This study analyses street-turn strategies for empty container repositioning in the hinterland using a double-ended queue model for matching operations. Containers arrive over time at the consignee and the demand for containers arises from the shipper. We prove that the matching time impacts matching proportion, while it marginally influences the consignee's inventory policy and cost per container. Also, the consignee's withholding level is mainly determined by the shipper's production rate.

In relay-based logistics, orders are routed through pit-stops with a different driver assigned to each segment. This paper formulates an integer optimization model to coordinate driver, truck and driver movements. We develop an arc-based column generation algorithm which expands time-space networks iteratively until convergence. Results show that relay operations, combined with our algorithm, can lead to faster deliveries, better driver lifestyles, and a lower environmental footprint.

In online retail, fulfillment optimization is the problem of dynamically determining, for every new order, the fulfillment node that will fulfill every item within the order. To solve the problem, we propose a novel policy function approximation approach based on genetic programming to generate interpretable fulfillment policies. Results show that policies more simple than mathematical programming based ones are able to significantly improve over a myopic assignment.

We study capacity flexibility via an innovative business practice: On-Demand Warehousing. In this emerging application, a platform connects independent warehouse providers, who are willing to sell excess capacity, with a firm that requires on-demand capacity. On-demand warehousing does not require long-term commitments, but rather provides flexible warehouse capacity, on-demand. Our results highlight how on-demand warehousing allows a firm to absorb demand fluctuations better.

Enabled through recent advances in technology, coupled with the advent of new system providers and decreased price points, automated and robotic order picking solutions evolved as a surging market. As implementation projects and the variety of solutions are rising, managers face the decision which ones to select for their specific business case. We contribute by proposing a mathematical optimization approach that assigns each stock keeping unit the most suitable solution under space constraints.

Online pickers encounter customer interference while picking orders, affecting productivity due to store traffic and queues, and service quality due to picking errors. Within the day, there are less-busy periods when stores resemble a warehouse. We match similar orders picked during peak vs non-peak periods to establish the value of picking during non-busy hours. Our research has implications for online grocers willing to be productive without incurring additional cost of maintaining dark stores

We study flexible versus dedicated technology choice and capacity investment of a two-product firm under demand uncertainty in the presence of subscription programs. With subscription programs, a proportion of customers allocated to a particular product are allowed to switch to the other product. We analyze how the switching proportion and demand correlation between two subscription demands affect capacity investment and profitability with each technology, and shape optimal technology choice.

High-tech products consist of many modules. We study an assembly system with one module sourced from a supplier with a fixed lead-time and one module produced in-house in a make-to-order (MTO) production system. Since unavailability of modules is costly, it is important to coordinate between the ordering policy for one module and the production of the other. We show optimality of an order policy for the lead-time module with base-stock levels depending on the state of the MTO production system.

The buffer allocation problem is a fundamental optimization problem if flow line planners need to cope with stochastic influences. Additionally, practitioners include spare part planning for manufacturing systems to increase the machine's availability directly. Hence, we tackle this crucial question and are the first to present a joint optimization of buffer capacities and spare part stocks for flow lines of arbitrary length. Among others, we generate new insights on spare part allocations.

Intermittent demand is difficult to forecast, as many periods have no demand. The time between demands is often not memoryless but –contrary to implicit model assumptions—displays periodicity. Consequently, the time since the last demand is a predictor for future demand. We propose a demand model that accommodates such periodicity and show that the optimal inventory policy is a state-dependent base-stock policy, where the order-up-to-levels depend on the time since the last demand.

We study how managers’ potential personal costs due to shareholder scrutiny affect inventory policies exploiting a quasi-natural experiment. Using a staggered DiD approach, we find that firms incorporated in constituency states increased inventory by 5.2% relative to control firms, indicating a heightened focus on customer service levels. To our best knowledge, our paper is the first to study managerial incentives pertaining to inventory management in a quasi-natural experimental setting.

