Multi-objective Dual-Sale Channel Supply Chain Network Design Based on NSGA-II

[[abstract]]In this study, we propose a two-echelon multi-objective dual-sale channel supply chain network (DCSCN) model. The goal is to determine (i) the set of installed DCs, (ii) the set of customers the DC should work with, how much inventory each DC should order and (iv) the distribution routes for physical retailers or online e-tailers (all starting and ending at the same DC). Our model overcomes the drawback by simultaneously tackling location and routing decisions. In addition to the typical costs associated with facility location and the inventory-related costs, we explicitly consider the pivotal routing costs between the DCs and their assigned customers. Therefore, a multiple objectives location-routing model involves two conflicting objectives is initially proposed so as to permit a comprehensive trade-off evaluation. To solve this multiple objectives programming problem, this study integrates genetic algorithms, clustering analysis, Non-dominated Sorting Genetic Algorithm II (NSGA-II). NSGA-II searches for the Pareto set. Several experiments are simulated to demonstrate the possibility and efficacy of the proposed approach.[[notice]]補正完畢[[incitationindex]]EI[[booktype]]紙

Locating collection centers for incentive-dependent returns under a pick-up policy with capacitated vehicles

We address the problem of locating collection centers of a company that aims to collect used products from product holders. The remaining value in the used products that can be captured by recovery operations is the company’s motivation for the collection operation. We assume that a pick-up strategy is in place according to which vehicles with limited capacity are dispatched from the collection centers to the locations of product holders to transport the returns. Each product holder has an inherent willingness to return, and makes the decision on the basis of the financial incentive offered by the company. The incentive depends on the condition of the returned item referred to as return type. We formulate a mixed-integer nonlinear facility location-allocation model to find both the optimal locations of a predetermined number of collection centers and the optimal incentive values for different return types. Since the problem is , we propose a heuristic method to solve medium and large-size instances. The main loop of the method is based on a tabu search method performed in the space of collection center locations. For each location set prescribed by tabu search, Nelder–Mead simplex search is called to obtain the best incentives and the corresponding net profit. We experiment with different quality profiles when there are two and three return types, and observe the effect of the uniform incentive policy (UIP) in which the same incentive is offered to product holders regardless of the quality of their returns. We conclude that the UIP is inferior to the quality-dependent incentive policy resulting in a higher profit loss when the proportion of lowest quality returns is relatively high

Design of a government-subsidized collection system for incentive-dependent returns

We address the problem of locating collection centers for a company that aims to collect used products (cores) in order to capture their remaining value by recovery operations. A pick-up strategy is in place according to which vehicles are dispatched from collection centers to the locations of product holders to transport their returns. Each product holder has an inherent willingness to return a core, and decides on the basis of the quality-dependent financial incentive offered by the company. Since the company seeks only economic profitability, the collected amounts may not be aligned with the target collection ratio imposed by the government. In this case, the government may alleviate the under-collection issue through a subsidy paid to the company for each core collected. From the government’s perspective the problem is to find the minimum subsidy level while meeting the target collection ratio. We propose a bilevel programming formulation for this collection system design problem. Since the problem is NP-hard, a heuristic method is developed to solve medium and large size instances. This approach explicitly focuses on the relationship between government authorities and profitoriented companies, and yields a frontier between the concurrent objectives of collection ratio satisfaction and subsidy minimization

Design and analysis of government subsidized collection systems for incentive-dependent returns

We present and solve two bilevel programming (BP) models describing the subsidization agreement between the government and a company engaged in collection and recovery operations. These enable the company to capture the remaining value in cores, referring to used products of different quality types. To pick up the cores, the company needs to open collection centers, and dispatch vehicles on direct routes to customer sites. Customers have an inherent willingness to return their core, and decide to do so according to the quality-dependent financial incentive offered by the company. In order to promote collection and recovery the government pays a uniform subsidy to the company for each core collected. We introduce a supportive and a legislative BP model to tackle this comprehensive collection system design problem. In both models, the outer problem of the BP formulation involves the government, which is the leader and wants to minimize the unit subsidy. The company is the follower in the inner problem, and tries to maximize its net profit from the cores subject to the government's subsidy decision. In the supportive model, the company itself is not bound by the minimum collection rate targeted by the government, thus the amount of collected cores may not be sufficient. The government tries to resolve this situation with increased subsidization. The legislative model assigns the minimum collection rate responsibility to the company, but also entitles it to a certain profitability ratio guaranteed by the government. The solution methodology proposed for both models consists of Brent's method for the outer problem and a tabu search heuristic solving the inner problem. Its effectiveness is tested in computational experiments. The results show that for the same collection rate and profitability ratio the government has to grant a higher subsidy in the supportive model than in the legislative model

Design of a government-subsidized collection system for incentive-dependent returns

We address the problem of locating collection centers for a company that aims to collect used products (cores) in order to capture their remaining value by recovery operations. A pick-up strategy is in place according to which vehicles are dispatched from collection centers to the locations of product holders to transport their returns. Each product holder has an inherent willingness to return a core, and decides on the basis of the quality-dependent financial incentive offered by the company. Since the company seeks only economic profitability, the collected amounts may not be aligned with the target collection ratio imposed by the government. In this case, the government may alleviate the under-collection issue through a subsidy paid to the company for each core collected. From the government’s perspective the problem is to find the minimum subsidy level while meeting the target collection ratio. We propose a bilevel programming formulation for this collection system design problem. Since the problem is NP-hard, a heuristic method is developed to solve medium and large size instances. This approach explicitly focuses on the relationship between government authorities and profitoriented companies, and yields a frontier between the concurrent objectives of collection ratio satisfaction and subsidy minimization