The green supply chain management (GSCM) is an enterprise’s effort to protect the environment and a key way to achieve sustainable environmental development. On the contrary, globalization brings more risks to the supply chain. Resilience has become a critical definition in supply chain management to help enterprises review the disruption and return to normal state. Therefore, choosing a resilient-green supplier to build a supply chain environment with flexibility and greenness under interruption becomes necessary for research works. However, the existing studies tended to focus on only one of the factors with resilience and greenness, and no comprehensive criteria system and performance value is expressed by a crisp number. Therefore, this paper proposes a hybrid method which integrates the Best-Worst method (BWM), Weighted Aggregated Sum-Product Assessment (WASPAS), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to solve the critical problems. Firstly, BWM is used to weigh the criteria; secondly, intuitionistic fuzzy numbers are introduced into the ranking stage. Then, the integrated WASPAS and TOPSIS are used to rank the alternatives to select the optimal resilient-green supplier. Finally, an illustrative example proves the feasibility of this method.

In this paper, an integrated production and distribution problem for perishable products is presented, which is an NP hard problem where a single machine, multi-customers, and homogenous vehicles with capacity constraints are considered. The objective is to minimize the total order weighted delivery time to measure the customer service level, by making two interacted decisions, production scheduling and vehicle routing, simultaneously. An integrated mathematical model is built, and the validity is measured by the linear programming software CPLEX by solving the small-size instances. An improved large neighborhood search algorithm is designed to address the problem. Firstly, a two-stage algorithm is constructed to generate the initial solution, which determines the order production sequence according to the given vehicle routing. Secondly, several removal/insertion heuristics are applied to enlarge the search space of neighbor solutions. Then, a local search algorithm is designed to improve the neighbor solutions, which further generates more chances to find the optimal solution. For comparison purposes, a genetic algorithm developed in a related problem is employed to solve this problem. The computational results show that the proposed improved large neighborhood search algorithm can provide higher quality solutions than the genetic algorithm.

Resilient suppliers can reduce supply chain risk, effectively avoid supply chain disruption, and bring profits to enterprises. However, there is no united measuring index system to evaluate the resilient supplier under supply chain environment, and the assessment language sets are usually crisp values. Therefore, in order to fill the research gap, this paper proposes a hybrid method, which combines triangular fuzzy number, the best-worst method (BWM), and the modular TOPSIS in random environments for group decision-making (GMo-RTOPSIS) to solve the above problem. Firstly, the weight of decision-maker is calculated by using fuzzy BWM which can deal with triangular fuzzy numbers. Secondly, triangular fuzzy number is introduced into GMo-RTOPSIS, and combined with fuzzy BWM, alternatives are sorted to select the best resilient supply chain partner. Finally, the feasibility and universality of this method are proved by illustrative examples.