PARALLEL MACHINE SCHEDULING WITH MONTE CARLO TREE SEARCH

In this article, a specific production scheduling problem (PSP), the Parallel Machine Scheduling Problem (PMSP) with Job and Machine Sequence Setup Times, Due Dates and Maintenance Times is presented. In this article after the introduction and literature review the mathematical model of the Parallel Machines Scheduling Problem with Job and Machine Sequence Setup Times, Due Dates and Maintenance Times is presented. After that the Monte Carlo Tree Search and Simulated Annealing are detailed. Our representation technique and its evaluation are also introduced. After that, the efficiency of the algorithms is tested with benchmark data, which result, that algorithms are suitable for solving production scheduling problems. In this article, after the literature review, a suitable mathematical model is presented. The problem is solved with a specific Monte Carlo Tree Search (MCTS) algorithm, which uses a neighbourhood search method (2-opt). In the article, we present the efficiency of our Iterative Monte Carlo Tree Search (IMCTS) algorithm on randomly generated datasets.

Just-in-time scheduling in identical parallel machine sequence-dependent group scheduling problem

<p style='text-indent:20px;'>In this research, a parallel machine sequence-dependent group scheduling problem with the goal of minimizing total weighted earliness and tardiness is investigated. First, a mathematical model is developed for the research problem which can be used for solving small-sized instances. Since the problem is shown to be NP-hard, this research focuses on proposing meta-heuristic algorithms for finding near-optimal solutions. In this regard, the main contribution of this research is to apply the Biogeography-based Optimization (BBO) algorithm as a novel meta-heuristic and Variable Neighborhood Search (VNS) algorithm as a best-known one. In order to evaluate the mathematical model and solution methods, several computational experiments are conducted. The computational experiments demonstrate the efficiency of the proposed meta-heuristic algorithms in terms of speed and solution quality. The maximum gap of BBO algorithm is 1.04% and for VNS algorithm, it is 1.35%.</p>

A Tabu Search for Multi-Objective Single Row Facility Layout Problem

This paper addresses the problem of multi-objective facility layout planning. The aim is to solve the single row facility layout problems (SRFLP) and find the linear machine sequence which minimizes the following: The total investment cost of machines; the total material handling cost; the total number of machines in the final sequence; and the total flow distance of the products in units. The tabu search algorithm (TSA) which has now become a very useful tool in solving a variety of combinatorial optimization problems is made use of here. TSA is developed to determine the product sequence based on which a common linear machine sequence is found out for multi-products with different machine sequences. We assume that, limited number of duplicate machine types available for job. The results are compared with other approaches and it shows the effectiveness of the TSA approach as a practical decision support tool to solve problems in SRFLP.