Time Window Restrictions Research Articles

Supporting scheduling decisions by using genetic algorithm based on tourists’ preferences

As the tourism industry switches from mass to personalized solutions, tourists’ preferences should be considered. This study aims to optimize tourists’ activity chains based on their preferences and 10 realistic parameters. A method using activity-based modeling and genetic algorithm is developed. The method handles flexible touristic activities with time window restrictions. Then scenarios including private vehicles (PV) and public transport (PT) are created. The results show high priority for security and provide significant reduction in journey times, particularly in case of flexible activities. The outcomes confirm that the proposed method provides an optimized solution to generate personalized travel itineraries that enhance tourists’ travel experience. To evaluate the performance of the algorithm, Wilcoxon Signed-Ranks Test is applied. The statistical results confirm that the performance of the GA significantly differs in the various scenarios. Travelers can benefit from the proposed approach since it offers a solution to complex real-world scheduling problems. In addition, travel patterns can be derived thus supporting the work of planners and decision-makers in the tourism industry.

Closed-loop multi-objective waste management through vehicle routing problem in neutrosophic hesitant fuzzy environment

Waste management contributes in various fields for global development. A multi-objective waste management (MOWM) problem is devised in an area that generates commercial, industrial and residential waste items. The target is to reduce the negative impact of waste items on social, economical and environmental sites by completing a closed-loop MOWM through a vehicle routing problem under time window restriction. This MOWM problem is optimizing the objectives including maximum profit, minimum carbon emission under carbon cap-and-trade policy, and minimum work load deviation to maintain the sustainability. Here, neutrosophic hesitant fuzzy (NHF) environment is preferred to overcome the hesitancy of MOWM problem. A new ranking approach is initiated for defuzzifying NHF data. The appropriateness of the formulated model is justified by evaluating two realistic applications. To derive the Pareto-optimal solution of the proposed MOWM problem, two fuzzy techniques, namely, neutrosophic linear programming and neutrosophic hesitant fuzzy programming, and one non-fuzzy technique global criterion method are utilized in NHF environment. The obtained Pareto-optimal solutions are compared by TOPSIS for determining the final Pareto-optimal solution and to select a better approach among the proposed three approaches. Comparison analysis, sensitivity analysis, managerial insights and conclusions with future research scopes are outlined at the end.

Optimization of vehicle automatic navigation path based on remote sensing and GIS

ObjectiveTaking a general view of the continuous convenience of modern social life, according to the rapid development of traffic network and communication network, this paper studies the vehicle path optimization problem. MethodsThis paper attempts to design a set of optimal vehicle path from the five aspects of algorithm development stage, algorithm solution idea, data storage mode, analysis data format and algorithm search strategy. According to the relevant theory of urban vehicle path, a mathematical model of vehicle path problem with time window restriction is constructed. Aiming at this model, a*, hierarchical algorithm, ant colony algorithm and genetic algorithm are used Line analysis and discussion, and according to the relevant algorithm examples in the literature to verify the model and algorithm. ResultIt is concluded that the combination of a*, ant colony algorithm and genetic algorithm is more suitable for the optimization of vehicle routing, and the hierarchical algorithm, road quality, congestion and other real-time traffic obstacles are improved to establish the vehicle routing optimization system in the traffic network and communication network. During the experiment, five road networks of different sizes were used successively. The modulus of these road networks increased sequentially, and the ratio of the number of road sections to the number of road network nodes was about 1698. And the conclusion drawn through experimental verification and algorithm verification proves the reliability and effectiveness of the system, has certain application value, and promotes the development of society.

Quest for Quality in Translational Stroke Research-A New Dawn for Neuroprotection?

Despite tremendous progress in modern-day stroke therapy, ischemic stroke remains a disease associated with a high socioeconomic burden in industrialized countries. In light of demographic change, these health care costs are expected to increase even further. The current causal therapeutic treatment paradigms focus on successful thrombolysis or thrombectomy, but only a fraction of patients qualify for these recanalization therapies because of therapeutic time window restrictions or contraindications. Hence, adjuvant therapeutic concepts such as neuroprotection are urgently needed. A bench-to-bedside transfer of neuroprotective approaches under stroke conditions, however, has not been established after more than twenty years of research, albeit a great many data have demonstrated several neuroprotective drugs to be effective in preclinical stroke settings. Prominent examples of substances supported by extensive preclinical evidence but which failed clinical trials are tirilazad and disodium 2,4-sulphophenyl-N-tert-butylnitrone (NXY-059). The NXY-059 trial, for instance, was retrospectively shown to have a seriously weak study design, a trial of insufficient quality and a poor statistical analysis, although it initially met the recommendations of the STAIR committee. In light of currently ongoing novel neuroprotective stroke trials, such as ESCAPE-NA, and to avoid the mistakes made in the past, an improvement in study quality in the field of stroke neuroprotection is urgently needed. In the present review, animal models closely reflecting the “typical” stroke patient, occlusion techniques and the appropriate choice of time windows are discussed. In this context, the STAIR recommendations could provide a useful orientation. Taking all of this into account, a new dawn for neuroprotection might be possible.

