Abstract: The job-shop scheduling problem (JSSP) is an important decision facing those involved in the fields of industry, economics and management. This problem is a class of combinational optimization problem known as the NP-hard problem. JSSPs deal with a set of machines and a set of jobs with various predetermined routes through the machines, where the objective is to assemble a schedule of jobs that minimizes certain criteria such as makespan, maximum lateness, and total weighted tardiness. Over the past several decades, interest in meta-heuristic approaches to address JSSPs has increased due to the ability of these approaches to generate solutions which are better than those generated from heuristics alone. This article provides the classification, constraints and objective functions imposed on JSSPs that are available in the literature.
Abstract: This paper proposes an application of probabilistic technique, namely Gaussian process regression, for estimating an optimal sequence of the single machine with total weighted tardiness (SMTWT) scheduling problem. In this work, the Gaussian process regression (GPR) model is utilized to predict an optimal sequence of the SMTWT problem, and its solution is improved by using an iterated local search based on simulated annealing scheme, called GPRISA algorithm. The results show that the proposed GPRISA method achieves a very good performance and a reasonable trade-off between solution quality and time consumption. Moreover, in the comparison of deviation from the best-known solution, the proposed mechanism noticeably outperforms the recently existing approaches.
Abstract: Total weighted tardiness is a measure of customer
satisfaction. Minimizing it represents satisfying the general
requirement of on-time delivery. In this research, we consider an ant
colony optimization (ACO) algorithm to solve the problem of
scheduling unrelated parallel machines to minimize total weighted
tardiness. The problem is NP-hard in the strong sense. Computational
results show that the proposed ACO algorithm is giving promising
results compared to other existing algorithms.
Abstract: Scheduling for the flexible job shop is very important
in both fields of production management and combinatorial
optimization. However, it quit difficult to achieve an optimal solution
to this problem with traditional optimization approaches owing to the
high computational complexity. The combining of several
optimization criteria induces additional complexity and new
problems. In this paper, a Pareto approach to solve the multi
objective flexible job shop scheduling problems is proposed. The
objectives considered are to minimize the overall completion time
(makespan) and total weighted tardiness (TWT). An effective
simulated annealing algorithm based on the proposed approach is
presented to solve multi objective flexible job shop scheduling
problem. An external memory of non-dominated solutions is
considered to save and update the non-dominated solutions during
the solution process. Numerical examples are used to evaluate and
study the performance of the proposed algorithm. The proposed
algorithm can be applied easily in real factory conditions and for
large size problems. It should thus be useful to both practitioners and
researchers.