| Summary: | With a growing focus on environmental conservation, organizations continuously reevaluate their conventional profit-driven business models. This shift has led to the development of green production planning models, attracting considerable attention in recent years. Previously, energy consumption or carbon emission were the most considered green parameters in aggregate production planning problems. Nevertheless, several other essential green aspects, such as reductions in chemical, solid waste and wastewater are usually neglected. Therefore, this study has proposed a multi-objective model for environmentally conscious aggregate production planning by simultaneously considering energy consumption, carbon emission, chemical consumption, solid waste generation and wastewater generation as green objectives. Additionally, the uncertain parameters of the model, including the green parameters, were modelled through fuzzy mathematical programming. This study also incorporated the time-of-use electricity tariff scheme into the model. To solve the proposed model, the initial step involved the defuzzification of uncertain attributes, followed by the transformation of the fuzzy multi-objective model into an equivalent single-objective crisp model. An optimization solver was utilized to solve the model based on the data collected from two case companies; precision engineering company (case company I) and plastic bottle manufacturing company (case company II). Both case companies were primarily capable of meeting demand through in-house production, either during on-peak or off-peak hours. Inventories and backorders for the products across different time periods were predominately not needed. The generated production plan achieved overall satisfaction levels of 70% and 85% for case company I and case company II, respectively, which were reasonably acceptable to the decision makers. Moreover, the validation of the model was conducted by performing sensitivity analyses of the optimism degree of the objective functions and the feasibility degree of the constraints. A general overview of the results obtained indicated that the optimum values of the objective functions deteriorate as the values of the parameters increase, though all remain within an acceptable range. The proposed model will assist decision makers in generating production plans that are both economically and environmentally feasible.
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