Successful stroke thrombolysis beyond guidelines: A case series

Intravenous (IV) thrombolysis is a safe and effective treatment for acute ischemic stroke. The therapeutic benefit is not extended to more than 4.5 hours in many patients due to the protocol’s time window restriction. Here, we reported two acute stroke cases with a moderate National Institutes of Health Stroke Scale (NIHSS) and onset greater than 4.5 hours that were successfully thrombolysed with intravenous recombinant tissue plasminogen activator (tPA) - low-dose Tenecteplase (TNK). The decision to thrombolysed both patients were based on Magnetic Resonance Imaging (MRI) Diffusion-weighted imaging (DWI)-Fluid Attenuation Inversion Recovery (FLAIR) mismatch – tissue basis rather than a time window, and this resulted in a good neurological recovery with a significant improvement in functional Modified Rankin Score (MRS) to zero at 90 days post stroke regardless of the stroke aetiology. In summary, Intravenous thrombolysis in acute ischemic stroke outside the therapeutic window but with significant penumbra based on MRI DWI-FLAIR tissue mismatch resulted in a remarkable neurological recovery after 90 days.

Delivering and picking goods under time window restrictions: an effective evolutionary algorithm

Routing of vehicles and scheduling of deliveries play a crucial role in logistics operations as they affect both the distribution cost and customer satisfaction. That is why researchers have intensively studied this problem in conjunction with the multiple variables and constraints involved in the logistics operations. In this paper, the cases of time windows and simultaneous pickups and deliveries, where goods are simultaneously delivered and collected from customers within a predetermined time slot, are studied. The objective of our research is to create efficient routes that minimize both the number of vehicles and the total distance travelled, as both of them affect the total distribution cost. Considering various plans of routes that are differentiated by the number of routes and the sequence of visitations, can be beneficial for decision-makers, since they have the opportunity to select the plan that better fits their needs. Therefore, in this paper we develop a multiobjective evolutionary algorithm (MOEA) that integrates an improved construction algorithm and a new crossover operator for efficient distribution services. Through the proposed MOEA a set of solutions (route plans), known as Pareto-optimal, is obtained, while single biased solutions are avoided. The proposed algorithm is tested in two well-known datasets in order to evaluate the algorithm’s efficiency. The results indicate that the algorithm’s solutions have small deviation from the best-published and some non-dominated solutions are also obtained.

A modified adaptive large neighbourhood search for a vehicle routing problem with flexible time window

Vehicle routing problems are widely available in real world application. In this paper, we tackle the resolution of a specific variant of the problem called in the literature vehicle routing problem with flexible time windows (VRPFlexTW), when the solution has to obey several other constraints, such as the consideration of travel, service, and waiting time together with time-window restrictions. There are proposed two modified versions of the Multi-objective Adaptive Large Neighbourhood Search (MOALNS). The MOALNS approach and its different components are described. Also it is listed a computational comparison between the MOALNS versions and the Ant colony optimiser (ACO) on a few instances of the VRPFlexTW.

Supporting the decision making process in the urban freight fleet composition problem

The Urban Freight Fleet Composition (UFFC) problem addresses the need of a logistics service provider to define the optimal fleet mix in terms of types and number of vehicles to serve the demand for goods delivery in an urban area. Urban areas can be subject to access restrictions (e.g. based upon the time of the day or vehicles’ characteristics) that could affect the performance of transport assets. In this paper, we consider time-window access restrictions based upon the characteristics of the vehicles, and we propose a human-in-the-loop decision support system (HIL-DSS) architecture using optimisation and simulation models to address the trade-off between vehicles characteristics, revenues, costs, and performance. We formulate both a deterministic and a stochastic optimisation decision model addressing the problem in the context of the HIL-DSS. In doing this, we emphasise the role of the human decision maker in tackling a complex problem affected by variability and uncertainty, and to overcome the rigidity of optimisation models thanks to the possibility to include qualitative information into the process.

