Bidding-Based Hyperconnected Service Network Design
Simon Kwona,∗ , Benoit Montreuila,b , and Mathieu Dahanaa Physical Internet Center, Supply Chain & Logistics Institute, School of Industrial andSystems Engineering, Georgia Institute of Technology, Atlanta, Georgia, USAb Coca-Cola Chair in Material Handling and Distribution
skwon82@gatech.edu, benoit.montreuil@isye.gatech.edu, mathieu.dahan@isye.gatech.edu∗ Corresponding author
Keywords: Hyperconnected Logistics, Physical Internet, Service Network Design, Parcel Logistics

1INTRODUCTION

The Physical Internet (PI) is a hyperconnected global logistics system enabling seamless open
asset sharing and flow consolidation through standardized encapsulation, modularization, protocols, and interfaces (Kim et al., 2021). PI enables a Logistic Web interconnecting multi-planeand multi-party meshed logistic networks (Montreuil et al., 2018) serving the multi-tier logistic
space as illustrated in Figure 1. Such networks comprise multi-tiers of logistics hubs and clusters
adapted to each plane, including unit zones at the lowest tier, local cells as groups of adjacentunit zones, areas as groups of local cells, regions as groups of areas, blocks as groups of regions,with the world being a group of blocks (Montreuil et al., 2018). Examples of unit zones include a
suburban neighborhood, an urban community, a high-rise building or a campus (Montreuil et al.,2018). Several logistics service providers use the concept of unit zones within their organization,
assigning a single courier or a small set of couriers taking charge of all their pickups and deliveries
within the zone (Montreuil et al., 2018). This Logistic Web allows parcels to flow through eachmeshed plane through operations characterized by openly shared access to logistics resources andservice outsourcing, increased cooperation and coordination, and information exchange.

Figure 1 – Multi-Plane Logistics Web Serving the Multi-Tier Logistic Space
Provided with a multi-tier set of interconnected logistic networks, this work introduces a methodology and optimization-simulation models to design a service network by leveraging outsourcing
to third-party logistic (3PLs) service providers, accounting for customers’ expectations to receive
accurate, fast and convenient service. Most of the service network design research is focused on
first-party service network design. Literature focused on designing a service network leveraging
3PLs is scarce. As a similar spirit to this work, Pan et al. (2014) introduces the use of Mechanism
Design theory to make a business model of the logistics service providers where every transport
service is auctioned and develops a simulation framework for auction-based transport service
allocation process in PI. Such service network design must cooperatively leverage the core competencies of various service providers to create a service network that is highly responsive andflexible at responding to customer demands. One of the key issues in developing such a networkis the selection of service providers as the performance of the network depends on every single
organization involved (Cakravastia et al., 2002).
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Simon Kwon, Benoit Montreuil, and Mathieu Dahan

2

The key contributions of this work are (1) introducing the design of hyperconnected service
networks where each network hub and each transport activity is outsourced to 3PL service
providers; (2) introducing a decision-making framework enabling to select the service providers
and to set service level expectations and agreements for each service hub and transport activity;(3) proposing a bidding scheme engaging multiple service providers and accounting for price-timetrade-offs in such logistic service network design (in the spirit of D’Amours et al. (1997)); (4)introducing a simulation-optimization model supporting this bidding based design approach.

