Time-varying Demand Research Articles

Weekly scheduling of emergency department physicians to cope with time-varying demand

Overcrowding, long waiting times and delays frequently occur in hospital Emergency Departments (EDs). The main causes are the stochastic and strongly time-varying demands of patient arrivals at an ED and the temporary overloading of EDs. Motivated by collaboration with large EDs, we investigate the physicians scheduling problem in the ED for a weekly planning horizon to address the stochastic and time-varying demands. The patient–physician service system is modeled as a time-varying and temporarily overloaded queueing system without abandonments. We employ a continuous-time Markov chain and uniformization method for the analytical evaluation of waiting times of patients. Based on an increasing convex order property, patient waiting times are proven to be convex in a system state. Based on this convexity, an approximation technique is established to model the physician scheduling problem as a mixed-integer program to decide the start and end working times of physicians. We also obtain a tight lower bound of the optimal solution to this scheduling problem. A local search-based algorithm is designed to solve this scheduling problem. Our method improves the physician schedule obtained via the approaches from the literature, significantly improves actual hospital scheduling, and simultaneously reduces physician working times and patient waiting times.

A smart meter data-driven distribution utility rate model for networks with prosumers

Distribution grids across the world are undergoing profound changes due to advances in energy technologies. Electrification of the transportation sector and the integration of Distributed Energy Resources (DERs), such as photo-voltaic panels and energy storage devices, have gained substantial momentum, especially at the grid edge. Transformation in the technological aspects of the grid could directly conflict with existing distribution utility retail tariff structures. We propose a smart meter data-driven rate model to recover distribution network-related charges, where the implementation of these grid-edge technologies is aligned with the interest of the various stakeholders in the electricity ecosystem. The model envisions a shift from charging end-users based on their KWh volumetric consumption, towards charging them a grid access fee that approximates the impact of end-users’ time-varying demand on their local distribution network. The proposed rate incorporates two cost metrics affecting distribution utilities (DUs), namely magnitude and variability of customer demand. The proposed rate can be applied to prosumers and conventional consumers without DERs.

Service Placement and Request Scheduling for Data-Intensive Applications in Edge Clouds

Mobile edge computing provides the opportunity for wireless users to exploit the power of cloud computing without a large communication delay. To serve data-intensive applications (e.g., video analytics, machine learning tasks) from the edge, we need, in addition to computation resources, storage resources for storing server code and data as well as network bandwidth for receiving user-provided data. Moreover, due to time-varying demands, the code and data placement needs to be adjusted over time, which raises concerns of system stability and operation cost. In this paper, we address these issues by proposing a two-time-scale framework that jointly optimizes service (code and data) placement and request scheduling, while considering storage, communication, computation, and budget constraints. First, by analyzing the hardness of various cases, we completely characterize the complexity of our problem. Next, we develop a polynomial-time service placement algorithm by formulating our problem as a set function optimization, which attains a constant-factor approximation under certain conditions. Furthermore, we develop a polynomial-time request scheduling algorithm by computing the maximum flow in a carefully constructed auxiliary graph, which satisfies hard resource constraints and is provably optimal in the special case where requests have homogeneous resource demands. Extensive synthetic and trace-driven simulations show that the proposed algorithms achieve 90% of the optimal performance.

Integrated optimization of bus bridging routes and timetables for rail disruptions

A rail disruption may lead to rapid degradation of the public transportation system. In response to rail disruptions, this study focuses on bus bridging service design considering a routing strategy that combines express and short-turn routes. An integrated optimization framework is proposed to jointly optimize the bus bridging routes and timetables under time-varying demand. A brute-force search method is developed to identify all candidate bus bridging routes. A mixed integer linear programming model is then formulated to solve the route selection, bus deployment, and bus timetabling problem simultaneously. To handle the arising computational challenges in solving this large-scale problem, a tabu search-based heuristic with LP rounding is developed. Numerical studies based on Cranbourne Line, Melbourne, demonstrates the applicability of the proposed approach in practice. The results indicate that the proposed framework can generate high-quality bus bridging solutions in a reasonable time, which allows quick response to rail line disruptions. The bus bridging strategy with multiple routes outperforms the standard bridging route in terms of bus line capacity and passenger travel time.