The traveling salesman problem with time windows in postal services

This article focuses on a variant of the travelling salesman problem with time windows (TSPTW) that arises in postal services and parcel deliveries. It differs from the classical TSPTW as follows. First, route duration is a significant concern, as human costs largely exceed vehicle costs, emphasising the importance of reducing waiting time. Second, customers are divided in two categories: Commercial ones with time windows and private customers without time window restrictions, making the NP-hard TSPTW even harder to solve. To support the first statement, we present a multi-objective approach based on a constraint programming formulation of the problem which allows to balance the optimisation of both human and material resources. To address the issue of large-scale sparsely time-constrained instances and reduce the size of the large real-world instances, we also introduce a cluster/solve/sequence approach that relies on a mathematical programming formulation to sequence the customer visits in the final step. This decomposition technique allows to produce high-quality solutions (with an average optimality gap of 1.31%) for industrial problems in less than one minute of computation time.

Customized bus routing problem with time window restrictions: model and case study

Considering that, in reality, passengers usually hold preferred time windows when waiting for a bus at a station, this study develops a mixed integer programming model for the customized bus routing problem (CBRP) with full spatial–temporal constraints based on one of our previous studies. Specifically, bus routing and passenger assignment are simultaneously optimized with better vehicle capacity utilization and more realistic considerations of partial service, characteristics of customized bus service, and a range of operational constraints. To solve the formulated model, an exact algorithm (i.e. the branch-and-cut algorithm) and two heuristics (i.e. the genetic and tabu search algorithms) are numerically compared through an illustrative example, and subsequently, a case study in Beijing is conducted to assess the proposed approach. A comparison with the practical customized bus system shows that the proposed approach realizes effective vehicle usage on several routes.

Optimization of departure runway scheduling incorporating arrival crossings

AbstractThe runway is a key airport resource and an efficient runway operation is critical to enhance the airport efficiency and to reduce delays. This paper addresses the problem of scheduling aircraft departures incorporating arrival crossings. Constraints for wake turbulence separations, flight time window restrictions, and holding queue capacity at runway threshold are explicitly considered. We present two integer linear programming models and a simulated annealing (SA) algorithm. Comparison tests are conducted for the south side of Paris Charles De Gaulle Airport and show that the three proposed methods can significantly improve the solutions based on the simple first‐come‐first‐served rule. Moreover, SA has sufficiently small computation time for real‐time deployment.

Determining time windows in urban freight transport: A city cooperative approach

Many cities have implemented delivery time windows, which limit city center access for freight vehicles. Each city determines its time windows independently. However, time window restrictions of one city also affect store deliveries in neighbouring cities, since retailers combine deliveries to customers in different cities in a single trip. We develop a cooperative game-theoretic approach to find better time windows than those currently used. A study of three cities and three retail chains illustrates the approach. Compared to the current situation, we find time windows that improve all the city satisfactions, while they hardly impact the retailers’ efficiencies.

Abstract TMP2: Is Eptifibatide a Viable and Safe Option as Stand-alone Therapy for Acute Ischemic Stroke Patients?

Introduction: The list of contraindications for IV tPA in acute ischemic stroke (AIS) is often too long and may lead to physicians opting to offer no treatment for certain strokes. An alternative treatment is proposed in cases where IV tPA is not an option due to time-window restrictions or contraindications. We compared the stroke severity, outcomes and safety of IV eptifibatide when compared with IV tPA. Methods: Patients who presented to a community based university affiliated comprehensive stroke center from 2012-15 with AIS over a two-year period were included in the study. Those who qualified for IV tPA, and were treated, were compared with patients who only received IV eptifibatide. The initial NIH Stroke Score (NIHSS), 24-hour NIHSS, discharge NIHSS (DCNIHSS), discharge mRS (DCmRS) and symptomatic ICH rates were compared with a paired samples t-test to determine significance of difference between the means. SPSS Version 22 was used for all data analysis. Results: A total of 864 patients presented with AIS in the evaluated time period and of those 166 met study criteria. There were 119 patients who received IV tPA alone (group A) and 47 patients received eptifibatide (group B). The mean initial NIHSS, 24-NIHSS, DCNIHSS, DCmRS and percent bleeding complications for group A were: 11.2, 10.8, 8.6, 3.1 and 6%. For group B the figures were: 6.7, 4.8, 4.3, 1.7 and 0%, respectively. Group A was compared with group B in a paired samples T-test and yielded -4.3, -6.2, -6, -1.5 (p=.0001 to .04) for initial, 24-hour, discharge NIHSS and discharge mRS, respectively. The difference between initial and discharge NIHSS between the two groups was -2.7 (p=.009), favoring IV tPA. Conclusion: In patients who are either outside the time-window or with contraindications to IV tPA, eptifibatide may be a safe alternative and appears to be efficacious. None of the patients who were started on eptifibatide had bleeding complications and they had a statistically significant improvement in their level of disability and stroke severity at discharge. A limitation of this study is that patients in group A had significantly worse initial NIHSS compared with group B. To better evaluate the efficacy of eptifibatide, a larger, prospective study should be initiated.

