Optimal bike allocations in a competitive bike sharing market

This paper studies the bike allocation problem in a competitive bike sharing market. To overcome computational challenges, a continuum approximation (CA) approach is applied, where the allocation points and user demand are assumed to be continuously distributed in a two-dimensional region. Companies offering bike sharing service bear both allocation cost and bike depreciation cost while earning revenue from fare collection. The user's selection of bike service is affected by both walking distance and preference towards bike quality. The elasticity of the demand is considered in relation to the density of allocation points in the market. A leader-follower Stackelberg competition model is developed to derive the optimal allocation strategy for market leader. Two sets of numerical studies - one hypothetical case and one from a real case - are conducted to specify the impact of the parameters on model performance and illustrate how the proposed model can be applied to support the decision making.<br/

Sovereign natural disaster insurance for developing countries : a paradigm shift in catastrophe risk financing

Economic theory suggests that countries should ignore uncertainty for public investment and behave as if indifferent to risk because they can pool risks to a much greater extent than private investors can. This paper discusses the general economic theory in the case of developing countries. The analysis identifies several cases where the government's risk-neutral assumption does not hold, thus making rational the use of ex ante risk financing instruments, including sovereign insurance. The paper discusses the optimal level of sovereign insurance. It argues that, because sovereign insurance is usually more expensive than post-disaster financing, it should mainly cover immediate needs, while long-term expenditures should be financed through post-disaster financing (including ex post borrowing and tax increases). In other words, sovereign insurance should not aim at financing the long-term resource gap, but only the short-term liquidity need.Debt Markets,Hazard Risk Management,Banks&Banking Reform,Insurance&Risk Mitigation,Natural Disasters

A resilient and sustainable supply chain: Is it affordable?

Developing environmentally and socially sustainable supply chains has become an integral part of corporate strategy for virtually every industry. However, little is understood about the broader impacts of sustainability practices on the capacity of the supply chain to tolerate disruptions. This article aims to investigate the sustainability-resilience relationship at the strategic supply chain design level using a multi-objective optimization model and an empirical case study. The proposed model utilizes a sustainability performance scoring method and a novel programming approach to perform a dynamic sustainability tradeoff analysis and design a “resiliently green” supply chain

Sustainable and reliable design of large-scale complex logistics systems under competition and uncertainties

Logistics systems generally involve multiple interacting stakeholders who endogenously make decisions based on their individual, sometimes conflicting, objectives. Meanwhile, many of such systems may be disrupted from time to time under extreme threats (e.g., natural or human-induced disasters). These endogenous and exogenous factors often adversely impact system performance and result in significant societal disutility. My dissertation research focuses on developing mathematical models for design and analysis of large-scale logistics systems, especially those under competition and uncertainties. It holistically captures interactions and joint impacts of various objectives in large-scale supply chains, including supply reliability (against disruptions), service competition (against competitors), as well as demand uncertainties.Built upon a general analysis framework, we seek applications and extensions to address concerns of the current renewable energy sector. Starting from a logistics angle, i.e., the biofuel supply chain design, we investigate its profound economic and societal impact. First, We develop game-theoretical models based on Continuum Approximation (CA) to study a reliable competitive location problem where facilities are simultaneously subject to (i) symmetric or leader-follower types of competitions, and (ii) location-dependent probabilistic failures. An optimization model is formulated to capture the symmetric Nash competition between two companies. The goal is to maximize the expected profit (service revenue minus the sum of initial facility construction costs and the expected customer transportation costs) under normal and failure scenarios. Building upon this result, we build a bilevel leader-follower Stackelberg competition model to derive the optimal facility location design when one of the companies has the first-mover advantage over its competitor. Our CA approach is able to effectively solve the models. For special cases, closed-form analytical solutions can be obtained. Numerical experiments with hypothetical data and a case study for competitive biofuel supply chain design in the State of Illinois are conducted. The results revealed managerial insights on how competing companies should optimally plan their facility locations. Then, we propose a systematic optimization framework to analyze how biofuel supply chain decisions are affected by (i) crop yield/supply uncertainty, (ii) refinery disruption risks, and (iii) competition against existing food supply chains. The interactions among the biofuel industry, farmers and food industry are captured by a Stackelberg-Nash game, formulated under a CA scheme. The expected profits of both the farmers and the biofuel industry are evaluated based on probability distributions of crop yield and refinery disruption risks over space. Functional optimization, e.g., variational calculus, is used to derive the equilibrium conditions and suggest numerical algorithms. A series of numerical experiments are conducted for both hypothetical test cases and a Midwest case study to (i) show computational performance and robustness of the modeling approach, (ii) analyze the impacts of system parameters, as well as (iii) draw managerial insights in realistic settings. In addition, we propose a heuristic modeling framework to overcome the challenge that applying CA in solving dynamic facility location problems. First, we formulate a continuous model for the dynamic version by augmenting the time dimension, while relaxing the location consistency constraints. To translate the CA output into a set of discrete facility locations, we extend the disk model (for one static time period) to a tube model (for multiple time periods). Then, the location consistency constraints are enforced through a nonlinear optimization model with penalty terms. Lastly, we propose an iterative tube regulation algorithm to solve the penalty-based optimization problem. We analyze the accuracy and convergence of our modeling framework and conduct numerical experiments to verify its performance. The model and the solution procedure we proposed are very generic and flexible; thus, it can be extended to variants (e.g., incorporating existing facilities at the beginning of the horizon). Finally, we investigate a difficult trilemma: with limited farmland, how does the government stimulate the growth of the biofuel industry while, at the same time, protect food security and preserve environmental sustainability? Our framework is applied to address such multiple cross-interacting systems associating with the biofuel industry development in a broader context. We aim to provide policy guidelines on governmental mandates to induce socially favorable farmland use configurations to support a sustainable bio-economy

