of a systematic groundwork for comprehensive performance evaluation of algorithms in online optimization with lookahead and the subsequent validation of the presented approaches in theoretical analysis and computational experiments

Online Stochastic and Robust Optimization

Abstract. This paper considers online stochastic optimization problems where uncertainties are characterized by a distribution that can be sampled and where time constraints severely limit the number of offline optimizations which can be performed at decision time and/or in between decisions. It reviews our recent progress in this area, proposes some new algorithms, and reports some new experimental results on two problems of fundamentally different nature: packet scheduling and multiple vehicle routing (MVR). In particular, the paper generalizes our earlier generic online algorithm with precomputation, least-commitment, service guarantees, and multiple decisions, all which are present in the MVR applications. Robustness results are also presented for multiple vehicle routing.

Online Stochastic and Robust Optimization

Abstract. This paper considers online stochastic optimization problems whereuncertainties are characterized by a distribution that can be sampled and where time constraints severely limit the number of offline optimizations which canbe performed at decision time and/or in between decisions. It reviews our recent progress in this area, proposes some new algorithms, and reports some newexperimental results on two problems of fundamentally different nature: packet scheduling and multiple vehicle routing (MVR). In particular, the paper general-izes our earlier generic online algorithm with precomputation, least-commitment, service guarantees, and multiple decisions, all which are present in the MVR ap-plications. Robustness results are also presented for multiple vehicle routing. 1 Introduction Online scheduling and routing problems arise naturally in many application areas andhave received increasing attention in recent years. Contrary to offline optimization, the data is not available a priori in online optimization. Rather it is incrementally revealedduring algorithm execution. In many online optimization problems, the data is a set of requests (e.g., packets in network scheduling or customers in vehicle routing) which arerevealed over time and the algorithm must decide which request to process next