Abstract
Industrial waste is a major environmental concern. This paper proposes an optimization approach to better manage the operations of a food production line, with the aim of minimizing material waste. The configuration considered is inspired by a real situation. It is a two-stage flow shop with dedicated machines at stage 1 and identical parallel machines at stage 2. A mathematical model is first proposed that considers the minimization of the number of idle times exceeding an acceptable threshold as an objective. A positive time lag for the product and machine setup is considered as a constraint. To solve large problem instances, a hybrid genetic algorithm (HGA) is proposed. In addition, we developed a two-phase procedure based on a decision tree to select the parameters of the HGA. The results show that we could achieve the minimization of material waste without altering too much, another industrial goal, which is the total production time.
Highlights
Many industries are facing two major environmental problems, such as energy consumption and material waste
The configuration presented in this paper that distinguishes this work from other studies is the objective of minimizing waste resulting from the occurrences of machine idle times exceeding the perishability limit (Nbr_IT+) while simultaneously considering several temporal constraints
The results indicate that the hybrid genetic algorithm (GA) generates either optimal or near-optimal solutions
Summary
Many industries are facing two major environmental problems, such as energy consumption and material waste. Jobs from J2 should be processed on the second dedicated machine M2 at S1; they are authorized to wait for a maximum time limit before going through S2. A drying cabin is considered a batch machine that can handle multiple tasks in one operation This could not be applied to our problem, as jobs do not have the same drying characteristics. The main contribution of this paper, compared to our previous work, is an approach to consider an important objective for the industry, which is the minimization of material waste through the minimization of Nbr_IT+ while giving special attention to satisfactory Cmax values. A second paper [1] presented a MIP for waste minimization without considering acceptable solutions to the industry.
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