Robust design of complex socio-technical systems against seasonal effects: a network motif-based approach

Seasonal effects can significantly impact the robustness of socio-technical systems (STS) to demand fluctuations. There is an increasing need to develop novel design approaches that can support capacity planning decisions for enhancing the robustness of STS against seasonal effects. This paper proposes a new network motif-based approach to supporting capacity planning in STS for an improved seasonal robustness. Network motifs are underlying nonrandom subgraphs within a complex network. In this approach, we introduce three motif-based metrics for system performance evaluation and capacity planning decision-making. The first one is the imbalance score of a motif (e.g., a local service network), the second one is the measurement of a motif’s seasonal robustness, and the third one is a capacity planning decision criterion. Based on these three metrics, we validate that the sensitivity of STS performance against seasonal effects is highly correlated with the imbalanced capacity between service nodes in an STS. Correspondingly, we formulate a design optimisation problem to improve the robustness of STS by rebalancing the resources at critical service nodes. To demonstrate the utility of the approach, a case study on Divvy bike-sharing system in Chicago is conducted. With a focus on the size-3 motifs (a subgraph consisting three docked stations), we find that there is a significant correlation between the difference of the number of docks among the stations in a motif and the return/rental performance of such a motif against seasonal changes. Guided by this finding, our design approach can successfully balance out the number of docks between those stations that have caused the most severe seasonal perturbations. The results also imply that the network motifs can be an effective local structural representation in support of STS robust design. Our approach can be generally applied in other STS where the system performances are significantly impacted by seasonal changes, for example, supply chain networks, transportation systems and power grids.

Designing of Container Feeder Service Networks Under Unstable Demand Conditions

The COVID-19 pandemic has greatly magnified supply challenges in all industries, and virus waves continue to cause an extraordinary amount of variation in both the demand for and the availability of necessary products. This uncertainty has also forced many organizations including container liner shipping to redesign their supply chain. Feeder services from hub ports are essential chain of shipping networks. This chapter addresses the design of feeder networks under consideration of demand fluctuations over the year. For this purpose, a perturbation-based variable neighbourhood search approach is developed in order to determine the feeder ship fleet size and mix, the fleet deployment, service routes, and voyage schedules to minimize operational costs. In the case study investigation, the authors consider the feeder network design problem faced by a feeder shipping company as a sample application. The performance of alternate network configurations is compared under dynamic demand conditions. Numerical results highlight the advantage of dynamic and flexible design of feeder service networks.

Controlling Mixed Connected and Non-Connected Vehicle Traffic Through a Diamond Interchange

Diamond interchanges (DIs) allow movement of vehicles between surface streets and freeways for all types of vehicles, including normal non-connected human-driven vehicle (NHDV) traffic and the connected vehicles (CVs). Unlike simple intersections, DIs consist of a pair of closely spaced intersections that are controlled together with complicated traffic movements and heavy demand fluctuations. This paper reviews the movements being controlled at DIs and presents a dynamic programming (DP)-based real-time proactive traffic control algorithm called MIDAS, to control both NHDVs and CVs. Like seminal cycle-free adaptive control methods such as OPAC and RHODES, MIDAS uses a forward recursion DP approach with efficient data structures for any large set of phase movements being controlled at DIs, over a finite-time horizon that rolls forward, and then uses a backward recursion to retrieve the optimal phase sequence and duration of phases. MIDAS captures Eulerian measurements from fixed loop detectors for all vehicles, and also captures Lagrangian measurements like in-vehicle GPS from CVs to estimate link travel times, arrival times, turning movements, etc. For every time horizon MIDAS predicts future arrivals, estimates queues at the interchange, and then minimizes a user-defined metric like delays, stops, or queues at an interchange. The paper compares performances of MIDAS with those of an optimal fixed cycle time signal control (OFTC) scheme and RHODES control on a simulated DI. The simulation is of Phoenix, AZ, DI (on I-17/19th Ave.) that uses the VISSIM micro-simulation platform. Performance is evaluated for various traffic loads and various CV market penetrations. Results show that MIDAS control outperforms RHODES and OFTC.

