Optimizing the WEEE Recovery Network Associated with Environmental Protection Awareness and Government Subsidy by Nonlinear Mixed Integer Programming

Abstract

Recovery of waste electrical and electronic equipment (WEEE) plays an important role in protecting environment and conserving resources. Design of a more efficient WEEE recovery system is an imperative need for the relevant decision-makers, such as alleviation of overcapacity or insufficient recycling in many developing countries. In this paper, we optimize the WEEE recovery network which is associated with recycling prices and government subsidies by a nonlinear mixed integer programming approach. An integrated model is first proposed to formulate a design problem of WEEE recovery network, being involved with collection centers, two types of transfer stations, processing centers, incineration plants, landfill plants, secondhand product markets, and government subsides. The recycling prices and the transported quantities of WEEE (the number of batches) are endogenous variables of the model, being subject to a number of practical constraints. For solving this model, an algorithm is developed based on the branch and bound method. Scenario analysis and numerical experiments indicate that: (1) appropriate capacities of transfer stations can be provided by the proposed model for designing an environmentally and economically efficient WEEE recycling system, especially for alleviating the existing overcapacity or insufficient recycle. (2) An optimal governmental subsidy can be obtained in virtue of the proposed model and algorithm. (3) Diversity of transportation modes and permission of more than one mode in the same delivery route can greatly reduce the cost of recycling WEEE. (4) Preferred awareness of environmental protection can increase the profit and the recycled quantities, as well as reduction of the total recycling cost.

Profit maximization multi-item Just-in-Time solid transportation problem with budget constraints based on carbon emission

“…According to the presented algorithm, we notice that if the time deviation is less than 3 days, then allocations cannot be made properly at the destinations. The obtained solution shows that allocation is made in the cells (2, 2), (3, 2), (1, 3), (2, 3), (3,3) and (2,4) correspond to the commodity G 1 ; the cells (3, 2), (3,3), (1,4) and (3,4) correspond to the commodity G 2 ; the cells (1, 1), (2, 1), (3, 2), (1, 3), (3,3) and (3,4) 7. Sensitivity analysis of the proposed study.…”

“…correspond to the commodity G 3 through first conveyance. The obtained solution shows that allocation is made in the cells (1, 1),(2,1),(2,3) and(2,4) correspond to the commodity G 1 ; the cells (1, 1),(3,3) and(1,4) correspond to the commodity G 2 ; the cells (2, 1), (1, 2) and(2,4) correspond to the commodity G 3 through second conveyance. Total numbers of tote bags (G 1 ) transported trough first conveyance is 379, total numbers of backpacks (G 2 ) transported through first conveyance is 520, total numbers of duffel bags (G 3 ) transported through first conveyance is 512.…”

“…Roy and Maity [46] explored the minimization of both cost and time through a single objective function within the context of multi-choice interval-valued transportation problems. Bo et al [3] employed a nonlinear mixed-integer programming approach to optimize the Waste Electrical and Electronic Equipment (WEEE) recovery network, considering environmental protection awareness and government subsidies. Hammer [20] extensively studied time minimizing transportation problems.…”

An integrated multi-stage vehicle routing and mixed-model job-shop-type robotic disassembly sequence scheduling problem for e-waste management system

“…Bal and Satoglu (2018) developed an operation planning for a comprehensive sustainability reverse supply chain network. Bo, Wang, and Wan (2019) formulated an optimization model for a reverse logistic network on e-waste and solved their model with Branch and bound method. Mohtashami, Aghsami, and Jolai (2020) studied a green supply chain with a reverse logistic in which the collected products are categorized into repairable, reusable, recyclable and waste products.…”