Distributed Services with Foreseen and Unforeseen Tasks: The Mobile Re-allocation Problem

In this paper we deal with a common problem found in the operations of security and preventive/corrective maintenance services: that of routing a number of mobile resources to serve foreseen and unforeseen tasks during a shift. We define the (Mobile Re-Allocation Problem) MRAP as the problem of devising a routing strategy to maximize the expected weighted number of tasks served on time. For obtaining a solution to the MRAP, we propose to solve successively a multi-objective optimization problem called the stochastic Team Orienteering Problem with Multiple Time Windows (s-TOP-MTW) so as to consider information about known tasks and the arrival process of new unforeseen tasks. Solving successively the s-TOP-MTW we find that considering information about the arrival process of new unforeseen tasks may aid in maximizing the expected proportion of tasks accomplished on time.location;reliability;routing;distributed services

Genetic and memetic algorithms for scheduling railway maintenance activities

Nowadays railway companies are confronted with high infrastructure maintenance costs. Therefore good strategies are needed to carry out these maintenance activities in a most cost effective way. In this paper we solve the preventive maintenance scheduling problem (PMSP) using genetic algorithms, memetic algorithms and a two-phase heuristic based on opportunities. The aim of the PMSP is to schedule the (short) routine activities and (long) unique projects for one link in the rail network for a certain planning period such that the overall cost is minimized. To reduce costs and inconvenience for the travellers and operators, these maintenance works are clustered as much as possible in the same time period. The performance of the algorithms presented in this paper are compared with the performance of the methods from an earlier work, Budai et al. (2006), using some randomly generated instances.genetic algorithm;heuristics;opportunities;maintenance optimization;memetic algorithm

Hybrid Meta-Heuristics for Robust Scheduling

The production and delivery of rapidly perishable goods in distributed supply networks involves a number of tightly coupled decision and optimization problems regarding the just-in-time production scheduling and the routing of the delivery vehicles in order to satisfy strict customer specified time-windows. Besides dealing with the typical combinatorial complexity related to activity assignment and synchronization, effective methods must also provide robust schedules, coping with the stochastic perturbations (typically transportation delays) affecting the distribution process. In this paper, we propose a novel metaheuristic approach for robust scheduling. Our approach integrates mathematical programming, multi-objective evolutionary computation, and problem-specific constructive heuristics. The optimization algorithm returns a set of solutions with different cost and risk tradeoffs, allowing the analyst to adapt the planning depending on the attitude to risk. The effectiveness of the approach is demonstrated by a real-world case concerning the production and distribution of ready-mixed concrete.Meta-Heuristics;Multi-Objective Genetic Optimization;Robust Scheduling;Supply Networks

The effect of canopy position on growth and mortality in mixed sapling communities during self-thinning

This research investigates how species in the sapling phase differ in growth and survival depending on light availability (as estimated by canopy position) by means of tree-ring analysis and modelling mortality. We harvested 120 live and 158 dead saplings in self-thinning communities consisting of Silver birch (Betula pendula Roth.), Scots pine (Pinus sylvestris L.), Japanese larch (Larix kaempferi Carr.) and Douglas fir (Pseudotsuga menziesii Mirb. Franco) in the Netherlands. Results are evaluated within the framework of a trade-off between high-light growth and low-growth survival. Radial growth, measured at ground level, generally declined over time. In addition, a decreasing light availability further reduced growth in all species except Douglas fir. Trees died when radial growth was reduced to about 0.5 mm year¿1. Mortality in all species except Scots pine was significantly related to recent growth, but mortality curves were not different. The light-demanding Silver birch and Japanese larch differed from the shade-tolerant Douglas fir in both high-light growth and low-growth mortality, in line with a growth-survival trade-off. The light-demanding Scots pine did not fit this pattern as it was unable to transfer high radial growth into height gain, leaving it in suppressed canopy positions. This indicates the importance of height growth in the growth-survival trade-off. Differences in mortality probabilities affect the potential for coexistence, however, in all species also fast-growing individuals died suggesting additional factors causing mortality during self-thinning, other than direct competition for ligh

Bayesian networks to explain the effect of label information on product perception

Interdisciplinary approaches in food research require new methods in data analysis that are able to deal with complexity and facilitate the communication among model users. Four parallel full factorial within-subject designs were performed to examine the relative contribution to consumer product evaluation of intrinsic product properties and information given on packaging. Detailed experimental designs and results obtained from analyses of variance were published [1]. The data was analyzed again with the machine learning modelling technique Bayesian networks. The objective of the current paper is to explain basic features of this technique and its advantages over the standard statistical approach regarding handling of complexity and communication of results. With analysis of variance, visualization and interpretation of main effects and interactions effects becomes difficult in complex systems. The Bayesian network model offers the possibility to formally incorporate (domain) experts knowledge. By combining empirical data with the pre-defined network structure, new relationships can be learned, thus generating an update of current knowledge. Probabilistic inference in Bayesian networks allows instant and global use of the model; its graphical representation makes it easy to visualize and communicate the results. Making use of the most of data from one single experiment, as well as combining data of independent experiments makes Bayesian networks for analysing these and similarly complex and rich data set