On-demand last-mile distribution network design with omnichannel inventory

E-commerce delivery deadlines are getting increasingly tight, driven by a growing ‘I-want-it-now’ instant gratification mindset of consumers and the desire of online and omnichannel retailers to capitalize on the growth of on-demand e-commerce. On-demand deliveries with delivery deadlines as tight as one or two hours force companies to rethink their last-mile distribution network, since tight delivery deadlines require decentralization of order picking and inventory holding to ensure close proximity to consumers. This fundamentally changes the strategic design process of last-mile distribution networks. We study the impact of incorporating inventory order-up-to level decisions into the strategic design process of last-mile distribution networks with tight delivery deadlines. We develop an approximate inventory model by including an estimate of the cost of late delivery and additional transportation due to local stock-outs in a newsvendor formulation. Such local stock-outs require an order to be delivered from a more distant facility, which may lead to late delivery and additional transportation cost. We integrate our approximate inventory model and a location-allocation mixed-integer program that determines optimal facility locations, associated order-up-to inventory levels, and fleet composition, into a metamodel simulation-based optimization approach. Our numerical analyses demonstrate that pooling the additional online inventory with brick-and-mortar (B&amp;M) inventories leads to cannibalization by the B&amp;M network and higher B&amp;M service levels. However, the pooling benefits to the online network outweigh the cost of inventory cannibalization. Furthermore, we show under which circumstances omnichannel retailers may have an incentive to consolidate online inventory in specific B&amp;M facilities

The Private and Public Economics of Renewable Electricity Generation

Generating electricity from renewable sources is more expensive than conventional approaches, but reduces pollution externalities. Analyzing the tradeoff is much more challenging than often presumed, because the value of electricity is extremely dependent on the time and location at which it is produced, which is not very controllable with some renewables, such as wind and solar. Likewise, the pollution benefits from renewable generation depend on what type of generation it displaces, which also depends on time and location. Without incorporating these factors, cost-benefit analyses of alternatives are likely to be misleading. However, other common arguments for subsidizing renewable power – green jobs, energy security and driving down fossil energy prices – are unlikely to substantially alter the analysis. The role of intellectual property spillovers is a strong argument for subsidizing energy science research, but less persuasive as an enhancement to the value of installing current renewable energy technologies.

Facility Location Planning Under Disruption

Facility Location Problems (FLPs) such as the Uncapacitated Facility Location (UFL) and the Capacitated Facility Location (CFL) along with the k-Shortest Path Problem (k-SPP) are important research problems in managing supply chain networks (SCNs) and related operations. In UFL, there is no limit on the facility serving capacity while in CFL such limit is imposed. FLPs aim to find the best facility locations to meet the customer demands within the available capacity with minimized facility establishment and transportation costs. The objective of the (k-SPP) is to find the k minimal length and partial overlapping paths between two nodes in a transport network graph. In the literature, many approaches are proposed to solve these problems. However, most of these approaches assume totally reliable facilities and do not consider the failure probability of the facilities, which can lead to notably higher cost. In this thesis, we investigate the reliable uncapacitated facility location (RUFL)and the reliable capacitated facility location (RCFL) problems, and the k-SPP where potential facilities are exposed to disruption then propose corresponding solution approaches to efficiently handle these problems. An evolutionary learning technique is elaborated to solve RUFL. Then, a non-linear integer programming model is introduced for the RCFL along with a solution approach involving the linearization of the model and its use as part of an iterative procedure leveraging CPLEX for facility establishment and customer assignment along with a knapsack implementation aiming at deriving the best facility fortification. In RUFL and RCFL, we assume heterogeneous disruption with respect to the facilities, each customer is assigned to primary and backup facilities and a fixed fortification budget allows to make a subset of the facilities totally reliable. Finally, we propose a hybrid approach based on graph partitioning and modified Dijkstra algorithm to find k partial overlapping shortest paths between two nodes on a transport network that is exposed to heterogeneous connected node failures. The approaches are illustrated via individual case studies along with corresponding key insights. The performance of each approach is assessed using benchmark results. For the k-SPP, the effect of preferred establishment locations is analyzed with respect to disruption scenarios, failure probability, computation time, transport costs, network size and partitioning parameters

A concise guide to existing and emerging vehicle routing problem variants

Vehicle routing problems have been the focus of extensive research over the past sixty years, driven by their economic importance and their theoretical interest. The diversity of applications has motivated the study of a myriad of problem variants with different attributes. In this article, we provide a concise overview of existing and emerging problem variants. Models are typically refined along three lines: considering more relevant objectives and performance metrics, integrating vehicle routing evaluations with other tactical decisions, and capturing fine-grained yet essential aspects of modern supply chains. We organize the main problem attributes within this structured framework. We discuss recent research directions and pinpoint current shortcomings, recent successes, and emerging challenges