scholarly journals A Distributionally Robust Chance-Constrained Approach for Modeling Demand Uncertainty in Green Port-Hinterland Transportation Network Optimization

Symmetry ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 1492
Author(s):  
Qian Dai ◽  
Jiaqi Yang
Keyword(s):  
Transportation Network ◽  
Chance Constraint ◽  
Planning Problem ◽  
Probability Level ◽  
Worst Case ◽  
Transportation Demand ◽  
Trade Offs ◽  
Network Costs ◽  
Distributionally Robust ◽  
Total Network

This paper discusses a bi-objective programming of the port-hinterland freight transportation system based on intermodal transportation with the consideration of uncertain transportation demand for green concern. Economic and environmental aspects are integrated in order to obtain green flow distribution solutions for the proposed port-hinterland network. A distributionally robust chance constraint optimization model is then established for the uncertainty of transportation demand, in which the chance constraint is described such that transportation demand is satisfied under the worst-case distribution based on the partial information of the mean and variance. The trade-offs among different objectives and the uncertainty theory applied in the modeling both involve the notion of symmetry. Taking the actual port-hinterland transportation network of the Yangtze River Economic Belt as an example, the results reveal that the railway-road intermodal transport is promoted and the change in total network CO2 emissions is contrary to that in total network costs. Additionally, both network costs and network emissions increase significantly with the growth of the lower bound of probability for chance constraint. The higher the probability level grows, the greater the trade-offs between two objectives are influenced, which indicates that the operation capacity of inland intermodal terminals should be increased to meet the high probability level. These findings can help provide decision supports for the green development strategy of the port-hinterland container transportation network, which meanwhile faces a dynamic planning problem caused by stochastic demands in real life.

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 A New Heuristic Method for Transportation Network and Land Use Problem

2016 ◽  
Vol 8 (3) ◽  
pp. 94 ◽  
Author(s):  
Mouhamadou A.M.T. Bald ◽  
Babacar M. Ndiaye
Keyword(s):  
Land Use ◽  
Transportation Network ◽  
Heuristic Method ◽  
Optimization Techniques ◽  
Geographical Information ◽  
Mixed Integer ◽  
Individual Agent ◽  
The Road ◽  
Agent Simulation ◽  
Network Costs

Our paper deals with the Transportation Network and Land Use (TNLU) problem.  It consists in finding, simultaneously, the best location of urban area activities, as well as of the road network design that may minimize the moving cost in the network, and the network costs. We propose a new mixed integer programming formulation of the problem, and a new heuristic method for the resolution of TNLU. Then, we give a methodology to find locations or relocations of some Dakar region amenities (home, shop, work and leisure places), that may reduce travel time or travel distance. The proposed methodology mixes multi-agent simulation with combinatorial optimization techniques; that is individual agent strategies versus global optimization using Geographical Information System. Numerical results which show the effectiveness of the method,  and simulations based on the scenario of Dakar city are given.

Systematic Design Principles for Cost-Effective Hard Constraint Management in Dynamic Nonlinear Systems

2011 ◽  
Vol 2 (1) ◽  
pp. 18-45 ◽  
Author(s):  
Satyakiran Munaga ◽  
Francky Catthoor
Keyword(s):  
Cost Effective ◽  
Greedy Heuristics ◽  
Worst Case ◽  
Hard Constraint ◽  
Video Decoder ◽  
Constraint Management ◽  
Fine Grain ◽  
Trade Offs ◽  
Decoder Design ◽  
Energy Gains

Modern cost-conscious dynamic systems incorporate knobs that allow run-time trade-offs between system metrics of interest. In these systems regular knob tuning to minimize costs while satisfying hard system constraints is an important aspect. Knob tuning is a combinatorial constrained nonlinear dynamic optimization problem with uncertainties and time-linkage. Hiding uncertainties under worst-case bounds, reacting after the fact, optimizing only the present, and applying static greedy heuristics are widely used problem simplification strategies to keep the design complexity and decision overhead low. Applying any of these will result in highly sub-optimal system realizations in the presence of nonlinearities. The more recently introduced System Scenarios methodology can only handle limited form of dynamics and nonlinearities. Existing predictive optimization approaches are far from optimal as they do not fully exploit the predictability of the system at hand. To bridge this gap, the authors propose the combined strategy of dynamic bounding and proactive system conditioning for the predicted likely future. This paper describes systematic principles to design low-overhead controllers for cost-effective hard constraint management. When applied to fine-grain performance scaling mode assignment problem in a video decoder design, proposed concepts resulted in more than 2x energy gains compared to state-of-the-art techniques.

scholarly journals The Distributionally Robust Optimization Reformulation for Stochastic Complementarity Problems

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Liyan Xu ◽  
Bo Yu ◽  
Wei Liu
Keyword(s):  
Robust Optimization ◽  
Linear Complementarity Problem ◽  
Complementarity Problem ◽  
Linear Complementarity ◽  
Uncertain Parameters ◽  
Distributionally Robust Optimization ◽  
Worst Case ◽  
Nonnegativity Constraints ◽  
Distributionally Robust ◽  
Stochastic Linear Complementarity Problem

We investigate the stochastic linear complementarity problem affinely affected by the uncertain parameters. Assuming that we have only limited information about the uncertain parameters, such as the first two moments or the first two moments as well as the support of the distribution, we formulate the stochastic linear complementarity problem as a distributionally robust optimization reformation which minimizes the worst case of an expected complementarity measure with nonnegativity constraints and a distributionally robust joint chance constraint representing that the probability of the linear mapping being nonnegative is not less than a given probability level. Applying the cone dual theory and S-procedure, we show that the distributionally robust counterpart of the uncertain complementarity problem can be conservatively approximated by the optimization with bilinear matrix inequalities. Preliminary numerical results show that a solution of our method is desirable.

