Introduction: Nowadays, the main objective of many companies and organizations is to improve profitability and competitiveness. These improvements can be obtained with a good optimization of resources allocation. The job-shop scheduling problem (JSP) is a possible representation of a typical problem of scheduling. Many real-life problems can be modeled as …
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Introduction: Nowadays, the main objective of many companies and organizations is to improve profitability and competitiveness. These improvements can be obtained with a good optimization of resources allocation. The job-shop scheduling problem (JSP) is a possible representation of a typical problem of scheduling. Many real-life problems can be modeled as a job-shop scheduling problem and can be applied in a variety of areas, such as process scheduling in an industry, departure and arrival times of trains at stations, the delivery times of orders in a company, etc. To solve this problem many techniques have been developed such as branch and bound, constraint satisfaction techniques, neural networks, genetic algorithms, or tabu search. In general, a scheduling problem is a combinatorial optimization problem. The aim of scheduling is the resource allocation of tasks when one or more objectives must be optimized. The resources can be workshop machines, work tools, working staff, etc. Tasks can represent operations of a production process, executions of a computational program, steps of a process, arrivals or departures of a train, etc. The objective might be to minimize the time execution, to minimize the number of tasks after a given date, etc. The job-shop scheduling problem with operators is an extension of the classical job-shop scheduling problem where each operation has to be assisted by one operator from a limited set of them. The job-shop scheduling problem with operators has been recently proposed by Agnetis et al. [1]. This problem is denoted as JSO(n, p) where n is the number of jobs and p denotes the number of operators. It is motivated by manufacturing processes in which part of the work is done by human operators sharing the same set of tools. The problem is formalized as a classical job-shop scheduling problem in which the processing of a task on a given machine requires the assistance of one of p available operators. The state of the art of JSO(n, p) is analyzed in Chapter 3.
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