In bilateral asymmetric information sharing, a retailer has private demand and a supplier private capacity information. We propose non-financial information-sharing mechanisms without power structure and examine analytically how one’s sharing affects the other’s sharing for cooperative, sequential sharing, and under risk aversion. We find that the retailer shares if demand is higher than a threshold. The supplier shares if capacity cost is within a range of upper and lower cost thresholds.

We consider inventory allocation of multiple products, across a network of warehouses. We propose a multi-period, multi-product newsvendor formulation over a network of capacitated warehouses with depth constraints, minimizing the e-tailer’s shipment cost. The efficient algorithm we propose balances the tradeoff between overage and underage costs across periods. We establish its rate of convergence and in collaboration with a fashion e-tailer, we perform a study showing a cost reduction of 9%.

From cloud computing to Covid quarantines, requests for service can arrive in batches. How should this impact the service's staffing? Here, we find that there is no economy of scale as batches grow large, a stark contrast with classical square root rules. By consequence, the queue length is not asymptotically normal; in fact, the fluid and diffusion-type limits coincide. When arrivals are both quick and in batches, an economy of scale can exist, but we show that it is weaker than expected.

Motivated by hotel housekeeping, we study shift construction decisions for room attendants amid uncertainty about customer arrival and departure times. We provide analytical results using the framework of M-convexity. A numerical case study for one hotel suggests that reallocating a small number of workers to later shifts can effectively eliminate guest waiting after the posted check-in time. We also identify alternate optimal solutions that can be useful for recruiting and retaining workers.

We consider the multi-period problem of jointly allocating resources to J supply nodes and assigning jobs of I different demand origins to the nodes. The goal is to maximize rewards for matching or minimize costs of waiting and assignment. We introduce a distributive decision rule, which represents the proportion of jobs served by each of the supply nodes. We test against benchmark models developed specifically for allocation or assignment decisions only and record 1-15% reductions in costs.

We consider a multi-class multi-server queueing system, in which customers of different types have heterogenous preferences over the many servers available. A service provider designs a menu of service classes that balances maximizing the customers’ average service reward and minimizing customers’ average waiting time. Customers act as rational self-interested utility maximizing agents when choosing which service class to join. We study the problem under heavy traffic conditions.

We consider a first-come-first-serve single-server system in which heterogeneous customers. Customers are both payment and lead-time sensitive and are heterogeneous in both immediate service valuation and lead-time sensitivity. The service provider considers incentive-compatible price/lead-time menus based on the system congestion to maximize revenue. The optimal policy suggests the timing to offer a state-independent inflated lead-time option which may be easy to implement in practice.

Recent empirical observations find that the service time at the non-dedicated service provider is significantly prolonged compared with that at the dedicated service provider. This study models these empirical observations by developing stochastic models to assess the impact of the prolonged service time and other parameters (system workload and asymmetry level) on flexibility configurations. We obtain conditions characterizing the optimal flexibility design in the parameter space.

We study advance selling and upgrading in a priority queue setting that emerges in the amusement park industry. Waiting sensitive customers who purchase cheaper advance tickets may suffer from demand uncertainty when consuming the service. Customers can purchase regular tickets in advance and upgrade to fast-track tickets on-site. We find if there are some offline customers who cannot purchase in advance, then allowing upgrading generates more revenue for the service provider.

We consider a system where customers have to go through two service stages. We study the fully-observable model, in which queue-length information of both queues is available at arrival: customers observe the state of the entire system and decide whether to join or not. To learn the value of information we compare the fully observable system with the partially observable system in which, instead of observing the full system state, customers observe queue length only at arrival to each queue.

We study a queueing system with returns where at each service completion epoch, the decision maker can choose to reduce the probability of return for the departing customer at a cost that is convex increasing in the amount of reduction in the return probability. We characterize the structure of optimal long-run average and bias-optimal transient control policies for associated fluid control problems. Our results provide insights on the design of post-service intervention programs.