In general, reverse logistics network design has been driven by a need to reduce costs and to improve customer service without considering its environmental impact. In this paper, we address a reverse logistics network design problem regarding carbon emission. The problem is formulated as a bi-objective, mixed-integer, and nonlinear programming model under various operation technologies and transport modes in the truck tire remanufacturing industry. An improved non-dominated sorting genetic algorithm II (NSGA-II) solves this NP-hard problem with bi-objectives. The numerical cases demonstrate the validation of the proposed model and the advantage of improved NSGA-II over the basic NSGA-II. Furthermore, we conducted extensive sensitivity analysis, and several managerial insights are derived.INDEX TERMS Carbon footprint, emission trading scheme, network design, NSGA-II, reverse logistics.

This study examines a location-inventory-routing problem in a closed-loop supply chain (LIRP-CL) that considers random demands, as well as random returns, from customers. In reality, some of the returned products can be remanufactured as new products, and they share the same channel with new products. This practice renders previous models no longer appropriate. We consider this practice in this paper and build a new LIRP-CL system. To minimize the total costs of the system, this paper determines the locations of distribution centers and remanufacturing centers, the inventory level of the system, and the delivery routes from distribution centers to customers and from customers to remanufacturing centers. A mixed integer nonlinear model was developed to solve this problem. To solve the model, a novel hybrid heuristic algorithm based on tabu search and simulated annealing is proposed. The computational results and sensitivity analysis are presented. In addition, some managerial insights are proposed.

We study a new problem of location-inventory-routing in forward and reverse logistic (LIRP-FRL) network design, which simultaneously integrates the location decisions of distribution centers (DCs), the inventory policies of opened DCs, and the vehicle routing decision in serving customers, in which new goods are produced and damaged goods are repaired by a manufacturer and then returned to the market to satisfy customers’ demands as new ones. Our objective is to minimize the total costs of manufacturing and remanufacturing goods, building DCs, shipping goods (new or recovered) between the manufacturer and opened DCs, and distributing new or recovered goods to customers and ordering and storage costs of goods. A nonlinear integer programming model is proposed to formulate the LIRP-FRL. A new tabu search (NTS) algorithm is developed to achieve near optimal solution of the problem. Numerical experiments on the benchmark instances of a simplified version of the LIRP-FRL, the capacitated location routing problem, and the randomly generated LIRP-FRL instances demonstrate the effectiveness and efficiency of the proposed NTS algorithm in problem resolution.

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Bin location in urban areas is a significant research problem. Prior research has considered the average distance that citizens reach bins and it turns out that a citizen may walk a very long distance to dispose the waste. In this paper, we intend to optimize the locating of bins in urban areas. By minimizing the maximum distance for citizens to bins, we find out the optimal location of a bin on a road. Then, a bi-objective model is developed to optimize the locations of bins in a road network, in which one objective is to minimize the maximum distance for citizens to bins, while the other minimizes the installation costs of bins. The solution method and a case analysis based on a real road network in Xi'an are given. The results verify the effectiveness of the model and method.