presenting a bi-objective integrated production planning model considering constraints on order acceptance, production and customer delivery using meta-heuristic algorithms

In this study, the problem of simultaneous determination of order acceptance, scheduling and batch delivery considering sequence-dependent setup and capacity constraint has been presented. this problem is a combination of the three problems of order acceptance, scheduling and batch delivery. the most important innovation of this research is the simultaneous optimization of profits and the total weighted earliness and tardiness as two conflicting objectives in the problem of combining order, scheduling and batch delivery. another innovation of this research is the use of multi-objective grey wolf optimization (gwo) algorithm, which has not been used in studies of this field so far. it has also been shown that the multi-objective grey wolf optimization algorithm is comparable to the exact solution methods. the second part of the numerical results compares the results of the ε-constraint method, nsga-ii and the multi-objective grey wolf optimization algorithm. the results of this section show that by increasing the scale of the problem, the efficiency of the multi- objective grey wolf optimization algorithm is better displayed, and in general, this method has a significant advantage relative to nsga-ii and ε-constraint in terms of dm, sns and nps indicators. also, the solving time of this method is very shorter than that of the ε-constraint. therefore, from a managerial point of view, a tool called the multi-objective grey wolf optimization algorithm can be used as an efficient tool for supply and production managers, which is able to provide several optimal solutions with different profits, earliness and tardiness.