Designing a Resilient and Sustainable Logistics Network under Epidemic Disruptions and Demand Uncertainty

To face the new challenges caused by modern industry, logistics operations managers need to focus more on integrating sustainability goals, adapt to unexpected disruptions and find new strategies and models for logistics management. The COVID-19 pandemic has proven that unforeseen fragilities, negatively affecting the supply chain performance, can arise rapidly, and logistics systems may confront unprecedented vulnerabilities regarding network structure disruption and high demand fluctuations. The existing studies on a resilient logistics network design did not sufficiently consider sustainability aspects. In fact, they mainly addressed the independent planning of decision-making problems with economic objectives. To fill this research gap, this paper concentrates on the design of resilient and sustainable logistics networks under epidemic disruption and demand uncertainty. A two-stage stochastic mixed integer programming model is proposed to integrate key decisions of location–allocation, inventory and routing planning. Moreover, epidemic disruptions and demand uncertainty are incorporated through plausible scenarios using a Monte Carlo simulation. In addition, two resiliency strategies, namely, capacity augmentation and logistics collaboration, are included into the basic model in order to improve the resilience and the sustainability of a logistics chain network. Finally, numerical examples are presented to validate the proposed approach, evaluate the performance of the different design models and provide managerial insights. The obtained results show that the integration of two design strategies improves resilience and sustainability.

Flexible Hotel Design: Rethinking Hotel Design to Address Short-Term Demand Fluctuations

<p><b>A hotel consists of two major components, the business operations and the physical property. These two components although distinctively different, are very much interdependent and affect the hotel’s ability to succeed. An understanding of this important duality is evident in the increasingly market driven nature of hotel design. More diverse and innovative new hotel concepts are constantly being developed based on the identification of gaps in hotel markets, or the creation of new market segments. However, the common perception of the hotel property as being a static and permanent entity remains the same. Despite the volatile and ever‐changing nature of hotel markets, shortterm demand fluctuations have always been one of the biggest concerns and topics of discussion for hotel management and marketing.</b></p> <p>While there has been plenty of research into the problems and implications that short‐term demand fluctuations have on hotel performance and profitability, common approaches to dealing with demand changes are very much restricted by the physical hotel design, and limited to strategic management and marketing tactics that are often inadequate to deal with the problem.</p> <p>This thesis identifies a gap in the knowledge between hotel design and short‐term demand fluctuations. Through research and design, it aims to bridge the gap by directing a design response targeted specifically at the nature of shortterm demand fluctuations. The outcome of this thesis is the design of a new hotel proposed for Wellington, New Zealand. The design demonstrates how particular flexible design interventions can allow the hotel property to be more responsive to short‐term demand fluctuations, and its potential to improve business performance and operating efficiency.</p>

Flexible Hotel Design: Rethinking Hotel Design to Address Short-Term Demand Fluctuations

<p><b>A hotel consists of two major components, the business operations and the physical property. These two components although distinctively different, are very much interdependent and affect the hotel’s ability to succeed. An understanding of this important duality is evident in the increasingly market driven nature of hotel design. More diverse and innovative new hotel concepts are constantly being developed based on the identification of gaps in hotel markets, or the creation of new market segments. However, the common perception of the hotel property as being a static and permanent entity remains the same. Despite the volatile and ever‐changing nature of hotel markets, shortterm demand fluctuations have always been one of the biggest concerns and topics of discussion for hotel management and marketing.</b></p> <p>While there has been plenty of research into the problems and implications that short‐term demand fluctuations have on hotel performance and profitability, common approaches to dealing with demand changes are very much restricted by the physical hotel design, and limited to strategic management and marketing tactics that are often inadequate to deal with the problem.</p> <p>This thesis identifies a gap in the knowledge between hotel design and short‐term demand fluctuations. Through research and design, it aims to bridge the gap by directing a design response targeted specifically at the nature of shortterm demand fluctuations. The outcome of this thesis is the design of a new hotel proposed for Wellington, New Zealand. The design demonstrates how particular flexible design interventions can allow the hotel property to be more responsive to short‐term demand fluctuations, and its potential to improve business performance and operating efficiency.</p>

A Learning-Based Optimization Approach for Autonomous Ridesharing Platforms with Service-Level Contracts and On-Demand Hiring of Idle Vehicles