Exactly solvable path integral for open cavities in terms of quasinormal modes

We evaluate the finite-temperature Euclidean phase-space path integral for the generating functional of a scalar field inside a leaky cavity. Provided the source is confined to the cavity, one can first of all integrate out the fields on the outside to obtain an effective action for the cavity alone. Subsequently, one uses an expansion of the cavity field in terms of its quasinormal modes (QNMs)-the exact, exponentially damped eigenstates of the classical evolution operator, which previously have been shown to be complete for a large class of models. Dissipation causes the effective cavity action to be nondiagonal in the QNM basis. The inversion of this action matrix inherent in the Gaussian path integral to obtain the generating functional is therefore nontrivial, but can be accomplished by invoking a novel QNM sum rule. The results are consistent with those obtained previously using canonical quantization.Comment: REVTeX, 26 pages, submitted to Phys. Rev.

Distributed Services with Foreseen and Unforeseen Tasks: The Mobile Re-allocation Problem

In this paper we deal with a common problem found in the operations of security and preventive/corrective maintenance services: that of routing a number of mobile resources to serve foreseen and unforeseen tasks during a shift. We define the (Mobile Re-Allocation Problem) MRAP as the problem of devising a routing strategy to maximize the expected weighted number of tasks served on time. For obtaining a solution to the MRAP, we propose to solve successively a multi-objective optimization problem called the stochastic Team Orienteering Problem with Multiple Time Windows (s-TOP-MTW) so as to consider information about known tasks and the arrival process of new unforeseen tasks. Solving successively the s-TOP-MTW we find that considering information about the arrival process of new unforeseen tasks may aid in maximizing the expected proportion of tasks accomplished on time

Genetic and memetic algorithms for scheduling railway maintenance activities

Nowadays railway companies are confronted with high infrastructure maintenance costs. Therefore good strategies are needed to carry out these maintenance activities in a most cost effective way. In this paper we solve the preventive maintenance scheduling problem (PMSP) using genetic algorithms, memetic algorithms and a two-phase heuristic based on opportunities. The aim of the PMSP is to schedule the (short) routine activities and (long) unique projects for one link in the rail network for a certain planning period such that the overall cost is minimized. To reduce costs and inconvenience for the travellers and operators, these maintenance works are clustered as much as possible in the same time period. The performance of the algorithms presented in this paper are compared with the performance of the methods from an earlier work, Budai et al. (2006), usin

Development of an intervention to stimulate physical activity in hard-to-reach physically disabled people and design of a pilot implementation:an intervention mapping approach

INTRODUCTION: Physically disabled people are less physically active compared with healthy people. Existing physical activity (PA) interventions are limited in reach, since they are primarily rehabilitation or school based. The current study aims to develop a community-based intervention for stimulating PA in hard-to-reach physically disabled people. METHODS AND ANALYSIS: To systematically develop a PA-stimulating intervention, intervention mapping (six steps) was applied. PA level and health-related quality of life of patients after rehabilitation was determined using questionnaires (step 1). Qualitative research was performed to study professionals' and physically disabled people's ideas about intervention objectives, determinants and design (steps 2 and 3). Since experts expressed no need for a new intervention, the existing intervention 'Activity coach' was adapted to the specific target population. The adapted intervention 'Activity coach+' composes a network of intermediate organisations that refers participants to an activity coach, who coaches participants during 1 year. After a preintervention physical assessment by a physiotherapist, participants will be individually guided to existing organised or non-organised activities. An activity tracker will be used to monitor and stimulate PA in daily life (step 4). To support adoption and implementation, meetings between involved parties are organised (step 5). 'Activity coach+' is implemented in community in March 2017, and will be evaluated using a mixed-method analysis. Quantitative evaluation of intervention effects on PA, health and social participation takes place after 0, 2, 4, 6 and 12 months. The implementation process and experiences with the intervention will be determined using qualitative research (step 6). ETHICS AND DISSEMINATION: Insights from this study will be used for dissemination and further development of the intervention. The Medical Ethical Committee of the University Medical Center Groningen confirmed that formal ethical approval was not required (METc 2016/630). TRIAL REGISTRATION NUMBER: NTR6858

Hybrid Meta-Heuristics for Robust Scheduling

The production and delivery of rapidly perishable goods in distributed supply networks involves a number of tightly coupled decision and optimization problems regarding the just-in-time production scheduling and the routing of the delivery vehicles in order to satisfy strict customer specified time-windows. Besides dealing with the typical combinatorial complexity related to activity assignment and synchronization, effective methods must also provide robust schedules, coping with the stochastic perturbations (typically transportation delays) affecting the distribution process. In this paper, we propose a novel metaheuristic approach for robust scheduling. Our approach integrates mathematical programming, multi-objective evolutionary computation, and problem-specific constructive heuristics. The optimization algorithm returns a set of solutions with different cost and risk tradeoffs, allowing the analyst to adapt the planning depending on the attitude to risk. The effectiveness of the approach is demonstrated by a real-world case concerning the production and distribution of ready-mixed concrete