A Multi-Scenario Optimization Model for Emergency Cold Chain Logistics Distribution

Cold chain logistics has been playing a more and more crucial role in modern society. As a special professional cold chain logistics, emergency cold chain logistics can provide quality assurance for temperature-sensitive products in emergency situations. Due to the fact that demand is uncertain in emergency situations, the cold chain logistics companies have to deal with the issue of uncertainty. However, there is no literature on the emergency cold chain logistics distribution optimization problem with uncertain demand. This research contributes to solving this problem. To deal with uncertain demand in emergency situations, an emergency cold chain logistics distribution optimization model with time windows is proposed based on scenario analysis. The objectives of the model are to minimize the total cost and shorten the delivery time simultaneously. The model can also optimize product procurement and refrigerated vehicle renting. The multi-scenario optimization model is applied to a Chinese cold chain logistics center to verify its effectiveness.

Optimizing the development strategy of combined steam flooding & cyclic steam stimulation for enhanced heavy oil recovery through reservoir proxy modeling

Thermal injection methods are usually used for high viscosity oil. The results of previous studies showed that the combination of SF and SFF had the highest increase in oil recovery but still requires further study to determine the optimum strategy. This work is purposed to optimize the development scenario of a combined CSS-SF applied to a heavy oil field located in Sumatera, Indonesia. The recovery factor and NPV become the objective function, and several given and controlled parameters sensitivity toward the objective function are studied. A proxy model based on quadratic multivariate regression is developed to evaluate and get the desired objective function. The reservoir simulation of the thermal recovery process is done using CMG-STARS simulator. The overall workflow of scenario optimization is conducted using CMOST™ module. Optimum development scenario is obtained through maximization of the objective function. This work shows that the combination of proxy model development and optimization results in the best scenario of combined CSS-SF for heavy oil recovery.

Differential Evolution with Estimation of Distribution for Worst-Case Scenario Optimization

Worst-case scenario optimization deals with the minimization of the maximum output in all scenarios of a problem, and it is usually formulated as a min-max problem. Employing nested evolutionary algorithms to solve the problem requires numerous function evaluations. This work proposes a differential evolution with an estimation of distribution algorithm. The algorithm has a nested form, where a differential evolution is applied for both the design and scenario space optimization. To reduce the computational cost, we estimate the distribution of the best worst solution for the best solutions found so far. The probabilistic model is used to sample part of the initial population of the scenario space differential evolution, using a priori knowledge of the previous generations. The method is compared with a state-of-the-art algorithm on both benchmark problems and an engineering application, and the related results are reported.

Minimization of Mean-CVaR Evacuation Time of a Crowd using Rescue Guides: a Scenario-based Approach

In case of a threat in a public space, the crowd in it should be moved to a shelter or evacuated without delays. Risk management and evacuation planning in public spaces should also take into account uncertainties in the traffic patterns of crowd flow. One way to account for the uncertainties is to make use of safety staff, or guides, that lead the crowd out of the building according to an evacuation plan. Nevertheless, solving the minimum time evacuation plan is a computationally demanding problem. In this paper, we model the evacuating crowd and guides as a multi-agent system with the social force model. To represent uncertainty, we construct probabilistic scenarios. The evacuation plan should work well both on average and also for the worst-performing scenarios. Thus, we formulate the problem as a bi-objective scenario optimization problem, where the mean and conditional value-at-risk (CVaR) of the evacuation time are objectives. A solution procedure combining numerical simulation and genetic algorithm is presented. We apply it to the evacuation of a fictional passenger terminal. In the mean-optimal solution, guides are assigned to lead the crowd to the nearest exits, whereas in the CVaR-optimal solution the focus is on solving the physical congestion occurring in the worst-case scenario. With one guide positioned behind each agent group near each exit, a plan that minimizes both objectives is obtained.