{'fraction_non_alphanumeric': 0.01237432327919567, 'fraction_numerical': 0.0, 'mean_word_length': 5.046728971962617, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Solving a multiobjective job shop scheduling problem using Pareto Archived Cuckoo Search

This paper investigates a new approach for solving the multiobjective job shop scheduling problem, namely the Cuckoo Search (CS) approach. The requirement is to schedule jobs on a single machine so that the total material waste is minimised as well as the total tardiness time. The material waste is quantified in terms of saving factors to show the reduction in material that can be achieved when producing two jobs with the same materials in sequence. The estimated saving factor is used to calculate a cost savings for each job based on its material type. A formulation of multiobjective optimisation problems is adopted to generate the set of schedules that maximise the overall cost savings and minimise the total tardiness time, where all trade-offs are considered for the two conflicting objectives. A Pareto Archived Multiobjective Cuckoo Search (PAMOCS) is developed to find the set of nondom-inated Pareto optimal solutions. The solution accuracy of PAMOCS is shown by comparing the closeness of the obtained solutions to the true Pareto front generated by the complete enumeration method. Results show that CS is a very effective and promising technique to solve job shop scheduling problems.