Tactical Design of Same-Day Delivery Systems

2021 ◽  
Author(s):  
Alexander M. Stroh ◽  
Alan L. Erera ◽  
Alejandro Toriello
Keyword(s):  
Operations Management ◽  
Census Data ◽  
Worst Case ◽  
Fleet Size ◽  
Approximation Techniques ◽  
Vehicle Dispatching ◽  
Design Variables ◽  
Single Vehicle ◽  
Approximation Guarantee

We study tactical models for the design of same-day delivery (SDD) systems. Same-day fulfillment in e-commerce has seen substantial growth in recent years, and the underlying management of such services is complex. Although the literature includes operational models to study SDD, they tend to be detailed, complex, and computationally difficult to solve, and thus may not provide any insight into tactical SDD design variables and their impact on the average performance of the system. We propose a simplified vehicle-dispatching model that captures the “average” behavior of an SDD system from a single stocking location by utilizing continuous approximation techniques. We analyze the structure of optimal vehicle-dispatching policies given our model for two important instance families—the single-vehicle case and the case in which the delivery fleet is large—and develop techniques to find these policies that require only simple computations. We also leverage these results to analyze the case of a finite fleet, proposing a heuristic policy with a worst-case approximation guarantee. We then demonstrate with several example problem settings how this model and these policies can help answer various tactical design questions, including how to select a fleet size, determine an order cutoff time, and combine SDD and overnight order delivery operations. We validate model predictions empirically against a detailed operational model in a computational case study using geographic and Census data for the northeastern metro Atlanta region, and we demonstrate that our model predicts the average number of orders served and dispatch time to within 1%. This paper was accepted by Jay Swaminathan, operations management.

scholarly journals Transportation-Mission-Based Optimization of Heterogeneous Heavy-Vehicle Fleet Including Electrified Propulsion

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3221
Author(s):  
Toheed Ghandriz ◽  
Bengt Jacobson ◽  
Manjurul Islam ◽  
Jonas Hellgren ◽  
Leo Laine
Keyword(s):  
Internal Combustion Engines ◽  
Optimization Problem ◽  
Target Range ◽  
Heavy Vehicles ◽  
Efficient Manner ◽  
Fleet Size ◽  
Design Variables ◽  
Wide Range ◽  
Capacity Selection

Commercial-vehicle manufacturers design vehicles to operate over a wide range of transportation tasks and driving cycles. However, certain possibilities of reducing emissions, manufacturing and operational costs from end vehicles are neglected if the target range of transportation tasks is narrow and known in advance, especially in case of electrified propulsion. Apart from real-time energy optimization, vehicle hardware can be meticulously tailored to best fit a known transportation task. As proposed in this study, a heterogeneous fleet of heavy-vehicles can be designed in a more cost- and energy-efficient manner, if the coupling between vehicle hardware, transportation mission, and infrastructure is considered during initial conceptual-design stages. To this end, a rather large optimization problem was defined and solved to minimize the total cost of fleet ownership in an integrated manner for a real-world case study. In the said case-study, design variables of optimization problem included mission, recharging infrastructure, loading–unloading scheme, number of vehicles of each type, number of trips, vehicle-loading capacity, selection between conventional, fully electric, and hybrid powertrains, size of internal-combustion engines and electric motors, number of axles being powered, and type and size of battery packs. This study demonstrated that by means of integrated fleet customization, battery-electric heavy-vehicles could strongly compete against their conventional combustion-powered counterparts. The primary focus has been put on optimizing vehicle propulsion, transport mission, infrastructure and fleet size rather than routing.

scholarly journals Hybrid Seven-bar Press Mechanism

2018 ◽  
Vol 12 (3) ◽  
pp. 181-187
Author(s):  
M. Erkan Kütük ◽  
L. Canan Dülger
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Design Optimization ◽  
Hybrid System ◽  
Degrees Of Freedom ◽  
Dimensional Synthesis ◽  
Design Variables ◽  
Optimization Study ◽  
Metal Stamping

An optimization study with kinetostatic analysis is performed on hybrid seven-bar press mechanism. This study is based on previous studies performed on planar hybrid seven-bar linkage. Dimensional synthesis is performed, and optimum link lengths for the mechanism are found. Optimization study is performed by using genetic algorithm (GA). Genetic Algorithm Toolbox is used with Optimization Toolbox in MATLAB®. The design variables and the constraints are used during design optimization. The objective function is determined and eight precision points are used. A seven-bar linkage system with two degrees of freedom is chosen as an example. Metal stamping operation with a dwell is taken as the case study. Having completed optimization, the kinetostatic analysis is performed. All forces on the links and the crank torques are calculated on the hybrid system with the optimized link lengths

scholarly journals Importance of Geospatial Heterogeneity in Chronic Disease Burden for Policy Planning in an Urban Setting Using a Case Study of Singapore

