Some Comments on the Stochastic Eulerian Tour Problem

The Stochastic Eulerian Tour Problem was introduced in 2008 as a stochastic variant of the well-known Eulerian Tour Problem. In a follow-up paper the same authors investigated some heuristics for solving the Stochastic Eulerian Tour Problem. After a thorough study of these two publications a few issues emerged. In this short research commentary we would like to discuss these issues.Comment: research commentary, 4 page

International Coercion, Emulation and Policy Diffusion: Market-Oriented Infrastructure Reforms, 1977-1999

Why do some countries adopt market-oriented reforms such as deregulation, privatization and liberalization of competition in their infrastructure industries while others do not? Why did the pace of adoption accelerate in the 1990s? Building on neo-institutional theory in sociology, we argue that the domestic adoption of market-oriented reforms is strongly influenced by international pressures of coercion and emulation. We find robust support for these arguments with an event-history analysis of the determinants of reform in the telecommunications and electricity sectors of as many as 205 countries and territories between 1977 and 1999. Our results also suggest that the coercive effect of multilateral lending from the IMF, the World Bank or Regional Development Banks is increasing over time, a finding that is consistent with anecdotal evidence that multilateral organizations have broadened the scope of the “conditionality” terms specifying market-oriented reforms imposed on borrowing countries. We discuss the possibility that, by pressuring countries into policy reform, cross-national coercion and emulation may not produce ideal outcomes.http://deepblue.lib.umich.edu/bitstream/2027.42/40099/3/wp713.pd

A critical analysis of the harmony search algorithm—How not to solve sudoku

This article presents a critical analysis of the harmony search metaheuristic framework. We formally prove that the harmony search algorithm is a special case of evolution strategies. First, this implies that the harmony search algorithm itself does not offer any novelty, apart from using a different terminology. Second, the performance of the best harmony search algorithm is always bounded by the performance that can be obtained by evolution strategies. Additionally, more than a decade of research about harmony search has not revealed any other sort of novelty or has led to any new insights or significant contributions in the field of heuristics. In short, there is no reason for harmony search to exist as a separate metaheuristic framework. Based on these findings, we carefully examine the results found in the paper Harmony search algorithm for solving sudoku. A theoretical investigation and a reimplementation of the harmony search algorithm both reveal that these results are fundamentally flawed

Stochastic vehicle routing: from theory to practice

In this thesis we discuss practical and theoretical aspects of various stochastic vehicle routing problems. These are combinatorial optimization problems related to the field of transportation and logistics in which input data is (partially) represented in a stochastic way. More in detail, we focus on two-stage stochastic vehicle routing problems and in particular on so-called a priori optimization problems. The results are divided into a theoretical part and a practical part. In fact, the theoretical results provide a strong motivation for the development and the usage of the methods presented in the practical part. We begin the theoretical part with a convergence result regarding vehicle routing problems with stochastic demands. This result can be used to give explanations for some phenomena related to these problems which have been reported in literature. We then continue with hardness results for stochastic vehicle routing problems on substantially stochastic instances. Here we show that several stochastic vehicle routing problems remain NP-hard even if they are restricted to instances which differ significantly from non-stochastic instances. Additionally, we give some inapproximability results for these problems restricted to substantially stochastic instances. After that, we focus on a stochastic vehicle routing problem which considers time dependencies in terms of deadlines. We show that various computational tasks related to this problem, including the evaluation of the objective function, are #P-hard even for Euclidean instances. Note that this is a very strong hardness result and it immediately implies that these computational tasks are also NP-hard. We then further investigate the objective function of this problem. Here we demonstrate that the existing approximations for this objective function are not able to guarantee any reasonable worst-case approximation ratio. Finally, we show that it is NP-hard to approximate the objective function of a slightly more general problem within any reasonable worst-case approximation ratio. In the practical part we develop and apply various methods for the optimization of stochastic vehicle routing problems. Since the theoretical results indicate that it is a great challenge to optimize these problems, we focus mainly on heuristic methods. We start with the development of strong local search algorithms for one of the most extensively studied stochastic vehicle routing problems. These algorithms use an efficient approximation of the objective function based on Monte Carlo sampling. They are then further used within different heuristics, leading to new state-of-the-art methods for this problem. We then transfer our results to a more intricate stochastic vehicle routing problem. Here we first present an approximation of the objective function using the novel method of quasi-parallel evaluation of samples. Then we again develop strong local search algorithms and use them within more complex heuristics to obtain new state-of-the-art methods. After that we change the scope towards a general framework for the optimization of stochastic vehicle routing problems based on general purpose computing on graphics processing units. Here we are exploiting the massive computational power for parallel computations offered by modern graphics processing units in the context of stochastic vehicle routing problems. More in detail, we propose to use an approximation of the objective function based on Monte Carlo sampling which can be parallelized in an extremely efficient way. The effectiveness of this framework is then demonstrated in a case study. We finish the practical part with an application of our methods to a real world stochastic vehicle routing problem. This problem is part of a project that has been initiated in 2010 by Caritas Suisse. It is still in an early stage, but with our work we were able to successfully support some of the decision processes at this stage

