Multiple Time Windows Research Articles

A decomposition heuristic for a rich production routing problem

We propose a decomposition heuristic to solve a rich production routing problem arising in the context of a make-to-order company. The problem is motivated by the operations of a Brazilian furniture manufacturer and considers several important features, such as multiple products, sequence-dependent setup times, a heterogeneous fleet of vehicles, routes extending over one or more periods, multiple time windows and customer deadlines, among others. An integrated mathematical model is presented and is used as a basis to develop the heuristic, which solves the problem by decomposing it in two parts that are solved iteratively. The first subproblem focuses on the production planning and customer assignment decisions, and uses an approximation for the routing costs and travel times. The second subproblem makes the routing decisions, which are further improved by a local search algorithm. The solution of the second subproblem is then used to update the approximation of the routing costs and travel times in the first subproblem. We use a large set of random instances to benchmark our heuristic against a general-purpose solver. Numerical results show that our method provides, in shorter computing times, solutions of similar quality as those obtained by the solver for instances with up to 15 customers. For larger instances, with 20 to 50 customers, the heuristic clearly outperforms the solver, which in most cases cannot find any solution after 24 h of computing time.

A multi-criteria vehicle routing approach to improve the compliance audit schedule for food banks

According to the US Department of Agriculture, more than 49 million Americans do not have access to a sufficient quantity of affordable, nutritious food. To address the issue of food insecurity, non-profit food banks service a number of regional agencies that provide emergency food relief. To maintain regulatory compliance, each agency serviced by a food bank must receive an on-site audit once every 12–18 months. All audits are made by a member of the food bank’s staff. In collaboration with our community partner, we develop a multi-criteria capacitated vehicle routing with multiple time windows approach to improve the efficiency of the auditing schedule. Using real data from The Foodbank Inc., in Dayton, OH, we evaluate our model using both exact and heuristic methods and analyze the trade-offs between three competing objectives which correspond to our system’s three key stakeholders. Our computational results demonstrate an ability to quickly find solutions which improve upon the current operations at The Foodbank Inc. thereby benefiting all stakeholders and ultimately the community.

Dissection of Bitcoin's Multiscale Bubble History

We present a detailed bubble analysis of the Bitcoin to US Dollar price dynamics from January 2012 to February 2018. We introduce a robust automatic peak detection method that classifies price time series into periods of uninterrupted market growth (drawups) and regimes of uninterrupted market decrease (drawdowns). In combination with the Lagrange Regularisation Method for detecting the beginning of a new market regime, we identify 3 major peaks and 10 additional smaller peaks, that have punctuated the dynamics of Bitcoin price during the analyzed time period. We explain this classification of long and short bubbles by a number of quantitative metrics and graphs to understand the main socio-economic drivers behind the ascent of Bitcoin over this period. Then, a detailed analysis of the growing risks associated with the three long bubbles using the Log-Periodic Power Law Singularity (LPPLS) model is based on the LPPLS Confidence Indicators, defined as the fraction of qualified fits of the LPPLS model over multiple time windows. Furthermore, for various fictitious present analysis times t2, positioned in advance to bubble crashes, we employ a clustering method to group LPPLS fits over different time scales and the predicted critical times tc (the most probable time for the start of the crash ending the bubble). Each cluster is argued to provide a plausible scenario for the subsequent Bitcoin price evolution. We present these predictions for the three long bubbles and the four short bubbles that our time scale of analysis was able to resolve. Overall, our predictive scheme provides useful information to warn of an imminent crash risk.

Variable Neighborhood Search for Vehicle Routing Problem with Multiple Time Windows

The Vehicle Routing Problem (VRP) with Multiple Time Windows is a generalization of VRP, where the customers have one or more time windows in which they can be visited. The best heuristic in the literature is a Hybrid Variable Neighborhood Tabu Search (HVNTS) that mostly deals with infeasible solutions, because it is assumed that one may not reach some regions of the search space without passing through infeasible solutions. In this short paper, we propose a simpler Variable Neighborhood Search heuristic where all the computational effort is spent on searching for feasible solutions. Computational experiments showed that the proposed heuristic is competitive with the best heuristic in the literature.

