bi-objective mathematical model

With the increasing competition between manufacturers to respond to customers' requirements, the production planning processes in the form of supply chains has become more complex. On the one hand, manufacturers have to produce at the lowest cost in order to create a competitive advantage. On the other hand, environmental requirements and attention to sustainable production have caused more attention to energy consumption in production systems. In this research, for the first time, the green hybrid flow shop scheduling problem is investigated with considering batch delivery system. For this purpose, a bi-objective integer linear programming model is presented. The first goal of the problem is to minimize the total cost related to the total jobs completion times and the costs of dispatching the batches. The second goal is to minimize energy consumption. The mathematical model is converted to a single objective model using Ɛ-constraint method and a number of random instances are evaluated and comprehensively analyzed with its help, and the effect of paying attention to energy consumption on the scheduling of orders is presented. Also, due to the NP-hardness of the problem, an MOTLBO algorithm has been adopted to solve large size instances.

The supply chain management is an important factor in current competitive market. In recent years, the shortage of resources for answering an increasing food demand has increased researchers’ attention to the food supply chain. Given the importance of fish in the Household Food Basket, the development of aquaculture and recycling of returned goods in reverse logistics would significantly help with preserving water resources, as well as sustainable development. Therefore, government agencies and aquaculture industry beneficiaries are interested in reverse logistics. This study is focused on the optimization of a closed-loop supply chain of fish. To this end, a new bi-objective mathematical model is proposed that both minimizes total costs and maximizes fulfilling customers demand in uncertainty situation. Several well-known multi-objective meta-heuristic algorithms and a proposed hybrid meta-heuristic algorithm are applied to identify Pareto solutions. The solutions are then compared in terms of performance metrics. Also, the epsilon-constraint method and sensitivity analysis are used to validate the algorithms and evaluate the performance of the model. Lastly, the VIKOR method is used to select the superior method. To demonstrate the capability of the proposed model, a closed-loop supply chain of trout in northern Iran is investigated as a case study. The results show that the developed model could be effective in reducing the costs and increasing customer satisfaction.