2PROPOSED METHODOLOGY

Figure 2 – Multi-Phase Design of Hyperconnected Service Networks
We consider an urban authority, a logistics company or set of such organizations desiring todesign a hyperconnected service network leveraging service providers to transport commodities
between a set of predetermined Origin-Destination (O-D) pairs in a time-sensitive manner. We
assume that the O-D pairs are grouped into three types of shipments: (1) Within-Local cell (LC)shipments, (2) Within-Urban area (UA) shipments (moving across LCs within the same UA),and (3) Within-Region shipments (moving across UAs within the same Region) and each typeof shipments is associated with target service guarantees (e.g., 6-hour delivery). We also assume
that there are a set of service providers interested in offering hub processing services and/or cluster transport services. Hub processing services refer to a set of operations that take place in a
hub to handle inbound parcels/containers to be ready for outbound shipment. Cluster transport
services refer to the practice of transporting parcels/containers within a specified cluster.
As shown in Figure 2, a hyperconnected service network planning is designed in multi phases.
In Phase 1, the organizations collect information and expectations on their multi-tier network
including O-D demand flows and service guarantees offered in each O-D pair. Such guarantees
can be estimated through simulation with historical data or by benchmarking competitors’ service offers. The process of estimating service guarantees is important to the organizations as theservice guarantees impact their overall business and service costs outsourcing service providers.
In Phase 2, service providers share the service activities on which they desire to bid to provide
services and such information is shared with the organizations. In Phase 3, the organizations
provide the service providers with network expectations required for their desired services. InPhase 4, based on the provided network expectations, each service provider conducts analyses
to create their service bids containing which activity to bid on, corresponding service agreement
such as robust hub processing times at hubs/robust transport times within a specified cluster,and corresponding cost. In Phase 5, collecting all bids submitted, the organizations choose asubset of the service bids to set service level expectations and agreements for each service huband transport activity that meet the service guarantees offered in O-D pairs while minimizing thetotal outsourcing cost and assess the performance of the selected bids. Throughout the planning,
there can be multiple rounds of bidding with organizations’ feedback shown as dashed lines in
Figure 2, which will drive different bidding behavior and bid outcomes. For example, after Phase2, if there are service activities that are not liked by any service providers, organizations canrevisit Phase 1 to amend their network expectations to attract service providers. After Phase5, should the assessment of the selected bids turn out to be dissatisfying (e.g., failing to meet
service guarantees), organizations could revisit Phase 4 to negotiate with the associated service
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Simon Kwon, Benoit Montreuil, and Mathieu Dahan

3providers their bids or revisit Phase 1 to amend their network expectations.
We model the multi-phased design of a hyperconnected service network using a simulation-based
optimization approach. For Phase 1, demand patterns for hubs and clusters depend on the huband cluster transport services offered to the network and thus they can vary depending on theservices provided. Hub service processing times can also depend on many factors including volume of in-bound flows and flows’ next destinations which could require different hub processing
times, and are also impacted by the transport services provided to the clusters associated withthe hubs. Cluster transport services also depend on the services provided to the hubs associatedwith the clusters as those impact vehicle planning. We suppose that such fluctuating demand
patterns and mutual dependency in the services can be modeled using a representative finite set ofservice level expectation scenarios ΩH and ΩC for hub and cluster transport services, respectively
in each of which service expectations of each logistic activity are approximately estimated. We
model Phase 4 using simulation to generate service bidders for hub and cluster transport services
and corresponding service bids. Each service bidder (provider) designs their bids, considering
the service level expectation scenarios provided for the other side of the logistics activities. For
example, hub service bidders generate their bids considering the service expectations provided
for the cluster transport services and vice versa for cluster transport service bidders. Each service
bidder uses different strategies to provide services. For hub processing services, one can offer
advanced technologies and practices of processing operations that cost high yet provide highspeed performance while others may offer mode manual operations that may be less expensive
yet of slower-speed. For cluster transport services, one can impose constraints on number ofstops vehicle can make per route and dwell times up to which vehicles can wait at hubs, which
will impact parcel’s total transport time and their service capabilities. Such various capability
options and constraints are considered in the simulation in generating bids.
We model Phase 5 using an assignment-oriented mixed-integer program formulation. Key is
to ensure solvability of the optimization model in large-scale networks with multiple service expectation scenarios and multiple bids per service provider. We rely on a predetermined set ofservice expectation levels for each hub and cluster transport service to generate service expectation scenarios. For example, for each hub and cluster transport service, we consider three levelsof service expectations including fast, medium, and slow and the expected service time range isassigned to each level. The expected time range for each level differs across hubs and clusters.
After a set of bids is generated, each bid is mapped to corresponding service expectation level.
We then feed such input to the optimization model to choose a subset of the service bids that
leads to achieving time-sensitive transports between O-D pairs in a cost minimization manner.