Current mobility services cannot compete on equal terms with self-owned mobility products concerning service quality. Because of supply and demand imbalances, ridesharing users invariably experience delays, price surges, and rejections. Traditional approaches often fail to respond to demand fluctuations adequately because service levels are, to some extent, bounded by fleet size. With the emergence of autonomous vehicles, however, the characteristics of mobility services change and new opportunities to overcome the prevailing limitations arise. In this paper, we consider an autonomous ridesharing problem in which idle vehicles are hired on-demand in order to meet the service-level requirements of a heterogeneous user base. In the face of uncertain demand and idle vehicle supply, we propose a learning-based optimization approach that uses the dual variables of the underlying assignment problem to iteratively approximate the marginal value of vehicles at each time and location under different availability settings. These approximations are used in the objective function of the optimization problem to dispatch, rebalance, and occasionally hire idle third-party vehicles in a high-resolution transportation network of Manhattan, New York City. The results show that the proposed policy outperforms a reactive optimization approach in a variety of vehicle availability scenarios while hiring fewer vehicles. Moreover, we demonstrate that mobility services can offer strict service-level contracts to different user groups featuring both delay and rejection penalties.

Investigation of Underground Bio-Methanation Using Bio-Reactive Transport Modeling

Underground bio-methanation is a promising technology for large-scale renewable energy storage. Additionally, it enables the recycling of CO2 via the generation of "renewable methane" in porous reservoirs using in-situ microbes as bio-catalysts. Potential candidate reservoirs are depleted gas fields or even abandoned gas storages, providing enormous storage capacity to balance seasonal energy supply and demand fluctuations. This paper discusses the underlying bio-methanation process as part of the ongoing research project "Bio-UGS – Biological conversion of carbon dioxide and hydrogen to methane," funded by the German Federal Ministry of Education and Research (BMBF). First, the hydrodynamic processes are assessed, and a review of the related microbial processes is provided. Then, based on exemplary field-scale simulations, the bio-reactive transport process and its consequences for operation are evaluated. The hydrogen conversion process was investigated by numerical simulations on field scale. For this, a two-phase multi-component bio-reactive transport model was implemented by (Hagemann 2018) in the open-source DuMux (Flemisch et al. 2011) simulation toolkit for porous media flow. The underlying processes include the transport of reactants and products, consumption of specific components, and the related growth and decay of the microbial population, resulting in a bio-reactive transport model. The microbial kinetic parameters of methanogenic reactions are taken from the available literature. The simulation study covers different scenarios on conceptional field-scale models, studying the impact of well placement, injection rates, and gas compositions. Due to a significant sensitivity of the simulation results to the bio-conversion kinetics, the field-specific conversion rates must be obtained. Thus, the Bio-UGS project is accompanied by laboratory experiments out of the frame of this paper. Other parameters are rather a matter of design; in the present case of depleted gas fields, those parameters are coupled and can be chosen to convert fully hydrogen and carbon dioxide to methane. Especially the well spacing can be considered the main design parameter in the likely case of a given injection rate and gas composition. This study extends the application of the previously developed code from a homogeneous-2D to the heterogeneous-3D case. The simulations mimic the co-injection of carbon dioxide and hydrogen from a 40 MW electrolysis.

Shipbuilding capacity optimization using shipbuilding demand forecasting model

The shipbuilding industry has been drastically affected by demand fluctuations. Currently, it faces intense global competition and a crisis because of an imbalance between supply and demand. This imbalance of supply and demand is caused by an excess of shipbuilding capacity. The Organisation for Economic Co-operation and Development has considered adjusting the shipbuilding capacity to reduce the imbalance based on the demand forecast. On the other hand, demand forecast of shipbuilding is a complex issue because the demand is influenced indirectly by adjustments in shipbuilding capacity. Therefore, it is important to examine the influence of construction capacity adjustments on the future demand of ships based on demand forecasting for the sustainable growth of the shipbuilding industry. In this study, shipbuilding capacity adjustment is considered using a proposed simulation system based on a demand-forecasting model. Additionally, the system dynamics model of a previous study is improved by developing a ship price-prediction model for evaluating the shipbuilding capacity-adjustment scenario. We conduct simulations using the proposed demand-forecasting model and system to confirm the effectiveness of the proposed model and system. Furthermore, several shipbuilding capacity-adjustment scenarios are discussed using the proposed system.