Robust Multi-Objective Sustainable Reverse Supply Chain Planning: An Application in the Steel Industry

Abstract

In the design of the supply chain, the use of the returned products and their recycling in the production and consumption network is called reverse logistics. The proposed model aims to optimize the flow of materials in the supply chain network (SCN), and determine the amount and location of facilities and the planning of transportation in conditions of demand uncertainty. Thus, maximizing the total profit of operation, minimizing adverse environmental effects, and maximizing customer and supplier service levels have been considered as the main objectives. Accordingly, finding symmetry (balance) among the profit of operation, the environmental effects and customer and supplier service levels is considered in this research. To deal with the uncertainty of the model, scenario-based robust planning is employed alongside a meta-heuristic algorithm (NSGA-II) to solve the model with actual data from a case study of the steel industry in Iran. The results obtained from the model, solving and validating, compared with actual data indicated that the model could optimize the objectives seamlessly and determine the amount and location of the necessary facilities for the steel industry more appropriately.

Multi-period green reverse logistics network design: An improved Benders-decomposition-based heuristic approach

“…In the second group i.e., MILP model with uncertain factors, some authors ( Antucheviciene, Jafarnejad, Mahdiraji, Hajiagha & Kargar, 2020 ;Shahparvari et al, 2021 ) provided MILP models that dealt with uncertainty for RLN Design. Baptista, Barbosa-Póvoa, Escudero, Gomes and Pizarro (2019) introduced a two-stage stochastic linear model to address CLSC design and production planning under the returns quantity and quality uncertainties along the time horizon.…”

“…In the second category (heuristics), some researchers have applied heuristics to solve the network design problem. For instance, Antucheviciene et al (2020) presented an NSGA-II algorithm to optimize the flow of materials in an RSC for the steel industry. Li, Guo and Zhang (2018) proposed a novel, improved hybrid differential evolution (IHDE) algorithm for the closed-loop supply chain to study location-inventory decisions with third-party logistics.…”

Exploiting Discrete Event Simulation to Improve a Chicken Supply Chain

“…One of the goals of Company X, like most organizations, is to exceed customers' expectations in a regular manner by reducing stockouts. Smart forecasting would be the most effective way to satisfy customers' expectations [2].…”

Data-Driven Design and Optimization for Smart Logistics Parks: Towards the Sustainable Development of the Steel Industry

“…A well-designed logistics park can facilitate reduction in inventory costs and ease of freight pick-up so as to enhance the overall operational efficiency. As it is acknowledged that the establishment of a new logistics park is considerably costly, especially for the steel industry, the operational optimization of steel logistics parks becomes critically important, attracting interest from an increasing number of researchers and practitioners towards the development of smart logistics [5,6]. Moreover, in practice, enterprises in the steel industry are commonly large-scale conglomerates, such as POSCO, TATA steel, JFE, Nippon Steel Corporation, Baosteel Group, etc., which exemplify the conventional operations of the steel industry and supply chain.…”