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This paper and its companion (Part 2) deal with modelling and optimization of the flexible job-shop problem (FJSP). The FJSP is a generalised form of the classical job-shop problem (JSP) which allows an operation to be processed on several alternatives machines. To solve this NP-hard combinatorial problem, this paper proposes a customised Genetic Algorithm (GA) which uses an array of real numbers as chromosome representation so the proposed GA is called a real-coded GA (RCGA). The novel chromosome representation is designed to produces only feasible solutions which can be used to effectively explore the feasible search space. This first part of the papers focuses on the modelling of the problems and discusses how the novel chromosome representation can be decoded into a feasible solution. The second part will discuss genetic operators and the effectiveness of the RCGA to solve various test bed problems from literature.

<span lang="EN-US">Route scheduling is a quite complicated process because it involves some determinant factors. Several methods have been used to help resolve the NP-hard problems. This research uses genetic algorithm to assist in optimizing ship scheduling, that where there are several ports to be visited by some ships. The goal is to divide the ship to go to a specific port so that each port is only visited by one ship to minimize the total distance of all ships. The computational experiment produces optimal parameters such as the number of popsize is 30, the number of generations is 100, crossover rate value is 0.3 and mutation rate values is 0.7. The final results is an optimal ship route by minimizing the distance of each ship.</span>

This paper addresses optimization of the flexible job-shop problem (FJSP) by using real-coded genetic algorithms (RCGA) that use an array of real numbers as chromosome representation. The first part of the papers has detailed the modelling of the problems and showed how the novel chromosome representation can be decoded into solution. This second part discusses the effectiveness of each genetic operator and how to determine proper values of the RCGAs parameters. These parameters are used by the RCGA to solve several test bed problems. The experimental results show that by using only simple genetic operators and random initial population, the proposed RCGA can produce promising results comparable to those achieved by other best-known approaches in the literatures. These results demonstrate the robustness of the RCGA.

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This paper addresses the multi-period part type selection and machine loading problems in flexible manufacturing system (FMS) with the objective of maximizing system throughput and maintaining balance of the system for the whole planning horizon. Various flexibilities including machine and tool flexibility, routing flexibility, and alternative production plans are considered. Hybridization of real coded genetic algorithms (RCGA) and variable neighborhood search (VNS) is proposed to simultaneously solve these NP-hard problems for the whole periods. The proposed hybrid genetic algorithms (HGA) are designed to balance the power of the algorithms to explore a huge search space and to exploit local search areas. The experiments show that addressing the problems for the whole periods simultaneously will produce better results comparable to those achieved by the sequential approach.

This research studied the application of multitrip vehicle routing problem with time windows (VRPTW) on the problems of the tourist routes in Banyuwangi. The problems of ordinary VRPTW has only one route to the finish line that will be targeted with specific time limits while the multi-trip VRPTW has several tourist routes and one central point as the reference point for determining the route of the tour as well as the deadline for each tour. Genetic algorithm used to solve this problem because it can overcome the problem of combinatorial effectively and efficiently, moreover it can reach solutions globally so that it can produce the optimum solution. Chromosome on the Genetic Algorithm represents the permutation of the overall tour. After decoding there are three chromosome segments created, where each segment represents a visit of tourist attractions in one day. This research provides the optimal result i.e. a solution route with the shortest commute time and a fast computing time so it is very helpful in determining the route of the tourist trips with the closest mileage based on their places to stay (centre point).