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Most scheduling problems are complex combinatorial problems and very difficult to solve [Manage. is why, lots of methods focus on the optimization according to a single criterion (makespan, workloads of machines, waiting times, etc.). The combining of several criteria induces additional complexity and new problems. In this paper, we propose a Pareto(More)
The problem of efficiently scheduling production jobs on several machines is an important consideration when attempting to make effective use of a multi-machines system such as a flexible job shop scheduling production system (FJSP). In most of its practical formulations, the FJSP is known to be NP-hard [8][9], so exact solution methods are unfeasible for(More)
Most complex scheduling problems are combinatorial problems and difficult to solve. That is why, several methods focus on the optimization according to a single criterion such as makespan, workloads of machines, waiting times, etc. In this paper, the Choquet integral is introduced as a general tool for dealing with multiple criteria decision making and used(More)
Most scheduling problems are highly complex combinatorial problems. However, stochastic methods such as genetic algorithm yield good solutions. In this paper, we present a controlled genetic algorithm (CGA) based on fuzzy logic and belief functions to solve job-shop scheduling problems. For better performance, we propose an efficient representational(More)