Optimal Design of Demand-Responsive Feeder Transit Services with Passengers’ Multiple Time Windows and Satisfaction

This paper presents a mixed-integer linear programming model for demand-responsive feeder transit services to assign vehicles located at different depots to pick up passengers at the demand points and transport them to the rail station. The proposed model features passengers’ one or several preferred time windows for boarding vehicles at the demand point and their expected ride time. Moreover, passenger satisfaction that was related only to expected ride time is fully accounted for in the model. The objective is to simultaneously minimize the operation costs of total mileage and maximize passenger satisfaction. As the problem is an extension of the nondeterministic polynomial problem with integration of the vehicle route problem, this study further develops an improved bat algorithm to yield meta-optimal solutions for the model in a reasonable amount of time. When this was applied to a case study in Nanjing City, China, the mileage and satisfaction of the proposed model were reduced by 1.4 km and increased by 7.1%, respectively, compared with the traditional model. Sensitivity analyses were also performed to investigate the impact of the number of designed bus routes and weights of objective functions on the model performance. Finally, a comparison of Cplex, standard bat algorithm, and group search optimizer is analyzed to verify the validity of the proposed algorithm.

A Non-Stationary Reconnaissance Drought Index (NRDI) for Drought Monitoring in a Changing Climate

Traditionally, drought indices are calculated under stationary condition, the assumption that is not true in a changing environment. Under non-stationary conditions, it is assumed the probability distribution parameters vary linearly/non-linearly with time or other covariates. In this study, using the GAMLSS algorithm, a time-varying location parameter of lognormal distribution fitted to the initial values (α0) of the traditional Reconnaissance Drought Index (RDI) was developed to establish a new index called the Non-Stationary RDI (NRDI), simplifying drought monitoring under non-stationarity. The fifteen meteorological stations having the longest records (1951–2014) in Iran were chose to evaluate the NRDI performances for drought monitoring. Trend analysis of the α0 series at multiple time windows was tested by using the Mann-Kendall statistics. Although all stations detected decreasing trend in the α0 series, eight of them were significant at the 5% probability level. The results showed that the time-dependent relationship is adequate to model the location parameter at the stations with the significant temporal trend. There were remarkable differences between the NRDI and the RDI, especially for the time windows larger than 6 months, implying monitoring droughts using the NRDI under non-stationarity. The study suggests using the NRDI where the significant time trend appears in the initial values of RDI due to changing climate.

A Distance Measurement Method Using A Time-of-Flight CMOS Range Image Sensor with 4-Tap Output Pixels and Multiple Time-Windows

In this study, advanced range measurement method using a TOF sensor with 4-tap output pixels and small-duty short light pulse is presented. A CMOS TOF range imager with pinnedphotodiode high-speed charge modulator pixels using lateral electric field (LEF) control has been implemented by a 0.11-μm CIS process with high near infrared sensitivity. In order to improve the range resolution while maintaining the measurable range, multiple time-windows are used for range measurements. Compared with the conventional single time window, the use of N time-windows theoretically improve the range resolution by a factor of N1.5 if the back ground light shot noise is dominant. In the measurement for N=2, the range resolution is improved by a factor of 2.8 compared with the case of N=1 while maintaining the same distance range.

Tabu Search for the Vehicle Routing Problem with Multiple Trips, Time Windows, and Simultaneous Delivery-Pickup

This paper discusses a vehicle routing problem with multiple trips, time windows, and simultaneous delivery-pickup (VRPMTTWSDP). This problem is a variant of the basic vehicle routing problem (VRP) including the following characteristics: multiple trips, time windows, and simultaneous delivery-pickup. In this paper, a solution approach based on tabu search (TS) is proposed. In the proposed TS, the sequential insertion (SI) algorithm is used to construct an initial solution. A neighbor structure is generated by applying an operator order consisting of eleven operators of relocation, exchange, and crossover operators. A tabu solution code (TSC) method is applied as a tabu restriction mechanism. Computational experiments are carried out to examine the performance of the proposed TS using hypothetical instances. The performance of the proposed TS is compared to the local search (LS) and the genetic algorithm (GA). The comparison shows that the proposed TS is better in terms of the objective function value.

