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In the running of multiple automated guided vehicles (AGVs) in warehouses, delay problems in motions happen unavoidably as there might exist some disabled components of robots, the instability of networks and the interference of people walking. Under this case, robots would not follow the designed paths and the coupled relationship between temporal and space domain for paths is broken. And there is no doubt that other robots are disturbed by the ones where delays happen. Finally, this brings about chaos or even breakdown of the whole system. Therefore, taking the delay disturbance into consideration in the path planning of multiple robots is an issue worthy of attention and research.

This paper proposes a prioritized path planning algorithm based on time windows to solve the delay problems of multiple AGVs. The architecture is a unity consisting of three components which are focused on scheduling AGVs under normal operations, delays of AGVs, and recovery of AGVs. In the components of scheduling AGVs under normal operations and recovery, this paper adopts a dynamic routing method based on time windows to ensure the coordination of multiple AGVs and efficient completion of tasks. In the component for scheduling AGVs under delays, a dynamical prioritized local path planning algorithm based on time windows is designed to solve delay problems. The introduced planning principle of time windows would enable the algorithm to plan new solutions of trajectories for multiple AGVs, which could lower the makespan. At the same time, the real-time performance is acceptable based on the planning principle which stipulates the parameters of local time windows to ensure that the computation of the designed algorithm would not be too large.

The simulation results demonstrate that the proposed algorithm is more efficient than the state-of-the-art method based on homotopy classes, which aims at solving the delay problems. What is more, it is validated that the proposed algorithm can achieve the acceptable real-time performance for the scheduling in warehousing applications.

By introducing the planning principle and generating delay space and local adjustable paths, the proposed algorithm in this paper can not only solve the delay problems in real time, but also lower the makespan compared with the previous method. The designed algorithm guarantees the scheduling of multiple AGVs with delay disturbance and enhances the robustness of the scheduling algorithm in multi-AGV system.

The traveling purchaser problem (TPP) has gained attention in academics to deal with different variants in real business world. This study aims to study a green TPP with quantity discounts and soft time windows (TPPQS), in which a firm needs to purchase products from a set of available markets and deliver the products to a set of customers.

Vehicles are available to visit the markets, which offer products at different prices and with different quantity discount schemes. Soft time windows are present for the markets and the customers, and earliness cost and tardiness may incur if a vehicle cannot arrive a market or a customer within the designated time interval. The environmental impact of transportation activities is considered. The objective of this research is to minimize the total cost, including vehicle-assigning cost, vehicle-traveling cost, purchasing cost, emission cost, earliness cost and tardiness cost, while meeting the total demand of the customers and satisfying all the constraints. A mixed integer programming (MIP) model and a genetic algorithm (GA) approach are proposed to solve the TPPQS.

The results show that both the MIP and the GA can obtain optimal solutions for small-scale cases, and the GA can generate near-optimal solutions for large-scale cases within a short computational time.

The proposed models can help firms increase the performance of customer satisfaction and provide valuable supply chain management references in the service industry.

The proposed models for TPPQS are novel and can facilitate firms to design their green traveling purchasing plans more effectively in today’s environmental conscious and competitive market.

Verklar is the leading maker of roof windows based in Europe. Its Austrian subsidiary has historically dominated the Austrian market, with about 85% market share. However, at the time of the case, its market share has dropped to about 75%, and many of its dealers have either dropped the line entirely or are buying not from the company, but from the few remaining large dealers who still buy directly from Verklar. This has prompted the president of the subsidiary to devise a new way—called the Quota System—to run the distribution channel in the country to improve performance. Asks the reader to examine the sources of market share decline and whether the proposed Quota System solves the channel's problems.

The current air traffic management (ATM) operational approach is changing; “time” is now integrated as an additional fourth dimension on trajectories. This notion will impose on aircraft the compliance of accurate arrival times over designated checkpoints (CPs), called time windows (TWs). This paper aims to clarify the basic requirements and foundations for the practical implementation of this functional framework.

