Pricing and Collaboration in Last Mile Delivery Services

Recently, last mile delivery has emerged as an essential process that greatly affects the opportunity of obtaining delivery service market share due to the rapid increase in the business-to-consumer (B2C) service market. Express delivery companies are investing to expand the capacity of hub terminals to handle increasing delivery volume. As for securing massive delivery quantity by investment, companies must examine the profitability between increasing delivery quantity and price. This study proposes two strategies for a company’s decision making regarding the adjustment of market density and price by developing a pricing and collaboration model based on the delivery time of the last mile process. A last mile delivery time function of market density is first derived from genetic algorithm (GA)-based simulation results of traveling salesman problem regarding the market density. The pricing model develops a procedure to determine the optimal price, maximizing the profit based on last mile delivery time function. In addition, a collaboration model, where a multi-objective integer programming problem is developed, is proposed to sustain long-term survival for small and medium-sized companies. In this paper, sensitivity analysis demonstrates the effect of delivery environment on the optimal price and profit. Also, a numerical example presents four different scenarios of the collaboration model to determine the applicability and efficiency of the model. These two proposed models present managerial insights for express delivery companies.

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Collaboration Model for Service Clustering in Last-Mile Delivery

Sustainability ◽

10.3390/su12145844 ◽

2020 ◽

Vol 12 (14) ◽

pp. 5844 ◽

Cited By ~ 2

Author(s):

Seung Yoon Ko ◽

Ratna Permata Sari ◽

Muzaffar Makhmudov ◽

Chang Seong Ko

Keyword(s):

Market Share ◽

Programming Model ◽

Collaboration Model ◽

Multi Objective Programming ◽

Last Mile ◽

Delivery Services ◽

Service Clustering ◽

Last Mile Delivery ◽

Market Density ◽

Express Delivery

As e-commerce is rapidly expanding, efficient and competitive product delivery system to the final customer is highly required. Recently, the emergence of a smart platform is leading the transformation of distribution, performance, and quality in express delivery services, especially in the last-mile delivery. The business to consumer (B2C) through smart platforms such as Amazon in America and Coupang in Korea utilizes the differentiated delivery rates to increase the market share. In contrast, the small and medium-sized express delivery companies with low market share are trying hard to expand their market share. In order to fulfill all customer needs, collaboration is needed. This study aims to construct a collaboration model to maximize the net profit by considering the market density of each company. A Baduk board game is used to derive the last-mile delivery time function of market density. All companies in collaboration have to specialize the delivery items into certain service clustering types, which consist of regular, big sized/weighted, and cold items. The multi-objective programming model is developed based on max-sum and max-min criteria. The Shapley value and nucleolus approaches are applied to find the profit allocation. Finally, the applicability of the proposed collaboration model is shown through a numerical example.

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New Express Delivery Service and Its Impact on CO2 Emissions

Sustainability ◽

10.3390/su12020456 ◽

2020 ◽

Vol 12 (2) ◽

pp. 456

Author(s):

Dragan Lazarević ◽

Libor Švadlenka ◽

Valentina Radojičić ◽

Momčilo Dobrodolac

Keyword(s):

Co2 Emissions ◽

Rapid Development ◽

Environmental Issues ◽

Modern Society ◽

Well Being ◽

Delivery Service ◽

Last Mile ◽

Courier Service ◽

Last Mile Delivery ◽

Express Delivery

A rapid development of Internet technologies creates new opportunities for e-commerce, which is one of the fastest-growing segments of the entire economy. For policymakers, the most important aspects of e-commerce are related to the cost reduction in transportation, facilitation of administration and communication, innovations at the market level, and environmental issues. An unavoidable part of the e-commerce production process is related to the postal service. New market expectations of modern society lead to the consideration of upgrading the traditional express delivery service in terms of time availability. In this paper, we propose a new 24-h availability of postal and courier service so-called “post express nonstop”. To assess the potential demand for this kind of service, we propose a forecasting procedure based on the Bass diffusion model. In particular, the research is directed toward the examination of environmental issues, considering both types of services—traditional and the proposed new one. A comparison is done by analyzing CO2 emissions in the last-mile delivery of goods to the users’ addresses. The experiment was carried out in the city of Belgrade, simulating the last-mile delivery under realistic conditions and controlling the fuel consumption and CO2 emissions. In accordance with the results of this experiment and the forecasted number of postal items, a projection of CO2 emissions for the new service from 2020 to 2025 was carried out. The results show a significant contribution of the proposed new express delivery service to environmental well-being and sustainability.