Vehicle routing optimization with multiple fuzzy time windows based on improved wolf pack algorithm

Vehicle routing optimization with multiple fuzzy time windows based on improved wolf pack algorithm

Multiple lapse time window analysis using solely single events in South Korea

Scattering (Qs–1) and intrinsic (Qi–1) attenuation, important parameters for inferring both the materials and the physical condition of the regional lithosphere, are generally separated by the method of multiple lapse time window analysis (MLTWA). Recently, depth variation of crustal Qs–1 and Qi–1 by applying single events to MLTWA was first shown; however, this application inevitably combined several events due to insufficient data caused by a window with limited range. In this study, we demonstrated that a flexible range window can be applied successfully to solely single events in MLTWA. In particular, a more reliable constraint was obtained than in the previous study, with a reduced amount of data. This technique may be particularly useful for seismically stable regions due to the limitation of available combinations of similar depth events.

Improved Phase Corrections for Transoceanic Tsunami Data in Spatial and Temporal Source Estimation: Application to the 2011 Tohoku Earthquake

AbstractSystemic travel time delays of up to 15 min relative to the linear long waves for transoceanic tsunamis have been reported. A phase correction method, which converts the linear long waves into dispersive waves, was previously proposed to consider seawater compressibility, the elasticity of the Earth, and gravitational potential change associated with tsunami motion. In the present study, we improved this method by incorporating the effects of ocean density stratification, actual tsunami raypath, and actual bathymetry. The previously considered effects amounted to approximately 74% for correction of the travel time delay, while the ocean density stratification, actual raypath, and actual bathymetry, contributed to approximately 13%, 4%, and 9% on average, respectively. The improved phase correction method accounted for almost all the travel time delay at far‐field stations. We performed single and multiple time window inversions for the 2011 Tohoku tsunami using the far‐field data (>3 h travel time) to investigate the initial sea surface displacement. The inversion result from only far‐field data was similar to but smoother than that from near‐field data and all stations, including a large sea surface rise increasing toward the trench followed by a migration northward along the trench. For the forward simulation, our results showed good agreement between the observed and computed waveforms at both near‐field and far‐field tsunami gauges, as well as with satellite altimeter data. The present study demonstrates that the improved method provides a more accurate estimate for the waveform inversion and forward prediction of far‐field data.

The sales force sizing problem with multi-period workload assignments, and service time windows

This work introduces the sales force sizing problem with multi-period workload assignments, and service time windows. The motivation of the problem arises from a real life situation faced by a goods distribution company. The problem consists in determining the size of the sales force (i.e. the number of vendors to hire) and their daily schedules, within a planning horizon, to serve a set of customers in order to minimize the total nominal wage. There are different categories of customers and each customer has multiple time windows depending on the service day. Furthermore, the nominal wage of a hired vendor is determined by the most expensive category of the customers he serves. A mixed integer linear formulation and a heuristic are proposed for this problem. The performance of the heuristic algorithm is assessed over a set of instances adapted from literature. Computational results reveal the efficiency and scalability of the proposed procedure, providing a better tradeoff in terms of solution quality and running time than a commercial solver.

The Virtual Trackpad: An Electromyography-Based, Wireless, Real-Time, Low-Power, Embedded Hand-Gesture-Recognition System Using an Event-Driven Artificial Neural Network

This brief presents a wireless, low-power embedded system that recognizes hand gestures by decoding surface electromyography (EMG) signals. Ten hand gestures used on commercial trackpads, including pinch, stretch, swipe left, swipe right, scroll up, scroll down, single click, double click, pat, and ok, can be recognized in real time. Features from four differential EMG channels are extracted in multiple time windows. Unlike traditional data segmentation methods, an event-driven method is proposed, with the gesture event detected in the hardware. Feature extraction is triggered only when an event is detected, minimizing computation, memory, and system power. A time-delayed artificial neural network (ANN) is used to predict the gesture from the transient EMG features instead of traditional steady-state features. The ANN is implemented in the microcontroller with a processing time less than 0.2 ms. The detection results are sent wirelessly to a computer. The device weights 15.2 g. A 4.6 g battery supports up to 40 h continuous operation. To our knowledge, this brief shows the first real-time, embedded hand-gesture-recognition system using only transient EMG signals. Experiments with four subjects show that the device can achieve a recognition of ten gestures with an average accuracy of 94%.

