Conference Agenda

Session Overview
Location: Forum 11
 
Date: Monday, 27/June/2022
MA 8:30-10:00MA7 - IL1: Logistics
Location: Forum 11
Session Chair: Sérgio Vasconcelos Castro
 

Management of empty containers by consignees in the hinterland

Benjamin Legros1, Jan Fransoo2, Oualid Jouini3

1EM Normandie Business School, France; 2Tilburg School of Economics and Managemnt; 3CentraleSupélec

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.



Vehicle routing optimization with relay: an arc-based column generation approach

Alexandre Jacquillat1, Alexandria Schmid2, Kai Wang3

1MIT Sloan School of Management, United States of America; 2MIT Operations Research Center, United States of America; 3Heinz College, Carnegie Mellon University, United States of America

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.



Optimizing order fulfillment via genetic programming generated policies

Sérgio Vasconcelos Castro1,2, Gonçalo Figueira1,2, Bernardo Almada-Lobo1,2

1INESC TEC - Institute for Systems and Computer Engineering, Technology and Science, Portugal; 2Faculty of Engineering, University of Porto

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.

 
MB 10:30-12:00MB7 - IL2: Warehouse management
Location: Forum 11
Session Chair: Reeju Guha
 

Capacity flexibility via on-demand warehousing

Soraya Fatehi1, Leela Nageswaran2, Michael R Wagner2

1The University of Texas at Dallas; 2University of Washington

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.



Decision model for selecting robotized order picking solutions

Fabian Schäfer, Fabian Lorson, Alexander Hübner

TUM Campus Straubing, Germany

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.



When the Customer is in my Warehouse: Analysis of Customer Interference on Picking Operations

Daniel Simon Corsten, Reeju Guha

IE Business School, Spain

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

 
MC 14:00-15:30MC7 - IL3: Manufacturing
Location: Forum 11
Session Chair: Florian E. Sachs
 

Stochastic Capacity Investment and Flexible vs. Dedicated Technology Choice in the Presence of Subscription Programs

Liling Lu, Onur Boyabatli, Yini Gao

Singapore Management University

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.



Synchronization in a two-supplier assembly system: Combining a fixed lead-time module with capacitated make-to-order production

Mirjam Meijer, Willem van Jaarsveld, Ton de Kok

Eindhoven University of Technology, the Netherlands

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.



Design of unreliable flow lines with limited buffer capacities and spare part provisioning

Florian E. Sachs1, Gudrun P. Kiesmüller1, Stefan Helber2

1Technical University of Munich, Germany; 2Leibniz University Hannover, Germany

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.

 
MD 16:00-17:30MD7 - IL4: Flexibility and sharing
Location: Forum 11
Session Chair: Karca D. Aral
 

Inventory control for periodic intermittent demand

Sarah Van der Auweraer, Joachim Arts, Thomas van Pelt

University of Luxembourg, Luxembourg

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.



Managerial flexibility and inventory management

Karca D. Aral1, Erasmo Giambona1, Luk Van Wassenhove2

1Syracuse University, United States of America; 2INSEAD

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.

 

 
Date: Tuesday, 28/June/2022
TA 8:30-10:00TA7 - IL5: Inventory management
Location: Forum 11
Session Chair: Ioannis Spantidakis
 

Capacity and demand information sharing in a supply chain with bilateral information asymmetry

Eunji Lee1, Stefan Minner1,2

1TUM School of Management, Technical University of Munich, 80333 Munich, Germany; 2Munich Data Science Institute (MDSI)

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.



A decomposition approach for constrained inventory replenishment

Georgia Perakis1, Divya Singhvi2, Ioannis Spantidakis1

1MIT, United States of America; 2NYU, United States of America

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%.

 
TB 10:30-12:00TB7 - SM7: Service staffing and capacity allocation
Location: Forum 11
Session Chair: Gar Goei Loke
 

How to staff when customers arrive in batches

Andrew Daw1, Robert Hampshire2, Jamol Pender3

1University of Southern California, Marshall School of Business; 2University of Michigan & US Department of Transportation; 3Cornell University

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.



Service staffing for shared resources

Buyun Li1, Vincent Slaugh2

1Indiana University, United States of America; 2Cornell University, United States of America

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.



Joint capacity allocation and job assignment under uncertainty

Peng Wang3, Yun Fong Lim2, Gar Goei Loke1

1Rotterdam School of Management, Netherlands, The; 2Singapore Management University, Singapore; 3National University of Singapore, Singapore

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.

 
TC 14:00-15:30TC7 - SM8: Queuing models in services 1
Location: Forum 11
Session Chair: Jingui Xie
 

Designing service menus for bipartite queueing systems

Rene Caldentey, Varun Gupta, Lisa Aoki Hillas

University of Chicago, United States of America

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.



Dynamic payment and lead-time control in queueing systems with heterogeneous customers and strategic delay

Chen-An Lin, Kevin Shang, Peng Sun

Duke University, United States of America

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.



The impact of prolonged service time under off-service placement on flexibility configurations

Yanting Chen1, Jingui Xie2, Taozeng Zhu3

1University of Shanghai for Science and Technology; 2Technical University of Munich; 3Dongbei University of Finance and Economics

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.

 
TD 16:00-17:30TD7 - SM9: Queuing models in services 2
Location: Forum 11
Session Chair: Ricky Roet-Green
 

Advance selling and upgrading in priority queues

Yaolei Wang1, Ping Cao1, Jingui Xie2, Dongyuan Zhan3

1UCL, United Kingdom; 2Technical University of Munich; 3University College London

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.



Foresee the next line: on information disclosure in tandem queues

Jingwei Ji1, Ricky Roet-Green2, Ran Snitkovsky3

1University of Southern California; 2University of Rochester; 3Columbia University

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.



Dynamic control of service systems with returns

Timothy C. Y. Chan, Simon Y. Huang, Vahid Sarhangian

Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON Canada

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.