Convergence results for vehicle routing problems with stochastic demands

In this work we investigate two variants of the Stochastic Vehicle Routing Problem: The Vehicle Routing Prob- lem with Stochastic Demands and the Vehicle Routing Problem with Stochastic Demands and Customers. We show that under some moderate conditions there is an asymptotic equivalence between the Vehicle Routing Problem with Stochastic Demands and the Traveling Salesman Problem, as well as between the Vehicle Routing Problem with Stochastic Demands and Cus- tomers and the Probabilistic Traveling Salesman Problem. Based on our results we give explanations for different observations in literature and we provide ideas for the development of new approximation algorithms and heuristics for these problems

Hardness results for the probabilistic traveling salesman problem with deadlines

The Probabilistic Traveling Salesman Problem with Deadlines (PTSPD) is a Stochastic Vehicle Routing Problem considering time dependencies. Even the evaluation of the objective function is considered to be a computationally demanding task. So far there is no evaluation method known that guarantees a polynomial runtime, but on the other hand there are also no hardness results regarding the PTSPD objective function. In our work we show that the evaluation of the objective function of the PTSPD, even for Euclidean instances, is #P-hard. In fact, we even show that computing the probabilities, with which deadlines are violated is #P-hard. Based on this result we additionally show that the decision variant of the Euclidean PTSPD, the optimization variant of the Euclidean PTSPD and delta evaluation in reasonable local search neighborhoods is #P-hard

An improved heuristic for the probabilistic traveling salesman problem with deadlines based on GPGPU

Stochastic combinatorial optimization problems have received increasing attention in recent years. These problems can be used to obtain more realistic models for real world applications. The drawback is that stochastic combinatorial optimization problems are usually much harder to solve than their non-stochastic counterparts and therefore efficient heuristics for these problems are of great importance. In this paper we focus on the Probabilistic Traveling Salesman Problem with Deadlines, a well-known stochastic vehicle routing problem. This problem can be efficiently solved using a heuristic based on general-purpose computing on graphics processing units. We show how such a heuristic can be further improved to allow a more efficient utilization of the graphics processing unit. We extensively discuss our results and point out how our techniques can be generalized for solving other stochastic combinatorial optimization problems

A metaheuristic framework for stochastic combinatorial optimization problems based on GPGPU with a case study on the probabilistic traveling salesman problem with deadlines

In this work we propose a general metaheuristic framework for solving stochastic combinatorial optimization problems based on general-purpose computing on graphics processing units (GPGPU). This framework is applied to the probabilistic traveling salesman problem with deadlines (PTSPD) as a case study. Computational studies reveal significant improvements over state-of-the-art methods for the PTSPD. Additionally, our results reveal the huge potential of the proposed framework and sampling-based methods for stochastic combinatorial optimization problems

Heuristics for the probabilistic traveling salesman problem with deadlines based on quasi-parallel Monte Carlo sampling

The Probabilistic Traveling Salesman Problem with Deadlines (PTSPD) is a Stochastic Vehicle Routing Problem with a computationally demanding objective function. In this work we propose an approximation for that objective function based on Monte Carlo Sampling and using the novel approach of quasi-parallel evaluation of samples. We perform comprehensive computational studies that reveal the efficiency of this approximation. Additionally, we examine different Local Search Algorithms and present a Random Restart Local Search Algorithm for solving the PTSPD together with an extensive computational study on a large set of benchmark instances