How much to tell your customer? – A survey of three perspectives on selling strategies with incompletely specified products

Gönsch, Jochen

doi:10.1016/j.ejor.2019.02.008

Cited by 17 publications

(6 citation statements)

References 148 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, considering instances from the scientific literature, Koch et al (2017) have shown that delaying the assignment may increase revenues by up to 2% if the demand forecast is accurate and up to 8% under forecast errors. Gönsch (2019) has provided a recent literature review on flexible products, also including opaque products and not restricted to RM. Thus, we only portray the key concepts and contributions regarding availability control in the following.…”

Section: Scientific Contributionsmentioning

confidence: 99%

A review of revenue management: Recent generalizations and advances in industry applications

Klein

Koch

Steinhardt

et al. 2020

European Journal of Operational Research

104

View full text Add to dashboard Cite

show abstract

Section: Scientific Contributionsmentioning

confidence: 99%

A review of revenue management: Recent generalizations and advances in industry applications

Klein

Koch

Steinhardt

et al. 2020

European Journal of Operational Research

104

View full text Add to dashboard Cite

show abstract

“…By offering delivery time windows of different lengths at different (static) prices, a concept known as differentiated pricing (Agatz et al 2013), service providers can exploit heterogeneities in customer flexibility. Gönsch (2020) surveys incompletely specified products (ICSPs, flexible products). For ICSPs, not all specifics of the product are disclosed to the customer at the moment of purchase.…”

Section: Demand Managementmentioning

confidence: 99%

Multitrip vehicle routing with delivery options: a data-driven application to the parcel industry

Janinhoff

Klein

Scholz³

2023

OR Spectrum

View full text Add to dashboard Cite

To make the last mile of parcel delivery more efficient, service providers offer an increasing number of modes of delivery as alternatives to the traditional and often cost-intensive home delivery service. Parcel lockers and pickup stations can be utilized to reduce the number of stops and avoid costly detours. To design smart delivery networks, service providers must evaluate different business models. In this context, a multitrip vehicle routing problem with delivery options and location-dependent costs arises. We present a data-driven framework to evaluate alternative delivery strategies, formulate a corresponding model and solve the problem heuristically using adaptive large neighborhood search. By examining large, real-life instances from a major European parcel service, we determine the potential and benefits of different delivery options. Specifically, we show that delivery costs can be mitigated by consolidating orders in pickup stations and illustrate how pricing can be applied to steer customer demand toward profitable, eco-friendly products.

show abstract

“…In order to widen the field of application of revenue management, some at-tempts have been done to drop typical assumptions. The introduction of incompletely specified products [15], like flexible products [14] and opaque products [10]has offered the possibility of dealing with a less segmented and hence potentially larger demand, as for such products industries can partially postpone resource-product assignment at the end of the booking horizon. Important attempts have also been made to relax some strong assumptions related to the customer behavior.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The objective function (12) minimizes the total routing cost, while constraints ( 13) and ( 14) guarantee that an arc enters and an arc leaves from each node which is visited. Constraints (15) ensure that each node is visited at most once, constraint ( 16) limits the number of routes (i.e., vehicles) to be at most K, and constraints (17) impose that the solution is connected, with constraints ( 21), (22), and ( 23) representing variables definitions. The side constraints (18) to (20) concern the fulfillment of the requests.…”

Section: Capacity Control For Distribution Logistics Problemsmentioning

confidence: 99%

Profit maximization via capacity control for distribution logistics problems

Giallombardo¹,

Guerriero²,

Miglionico³

2020

Preprint

View full text Add to dashboard Cite

We consider a distribution logistics scenario where a shipping operator, managing a limited amount of resources, receives a stream of collection requests, issued by a set of customers along a booking time-horizon, that are referred to a future operational period. The shipping operator must then decide about accepting or rejecting each incoming request at the time it is issued, accounting for revenues, but also considering resource consumptions. In this context, the decision process is based on dynamically finding the best trade-off between the immediate return of accepting the request and the convenience of preserving capacity to possibly exploit more valuable future requests. We give a dynamic formulation of the problem aimed at maximizing the operator revenues, accounting also for the operational distribution costs. Due to the "curse of dimensionality", the dynamic program cannot be solved optimally. For this reason, we propose a mixed-integer linear programming approximation, whose exact or approximate solutions provide the relevant information to apply some commonplace revenue management policies in the real-time decision-making. Adopting a capacitated vehicle routing problem as an underlying distribution application, we analyze the computational behaviour of the proposed techniques on a set of academic

show abstract

How much to tell your customer? – A survey of three perspectives on selling strategies with incompletely specified products

Cited by 17 publications

References 148 publications

A review of revenue management: Recent generalizations and advances in industry applications

A review of revenue management: Recent generalizations and advances in industry applications

Multitrip vehicle routing with delivery options: a data-driven application to the parcel industry

Profit maximization via capacity control for distribution logistics problems

Contact Info

Product

Resources

About