This paper aims to introduce a novel bi-objective model for a Job Shop Scheduling Problem (JSSP) in order to minimize makespan and maximum tardiness simultaneously. Some realistic assumptions namely fuzzy processing times and due dates involving triangular possibility distributions, transportation times, availability constraints, modi ed position-based learning e ects on processing times, and sum-of-processing-times based learning e ects on duration of maintenance activities were considered to provide a more general and practical model for the JSSP. Based on the learning e ects, processing times decrease as the machine performs an operation frequently and workers gain working skill and experience. In this paper, based on DeJong's learning e ect, a novel and modi ed formulation is proposed for this e ect. According to the above-mentioned assumptions, a novel Mixed-Integer Linear Programming (MILP) model for the JSSP is suggested. The proposed model is rst converted into an auxiliary crisp model, given that the model is a possibilistic programming and is then solved by TH and "-constraint methods in the case of small-sized instances. Finally, the results are compared. For medium- and large- sized instances, ve metaheuristic algorithms including Non-dominated Sorting Genetic Algorithm (NSGA-III), Pareto Envelope-based Selection Algorithm (PESA-II), Strength Pareto Evolutionary Algorithm (SPEA-II), NSGA-II, and Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) are utilized, and the results are nally compared in terms of three performance metrics