3EMPIRICAL CASE STUDY

We apply the developed decision-making framework to design a hyperconnected urban parcel
logistic service network for a network topology for a Chinese megacity to demonstrate how the
proposed framework can be used for large-scale cases and to investigate the performance achievedby using the approach under representative sets of assumptions.
We consider a megacity instance with a multi-plane spatial structuring in line with Figure 1
composed of 110 clusters. The megacity logistic networks leverage 4522 hubs to serve a daily
demand on the order of 7 million parcels daily across 26,000 origin-destination (O-D) pairs. We
consider 162 hub service bidders and 140 cluster transport service bidders and 5 service level
expectation scenarios for hub and cluster transport services, respectively. Hub service bids fromeach bidder are generated through simulation with different capability options aforementioned.
Cluster transport service bids for each bidder are generated through Vehicle Routing Problem(VRP) simulation with different options of vehicle sizes and vehicle operations (e.g., on-demand
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Simon Kwon, Benoit Montreuil, and Mathieu Dahan

4or bus-type operations), and constraints aforementioned. We use the service guarantee offer
settings as shown in Table 1 to assess the network’s service guarantee’s performance.
Table 1 – O-D Service Guarantee Offer Settings
Service Offer Settings
1234
56
7
8
9Within LC
6 hours
6 hours
6 hours
6 hours
6 hours
4 hours
4 hours
4 hours
4 hours

Within UA
10 hours
10 hours
8 hours
8 hours
8 hours
8 hours
8 hours
6 hours
6 hours

Within Region
12 hours
11 hours
12 hours
11 hours
10 hours
11 hours
10 hours
9 hours
8 hours

We here present a minimal preview of empirical results of our bidding-based optimizationsimulation approach. Figures 3 focus on the achieved customer service performance overall(left) and disaggregated by each type of shipments (right), respectively. As the order-to-delivery
time requirements of each shipment are challenged, the percentage of service guarantee satisfaction decreases from 100% to 87.52%, with the steepest decrease felt when imposing 8 hours
on within-region shipments instead of 12 hours max. The shorter within-local cell and within
urban-area service offerings are overall better served. This illustrates the compromises between
targeted service offers, service levels, and the reality of the urban geography and the capabilitiesof the service providers.

Figure 3 – Customer Service Performance Measurement: (Left) Proportion of total service guarantee satisfaction. (Right) Proportion of service capability violation in each shipment typeIn the presentation, we plan to study each phase of the developed framework in detail and present
more elaborate and completed experiments, enabling to assess the approach in terms of multiple
key performance indicators, including service performance, cost analyses, and price-time tradeoffs. We will also present further research directions, notably in terms of profit maximization.

References
Cakravastia, Andi, Toha, Isa S, & Nakamura, Nobuto. 2002. A two-stage model for the design of supply chainnetworks. International journal of production economics, 80(3), 231–248.
D’Amours, Sophie, Montreuil, Benoit, & Soumis, François. 1997. Price-based planning and scheduling of multiproduct orders in symbiotic manufacturing networks. European journal of operational research, 96(1), 148–166.
Kim, Nayeon, Montreuil, Benoit, Klibi, Walid, & Kholgade, Nitish. 2021. Hyperconnected urban fulfillment anddelivery. Transportation Research Part E: Logistics and Transportation Review, 145, 102104.
Montreuil, Benoit, Buckley, Shannon, Faugere, Louis, Khir, Reem, & Derhami, Shahab. 2018. Urban parcel
logistics hub and network design: The impact of modularity and hyperconnectivity.
Pan, Shenle, Xu, Xiaozhou, Ballot, Eric, et al. 2014. Auction based transport services allocation in PhysicalInternet: a simulation framework. In: 5th international conference on information systems, logistics andsupply chain.

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Original abstract submittal