An Adaptive Memetic Algorithm for Dynamic Electric Vehicle Routing Problem with Time-Varying Demands

Dynamic electric vehicle routing problem (DEVRP) is an extension of the electric vehicle routing problem (EVRP) into dynamic logistical transportation system such that the demand of customer may change over time. The routing decision of DEVRP must concern with the driving range limitation of electric vehicle (EV) in a dynamic environment since both load degree and battery capacity are variable according to the time-varying demands. This paper proposes an adaptive memetic algorithm, where a special encoding strategy, an adaptive local search operator, and an economical random immigrant scheme are employed in the framework of evolutionary algorithm, to solve DEVRP efficiently. Numeric experiments are carried out upon a series of test instances that are constructed from a stationary VRP benchmark. The computational results show that the proposed algorithm is more effective in finding high-quality solution than several peer algorithms as well as significant in improving the capacity of the routing plan of EVs in dynamic transportation environment.

Channel choice and coordination of fresh agricultural product supply chain

By constructing a dual-channel fresh agricultural product (FAP) supply chain consisting of a retailer and a supplier, this paper considers the effect of fresh-keeping level on the freshness of perishable products and constructs a time-varying demand function based on freshness. The operating cost of the internet channel to the supplier has also been considered in the model. Optimal pricing strategy and profits of supply chain members under dual channels are investigated respectively in this paper. Comparing the optimal profit under traditional single-channel and dual-channel supply chain, we obtain the condition that the internet operating cost should satisfy. Given the situation where the supplier obtains profit while the retailer loses after the supplier introduced the internet channel, this paper proposes a revenue-sharing contract to make up for the loss of the retailer and achieves a win–win situation. Research shows that in the numerical analysis the supplier’s and the retailer’s profit can only be improved when the operating cost of the internet channel c0 and revenue-sharing ratio ψ are within a certain range. When ψ ≥ 0.4 and $ 0\le {c}_0\le \sqrt{\frac{2.34{\psi }^2+1.25\psi +7.39}{8.43{\psi }^3+4.62{\psi }^2+5.72\psi +9.05}}+8.64$, Pareto improvement will be attained on both sides in the supply chain.

A Data-Driven Quasi-Dynamic Traffic Assignment Model Integrating Multi-Source Traffic Sensor Data on the Expressway Network

Static traffic assignment (STA) models have been widely utilized in the field of strategic transport planning. However, STA models cannot fully represent the dynamic road conditions and suffer from inaccurate assignment during traffic congestion. At the same time, an increasing number of installed sensors have become an important means of detecting dynamic road conditions. To address the shortcomings of STA models, we integrate multi-source traffic sensor datasets and propose a novel data-driven quasi-dynamic traffic assignment model, named DQ-DTA. In this model, records of toll stations are used for time-varying travel demand estimation. GPS trajectory datasets of vehicles are further used to calculate the dynamic link costs of the road network, replacing the imprecise Bureau of Public Roads (BPR) function. Moreover, license plate recognition (LPR) data are used to design a statistical probability-based multipath assignment method to capture travelers’ route choices. The expressway network in the Hunan province is selected as the study area, and several classic STA models are also chosen for performance comparison. Experimental results demonstrate that the accuracy of the proposed DQ-DTA model is about 6% higher than that of the chosen STA models.

A two-stage heuristic approach for fleet management optimization under time-varying demand

An efficient two-stage heuristic approach is developed for solving the fleet management problem under time-varying demand. Stage 1 of the approach optimizes the vehicles’ utilization schedule. Continuous-time approximation is employed to yield a set of near-optimality conditions that can greatly reduce the solution space of this stage. Stage 2 then optimizes the vehicle purchase and retirement schedules. Numerical experiments showed that our approach outperformed a number of previous methods and commercial solvers by large margins in terms of solution quality, computational efficiency, or both.

