The environmental impact has been a critical issue in supply chain network research. Handling it efficiently will help the firm reduce pollution and save cost. In order to improve the environmental effect on this supply chain network, recovery activity becomes a significant part in the reverse logistics network, due to complexity and heterogeneity. In a reverse logistics network, there are many decision problems to be taken into consideration such as facility location, capacity allocation, production planning and vehicle routing. Each of these problems can be categorized into three types of decision planning: strategic, tactical or operational, based on the time planning horizon. In this paper, we review the literature of supply chain network in an effort to achieve green through implementing reverse logistic practices, and classify the defined problems by the group of decision planning. In general, most of the literatures are focused on the strategic and tactical decision planning, only a few are operational-based. The objective of this paper is to analyze the decision planning being done in this area of research and provide future insights on how to design the reverse logistics network with different decision planning which will reflect the real life scenario.

This paper considers a location-inventory-routing problem (LIRP) that integrates the strategic, tactical, and operational decision planning in supply chain network design. Both defect and non-defect items of returned products are considered in the cost of reverse logistics based on the economic production quantity model. The objective of the LIRP is to minimize the total cost of location-allocation of established depots, the cost of inventory, including production setup and holding cost, as well as the cost of travelled distance by the vehicles between the open depots and assigned customers. A Hybrid Harmony Search-Simulated Annealing (HS-SA) algorithm is proposed in this paper. This algorithm incorporates the dynamic values of harmony memory considering rate and pitch adjustment rate with the local optimization techniques to hybridize with the idea of probabilistic acceptance rule from simulated annealing, to avoid the local extreme points. Computational experiments on popular benchmark data sets show that the proposed hybrid HS-SA algorithm outperforms a standard HS and an improved HS for all cases.

The advancement of supply chain network design in reverse logistics is gaining interest from the industries. In recent years, the multi-objective framework of the problem has been widely studied by researchers. This paper integrates three different levels of decision planning in supply chain network design: location-allocation problem for strategic planning, inventory planning management for tactical planning, and vehicle routing for operational planning. A location-inventory-routing problem based on the economic production quantity model with environmental concerns is considered. This study aims to minimise the total cost of operating facilities, inventory and distance travelled by the vehicles as the first objective while minimising the CO2 emission cost as the second objective. Due to the complexity of the problem, a non-dominated sorting and ranking procedure is applied into a Multi-Objective Hybrid Harmony Search-Simulated Annealing (MOHS-SA) algorithm to find the trade-off between these two objectives. Computational experiments on the benchmark instances indicate that the proposed MOHS-SA algorithm can produce well-distributed Pareto-optimal solutions for multi-objective problems.