This paper reviews the operational deployment of 4D trajectories, by defining its relationship with other concepts and systems of the future ATM and communications, navigation and surveillance (CNS) context. This allows to establish the main tools that should be considered to ease the application of the 4D-trajectories approach. This paper appraises how 4D trajectories must be managed and planned (negotiation, synchronization, modification and verification processes). Then, based on the evolution of a simulated 4D trajectory, the necessary corrective measures by evaluating the degradation tolerances and conditions are described and introduced.

The proposed TWs model can control the time tolerance within less than 100 s along the passing CPs of a generic trajectory, which is in line with the expected future ATM time-performance requirements.

The main contribution of this work is the provision of a holistic vision of the systems and concepts that will be necessary to implement the new 4D-trajectory concept efficiently, thus enhancing performance. It also proposes tolerance windows for trajectory degradation, to understand both when an update is necessary and what are the conditions required for pilots and air traffic controllers to provide this update.

The pace of booking is a critical element in the accuracy of revenue management (RM) systems. Anecdotal evidence suggests that booking windows exhibit persistent shifts due to a variety of macro and micro factors. The article outlines several causes and tests the impact of the shifts on forecasting accuracy.

A novel methodological approach is utilized to empirically shift hotel reservation windows into smaller increments. Forecasts are then estimated and tested on the incremental shifts with popular RM techniques characteristic of advance booking data. A random effects model assesses the impact of the shifts on forecast accuracy.

The results show that shifts in booking behavior can cause the accuracy of forecasting models to deteriorate. The findings stress the importance of considering these shifts in model estimation and evaluation.

The results demonstrate that changes in booking behavior can be detrimental to the accuracy of RM forecasting algorithms. It is recommended that revenue managers monitor booking window shifts when forecasting with advanced booking data.

This study is the first to systematically assess the impact of booking window shifts on forecasting accuracy. The demonstrated approach can be implemented in future research to assess model accuracy as booking behavior changes.

The purpose of this paper is to provide an effective solution for a complex planning problem encountered in heavy industry. The problem entails selecting a set of projects to produce from a larger set of solicited projects and simultaneously scheduling their production to maximize profit. Each project has a due window inside of which, if accepted, it must be shipped. Additionally, there is a limited inventory buffer where lots produced early are stored. Because scheduling affects which projects may be selected and vice-versa, this is a particularly difficult combinatorial optimization problem.

The authors develop an algorithm based on the Metaheuristic for Randomized Priority Search (Meta-RaPS) as well as a greedy heuristic and an integer programming (IP) model. The authors then perform computational experiments on a large set of benchmark problems over a wide range of characteristics to compare the performance of each method in terms of solution quality and time required.

The paper shows that this problem is very difficult to solve using IP, with even small instances unable to be solved optimally. The paper then shows that both proposed algorithms will in seconds often outperform IP by a large margin. Meta-RaPS is particularly robust, consistently producing the best or very near-best solutions.

The Meta-RaPS algorithm developed enables companies facing this problem to achieve higher profits through improved decision making. Moreover, this algorithm is relatively easy to implement.

This research provides an effective solution for a difficult combinatorial optimization problem encountered in heavy industry which has not been previously addressed in the literature.

A new research domain known as the Quantified Self has recently emerged and is described as gaining self-knowledge through using wearable technology to acquire information on self-monitoring activities and physical health related problems. However, very little is known about the impact of time window models on discovering self-quantified patterns that can yield new self-knowledge insights. This paper aims to discover the self-quantified patterns using multi-time window models.

This paper proposes a multi-time window analytical workflow developed to support the streaming k-means clustering algorithm, based on an online/offline approach that combines both sliding and damped time window models. An intervention experiment with 15 participants is used to gather Fitbit data logs and implement the proposed analytical workflow.

The clustering results reveal the impact of a time window model has on exploring the evolution of micro-clusters and the labelling of macro-clusters to accurately explain regular and irregular individual physical behaviour.