Development of new hybrid discrete-time perishable inventory model based on Weibull distribution with time-varying demand using system dynamics approach

In the 20th century, there has been a significant development in logistics systems owing to market globalisation. From this perspective, our study focuses on designing novel models of inventory systems to reflect these real-time systems with improved accuracy and eventually design optimal control systems. Many mathematical inventory models lack details. Most of the existing models in the literature neglect the inclusion of closing days despite the shortening of the shelf-lifes of perishable items daily. In this study, a hybrid dynamic discrete-time inventory model with random lifetime perishable products, first in–first out (FIFO) and last in-first out (LIFO) issuing policies, positive lead time, and time-varying demand, including closing days, is developed. It is assumed that the demand is an external input to the model, and thus, it is easy to implement or test various market scenarios. The developed model is fitted to real data obtained from the research on the formation of botulinum toxin on packaged cubed melons and microbial spoilage at various incubation temperatures. Our study shows that the implementation of the Weibull distribution in modelling perishability can improve the accuracy of reflecting the performance of real physical inventory systems. We compare the developed model to an existing one in the literature, considering different issuing policies: FIFO, LIFO, and their combination. The study also includes a comparative analysis of the fixed and random lifetimes of products in terms of closing days.

Optimization on metro timetable considering train capacity and passenger demand from intercity railways

Purpose To evacuate passengers arriving at intercity railway stations efficiently, metros and intercity railways usually share the same station or have stations close to each other. When intercity trains arrive intensively, a great number of passengers will burst into the metro station connecting with the intercity railway station within a short period, while the number of passengers will decrease substantially when intercity trains arrive sparsely. The metro timetables with regular headway currently adopted in real-world operations cannot handle the injected passenger demand properly. Timetable optimization of metro lines connecting with intercity railway stations is essential to improve service quality. Design/methodology/approach Based on arrival times of intercity trains and the entire process for passengers transferring from railway to metro, this paper develops a mathematical model to characterize the time-varying demand of passengers arriving at the platform of a metro station connecting with an intercity railway station. Provided the time-varying passenger demand and capacity of metro trains, a timetable model to optimize train departure time of a bi-direction metro line where an intermediate station connects with an intercity railway station is proposed. The objective is to minimize waiting time of passengers at the connecting station. The proposed timetable model is solved by an adaptive large neighborhood search algorithm. Findings Real-world case studies show that the prediction accuracy of the proposed model on passenger demand at the connecting station is higher than 90%, and the timetable model can reduce waiting time of passengers at the connecting station by 28.47% which is increased by 5% approximately than the calculation results of the generic algorithm. Originality/value This paper puts forward a model to predict the number of passengers arriving at the platform of connection stations via analyzing the entire process for passengers transferring from intercity trains to metros. Also, a timetable optimization model aiming at minimizing passenger waiting time of a metro line where an intermediate station is connected to an intercity railway station is proposed.

A model of a production-repair inventory system with time-varying demand and quality-dependent recovery channels

In this paper, we study an inventory system over an infinite planning horizon where a time-varying demand is satisfied by process cycles that consist of a production batch followed by a recovery batch. Our model considers three types of inventory—returned items, serviceable items, and raw material. Furthermore, our model considers two recovery channels—recovery into serviceable items and recovery into raw material. Serviceable items are thus sourced from two inputs—direct recovery and production from raw material. These raw materials can be salvaged from returned items, as well as bought from external sources whenever required. We propose an expression for the unit time total cost as well as a numerical method to find the optimal policy. The properties of the model are studied through numerical experiments, in particular, the feasible situations where hybrid policies are better than pure policies.

