Motivated by express and e-commerce companies’ distribution practices, we study a two-echelon electric vehicle routing problem. In this problem, fuel-powered vehicles are used to transport goods from a depot to intermediate facilities (satellites) in the first echelon, whereas electric vehicles, which have limited driving ranges and need to be recharged at recharging stations, are used to transfer goods from the satellites to customers in the second echelon. We model the problem as an arc flow model and decompose the model into a master problem and pricing subproblem. We propose a branch-and-price algorithm to solve it. We use column generation to solve the restricted master problem to provide lower bounds. By enumerating all the subsets of the satellites, we generate feasible columns by solving the elementary shortest path problem with resource constraints in the first echelon. Then, we design a bidirectional labeling algorithm to generate feasible routes in the second echelon. Comparing the performance of our proposed algorithm with that of CPLEX in solving a set of small-sized instances, we demonstrate the former’s effectiveness. We further assess our algorithm in solving two sets of larger scale instances. We also examine the impacts of some model parameters on the solution.

In recent years, many online retailers in China set low prices on 11 November, which stimulates huge delivery demand and results in many problems although carriers make an effort to increase their delivery capacities temporarily. To circumvent this difficulty, we consider a supply chain consisting of an online retailer, who can set price to influence the demand, and a capacitated carrier, whose capacity can be expanded at a high cost. We derive the optimal decisions in the centralised and decentralised decision systems, and compare the performances of the two systems. We find that the optimal decisions, and which system has lower price, larger capacity increment, and more late delivered goods depends on the model parameters (the market scale, the late delivery costs, the capacity expansion cost, the delivery fee, and the demand uncertainty). Specially, we show that, contrary to the traditional channel, the online retailer in the decentralised system may set lower price and the carrier has less incentive to expand capacity in the decentralised system in some situations, which underlines the need for coordination. In addition, we propose coordination contracts to improve the overall performance of the supply chain under deterministic and random demands.

We consider a two-stage newsvendor model in which the retailer orders a product and sells it at the full price in the first stage, and sells the leftover stock, if any, at a discounted price in the second stage. The customers are strategic, who are boundedly rational and risk averse, and their valuations of the product decrease with time. The customers make purchase decisions based on their beliefs of the product’s availability and their expected utility in the two stages. The customers who purchase in the second stage may not get the product. Characterising the retailer’s optimal inventory policy, we show that the retailer’s optimal profit decreases with the rate of the customers’ decreasing product valuation. Moreover, as the customers become less rational or more risk averse, the retailer’s profit vary with the model parameters. We conduct numerical studies to examine the impacts of the customers’ bounded rationality on the retailer’s order quantity and optimal profit. In addition, considering the value of the quick response (QR) strategy, we show that the QR strategy can increase the retailer’s profit, while the customers’ bounded rationality may increase or decrease the value of QR to the retailer.

This paper addresses the impact of the quick response (QR) strategy on the profit of a retailer that sells a product to strategic consumers with risk preference and decreasing valuation, and makes pricing and order quantity decisions. We find that the optimal pricing and expected profit decrease in consumers’ risk preference and valuation decreasing rate, and the optimal inventory is unrelated to them. The effect of the QR strategy depends on the QR cost, consumers’ risk preference, and valuation decreasing rate. When the QR cost is low, the QR strategy can mitigate strategic consumers’ behaviour and bring profit to the retailer. Otherwise, the QR strategy may further reduce the retailer’s expected profit and intensify the negative effect of strategic consumers’ behaviour. The profit brought by the QR strategy decreases in consumers’ risk preference and valuation decreasing rate. Furthermore, we consider the case involving heterogeneous strategic consumers. We find that the proportion of high-valuation consumers in the market has an impact on the optimal decisions of the retailer and the effect of the QR strategy. The more high-valuation consumers there are, the greater is the profit brought by the QR strategy.