Ying Rong

OR/MS Models for Supply Chain Disruptions: A Review

ABSTRACT We review the Operations Research/Management Science (OR/MS) literature on supply chain disruptions in order to take stock of the research to date and to provide an overview of the research questions that have been addressed. We first place disruptions in the context of other forms of supply uncertainty and discuss common modeling approaches. We then discuss 180 scholarly works on the topic, organized into six categories: evaluating supply disruptions; strategic decisions; sourcing decisions; contracts and incentives; inventory; and facility location. We conclude with a discussion of future research directions.

Infrastructure Planning for Electric Vehicles with Battery Swapping

The transportation sector is a major source of greenhouse gas (GHG) emissions. As a step toward a greener environment, solutions involving electric vehicles (EVs) have been proposed and discussed. When powered by electricity from efficient and environmentally-friendly generators, EVs have significantly lower per-mile running costs compared to gasoline cars, while generating lower emissions. Unfortunately, due to the limited capacity of batteries, typical EVs can only travel for about 100 miles on a single charge. Because recharging takes several hours, it is impossible to recharge an EV in the middle of a long (round) trip exceeding 100 miles. Better Place (BP), a start-up based in Palo Alto, CA, proposed a novel strategy that potentially overcomes the recharging problem. In the plan, in addition to charging adaptors at homes, work places and shopping malls, swapping stations"", at which depleted batteries can be exchanged for recharged ones in the middle of long trips, will be located at strategic locations along freeways. With its battery swapping equipment, BP has demonstrated how to effectively refuel an EV in less than two minutes. The possible success of EV solutions based on the idea of battery swapping hinges on the ability of the charging service provider (BP or other similar firms) to deploy a cost-effective infrastructure network with comprehensive coverage. Unfortunately, since the adoption rate of electric vehicles, and thus demand for swapping service, is still highly uncertain, the service provider must make deployment plans with incomplete information on hand. In this paper, we develop models that aid the planning process for deploying battery swapping infrastructure, based on a robust optimization framework. We further show that our models can be tightly approximated by mixed-integer second-order cone programs (MISOCPs), which are readily solvable by commercial solvers. Using these models, we demonstrate the potential impacts of battery standardization and various technology advancements on the optimal infrastructure deployment strategy.

Supply disruptions with time-dependent parameters

We consider a firm that faces random demand and receives shipments from a single supplier who faces random supply. The suppliers availability may be affected by events such as storms, strikes, machine breakdowns, and congestion due to orders from its other customers. In our model, we consider a dynamic environment: the probability of disruption, as well as the demand intensity, can be time dependent. We model this problem as a two-dimensional non-homogeneous continuous-time Markov chain (CTMC), which we solve numerically to obtain the total cost under various ordering policies. We propose several such policies, some of which are time dependent while others are not. The key question we address is: How much improvement in cost is gained by using time-varying ordering policies rather than stationary ones? We compare the proposed policies under various cost, demand, and disruption parameters in an extensive numerical study. In addition, motivated by the fact that disruptions are low-probability events whose non-stationary probabilities may be difficult to estimate, we investigate the robustness of the time-dependent policies to errors in the supply parameters. We also briefly investigate sensitivity to the repair-duration distribution. We find that non-stationary policies can provide an effective balance of optimality (low cost) and robustness (low sensitivity to errors).

Appointment Scheduling with Limited Distributional Information

In this paper, we develop distribution-free models that solve the appointment sequencing and scheduling problem by assuming only moments information of job durations. We show that our min--max appointment scheduling models, which minimize the worst-case expected waiting and overtime costs out of all probability distributions with the given marginal moments, can be exactly formulated as tractable conic programs. These formulations are obtained by exploiting hidden convexity of the problem. In the special case where only the first two marginal moments are given, the problem can be reformulated as a second-order cone program. Based on the structural properties of this formulation, under a mild condition, we derive the optimal time allowances in closed form and prove that it is optimal to sequence jobs in increasing order of job duration variance. We also prove similar results regarding the optimal time allowances and sequence for the case where only means and supports of job durations are known. This paper was accepted by Dimitris Bertsimas, optimization.

Toward Mass Adoption of Electric Vehicles: Impact of the Range and Resale Anxieties

Key to the mass adoption of electric vehicles (EVs) is the establishment of successful business models based on sound understanding of consumer behavior in adopting this new technology. In this paper, we study the impact of two major barriers to mass adoption of EVs: (i) range anxiety, the concern that the driving range of EVs may be insufficient to meet the driving needs, and (ii) resale anxiety, the concern that used values of EVs may deteriorate quickly. Using a stylized model calibrated to a data set based on the San Francisco Bay Area, we show that although both types of consumer anxieties typically harm the firm’s profit, they often improve consumer surplus. In addition, we show that a business model that requires consumers to lease the EV batteries (rather than purchase them) may lead to a greater level of adoption and emission savings when the level of resale anxiety is high. Further, a business model that offers EV range improvement through enhanced charging infrastructure typically yields greater adoption and consumer surplus, but lowers the firm’s profit, compared with one that offers enlarged batteries. Overall, we find that the combinations of battery owning/leasing with enhanced charging service, referred to as the (O, E) and (L, E) models in our paper, typically yield the best balance among the objectives of EV adoption, emission savings, profitability, and consumer surplus, when the degree of resale anxiety is low and high, respectively.