A Modified Hybrid Particle Swarm Optimization With Bat Algorithm Parameter Inspired Acceleration Coefficients for Solving Eco-Friendly and Economic Dispatch Problems

The paper presents a modified hybrid particle swarm optimization with bat algorithm parameter inspired acceleration coefficients (MHPSO-BAAC) without and with the constriction factor to find the optimal solution of the economic dispatch problems (EDPs) incorporating conventional as well as hybrid and renewable energy sources (RESs) based plants. The algorithm is designed by modifying the recently presented hybrid PSO and BA (HPSOBA) algorithm applied for the achievement of the optimal solution of the EDPs. The modified algorithm is implemented to solve EDPs of all RESs-based power systems for three scenarios, without constraints, with time-varying demand, and with the consideration of regional load sharing dispatch (RLSD). The performance of the algorithm is also verified through the implementation of various combinations of hybrid as well as thermal power plants (TPPs). The case of TPPs consists of three different scenarios: 1) a small-scale system with constraints like ramp-rate limits (RRLs), prohibited operating zones (POZs), and power losses; 2) a medium-scale power system with consideration of emission-economic dispatch (EED); 3) a large-scale power system with valve-point loading (VPL) effect. The results of the designed MHPSO-BAAC algorithm are compared with the various metaheuristic algorithms available in the literature and the comparative analysis shows the superior performance of the developed algorithm in terms of fuel cost reduction, fast convergence, and computational time.

Solving the one-dimensional cutting stock problem under discrete, uncertain, time-varying demands using a hybrid of special-purpose Benders' decomposition and column generation

Solving the one-dimensional cutting stock problem under discrete, uncertain, time-varying demands using a hybrid of special-purpose Benders' decomposition and column generation

Optimal Reactive Power Dispatch With Time-Varying Demand and Renewable Energy Uncertainty Using Rao-3 Algorithm

The appropriate control and management of reactive power is of great relevance in the electrical reliability, stability, and security of power grids. This issue is considered in order to increase system efficiency and to maintain voltage under the acceptable value range. In this regard, novel technologies as FACTS, renewable energies, among others, are varying conventional grid behavior leading to unexpected limit capacity reaching due to large reactive power flow. Thus, optimal planning of this must be considered. This paper proposes a new application for a simple and easy implementation optimization algorithm, called Rao-3, to solve the constrained non-linear optimal reactive power dispatch problem. Moreover, the integration of solar and wind energy as the most applied technologies in electric power systems are exploited. Due to the continuous variation and the natural intermittence of wind speed and solar irradiance as well as load demand fluctuation, the uncertainties which have a global concern are investigated and considered in this paper. The proposed single-objective and multi-objective deterministic/stochastic optimal reactive power dispatch algorithms are validated using three standard test power systems, namely IEEE 30-bus, IEEE 57-bus, and IEEE 118-bus. The simulation results show that the proposed optimal reactive power dispatch algorithms are superior compared with two recent algorithms (Artificial electric field algorithm (AEFA) and artificial Jellyfish Search (JS) algorithm) and other optimization algorithms used for solving the same problem.

Errors in the Model Formulation of 'Impact of a Large Penetration of Wind Generation on the GB Gas Network', Energy Policy 38 (2010) 5684–5695, DOI: 10.1016/j.enpol.2010.05.016

This manuscript addresses the scientific errors of the gas model of the paper by Qadrdan et al. entitled “Impact of a large penetration of wind generation on the GB gas network” on Energy Policy 38 (2010) 5684–5695. The gas network model formulation of the original paper does not obey the mass and momentum equations for the (successive) steady-state or unsteady-state conditions. To illustrate the scientific flaws of the gas model of the original paper, a gas pipe with a time-varying demand profile and a fixed inlet pressure was used to find the outlet pressure, gas intake, change of linepack, and flow imbalance by three methods: 1) the proposed model of the original paper for successive steady-state conditions (Method 1), 2) PipelineStudio successive steady-state (Method 2), and 3) PipelineStudio unsteady-state (Method 3). There are large deviations between the results obtained from the original paper (Method 1) and the ones from the PipelineStudio gas simulator (Method 2 and 3).