The preliminary results demonstrate the impact they have on finding meaningful patterns.

The purpose of this paper is to present a decision support system (DSS) for a Colombian public utility company in order to aid decision-making at the operational level regarding route planning and travel time. The aim is to provide a tool to assist technicians that perform interruption and reconnection of domiciliary services for about 2,000 customers a day.

The real-life problem is modeled as a Single Depot Vehicle Routing Problem with Time Windows (SDVRP-TW), which is a well-known optimization problem in Operations Research/Management Science. A two-stage approach integrated into decision-making software is provided. The first stage considers the clustering of customers generated by a combination of the sweep and the k-means algorithms, while the second phase plans the routing of technicians using the nearest-neighbor and the Or-opt heuristics. The proposed approach is tested using real data sets.

In comparison with the current route planning approach, the proposed method is able to obtain savings in total travel times, improving operational productivity by 22.2 percent.

Since the analysis is carried out based on mathematical modeling, assumptions about the relationships between variables and elements of the actual complex problem might be simplified. Although the proposed approach aids the route planning, decision makers make the final decisions.

The proposed DSS has a critical impact on actual operational practices at the company. Productivity and service level are improved, while reducing operational costs. The decision-making process itself will be improved so technicians and higher decision makers can focus on performing other tasks.

The real-life problem is modeled using mathematical programming and efficiently solved through a two-stage approach based on simple, quite intuitive, solution procedures that have not been implemented for such services. In addition, as actual data from the company is employed for experimental purposes, the solution approach is tested and its efficiency and efficacy are both validated in a realistic setting, hence providing realistic behavior for decision makers at the company.

presentar un sistema de soporte a las decisiones (Decision Support System, DSS) para una empresa colombiana de servicios públicos con el fin de apoyar el proceso de toma de decisiones a nivel operativo en lo relacionado con la planeación de rutas y el tiempo de servicio. El objetivo es suministrar una herramienta que ayude a los técnicos a desempeñar el servicio de corte y reconección de servicios domiciliarios para aproximadamente 2000 clientes por día.

el problema de una empresa real es modelado como un problema de enrutamiento de vehículos un único depósito y ventanas de tiempo (Single Depot Vehicle Routing Problem with Time Windows, SDVRP-TW). Éste es un problema de optimización muy conocido en Investigación de Operaciones / Ciencias de la Administración. Se presenta un enfoque de dos etapas integrado en un software de ayuda a la toma de decisiones. La primera etapa considera el agrupamiento de los clientes generado por una combinación de los algoritmos del barrido y el k-media, mientras que la segunda fase define el plan de rutas para los técnicos utilizando las heurísticas de vecino más cercano y Or-opt. El enfoque propuesto es validado empleando datos reales.

en comparación con el plan de rutas actualmente utilizado por la empresa, el método propuesto es capaz de obtener ahorros en el tiempo total de viaje incrementando la eficiencia operativa en un 22.2%.

puesto que el análisis se lleva a cabo a partir de un modelo matemático, los supuestos sobre las relaciones entre las variables y los elementos del sistema real complejo podrían simplificarse. Además, aunque el sistema propuesto realiza la planeación de rutas, la decisión final es tomada finalmente por las personas.

el DSS propuesto tiene un impacto crítico en la práctica operativa real de la empresa. La productividad y el nivel de servicio se mejoran, mientras se reducen los costos operativos. El proceso de toma de decisiones en sí mismo se verá mejorado pues los técnicos y los tomadores de decisiones pueden enfocarse en realizar otras tareas.

el problema real es modelado utilizando programación matemática y se resuelve de forma efectiva con un procedimiento de dos etapas sencillo y básicamente intuitivo que no ha sido puesto en marcha antes para tales empresas de servicios. Además, puesto que datos reales de la empresa son utilizados en la experimentación, el enfoque de solución es validado y su eficiencia y eficacia son comprobadas en un ambiente real, suministrando así un comportamiento real para los tomadores de decisiones en la empresa.