A Heuristic for the Two-Echelon Multi-Period Multi-Product Location–Inventory Problem with Partial Facility Closing and Reopening

In this paper, the two-echelon multi-period multi-product location–inventory problem with partial facility closing and reopening is studied. For each product and period, plants serve warehouses, which serve consolidation hubs, which service customers with independent, normally distributed demands. The schedule of construction, temporary partial closing, and reopening of modular capacities of facilities, the continuous-review inventory control policies at warehouses, the allocation of customer demands to hubs, and the allocation of hubs to warehouses are determined. The service levels for stockout at warehouses during lead time and the violation of warehouse and hub capacities are explicitly considered. The proposed mixed-integer non-linear program minimizes the weighted summation of the number of different facilities and logistical costs, so that the number of different facilities can be controlled. Since the proposed model is np-hard, the multi-start construction and tabu search improvement heuristic (MS-CTSIH) with two improvement strategies and the modified MS-CTSIH incorporating both strategies are proposed. The experiment shows that the two improvement strategies appear non-dominated, and the modified MS-CTSIH yields the best results. The comparison of the modified MS-CTSIH and a commercial solver on a small instance shows the efficiency and effectiveness of the modified MS-CTSIH. The sensitivity analyses of problem parameters are performed on a large instance

Dynamic traffic assignment approximating the kinematic wave model: system optimum, marginal costs, externalities and tolls

System marginal costs, externalities and optimal congestion tolls for traffic networks are generally derived from system optimizing (SO) traffic assignment models and when these are treated as varying over time they are all referred to as dynamic. In dynamic SO network models the link flows and travel times or costs are generally modelled using so-called ‘whole link’ models. Here we instead develop an SO model that more closely reflects traffic flow theory and derive the marginal costs and externalities from that. The most widely accepted traffic flow model appears to be the LWR (Lighthill, Whitham and Richards) model and a tractable discrete implementation or approximation to that is provided by the cell transmission model (CTM) or a finite difference approximation (FDA). These handles spillbacks, traffic controls and moving queues in a way that is consistent with the LWR model (hence with the kinematic wave model and fluid flow model). An SO formulation using the CTM is already available, assuming a single destination and a trapezoidal flow-density function. We extend the formulation to allow more general nonlinear flow density functions and derive and interpret system marginal costs and externalities. We show that if tolls computed from the DSO solution are imposed on users then the DSO solution would also satisfy the criteria for a dynamic user equilibrium (DUE). We introduce constraints on the link outflow proportions at merges and inflow proportions at diverges. We also extend the model to elastic demands and establish links with previous dynamic traffic assignment (DTA) models

Robustness approach to the integrated network design problem, signal optimization and dynamic traffic assignment problem

textThis dissertation focuses on formulating robust optimization models and developing exact algorithms and various metaheuristics for the integrated network design (NDP), signal setting design (SSD) and dynamic traffic assignment (DTA) problem (NDP-SSD) when accounting for the bi-level nature and the long-term origin-destination demand uncertainty. The NDP determines the optimal budget allocation to improve link capacity, given the available budget. The SSD determines the optimal signal setting (cycle lengths, phase sequencing, green splits and time offsets). NDP-SSD provides a mutually consistent solution where the traffic flows are at dynamic user equilibrium, signal settings are optimal, and link capacity improvement decisions are most favorable. Three bi-level NDP, SSD and NDP-SSD models are proposed: deterministic, stochastic and robust. Also, three single-level SSD and NDP-SSD models are developed: useroptimal, stochastic system-optimal and stochastic user-optimal. The stochastic models minimize the expected cost, whereas the robust models minimize the tradeoff between the expected cost and risk. A solution of robust optimization remains close to optimal for all demand scenarios, while a solution of stochastic optimization may yield large changes in the objective value among different scenarios. v The proposed exact solution methods for bi-level NDP models are the Kth-best algorithm and the Karush-Kuhn-Tucker based mixed-integer programming reformulation. For the exact solution methods of bi-level SSD and NDP-SSD models, we show two mathematical proofs for reducing a mixed-zero-one continuous linear bi-level program and a mixed-zero-one continuous quadratic-linear bi-level program to a parametric linear bi-level program and a parametric quadratic-linear bi-level program, respectively. Although the analytical approach is limited to solving a small single-destination network, it is mathematically tractable and provides insight into the problems. For metaheuristic methods, many assumptions of analytical approach can be relaxed, and multi-destination problems can be solved with the use of existing simulation-based DTA software. Four metaheuristics are developed: random search, simulated annealing, genetic algorithm and reactive tabu search. Extensive numerical experiments are conducted to assess the performance of the metaheuristic algorithms, demonstrate the worthiness of the proposed robust formulations and show the benefit of the integrated approach over the sequential approach. The proposed models and metaheuristic algorithms are tested on limited transportation networks.Civil, Architectural, and Environmental Engineerin

Integrated Network Capacity Expansion and Traffic Signal Optimization Problem: Robust Bi-level Dynamic Formulation

Network design problem, Signal optimization, Dynamic traffic assignment, Robust optimization, Bilevel programming,

Empirical Proof of the Characteristics of the Queue Discharge Rate under Different Rainfall Conditions on an Active On-Ramp Bottleneck

Empirical studies show that queue discharge rate is lower than pre-queue capacity in congestion. This is the the capacity drop phenomenon. All previous research about this event used data during clear weather conditions. This is the first time that empirical relationships between queue discharge rate and weather conditions have been studied. Previous studies show that the capacity drop is triggered by a critical density. Once this density is reached, a drop in the discharge rate is expected. We show that this critical density decreases during any weather condition. Previous studies also prove that the capacity drop is related to speed in congestion but that this might not be true during inclement weather. We show that queue discharge rate is correlated to the speed of congestion in any weather condition. We have also shown for the first time that the speed in congestion and the percentage of the capacity drop have a negative linear relationship

Effects of COVID-19 on Travel Behavior and Mode Choice: A Case Study for the Bangkok Metropolitan Area

This research compared the primary purpose of travelling, mode choice, factors affecting mode choice, and frequency of working from home before and during the COVID-19 pandemic in Bangkok, Thailand using statistical tests and multinomial regression modelling. The Thailand Government applied various restrictions such as limited gathering, mandatory face masks, and closure of institutes to control the pandemic’s spread. The study results show a significant difference in the purpose of primary trips, distance travelled, travel time, number of primary trips, and mode chosen for the primary trips before and during the pandemic. People shifted from working to shopping trips, public to private transport or active modes, and in-person/office work to online working during COVID-19. Male respondents showed higher chances of using public transport than female respondents and gave higher preference to pandemic-related factors for the mode selection during the pandemic. The Government should take actions based on COVID-19 measures such as mandatory face masks, contactless tickets, and sanitization disinfectant booths on every transit station to promote public transport usage. People shifted from public to active modes during the pandemic. There is a need for the improvements of the walkways and footpaths to attract people to use active modes in the future