Customer acceptance mechanisms for home deliveries in metropolitan areas

Ehmke, Jan Fabian; Campbell, Ann Melissa

doi:10.1016/j.ejor.2013.08.028

Cited by 116 publications

(59 citation statements)

References 25 publications

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“…Early research on the last-mile delivery was aimed at analyzing and quantifying the impact of time slots on transportation costs (Punakivi and Saranen, 2001;Lin and Mahmassani, 2002). Then, researchers looked at the dynamic, or operational, management of time slots in attended home delivery by considering, for example, rules for the acceptance and rejection of deliveries (Bent and van Hentenryck, 2004;Campbell and Savelsbergh, 2005a;Ehmke and Campbell, 2014) or by introducing incentive schemes to encourage the customers to select time slots that could lead to lower transportation costs (Campbell and Savelsbergh, 2005b;Agatz et al, 2008b;Yang et al, 2016).…”

Section: Literature Reviewmentioning

confidence: 99%

Heuristics for tactical time slot management: a periodic vehicle routing problem view

Hernandez

Gendreau

Potvin

2017

Int Trans Operational Res

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In this study, we consider a tactical problem where a time slot schedule for delivery service over a given planning horizon must be selected in each zone of a geographical area. A heuristic search evaluates each schedule selection by constructing a corresponding tactical routing plan of minimum cost based on demand and service time estimates. At the end, the schedule selection leading to the best tactical routing plan is selected. The latter can then be used as a blueprint when addressing the operational problem (i.e., when real customer orders are received and operational routes are constructed). We propose two heuristics to address the tactical problem. The first heuristic is a three‐phase approach: a periodic vehicle routing problem (PVRP) is first solved, followed by a repair phase and a final improvement phase where a vehicle routing problem (VRP) with time windows is solved for each period of the planning horizon. The second heuristic tackles the problem as a whole by directly solving a PVRP with time windows. Computational results compare the two heuristics under various settings, based on instances derived from benchmark instances for the VRP with time windows.

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Section: Literature Reviewmentioning

confidence: 99%

Heuristics for tactical time slot management: a periodic vehicle routing problem view

Hernandez

Gendreau

Potvin

2017

Int Trans Operational Res

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“…or calling the purchaser to confirm attendance (Punakivi and Saranen, 2001;Ehmke, 2012a). These solutions are helpful to the problem, but their disadvantages are significant: time wasted for phone calls or for waiting for delivery, product security issues, lockers storage condition issue, reception boxes size issue (Iwan et al, 2016;Lowe and Rigby, 2014;Ehmke and Campbell, 2014). These disadvantages are particularly noticeable in food delivery.…”

Section: E-grocery Home Deliverymentioning

confidence: 99%

“…As regards the first method, due to the perishability and storage condition-sensitivity of food, attendance of customer (or receiver in general) is often required at the moment of home delivery (Hsu et al, 2007). This problem is known in the literature as the AHDP (Ehmke, 2012a;Ehmke and Campbell, 2014). As the attendance of a customer is hard to predict, home delivery usually results in high rate of failures (Agatz et al, 2011;Gevaers et al, 2011;Lowe and Rigby, 2014) and can lead to high delivery costs, waste of waiting time ( for the customer), and waste of energy spent in transportation.…”

Section: E-grocery Home Deliverymentioning

confidence: 99%

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Using customer-related data to enhance e-grocery home delivery

Pan

Giannikas

Han

et al. 2017

IMDS

112

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If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation.*Related content and download information correct at time of download. Purpose -The development of e-grocery allows people to purchase food online and benefit from home delivery service. Nevertheless, a high rate of failed deliveries due to the customer's absence causes significant loss of logistics efficiency, especially for perishable food. The purpose of this paper is to propose an innovative approach to use customer-related data to optimize e-grocery home delivery. The approach estimates the absence probability of a customer by mining electricity consumption data, in order to improve the success rate of delivery and optimize transportation. Design/methodology/approach -The methodological approach consists of two stages: a data mining stage that estimates absence probabilities, and an optimization stage to optimize transportation. Findings -Computational experiments reveal that the proposed approach could reduce the total travel distance by 3-20 percent, and theoretically increase the success rate of first-round delivery approximately by18-26 percent.Research limitations/implications -The proposed approach combines two attractive research streams on data mining and transportation planning to provide a solution for e-commerce logistics. Practical implications -This study gives an insight to e-grocery retailers and carriers on how to use customer-related data to improve home delivery effectiveness and efficiency. Social implications -The proposed approach can be used to reduce environmental footprint generated by freight distribution in a city, and to improve customers' experience on online shopping. Originality/value -Being an experimental study, this work demonstrates the effectiveness of data-driven innovative solutions to e-grocery home delivery problem. The paper also provides a methodological approach to this line of research. The current issue and full text archive of this journal is available on Emerald Insight at: www.emeraldinsight.com/0263-5577.htm IntroductionRecent developments of e-commerce have had a significant impact on food supply chains. Today, many traditional grocery retailers offer their customers the opportunity to purchase food items online and have them deliv...

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“…The DOD is defined by the ratio between expected numbers of LRC #LRC and overall customers #ERC + #LRC:The DOD is one main dimension to classify DVRPs with stochastic requests. A moderate DOD may be experienced for applications such as oil distribution, patient transports, or grocery delivery [9,38]. The range of applications with high DOD comprises emergency vehicles or courier services [27,47].…”

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confidence: 99%

Anticipation versus reactive reoptimization for dynamic vehicle routing with stochastic requests

Ulmer

2018

Networks

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Due to new business models and technological advances, dynamic vehicle routing is gaining increasing interest. Especially solving dynamic vehicle routing problems with stochastic customer requests becomes increasingly important, for example, in e‐commerce and same‐day delivery. Solving these problems is challenging, because it requires optimization along two dimensions. First, as a reaction to new customer requests, current routing plans need to be reoptimized. Second, potential future requests need to be anticipated in current decision making. Decisions need to be derived in real‐time. The limited time often prohibits extensive optimization in both dimensions and the question arises how to utilize the limited calculation time effectively. In this paper, we analyze the merits of reactive route reoptimization and anticipation for a dynamic vehicle routing problem with stochastic requests. To this end, we compare an existing method from each dimension as well a policy allowing for a tunable combination of the two approaches. We show how the appropriate optimization combination is strongly connected to the degree of dynamism, the percentage of unknown requests. We also show that our combination does not provide significant benefit compared to the respectively best optimization dimension.

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Customer acceptance mechanisms for home deliveries in metropolitan areas

Cited by 116 publications

References 25 publications

Heuristics for tactical time slot management: a periodic vehicle routing problem view

Heuristics for tactical time slot management: a periodic vehicle routing problem view

Using customer-related data to enhance e-grocery home delivery

Anticipation versus reactive reoptimization for dynamic vehicle routing with stochastic requests

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