Motivated by Hema Fresh’s new-retail case, we study the coordination of a two-echelon fresh-product supply chain consisting of a single supplier and a single retailer. Due to a long production lead time, the supplier has to make production decision in advance based on early demand information. The market demand can be updated during the supplier’s production lead time. Hence, the retailer would make order decision according to the latest demand information. Incorporating risk-sharing mechanism of overproduction and overstock, we propose a novel bi-directional risk-sharing contract to coordinate such a supply chain with demand information updating. We construct a two-stage optimization model in which the supplier first decides production quantity, and then the retailer decides final order quantity not exceeding the supplier’s initial production. In both the centralized and decentralized systems, we analytically derive the unique equilibrium of production and order decisions in a Stackelberg supplier-led game. We prove that the proposed contract can realize supply chain perfect coordination and explore how the proposed contract affects the members’ decisions. The theoretical results show that, by turning the risk-sharing proportions, the supply chain profit can be arbitrarily split between the members, which is a desired property for supply chain coordination. Compared with the single risk-sharing contract, the proposed contract results in a greater supply chain profit and achieves Pareto improvement for both members. Furthermore, we also explore how the risk preference and negotiating power affect the contract selection and the additional profit allocation of the supply chain. Numerical examples are presented to verify our theoretical results.

We study the optimal retail price and service level in a dual-channel supply chain in which the manufacturer may sell products through its online channel and offline retailer. Consumers with channel preference make their purchasing decision based on retail price, service level and channel valuation difference. We consider the reference price effect into a Hotelling utility function to formulate the competition of retail price and service level. We analytically derive the unique equilibrium separately in three power structures: (1) Manufacturer-led Stackelberg game (MS), (2) Retailer-led Stackelberg game (RS) and (3) Nash game (N). Particularly, we characterize the impacts of channel valuation difference and reference price effect intensity on the optimal decision and expected profit. In contrast to the previous literature on the dominance of the supply chain in that the dominance always can raise the dominant enterprise's profit, our findings indicate that supply chain members with channel advantages and channel disadvantages have different preferences for power structure. The member with channel disadvantages should give up dominance. Only when channel valuation difference is significant or reference price effect intensity is weak, the member with channel advantages will fight for dominance. Nash game is highly competitive in that the members only earn less profit. Numerical examples verified theoretical results.

This study investigates the scheduling problem of return communication tasks for unmanned aerial vehicle (UAV) swarms, where disaster relief environmental global positioning is hampered. To characterize the utility of these tasks and optimize scheduling decisions, we developed a time window-constrained scheduling model that operates under constraints, including communication base station time windows, battery levels, and task uniqueness. To solve the above model, we propose an enhanced algorithm through integrating Dueling Deep Q-Network (Dueling DQN) into adaptive large neighborhood search (ALNS), referred to as Dueling DQN-ALNS. The Dueling DQN component develops a method to update strategy weights, while the action space defines the destruction and selection strategies for the ALNS scheduling solution across different time windows. Meanwhile, we design a two-stage algorithm framework consisting of centralized offline training and decentralized online scheduling. Compared to traditionally optimized search algorithms, the proposed algorithm could continuously and dynamically interact with the environment to acquire state information about the scheduling solution. The solution ability of Dueling DQN is 3.75% higher than that of the Ant Colony Optimization (ACO) algorithm, 5.9% higher than that of the basic ALNS algorithm, and 9.37% higher than that of the differential evolution algorithm (DE). This verified its efficiency and advantages in the scheduling problem of return communication tasks for UAVs.

This paper studies production-and-order strategies under three production systems in which the supplier’s production quantity is constrained by the upfront preparations of capacity due to a long production season. The newsvendor-like retailer may make an advance order at the beginning of the production season, relying on early demand information, or a delay order at the end of the production season, depending on the updated demand information. In two special cases of perfect and imperfect demand information updates, this paper analytically derives the optimal decision equilibrium of a supplier-led Stackelberg game for three production-and-order strategies: (1) advance order under single regular production, (2) delay order under single emergency production and (3) delay order under two-mode production. In the case of perfect information updates, all three strategies likely become the equilibrium strategies that depend on the instant wholesale price and on the production cost for emergency production. In the case of imperfect information updates, the strategy of advance orders under regular production represents the unique equilibrium of the supply chain. Furthermore, the effect of general demand information updates on the equilibrium strategy is analyzed. Counterintuitively, the research results show that the retailer is more profitable with imperfect information updates than with perfect information updates when a high instant wholesale price is charged. In sharp contrast to previous studies, where the supplier always benefits from a two-mode production system due to its flexibility, our results indicate that the supplier would prefer a single regular production system when the production cost under emergency production is large, or the quality of demand information is high. Numerical examples verify our theoretical results.