2021 ◽  
Vol 18 (9) ◽  
pp. 4406
Author(s):  
Ken Wei Tan ◽  
Joel R. Koo ◽  
Jue Tao Lim ◽  
Alex R. Cook ◽  
Borame L. Dickens
Keyword(s):  
Diabetes Mellitus ◽  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Chronic Disease ◽  
Type Ii Diabetes ◽  
Census Data ◽  
Type Ii Diabetes Mellitus ◽  
Urban Setting ◽  
Type Ii

Chronic disease burdens continue to rise in highly dense urban environments where clustering of type II diabetes mellitus, acute myocardial infarction, stroke, or any combination of these three conditions is occurring. Many individuals suffering from these conditions will require longer-term care and access to clinics which specialize in managing their illness. With Singapore as a case study, we utilized census data in an agent-modeling approach at an individual level to estimate prevalence in 2020 and found high-risk clusters with >14,000 type II diabetes mellitus cases and 2000–2500 estimated stroke cases. For comorbidities, 10% of those with type II diabetes mellitus had a past acute myocardial infarction episode, while 6% had a past stroke. The western region of Singapore had the highest number of high-risk individuals at 173,000 with at least one chronic condition, followed by the east at 169,000 and the north with the least at 137,000. Such estimates can assist in healthcare resource planning, which requires these spatial distributions for evidence-based policymaking and to investigate why such heterogeneities exist. The methodologies presented can be utilized within any urban setting where census data exists.

scholarly journals Queue Length Forecasting in Complex Manufacturing Job Shops

Forecasting ◽  
2021 ◽  
Vol 3 (2) ◽  
pp. 322-338
Author(s):  
Marvin Carl May ◽  
Alexander Albers ◽  
Marc David Fischer ◽  
Florian Mayerhofer ◽  
Louis Schäfer ◽  
...  
Keyword(s):  
Operations Management ◽  
Queue Length ◽  
Manufacturing Systems ◽  
Production Control ◽  
Data Sets ◽  
Job Shops ◽  
Static Data ◽  
Queue Lengths ◽  
Operational Excellence

Currently, manufacturing is characterized by increasing complexity both on the technical and organizational levels. Thus, more complex and intelligent production control methods are developed in order to remain competitive and achieve operational excellence. Operations management described early on the influence among target metrics, such as queuing times, queue length, and production speed. However, accurate predictions of queue lengths have long been overlooked as a means to better understanding manufacturing systems. In order to provide queue length forecasts, this paper introduced a methodology to identify queue lengths in retrospect based on transitional data, as well as a comparison of easy-to-deploy machine learning-based queue forecasting models. Forecasting, based on static data sets, as well as time series models can be shown to be successfully applied in an exemplary semiconductor case study. The main findings concluded that accurate queue length prediction, even with minimal available data, is feasible by applying a variety of techniques, which can enable further research and predictions.

scholarly journals THE RAILWAY NETWORK AND THE PROCESS OF POPULATION CONCENTRATION IN SPAIN, 1900-2001

2014 ◽  
Vol 32 (3) ◽  
pp. 351-379 ◽  
Author(s):  
Xavier Franch-Auladell ◽  
Mateu Morillas-Torné ◽  
Jordi Martí-Henneberg
Keyword(s):  
Total Population ◽  
Census Data ◽  
Population Distribution ◽  
Historical Analysis ◽  
Railway Network ◽  
Population Concentration ◽  
Central Hypothesis ◽  
Municipal Level

ABSTRACTThis paper proposes a methodology for quantifying the territorial impact on population distribution of the railway. The central hypothesis is that access to railway services provides the best-connected areas with a long-term comparative advantage over others that are less accessible. Carrying out a historical analysis and providing comparable data at the municipal level allows us to determine the extent to which the railway has fostered the concentration of population within its immediate surroundings. The case study presented here is that of Spain between 1900 and 2001, but the same methodology could equally be applied to any other country for which the required data are available. In this case, key data included a Geographic Information System with information about both the development of the railway network and census data relating to total population at the municipal level. The results obtained suggest the relevance of this methodology, which makes it possible to identify the periods and areas in which this influence was most significant.