The Nonstationary Newsvendor: Data-Driven Nonparametric Learning

We study a newsvendor problem with unknown demand distribution in a nonstationary demand environment over a multi-period time horizon. The demand in each period consists of a time-varying demand level and an additive random shock. Neither the demand level nor the random shock is separately observable. The amount of change in the demand level over the time horizon is measured by a cumulative variation metric. The problem has widespread applications, such as perishable inventory planning, staffing, and medical resource capacity planning in the wake of COVID-19. We design a nonparametric dynamic ordering policy, termed the moving window ordering policy, that tracks the shifts in the unknown demand level while accounting for the unobservable random demand shocks. To compute the order quantity in each period, this policy only needs the past demand observations, without any access to the underlying demand distribution. For a finite variation budget, we prove that our ordering policy is first-order optimal in the sense that its regret grows at the smallest possible rate. We also extend our analysis to the case of asymptotically large variation budgets. Through case studies based on real-life data, we show that our policy can save 20-80% of overage and underage costs, relative to policies widely used for perishable inventory replenishment and nurse staffing.

The Flight to Safety and International Risk Sharing

We study a business cycle model of the international monetary system featuring a time-varying demand for safe dollar bonds, greater risk-bearing capacity in the U.S. than the rest of the world, and nominal rigidities. A flight to safety generates a dollar appreciation and decline in global output. Dollar bonds thus command a negative risk premium and the U.S. holds a levered portfolio of capital financed in dollars. We quantify the effects of safety shocks and heterogeneity in risk-bearing capacity for global macroeconomic volatility; U.S. external adjustment; and the international transmission of monetary and fiscal policies, including dollar swap lines.

Scientific errors of the gas model of “Role of power-to-gas in an integrated gas and electricity system in Great Britain”, International Journal of Hydrogen Energy 40 (2015) 5763–5775

This paper addresses the errors associated with the gas model of the paper entitled “Role of power-to-gas in an integrated gas and electricity system in Great Britain”, International Journal of Hydrogen Energy 40 (2015) 5763–5775. The equations used for the gas model do not obey the mass and momentum equations for the (successive) steady-state or unsteady-state conditions. In addition, the impact of hydrogen concentration on some physical properties of the mixture and key performance indicators of the system was not addressed in the original paper. To illustrate the scientific flaws of the gas model of the original paper, a gas pipe with a time-varying demand profile and a fixed inlet pressure was used to find the outlet pressure, gas intake, change of linepack, and flow imbalance by three methods: 1) the proposed model of the original paper for successive steady-state conditions (Method 1), 2) PipelineStudio successive steady-state (Method 2), and 3) PipelineStudio unsteady-state (Method 3). There are large deviations between the results obtained from the original paper (Method 1) and the ones from the PipelineStudio gas simulator (Method 2 and 3).

Optimal scheduling of emergency resources for major maritime oil spills considering time-varying demand and transportation networks

During an emergency response to a major oil spill accident, the features of the motion of the oil films affect the response decisions. A highly dynamic optimal solution is needed to tackle the continuous changes in the demand for emergency resources and transportation networks for logistics deliveries that must occur. To effectively balance the responsiveness and the total response cost in emergency operations, this paper proposes a dynamic multi-objective location-routing model to address new challenges, such as the time-varying conditions in the response to oil spills and the interrelationship between the decision-making environment and emergency operations. Since the problem is NP-hard, to efficiently obtain Pareto solutions, a novel implementation of a heuristic framework based on particle swarm optimization is developed to conduct numerical experiments. Additionally, to handle the multi-objective model, an alternative solution based on the cost performance method is adopted to help decision makers select the ideal options for Pareto solutions. A case study of a major oil spill accident that occurred in the Bohai Bay is conducted to demonstrate the application of the proposed model and approaches and the real-world implications.

A PERFORMANCE-BASED COASTAL ENGINEERING FRAMEWORK

Coastal regions are exposed to both chronic and punctuated hazards, such as sea level rise and hurricane events, that can jeopardize entire coastal communities. Therefore, to effectively assess the risk and resilience of coastal communities subjected to multi-hazard environments, evaluation of the capacity of individual structures and infrastructure systems to withstand the different time-varying demands imposed in coastal settings is of paramount importance. This study proposes a comprehensive probabilistic framework for the design, risk and resilience assessment of coastal structures. The methodology also provides useful tools to inform decision-making, facilitate recovery efforts and improve resource allocation.Recorded Presentation from the vICCE (YouTube Link): https://youtu.be/8SI3Dw30yes