Inefficiencies in the food supply chain account for up to 60% of food wasted in the United States, significantly inhibiting efforts to tackle food insecurity. In this work, this problem is addressed by developing a su...
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Inefficiencies in the food supply chain account for up to 60% of food wasted in the United States, significantly inhibiting efforts to tackle food insecurity. In this work, this problem is addressed by developing a supply chain decision-making framework that explicitly considers complex biochemical product quality degradation processes as a function of environmental conditions (e.g., temperature, humidity, atmospheric composition). The resulting optimization problem is solved online in real-time to mitigate demand uncertainty, reducing operating costs, and inventory spoilage. We demonstrate that this approach is equivalent to a data-driven, feedback-based control strategy that relies on manipulating environmental conditions at storage facilities and in transportation equipment. Since large-scale supply chain network instances result in computationally prohibitive optimization problems, a novel and highly efficient heuristic is introduced, that allows for obtaining solutions in practical amounts of time and with negligible degradation in the value of the objective function. The performance of our proposed approach is benchmarked with extensive numerical simulations based on a realistic, large-scale study of the produce supply chain from Mexico to the United States.
The traffic equilibrium assignment problem under tradable credit scheme(TCS) in a bi-modal stochastic transportation network is investigated in this paper. To describe traveler’s risk-taking behaviors under uncertain...
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The traffic equilibrium assignment problem under tradable credit scheme(TCS) in a bi-modal stochastic transportation network is investigated in this paper. To describe traveler’s risk-taking behaviors under uncertainty, the cumulative prospect theory(CPT) is adopted. Travelers are assumed to choose the paths with the minimum perceived generalized path costs, consisting of time prospect value(PV) and monetary cost. At equilibrium with a given TCS, the endogenous reference points and credit price remain constant, and are consistent with the equilibrium flow pattern and the corresponding travel time distributions of road sub-network. To describe such an equilibrium state, the CPT-based stochastic user equilibrium(SUE) conditions can be formulated under TCS. An equivalent variational inequality(VI) model embedding a parameterized fixed point(FP) model is then established, with its properties analyzed theoretically. A heuristic solution algorithm is developed to solve the model, which contains two-layer iterations. The outer iteration is a bisection-based contraction method to find the equilibrium credit price, and the inner iteration is essentially the method of successive averages(MSA) to determine the corresponding CPT-based SUE network flow pattern. Numerical experiments are provided to validate the model and algorithm.
When the direction of traffic flow is asymmetric and the road capacity is insufficient, the two-way traffic capacity of the road can adapt to traffic demand dynamically through changing the direction of the lane, whic...
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ISBN:
(纸本)9783642549274;9783642549267
When the direction of traffic flow is asymmetric and the road capacity is insufficient, the two-way traffic capacity of the road can adapt to traffic demand dynamically through changing the direction of the lane, which can ease traffic congestion. This paper proposes the implement conditions of variable lanes for road section and verifies that the intersections connected with variable lanes can deal with the increased traffic successfully. Meanwhile, a bi-level programming model was established. The upper-level is the integrated optimization of lane allocating and the lower-level represents a network equilibrium mode, which predicts the drivers' reaction to any given lane allocation. Then, a heuristic solution algorithm is proposed to solve the bi-level programming model. The numerical test results show that the algorithm increases the capacity of the road network significantly and the two-way traffic is more balanced.
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