Beyond the Known: Detecting Novel Feasible Domains Over an Unbounded Design Space

2017 ◽  
Vol 139 (11) ◽  
Author(s):  
Wei Chen ◽  
Mark Fuge
Keyword(s):  
Real World ◽  
Adaptive Sampling ◽  
Design Space ◽  
Fundamental Problem ◽  
Domain Boundary ◽  
Sampling Technique ◽  
Standard Test ◽  
Worst Case ◽  
Trade Offs ◽  
Design Variables

To solve a design problem, sometimes it is necessary to identify the feasible design space. For design spaces with implicit constraints, sampling methods are usually used. These methods typically bound the design space; that is, limit the range of design variables. But bounds that are too small will fail to cover all possible designs, while bounds that are too large will waste sampling budget. This paper tries to solve the problem of efficiently discovering (possibly disconnected) feasible domains in an unbounded design space. We propose a data-driven adaptive sampling technique—ε-margin sampling, which learns the domain boundary of feasible designs and also expands our knowledge on the design space as available budget increases. This technique is data-efficient, in that it makes principled probabilistic trade-offs between refining existing domain boundaries versus expanding the design space. We demonstrate that this method can better identify feasible domains on standard test functions compared to both random and active sampling (via uncertainty sampling). However, a fundamental problem when applying adaptive sampling to real world designs is that designs often have high dimensionality and thus require (in the worst case) exponentially more samples per dimension. We show how coupling design manifolds with ε-margin sampling allows us to actively expand high-dimensional design spaces without incurring this exponential penalty. We demonstrate this on real-world examples of glassware and bottle design, where our method discovers designs that have different appearance and functionality from its initial design set.

scholarly journals Assessing storm surge hazard and impact of sea level rise in the Lesser Antilles case study of Martinique

2017 ◽  
Vol 17 (9) ◽  
pp. 1559-1571 ◽  
Author(s):  
Yann Krien ◽  
Bernard Dudon ◽  
Jean Roger ◽  
Gael Arnaud ◽  
Narcisse Zahibo
Keyword(s):  
Sea Level Rise ◽  
Sea Level ◽  
Storm Surge ◽  
Numerical Models ◽  
Lesser Antilles ◽  
Adaptation Measures ◽  
Worst Case ◽  
Mitigation And Adaptation ◽  
The Impact

Abstract. In the Lesser Antilles, coastal inundations from hurricane-induced storm surges pose a great threat to lives, properties and ecosystems. Assessing current and future storm surge hazards with sufficient spatial resolution is of primary interest to help coastal planners and decision makers develop mitigation and adaptation measures. Here, we use wave–current numerical models and statistical methods to investigate worst case scenarios and 100-year surge levels for the case study of Martinique under present climate or considering a potential sea level rise. Results confirm that the wave setup plays a major role in the Lesser Antilles, where the narrow island shelf impedes the piling-up of large amounts of wind-driven water on the shoreline during extreme events. The radiation stress gradients thus contribute significantly to the total surge – up to 100 % in some cases. The nonlinear interactions of sea level rise (SLR) with bathymetry and topography are generally found to be relatively small in Martinique but can reach several tens of centimeters in low-lying areas where the inundation extent is strongly enhanced compared to present conditions. These findings further emphasize the importance of waves for developing operational storm surge warning systems in the Lesser Antilles and encourage caution when using static methods to assess the impact of sea level rise on storm surge hazard.

scholarly journals A proposal for a course of Operations Management for the Degree in Electronics and Automatic

2017 ◽  
Vol 8 ◽  
pp. 177
Author(s):  
Pilar I. Vidal-Carreras ◽  
Julio J. Garcia-Sabater ◽  
Lourdes Canos-Daros
Keyword(s):  
Group Work ◽  
Operations Management ◽  
Problem Based Learning ◽  
Project Based Learning ◽  
Project Learning ◽  
Large Groups

At this work a methodology is proposed for a course of the discipline of Operations Management with a focus on active methodologies in the degree of Electronics and Automatic. For the course is combined: lecture, group work, problem-based learning, project-based learning and presentation of group work. Previous experiences in the same course allow us to conclude the importance of the lecture in this environment in what is the only course of the discipline in all the degree. The importance of feedback in project learning is not easy for large groups such as the case study, suggesting the presentation of group work as a good solution to the problem

scholarly journals Robustness of electricity systems with nearly 100% share of renewables: A worst-case study

2022 ◽  
Vol 155 ◽  
pp. 111932
Author(s):  
Francisco Gutierrez-Garcia ◽  
Angel Arcos-Vargas ◽  
Antonio Gomez-Exposito
Keyword(s):  
Worst Case ◽  
Electricity Systems
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