Carrier collaboration in transportation means multiple carriers form an alliance to optimize their transportation operations through sharing transportation requests and vehicle capacities. In this paper, we propose a multi-agent and auction-based framework and approach for carrier collaboration in less than truckload transportation. In this framework, the carriers outsource/acquire requests through multiple auctions, one for outsourcing each request; a carrier acts as an auctioneer when it wants to outsource a request to other carriers, whereas the carrier acts as a bidder when it wants to acquire a request from other carriers; for each carrier, which requests it should outsource and acquire are determined by solving its outsourcing requests selection problem and requests bidding problem, respectively. These two decision problems are formulated as mixed integer programming problems. The auction of each request is multiround; in each round, the auctioneer determines the outsourcing price of the request and each bidder determines whether it acquires the request at the given price; the auctioneer lowers the outsourcing price if multiple carriers bid for the request or raises the price if no carrier bids for it. The auction process continues until only one carrier bids for the request or a given number of rounds are achieved. In the second case, if no agent bids for the request, then it is returned to the outsourcing agent; if multiple bidding agents compete for the request, a conflict resolution procedure is used to determine which carrier wins it. The approach is decentralized, asynchronous, and dynamic, where multiple auctions may occur simultaneously and interact with each other. The performance of the approach is evaluated by randomly generated instances and compared with an individual planning approach and a centralized planning approach.
This research investigates the effect of fairness concerns on a sustainable low-carbon supply chain (LCSC) with a carbon quota policy, in which a manufacturer is in charge of manufacturing low-carbon products and sells them to a retailer. The demand is affected by price and the carbon emission reduction rate. The optimal decisions of pricing and carbon emission reduction rate are analyzed under four decision models: (i) centralized decision, (ii) decentralized decision without fairness concern, (iii) decentralized decision with manufacturer’s fairness concern, (iv) decentralized decision with retailer’s fairness concern. The results indicate that the profits in the centralized LCSC are higher than those in the decentralized LCSC with fairness concern. If a manufacturer pays close attention to fairness, the fairness concern coefficient will reduce the carbon emission reduction rate and the profit of the LCSC and increase the wholesale price and the retail price of the product. If a retailer pays close attention to fairness, and the preference of consumers for a low-carbon product is low, the fairness concern coefficient of the retailer increases the total profit of the LCSC and decreases the carbon emission reduction rate and retail price of the product. Otherwise, if the preference of consumers for a low-carbon product is great, the fairness concern coefficient of the retailer would lead to a lower retail price compared with the retail price in the centralized decision and decrease the total profit of the LCSC.
This study takes a sustainable closed-loop supply chain composed of one manufacturer and two price-competitive retailers as the object and considers the two-way risk aversion characteristics of manufacturers and retailers in examining the coordination mechanism in a closed-loop supply chain. Using game theory, optimal decision-making on wholesale prices, retail prices, and recycling prices are explored under decentralized and centralized decision-making scenarios, and representative expressions are established. By analyzing the effects of the risk aversion coefficient on players’ optimal strategies, we found that the manufacturer’s and retailers’ risk aversion coefficients have different effects on the wholesale price, retail price, and recycling price under decentralized decision-making, while in a centralized decision-making scenario, the effects are the same. The comparison also found that the wholesale price and recovery price under the centralized decision-making scenario are higher than those under decentralized decision-making. To achieve closed-loop supply chain coordination, we propose a revenue-sharing contract that we demonstrate by coordinating price competition with risk aversion and analyze a range of parameters that influence the revenue-sharing contract. The results show that the proposed contract can increase the profits of supply chain members by identifying the optimal revenue-sharing ratio.
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