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Crowd Sourcing Dynamic Pickup & Delivery Problem considering Task Buffering and Drivers’ Rejection -Application of Multi-agent Reinforcement Learning-

WSEAS TRANSACTIONS ON BUSINESS AND ECONOMICS ◽

10.37394/23207.2021.18.63 ◽

2021 ◽

Vol 18 ◽

pp. 636-645

Author(s):

Junyi Mo ◽

Shunichi Ohmori

Keyword(s):

Reinforcement Learning ◽

Numerical Experiments ◽

Delivery Time ◽

Crowd Sourcing ◽

Employment Opportunities ◽

Matching Algorithm ◽

Delivery Problem ◽

Last Mile ◽

Multi Agent ◽

Last Mile Delivery

In the last decade, dynamic and pickup delivery problem with crowd sourcing has been focused on as a means of securing employment opportunities in the field of last mile delivery. However, only a few studies consider both the driver's refusal right and the buffering strategy. This paper aims at improving the performance involving both of the above. We propose a driver-task matching algorithm that complies with the delivery time constraints using multi-agent reinforcement learning. Numerical experiments on the model show that the proposed MARL method could be more effective than the FIFO and the RANK allocation methods

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Understanding customers' adoption of express delivery service for last-mile delivery in the UK

International Journal of Logistics Research and Applications ◽

10.1080/13675567.2021.1914563 ◽

2021 ◽

pp. 1-18

Author(s):

Shuya Zhong ◽

Carl Lomas ◽

Tracey Worth

Keyword(s):

Delivery Service ◽

Last Mile ◽

The Uk ◽

Last Mile Delivery ◽

Express Delivery

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PROSPECTS AND PROBLEMS OF ROBOTIC DELIVERY OF THE LAST MILE. THE RELEVANCE OF THE PROBLEM AND THE CONCEPT OF THE SOLUTION. PART I.

EurasianUnionScientists ◽

10.31618/esu.2413-9335.2021.2.92.1516 ◽

2021 ◽

pp. 24-32

Author(s):

Yu. Khamukov ◽

M. Kanokova

Keyword(s):

New Technologies ◽

Freight Transportation ◽

Delivery Mode ◽

Air Cargo ◽

Technological Breakthrough ◽

Last Mile ◽

Human Labor ◽

New Thinking ◽

Last Mile Delivery ◽

Express Delivery

The express delivery market in recent years has been growing at the level of 3-4%, and even in these conditions, not only is it not saturated, but the demand for it is growing. According to Oxford Economics, the growth of the air cargo market, which determines the volume of the express delivery market, accelerated at times up to 7% per year from 2013 to 2018 [1]. The biggest changes took place in 2016-17 due to a technological breakthrough in the field of logistics with the introduction of services such as drone delivery, processing orders on the blockchain, calculation of the delivery mode using artificial intelligence, etc. It was expected that due to the growing demand on fast delivery guaranteed, the number of express delivery employees worldwide will grow to 4.5 million over the next few years. But the coronavirus pandemic has accelerated this process. In the study “The Future of Freight Transportation. How new technologies and new thinking can change the movement of goods”, presented by the international network of consulting companies Deloitte in 2017, states that carriers have already solved many of the problems associated with the transportation of goods. But the “last mile delivery” stage has remained limiting the development of the delivery service. At this stage, companies suffer losses due to the concentration of logistics, algorithmic and kinematic tasks that cannot be automated with modern means and technologies for replacing human labor. Consequently, the use of alternative, unconventional technologies at this stage is a key condition for the mass development of delivery.

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Data-driven optimization for last-mile delivery

Complex & Intelligent Systems ◽

10.1007/s40747-021-00293-1 ◽

2021 ◽

Author(s):

Hongrui Chu ◽

Wensi Zhang ◽

Pengfei Bai ◽

Yahong Chen

Keyword(s):

Machine Learning ◽

Heuristic Algorithms ◽

Computational Study ◽

Mutual Effect ◽

Data Driven ◽

Optimization Approach ◽

Delivery Time ◽

Last Mile ◽

Order Assignment ◽

Last Mile Delivery

AbstractThis paper considers how an online food delivery platform can improve last-mile delivery services’ performance using multi-source data. The delivery time is one critical but uncertain factor for online platforms that also regarded as the main challenges in order assignment and routing service. To tackle this challenge, we propose a data-driven optimization approach that combines machine learning techniques with capacitated vehicle routing optimization. Machine learning methods can provide more accurate predictions and have received increasing attention in the operations research field. However, different from the traditional predict-then-optimize paradigm, we use a new smart predict-then-optimize framework, whose prediction objective is constructed by decision error instead of prediction error when implementing machine learning. Using this type of prediction, we can obtain a more accurate decision in the following optimization step. Efficient mini-batching gradient and heuristic algorithms are designed to solve the joint order assignment and routing problem of last-mile delivery service. Besides, this paper considers the mutual effect between routing decision and delivery time, and provides the corresponding solution algorithm. In addition, this paper conducts a computational study and finds that the proposed method’s performance has an approximate 5% improvement compared with other methods.

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