Abstract
In this work, we study a multi-objective problem in a pharmaceutical production unit composed of several production stations that produce several kinds of products. The goal of this work, in a first step, is to propose a scheduling at the production unit in order to establish a synchronization of the different tasks on the m-machines. The problem can be formulated by the scheduling of a multi-product flow-shop.Then, we take into account the energy constraints of each task to be done. The problem can be formulated by a linear multi-objective MILP program, where the first objective is to determine the best sequence that minimizes the production and launch costs. In addition, the second objective is to find the best sequence that minimizes energy consumption. The problem is formulated and solved by combining the NEH heuristic and the LP-metric method to determine an intermediate solution between minimizing production costs and minimizing energy consumption.
Highlights
The evolution of technology and the increasing competition from more exigent customers impact the industry at all different levels
To position our work in this area, namely that our work addresses two phases, the first phase related to the scheduling of a multi-product flow-shop and a second phase related to energy consumption in a multiobjective framework, an overview of existing work will be given subsequently, where several researchers are interested in the multi-objective aspect of the criteria for optimizing production units as given by Mouzon and al. (2007)[1] who have proposed several algorithms that can solve a single machine problem aiming to reduce energy consumption and total operating time
Yan and al. (2016)[4] A multi-level optimization approach for a flexible flowshop type scheduling problem focused on energy consumption and whose resolution is based on genetic algorithms
Summary
The evolution of technology and the increasing competition from more exigent customers impact the industry at all different levels. (2016)[4] A multi-level optimization approach for a flexible flowshop type scheduling problem focused on energy consumption and whose resolution is based on genetic algorithms. (2020)[11] developed a two-objective optimization model for a hybrid flow-shop type scheduling problem considering the energy consumption costs based on the time of use (TOU) of electricity. (2021)[16] we established a multi-objective optimization method by adaptive selection of algorithms for solving a hybrid flow-shop type scheduling problem under constraints of finite variable parameters, including the makespan and the consumption of 'energy. 2021) combined in a multi-objective context with energy consumption applied to the production company SOPHAL SPA pharmaceutical products This prompted us to try a more interactive and simplistic resolution for the case of the flow shop m machines problem
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