Preface

Abstract

Nowadays, most of multinational enterprises faces the issues of sustainable development for their logistics systems in order to meet or exceed customer expectations. Sustainable development attracts both researchers and industrial practitioners who are focused on the design and implementation of logistics system. AI-based meta-heuristics approaches has emerged as a capable method for quickly providing optimal or near-optimal solutions for the problems that exact optimization cannot solve. Recent advances in various AI-based meta-heuristics approaches can resolve various and complex logistics and supply chain problem types. In this special issue, recent trends and studies of sustainable logistics systems using AI-based meta-heuristics approaches have been introduced and analyzed. In the first contribution ‘Applying GA-VNS Approach to Supply Chain Network Model with Facility and Route Disruptions,’ by Anudari, Yun, and Gen introduced a supply chain network (SCN) model which simultaneously considers the disruption risks of facility and route. As most of conventional studies have focused either on facility disruption solely or on route disruption solely, simultaneously considering the disruption risks of facility and route in the SCN model can reinforce the efficiency and stability for its implementation. For solving the SCN model, they proposed a GA-VNS approach which combines genetic algorithm (GA) with variable neighborhood search (VNS), as one of hybrid meta-heuristics approaches. The performance of the proposed GA-VNS approach was compared with those of some conventional single and hybrid meta-heuristic approaches, and the experimental results shown that the former outperforms the latter. In the second contribution ‘Edge Boundary Variable Neighborhood Strategy Adaptive Search for a Vegetable Crop Land Allocation Problem,’ by Nitisiria, Sethanana, Pitakaso, and Gen introduced a meta-heuristic approach to optimize crop land allocation for planting vegetables. For the meta-heuristic approach, edge boundary variable neighborhood strategy adaptive search (EB-VaNSAS) was applied to significantly improve the solution quality of the traditional variable neighborhood strategy. The numerical results shown that the proposed EBVaNSAS outperforms competing methods. In the third contribution ‘Multi-criteria decision-making methods for the evaluation of a real-green supply chain in companies with fast-moving consumer goods,’ by Rastpour, Kayvanfar, and Rafiee proposed a green supply chain management (GSCM) model to assess and compare the greenness of Iran’s industry. A step by step analysis using fuzzy Delphi method, fuzzy DEMATEL method, and weighted aggregated sum product assessment method were conducted and through real-case study in Iran’s industry, the importance of GSCM implementation was emphasized. In the fourth contribution ‘Multi-Objective Grouping Genetic Algorithm for the Joint Order Batching, Batch Assignment, and Sequencing Problem,’ by Cano, Cortes, Campo, Correa-Espinal” by Cano, Cortes, Campo, CorreaEspinal developed a multi-objective grouping genetic algorithm (GGA) to minimize total travel time and total tardiness by implementing an encoding scheme. Computer simulations showed that the proposed algorithm performs 25.4% better than a first come, first served (FCFS) rule-based heuristic and 10.2% better than an earliest due date (EDD) rule–based heuristic. In the fifth contribution ‘Green supply chain management framework for supplier selection: an integrated multi-criteria decision-making approach,’ by Ghosh, Mandal and Ray proposed a GSCM framework to evaluate three supplier organizations (service organization, manufacturing organization, and process organization). Using six important criteria including environmental, economic, and operational aspects of sustainability, a MCDM approach was implemented for evaluating the three supplier organizations. Experimental results showed that (i) the manufacturing organization is the benchmark organization and its strategies can guide other organizations to enhance their performances, and (ii) three parameters (total energy consumption, total scrap material generation, and renewable energy utilization) are the influential parameters that should predominantly be considered for green supplier selection. In the sixth contribution ‘A dynamic multi-objective green supply chain network design for perishable products in uncertain environments, the coffee industry case study’ by Torabzadeh, Nejati, Aghsami, Rabbani developed a mathematical model for the location-allocation-inventory problem based on a real-case study to design a three-echelon coffee supply chain network. Minimizing the CO2 emission was also considered to address the increasing eco-friendly challenges. The problem includes various strategic and tactical decisions. The problem’s parameters are considered