Learning topic description from clustering of trusted user roles and event models characterizing distributed provenance networks: a reinforcement learning approach

This paper proposes a reinforcement learning based message transfer model for transferring news report messages through a selected path in a trusted provenance network with the objective of maximizing the reward values based on trust or importance based and network congestion or utility based cost measures. The reward values are calculated along a dynamically defined policy path connecting start topic or event node to a goal topic or event or issue nodes for incrementally defined time windows for a given network congestion situation. A hierarchy of agents of trusted roles is used to accomplish the sub-goals associated with sub-story or subtopic in the provenance structure where an agent role may assume the semantic role of the associated sub-topic. The twitted news story thread or plan of events is defined in this work from the starting topic or event node to the goal topic or event node for incrementally defined intervals of time. The graphs are clustered into subtopic and these sub-goals or sub topic nodes of a topic node at every level of granularity are associated with cluster of news reports which describe activities associated with sub-goal or sub-topic events. Such cluster of nodes may also represent drilled down sequence of sub-events describing a sub-topic or sub-goal node. The policy path in a topic or story graph model is defined by applying reinforcement learning principles on dynamically defined event models associated with evolution of topic definition observed from incrementally acquired samples of input training data spanning multiple time windows. We provide a methodology for unifying similar provenance graph models for adapting and averaging the policy path classifiers associated with individual models to produce a reduced set of unified models derived during training. A minimum set cover of classifiers is identified for the models and a clustering procedure of the models is suggested based on these classifiers. Other database clustering methods have also been suggested as alternatives for clustering these models. A collection of unified models are identified from the models identified within a cluster and the policy path classifiers associated with these models provide the story or topic descriptions destined to goal topic or event nodes characterizing these models within a cluster.

Behavioral relevance impacts utilization of diagnostic information for scene categorization at multiple time windows: Electrophysiological evidence

Behavioral relevance impacts utilization of diagnostic information for scene categorization at multiple time windows: Electrophysiological evidence

Acoustical Emission Analysis by Unsupervised Graph Mining: A Novel Biomarker of Knee Health Status.

To study knee acoustical emission patterns in subjects with acute knee injury immediately following injury and several months after surgery and rehabilitation. We employed an unsupervised graph mining algorithm to visualize heterogeneity of the high-dimensional acoustical emission data, and then to derive a quantitative metric capturing this heterogeneity-the graph community factor (GCF). A total of 42 subjects participated in the studies. Measurements were taken once each from 33 healthy subjects with no known previous knee injury, and twice each from 9 subjects with unilateral knee injury: first, within seven days of the injury, and second, 4-6 months after surgery when the subjects were determined to start functional activities. Acoustical signals were processed to extract time and frequency domain features from multiple time windows of the recordings from both knees, and k-nearest neighbor graphs were then constructed based on these features. The GCF calculated from these graphs was found to be 18.5 ± 3.5 for healthy subjects, 24.8 ± 4.4 (p = 0.01) for recently injured, and 16.5 ± 4.7 (p = 0.01) at 4-6 months recovery from surgery. The objective GCF scores changes were consistent with a medical professional's subjective evaluations and subjective functional scores of knee recovery. Unsupervised graph mining to extract GCF from knee acoustical emissions provides a novel, objective, and quantitative biomarker of knee injury and recovery that can be incorporated with a wearable joint health system for use outside of clinical settings, and austere/under resourced conditions, to aid treatment/therapy.

Relative role of intrinsic and scattering attenuation beneath the Andaman Islands, India and tectonic implications

The attenuation characteristics of seismic waves traversing the Andaman Nicobar subduction zone (ANSZ) are investigated using high quality data from a network of broadband stations operational since 2009. We initially studied the Coda wave attenuation (Qc-1) under the assumption of a single isotropic scattering model. Subsequently, following the multiple isotropic scattering hypothesis, we isolated the relative contributions of intrinsic (Qi-1) and scattering (Qsc-1) attenuation employing the Multiple Lapse Time Window Analysis (MLTWA) method within a frequency range 1.5–18Hz. Results reveal a highly attenuative nature of the crust, with the values of Qc being frequency dependent. The intrinsic absorption is mostly found to be predominant compared to scattering attenuation. The dominance of Qi-1 in the crust may be attributed to the presence of fluids associated with the subducted slab. Our results are consistent with the low velocity zone reported for the region. A comparison of our results with those from other regions of the globe shows that the ANSZ falls under the category of high intrinsic attenuation zone. Interestingly, the character of ANSZ is identical to that of eastern Himalaya and southern Tibet, but entirely different from the Garhwal-Kumaun Himalaya and the source zone of Chamoli earthquake, due to the underlying mechanisms causing high attenuation.