Clean Last Mile Transport and Logistics Management for Smart and Efficient Local Governments in Europe

C-LIEGE “Clean Last mile transport and logistics management for smart and efficient local Governments in Europe” is a STEER Intelligent Energy Europe Programme 2010 project focused on developing integrated framework for energy-efficient urban freight transport demand management and planning. C-LIEGE is conceived as a showcase for good practices and a helping hand for European cities striving for cleaner and sustainable urban freight transport and logistics. C-LIEGE provides solutions for decision makers and professionals in Local Authorities and for freight operators. C-LIEGE empowers a cooperative approach between public and private stakeholders targeted on the reduction of energetic and environmental impacts of freight transport in European cities and regions. C-LIEGE successfully developed and tested freight transport demand-oriented measures and decision supporting tools towards reduction of energy, environmental and economic impacts of freight transport in urban environments. A set of integrated solutions and measures were implemented and tested by the support of newly developed C-LIEGE tools: the Stakeholder Engagement Manual, Good Practice Database, Push and Pull Measures Database, Guideline for the development of Urban Freight Mobility Plan as well as the C-LIEGE Toolbox for the establishment of a City Logistics Manager. Seven pilot experiments in six European countries assessed effectiveness of the C-LIEGE approach: Bulgaria, Italy, Poland, United Kingdom, Germany and Malta. The pilot cities selected and implemented a package of demand-oriented measures such as access fees and time windows restrictions, environmental zones, recognition schemes, freight maps, Freight Quality Partnerships, etc. A total of 35 demand-oriented measures were implemented in the C-LIEGE pilot cities, based upon the C-LIEGE toolbox and guidelines.The C-LIEGE pilot sites have introduced a wide range of measures aimed at reducing the impacts of urban freight transport: Fleet Operator Recognition Scheme (FORS) in Newcastle (UK), introduction of access restrictions for heavier freight vehicles in Montana (Bulgaria), new freight mapping in Leicester (UK) accessible via smart phone, re-routing measures in Stuttgart (Germany) as well as Hal-Tarxien (Malta), the establishment of Freight Quality Partnerships (FQPs) as well as Local Freight Development Plans (LFDPs) in all pilot cities. The results achieved in each C-LIEGE pilot site is summarized as follows: decreasing of CO, CO2, NOx and PM10 emissions (tons/year) generated by freight vehicles (Leicester: 15.3%; Newcastle: 9.5%; Stuttgart: 13.2%; Szczecin: 27.8%; Montana: 11.9%; Hal-Tarxien: 10.6%; Piacenza/Emilia-Romagna: 41.2%/44.4%), decreasing fossil fuel consumption (Leicester: 10.5%; Newcastle: 9.4%; Stuttgart: 13.1%; Szczecin: 27.8%; Montana: 11.9%; Hal-Tarxien: 10.5%; Piacenza/Emilia-Romagna: 1.7%/1.5%), decreasing operating costs for freight carriers (Leicester: 6.5% Newcastle: 5.4%; Stuttgart: 7.4%; Szczecin: 18.2%; Montana: 7.7%; Hal-Tarxien: 9.1%; Piacenza/Emilia-Romagna: 17.5%/20.1%). C-LIEGE finally filled the existing gap in know-how and professionalism in the sector by designing, fine-tuning and delivering the complimentary capabilities of City Logistics Manager. For the first time in Europe, the well-known Mobility Manager whose duties are focused on passenger transport only can be paired with a City Logistics Manager. We are convinced that the C-LIEGE tools and experiences will be of great value for urban freight transport management and logistics in other European cities and regions.

History-dependent scheduling: Models and algorithms for scheduling with general precedence and sequence dependence

In this paper, we extend job scheduling models to include aspects of history-dependent scheduling, where setup times for a job are affected by the aggregate activities of all predecessors of that job. Traditional approaches to machine scheduling typically address objectives and constraints that govern the relative sequence of jobs being executed using available resources. This paper optimises the operations of multiple unrelated resources to address sequential and history-dependent job scheduling constraints along with time window restrictions. We denote this consolidated problem as the general precedence scheduling problem (GPSP). We present several applications of the GPSP and show that many problems in the literature can be represented as special cases of history-dependent scheduling. We design new ways to model this class of problems and then proceed to formulate it as an integer program. We develop specialized algorithms to solve such problems. An extensive computational analysis over a diverse family of problem data instances demonstrates the efficacy of the novel approaches and algorithms introduced in this paper.

