Venue: H10-31
Time: 12:00

Joydeep Paul (RSM)

8 september 2017

Vehicle routing with shared customer consolidation in multi-channel retail


A multi-channel fulfillment model that is currently very popular is the buy-online-pick-up-in-store (BOPS) in which customers can buy goods online and pick them up in store. It is a common practice for multi-channel retailers using BOPS to have separate warehouses for online and offline channels to ensure efficient order fulfillment operations, and then transfer the goods from the individual warehouses to the stores. As a result, same stores are visited by vehicles from different channels. In this paper, we introduce a novel consolidation strategy in which the carrier of one distribution channel has the possibility to piggyback on the routes of the carrier  of another distribution channel for the delivery of goods to a set of shared customers. Since there is not enough room to outsource all the customers, we have to decide which customers to visit directly and which customers to outsource. However, there are additional costs to transfer the demand of outsourced customers from the warehouse of one to the other's. This gives rise to a trade-off between the savings achieved by outsourcing and the transfer costs. We apply our consolidation strategy to a real grocery retail case to test that substantial savings can be achieved in terms of the travel distance and the number of customer-visits.

Florian Arnold (Faculty of Applied Economics, University of Antwerp)

22 september 2017

Fast and useful - Efficient routing and its application in practice


Routing problems are among the most-studied and practical-relevant problems in combinatorial optimization. To tackle their complexity, a plethora of heuristics has been developed in the last decades to compute better and better solutions in a feasible time. However, this race for better solutions has caused heuristics to become extremely intricate and difficult to study or to apply in practice. Furthermore, almost all research has been targeted to solve problems of several hundred customers, even though some applications like waste collection and parcel distributions can involve thousands or even tens of thousands of customers.

In this talk, I will demonstrate that complexity is not the only way to obtain successful heuristics. We developed a well-implemented local search which - guided by problem knowledge, obtained through data mining - is among the most efficient heuristics for the Vehicle Routing Problem. Moreover, its linear scaling can be used to solve very large scale routing problems with 10,000 and more customers in a feasible time. Such a fast routing algorithm can be used to tackle real-world problems, for instance, to simulate whether parcel deliveries via cargo bikes are a decent alternative to classical home deliveries via vans. Finally, efficient solution techniques for routing problems can be used to address more advanced problems, especially when it comes to integrating different decisions along the supply-chain in one optimisation framework. As an example, I will outline the idea of optimising the inventory strategy as well as the distribution jointly.

Dirk Siegag

26 September 2017

Choice-Based Network Revenue Management under Online Reviews


A choice-based network revenue management model is proposed that integrates the effect of reviews. Application areas include airlines, hotels, and rental cars. The dependency between reviews and revenue is two-fold: the content of a review depends on the product the customer purchases, and reviews impact the demand. A complicating factor in this model is that the effects of reviews are delayed, i.e., by sacrificing revenue now in order to get better reviews, long-term revenue can be increased. Novel solution methods are proposed that exploit the presence of reviews in order to optimise revenue.

Dmitry Krushinsky (Wageningen University)

27 September 2017

Old problems, new ideas


In this talk, he will give a brief overview of his past and current logistics-related research. The talk is focused on the idea that a careful model choice is crucial for successful optimisation. In particular, the use of non-standard approaches can provide new insights into the properties of a problem and substantially improve the performance of the related algorithms. Among others, this point will be illustrated on three rather different problems:
- simulation of pedestrian crowd movement;
- cell formation in industrial engineering;
- vehicle routing.

Niels Agatz (RSM)

6 October 2017

Dynamic Programming Approaches for the Traveling Salesman Problem with Drone


The fast and cost-efficient home delivery of goods ordered online is logistically challenging. Many companies are looking for new ways to cross the last-mile to their customers. One technology-enabled opportunity that recently has received much attention is the use of a drone to support deliveries. A new delivery model in this area entails the use of a delivery truck that collaborates with a drone to make deliveries. Effectively combining a drone and a truck gives rise to a new planning problem that we call the Traveling Salesman Problem with Drone (TSP-D). In this talk, I will present solution approaches to the TSP-D based on dynamic programming and discuss various new numerical results. 

Christos Orlis (Vrije Universiteit Amsterdam

20 October 2017

Distributing cash with KPI considerations: the capacitated routing problem with profits and service level requirements


Inspired by a problem arising in cash logistics, we propose the Capacitated Routing Problem with Profits and Service Level Requirements (CRPPSLR). The CRPPSLR extends the class of Routing Problems with Profits by considering customers requesting deliveries to their (possibly multiple) service points. Moreover, each customer imposes a service level requirement specifying a minimum-acceptable bound on the fraction of its service points being delivered. A customer-specific financial penalty is incurred by the logistics service provider when this requirement is not met. The CRPPSLR consists of finding vehicle routes maximizing the difference between the collected revenues and the incurred transportation and penalty costs in such a way that vehicle capacity and route duration constraints are met. A fleet of homogeneous vehicles is available for serving the customers. We design a branch-and-cut algorithm and evaluate the usefulness of valid inequalities that have been effectively used for the capacitated vehicle routing problem and, more recently, for other routing problems with profits. Real-life case studies coming from the cash supply chain in the Netherlands highlight the relevance of the problem under consideration. Computational results illustrate the performance of the proposed solution approach under different input parameter settings for the synthetic instances. For the real-life problem instances we distinguish between coin and banknote distribution since vehicle capacities only matter when considering the former case. 

Lisa Maillart (Department of Industrial Engineering at the University of Pittsburgh)

17 November 2017 (H12-30)

Optimizing Donor Milk Bank Operations


Donated human milk – collected, processed and dispensed via milk banks – is the standard of care for premature inpatient infants and unhealthy outpatient infants whose mothers cannot provide adequate supply. We take a multi-criteria mixed-integer program approach to optimize (1) the daily decisions involved in the pooling of milk from different donors to meet macronutrient requirements across different product types, and (2) the batching of pooled milk for efficient pasteurization. Our numerical results demonstrate significant improvements compared to historical decisions at our partner milk bank.


Remy Spliet
Room: 11-05
Phone: 010-4081342