The reliable design of one-piece flow production system using fuzzy ant colony optimization

In this work, one-piece flow production system is designed with the purpose of ensuring just-in-time production. Three approaches are applied to achieve the goal: adopting straightforward schedule policies, relaxing the Takt time and decreasing the risk of machine failures and operator mistakes. Consequently, a multi-objective design model is proposed, whose aim is to minimize cycle time, changeover count, cell load variation and the number of cells and maximize the extent to which items are completed in a cell. The fuzzy ant colony optimization (FACO) is also presented to solve the formulated problem. In FACO, the fuzzy logic controller (FLC) is used to adapt the evaporated and deposited value of pheromone trail based on the ants fitness and pheromone trail age. Furthermore, domain knowledge of facility layout, generated based on the travel chart method, is also adaptively injected to improve the performance of FACO. The proposed method is evaluated with the real-world data and experimental results demonstrate that our method outperforms many other methods in efficiency, solution quality and facilitation measures.

Service Region Design for Urban Electric Vehicle Sharing Systems

Emerging collaborative consumption business models have shown promise in terms of both generating business opportunities and enhancing the efficient use of resources. In the transportation domain, car-sharing models are being adopted on a mass scale in major metropolitan areas worldwide. This mode of servicized mobility bridges the resource efficiency of public transit and the flexibility of personal transportation. Beyond the significant potential to reduce car ownership, car sharing shows promise in supporting the adoption of fuel-efficient vehicles, such as electric vehicles (EVs), because of these vehicles’ special cost structure with high purchase but low operating costs. Recently, key players in the car-sharing business, such as Autolib’, car2go, and DriveNow, have begun to employ EVs in an operations model that accommodates one-way trips. On the one hand (and particularly in free-floating car sharing), the one-way model results in significant improvements in coverage of travel needs and therefore i...

Sequencing Appointments for Service Systems Using Inventory Approximations

Managing appointments for service systems with random job durations is a challenging task. We consider a class of appointment planning problems that involve two sets of decisions: job sequencing , i.e., determining the order in which a list of jobs should be performed by the server, and appointment scheduling , i.e., planning the starting times for jobs. These decisions are interconnected because their joint goal is to minimize the expected server idle time and job late-start penalty costs incurred because of randomness in job durations. In this paper, we design new heuristics for sequencing appointments. The idea behind the development of these heuristics is the structural connection between such appointment scheduling problems and stochastic inventory control in serial supply chains. In particular, the decision of determining time allowances as buffers against random job durations is analogous to that of selecting inventory levels as buffers to accommodate random demand in a supply chain; having excess buffers in appointment scheduling and supply chain settings incurs idle time and excess inventory holding costs, respectively, and having inadequate buffers leads to delays of subsequent jobs and backorders, respectively. Recognizing this connection, we propose tractable approximations for the job sequencing problem, obtain several insights, and further develop a very simple sequencing rule of ordering jobs by duration variance to late-start penalty cost ratio. Computational results show that our proposed heuristics produce close-to-optimal job sequences with significantly reduced computation times compared with those produced using an exact mixed-integer stochastic programming formulation based on the sample-average approximation approach.

Bullwhip and Reverse Bullwhip Effects under the Rationing Game

When an unreliable supplier serves multiple retailers, the retailers may compete with each other by inflating their order quantities in order to obtain their desired allocation from the supplier, a behavior known as the rationing game. We introduce capacity information sharing and a capacity reservation mechanism in the rationing game and show that a Nash equilibrium always exists. Moreover, we provide conditions guaranteeing the existence of the reverse bullwhip effect upstream, a consequence of the disruption caused by the supplier. In contrast, we also provide conditions under which the bullwhip effect does not exist. In addition, we show that a smaller unit reservation payment leads to more bullwhip and reverse bullwhip effects, while a large unit underage cost results in a more severe bullwhip effect.

Cheaper by the pallet? Multi-item procurement with standard batch sizes

This research was motivated by challenges facing inventory managers at a major retail chain in deciding how often to order each product and whether to use a standard batch size of a pallet, a half-pallet, or even less. The retailer offers thousands of different products, but the total demand for a typical product is only a few pallets per year. Manufacturers offer lower per unit prices for larger standard batch sizes, but larger order quantities increase inventory holding costs. The inventory managers are also concerned about how the ordering strategy might affect transportation costs and material handling costs at the warehouse. We develop a framework and solution strategy to determine the best shipment frequency, standard batch size (from a set of options), and an ordering plan for a set of products procured from a single supply location. To do so, the inventory holding and material handling costs incurred by a single product are derived for a given review interval and standard batch size. We incorporate the individual product costs into an optimization model to find, for a given transportation interval (with a limit on transport capacity for each shipment), the best procurement plan considering variable procurement, inventory, material handling, and excess transportation costs. With this, several transportation intervals can be compared and the best one selected. To the best of our knowledge, this work is the first to consider the effects of transportation capacity and standard batch sizes in a multi-item procurement problem with the goal of minimizing transportation, inventory, and material handling costs.