Electric Vehicle Routing Problem with Industry Constraints: Trends and Insights for Future Research

This paper presents and analyzes the one-to-many vehicle routing and scheduling problem with electric vehicles. Initially, focus is given on the problem formulation and the restrictions imposed in practice are examined. EVRP is NP-hard in the strong sense since it is natural extension of the well-known Capacitated Vehicle Routing Problem and requires substantial computational effort for determining optimal or near optimal solutions for medium and large scale problem instances. A comprehensive mathematical formulation is developed in order to model the EVRP and the multiple constraints appeared due to capacity limitations, time window restrictions and the predefined charging level of the vehicles. In addition, recent trends for the EVRP are analyzed producing valuable insights for future research regarding extra operational constraints, real-life data sets and solution frameworks that embody approximation algorithms for an efficient and effective search of the solution space.

Robust optimisation model for the cold food chain logistics problem under uncertainty

In the last two decades, food safety has become one of the main concerns in the area of logistics and supply chain management and also in cold chain. Safety is a critically sensitive area in this category as if the required safety conditions are not satisfied during the logistics process, foods will soon deteriorate and probably become unsafe to use by customers. Thus, the problem of cold food safety has encouraged serious attentions among the logistics practitioners. However, because of the complexity in nature of such problems, research so far is limited to the quantitative models with deterministic parameters and the robustness of this nature still remains unanswered. In this paper, a robust optimisation model has been developed aiming to maximise the food safety aspects and thus to minimise the logistics cost of the cold chain system under various uncertainties and customers time windows restrictions. The model has been solved by artificial bee colony intelligence algorithm through MATLAB 8 software. Finally, the results are analysed for possible real world considerations in order to propose some key practical highlights.

Estimating the extra costs imposed on delivery vehicles using access time windows in a city

Analyses performed before introducing access time window policies in the center of European cities often do not evaluate the extra costs imposed on carriers from the additional number of vehicles required and increase in tour length. To facilitate this evaluation, we have developed a vehicle routing algorithm that considers the existence of access time windows and adapts tours to this restriction in the best possible manner. The procedure is based on a genetic algorithm, which we calibrate by analyzing several experiments in a test network. We then apply the algorithm to a real case study in the city of Seville, where local authorities are considering increases in the duration of the time window restriction and the size of the restricted area.

A Stochastic Savings Algorithm for Vehicle Routing Problem with a Centralized Distribution Center and Delivery Time Window

The vehicle routing problem (VRP) under study involves managing a fleet of trucks existing to provide transportation services from a centralized distribution center (DC) to a set of geographically dispersed customers. The problem is motivated by a real problem from one of the large chains of high-end retail stores in Thailand. The objective of this study is to develop a simple and effective algorithm that can help the user manage the fleet in order to reduce the total transportation cost. The developed algorithm is based on the parallel savings algorithm but with a stochastic component that has a capability of escaping the local optima. The stochastic component applied a biased random sampling (BRS) and regret based random sampling (RBRS) to the selection of customers to add to delivery routes. Specifically, the selection probabilities are computed proportionally from the savings value of merging customers. The algorithm generates solutions containing delivery routes and schedules for all trucks in the fleet that satisfy daily store demands under limited truck capacity and delivery time window restrictions. Three measures of performance include the total distance, total travel time, and total cost. Computational test of the algorithm was performed on a set of standard problems, and promising results are obtained.

A General Vehicle Routing Problem

In this paper, we study a rich vehicle routing problem incorporating various complexities found in real-life applications. The General Vehicle Routing Problem (GVRP) is a combined load acceptance and generalised vehicle routing problem. Among the real-life requirements are time window restrictions, a heterogeneous vehicle fleet with different travel times, travel costs and capacity, multi-dimensional capacity constraints, order/vehicle compatibility constraints, orders with multiple pickup, delivery and service locations, different start and end locations for vehicles, and route restrictions for vehicles. The GVRP is highly constrained and the search space is likely to contain many solutions such that it is impossible to go from one solution to another using a single neighbourhood structure. Therefore, we propose iterative improvement approaches based on the idea of changing the neighbourhood structure during the search.