Mixed integer linear programming for a multi-attribute technician routing and scheduling problem

ABSTRACTIn this paper, we consider a multi-attribute technician routing and scheduling problem motivated by an application for the maintenance and repair of electronic transaction equipment. This problem is aimed at routing technicians to perform tasks at different customer locations so as to maximize the total gain associated with served customers, minus the operations costs (total travelled distance and overtime of the technicians). At the same time, a number of constraints must be satisfied like technician skills, breaks, maximum distance, multiple time windows and parts inventory. A mixed integer linear programming model is proposed to address this problem, which is then solved with a commercial solver. The computational results explore the difficulty of the problem along various dimensions and underline its inherent complexity.

Optimization model for a production, inventory, distribution and routing problem in small furniture companies

Production and distribution are two key decisions in supply chain planning. In order to achieve an effective operational performance, it is important for these two decisions to be integrated, especially in supply chains with low inventory levels. In this paper, we propose a mixed integer programming model to integrate production, inventory, distribution and routing decisions in a single framework. The model was inspired by small Brazilian furniture companies and focuses on production and distribution decisions at an operational level. In particular, we consider a scenario in which only one production line and one vehicle, which makes multiple trips over the planning horizon, are available to produce items and deliver final products, respectively. We also take into account some features rarely considered in the literature, but commonly found in real-world applications, such as producing and stocking multiple items, distribution routes extending over one or more periods, multiple time windows and customers’ due dates. Computational tests on a set of randomly generated instances were carried out using a well-known optimization software and six relax-and-fix heuristics, which explore different criteria for partitioning and fixing variables. We also implemented two hybrid heuristics in which an initial solution is first constructed and then fed into the optimization software to improve it. The results showed that one relax-and-fix and the two hybrid heuristics performed better than the solver on the largest instances.

Traffic-Related Air Pollution and Childhood Asthma: Recent Advances and Remaining Gaps in the Exposure Assessment Methods

Background: Current levels of traffic-related air pollution (TRAP) are associated with the development of childhood asthma, although some inconsistencies and heterogeneity remain. An important part of the uncertainty in studies of TRAP-associated asthma originates from uncertainties in the TRAP exposure assessment and assignment methods. In this work, we aim to systematically review the exposure assessment methods used in the epidemiology of TRAP and childhood asthma, highlight recent advances, remaining research gaps and make suggestions for further research. Methods: We systematically reviewed epidemiological studies published up until 8 September 2016 and available in Embase, Ovid MEDLINE (R), and “Transport database”. We included studies which examined the association between children’s exposure to TRAP metrics and their risk of “asthma” incidence or lifetime prevalence, from birth to the age of 18 years old. Results: We found 42 studies which examined the associations between TRAP and subsequent childhood asthma incidence or lifetime prevalence, published since 1999. Land-use regression modelling was the most commonly used method and nitrogen dioxide (NO2) was the most commonly used pollutant in the exposure assessments. Most studies estimated TRAP exposure at the residential address and only a few considered the participants’ mobility. TRAP exposure was mostly assessed at the birth year and only a few studies considered different and/or multiple exposure time windows. We recommend that further work is needed including e.g., the use of new exposure metrics such as the composition of particulate matter, oxidative potential and ultra-fine particles, improved modelling e.g., by combining different exposure assessment models, including mobility of the participants, and systematically investigating different exposure time windows. Conclusions: Although our previous meta-analysis found statistically significant associations for various TRAP exposures and subsequent childhood asthma, further refinement of the exposure assessment may improve the risk estimates, and shed light on critical exposure time windows, putative agents, underlying mechanisms and drivers of heterogeneity.