EFFECTS OF UNTREATED AND CHEMICALLY DISPERSED OIL ON TROPICAL MARINE COMMUNITIES: A LONG-TERM FIELD EXPERIMENT

1989 ◽  
Vol 1989 (1) ◽  
pp. 447-454 ◽  
Author(s):  
Thomas G. Ballou ◽  
Stephen C. Hess ◽  
Richard E. Dodge ◽  
Anthony H. Knap ◽  
Thomas D. Sleeter
Keyword(s):  
Field Experiment ◽  
Sea Urchins ◽  
Exposure Level ◽  
Long Term Effects ◽  
Worst Case ◽  
Dispersed Oil ◽  
Trade Offs ◽  
Chemically Dispersed Oil ◽  
Term Field

ABSTRACT A multidisciplinary long-term field experiment was conducted to evaluate the use of chemical dispersants to reduce the adverse environmental effects of oil spills in nearshore, tropical waters. Three study sites, whose intertidal and subtidal components consisted of mangroves, seagrass beds, and coral reefs, were studied in detail before, during, and after exposure to untreated crude oil or chemically dispersed oil. This study simulated an unusually high (“worst case”) exposure level of dispersed oil and a moderate exposure level of untreated oil. The third site served as an untreated reference site. Assessments were made of the distribution and extent of contamination by hydrocarbons over time, and the short- and long-term effects on survival, abundance, and growth of the dominant flora and fauna of each habitat. The whole, untreated oil had severe, long-term effects on survival of mangroves and associated fauna, and relatively minor effects on seagrasses, corals, and associated organisms. Chemically dispersed oil caused declines in the abundance of corals, sea urchins, and other reef organisms, reduced coral growth rate in one species, and had minor or no effects on seagrasses and mangroves. Conclusions were drawn from these results on decision making for actual spills based on trade-offs between dispersing or not dispersing the oil. This report deals only with the major results of the study. A large number of parameters were monitored, but in the interest of brevity only the most important aspects of the study are reported here. A detailed description of the methods used and a complete presentation and discussion of results is given in Ballou et al.2

Study on Park and Ride Locating Based on Transportation Demand Management

2014 ◽  
Vol 1030-1032 ◽  
pp. 2254-2259
Author(s):  
Jin De Cai ◽  
Ke Zhang
Keyword(s):  
Traffic Congestion ◽  
Transportation Network ◽  
Demand Management ◽  
Urban Transportation ◽  
Urban Traffic ◽  
Transportation Costs ◽  
Construction Costs ◽  
Transportation Management ◽  
Transportation Demand ◽  
Park And Ride

With the increasingly serious problem of urban traffic congestion, more attention is focused on the Park and Ride (P&R) schemes based on urban transportation demand (TDM) management. The P&R locating research, as an important part of the scheme, plays an important role to strengthen the transportation management. On the basis of identifying all the potential P&R locations, and from the macroscopic perspective of urban transportation network, this paper establishes a model of P&R locating in order to minimize their construction costs as well as the total transportation costs. Example analysis is finally carried out with the help of Lingo software, thus testifying the validity of this research.

scholarly journals Optimization Models and Solution Algorithms for Freight Routing Planning Problem in the Multi-Modal Transportation Networks: A Review of the State-of-the-Art

2015 ◽  
Vol 9 (1) ◽  
pp. 714-723 ◽  
Author(s):  
Yan Sun ◽  
Maoxiang Lang ◽  
Danzhu Wang
Keyword(s):  
Heuristic Algorithms ◽  
Transportation Network ◽  
Algorithm Design ◽  
Global Market ◽  
Optimization Models ◽  
Planning Problem ◽  
Total Production ◽  
Solution Algorithms ◽  
Freight Routing ◽  
A Company

With the remarkable development of international trade, global commodity circulation has grown significantly. To accomplish commodity circulation among various regions and countries, multi-modal transportation scheme has been widely adopted by a large number of companies. Meanwhile, according to the relevant statistics, the international logistics costs reach up to approximate 30-50% of the total production cost of the companies. Lowering the transportation costs has become one of the most important sources for a company to raise profits and maintain competitiveness in the global market. Thus, how to optimize freight routes selection to move commodities through the multi-modal transportation network has gained great concern of both the decision makers of the companies and the multi-modal transport operators. In this study, we present a systematical review on the multi-modal transportation freight routing planning problem from the aspects of model formulation and algorithm design. Following contents are covered in this review: (1) distinguishing the formulation characteristics of various optimization models; (2) identifying the optimization models in recent studies according to the formulation characteristics; and (3) discussing the solution approaches that are developed to solve the optimization models, especially the heuristic algorithms.

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