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In this paper, a multi-objective flexible job shop scheduling problem with machines capacity constraints is studied. Minimizing the makespan and overtime costs of machines are considered as two objectives for evaluating solutions. First, a new nonlinear integer programming model is presented to formulate the problem. Inasmuch as this problem is well-known as a NP-hard problem, a hybrid meta-heuristic algorithm (CFJSP II) is developed to overcome its complexity. Regarding to the solution space of the problem, for assigning and sequencing operations, a multi-objective genetic algorithm based on the ELECTRE method is presented. Also, a powerful heuristic approach to tradeoff the objective functions is developed. Finally, the proposed algorithm is compared with some well-known multi-objective algorithms such as NSGAII, SPEA2, and VEGA. Regarding to the computational results, it is clear that the proposed algorithm has a better performance especially in the closeness of the solutions to the Pareto optimal front. [ABSTRACT FROM AUTHOR]

In order to obtain efficiency and flexibility, assignment of machines' layout and determination of jobs' schedule on each machine are among the most important decisions. These decisions are interrelated and may impact each other but they are often treated separately or as a sequential decision in prior researches. In this paper, we propose a new approach to concurrently make the layout and scheduling decisions in a job shop environment. In other words, we consider an extension of the well-known job shop scheduling problem with transportation delay in which in addition to decisions made in the classic problem, the locations of machines have to be selected among possible sites. The only goal of the problem is the minimization of the makespan. A hybrid metaheuristic approach based on the scatter search algorithm is developed to tackle this problem. Using 43 randomly generated benchmark instances, the performance of the scatter search and its components are evaluated. We also applied our procedure to the classic job shop scheduling problems. Computational results show that our procedure is efficient. [ABSTRACT FROM AUTHOR]

This paper focuses on a hub reentrant shop scheduling problem which is motivated by actual packing production line in the iron and steel industry. There are five operations for processing a job where three operations must be processed on a hub machine, and between any two consecutive operations on the hub machine, two operations must be processed on other two secondary machines, respectively. We mainly consider two types of the problem. The first is basic hub reentrant shop (BHRS) problem where only one machine in each secondary machine center. The second is hybrid hub reentrant shop (HHRS) problem where multiple machines in each secondary machine center. The objective is to minimize the makespan. For BHRS problem, we show that the problem is NP-hard in the strong sense. Some properties are derived for identifying an optimal schedule. Furthermore, a heuristic algorithm is proposed with the worst performance ratio 6:5, and this ratio is demonstrated tight. For HHRS problem, two well-solvable cases are proposed, respectively. Under a split condition, HHRS is equivalent to a two-stage hybrid flow shop problem. The worst case of a class of proposed algorithms is analyzed. The performance ratio is demonstrated relatively with the secondary machine numbers. [ABSTRACT FROM AUTHOR]

This paper presents a hybrid Pareto-based discrete artificial bee colony algorithm for solving the multi-objective flexible job shop scheduling problem. In the hybrid algorithm, each solution corresponds to a food source, which composes of two components, i.e., the routing component and the scheduling component. Each component is filled with discrete values. A crossover operator is developed for the employed bees to learn valuable information from each other. An external Pareto archive set is designed to record the non-dominated solutions found so far. A fast Pareto set update function is introduced in the algorithm. Several local search approaches are designed to balance the exploration and exploitation capability of the algorithm. Experimental results on the well-known benchmark instances and comparisons with other recently published algorithms show the efficiency and effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Generating schedules such that all operations are repeated every constant period of time is as important as generating schedules with minimum delays in all cases where a known discipline is desired or obligated by stakeholders. In this paper, a periodic job shop scheduling problem (PJSSP) based on the periodic event scheduling problem (PESP) is presented, which deviates from the cyclic scheduling. The PESP schedules a number of recurring events as such that each pair of event fulfills certain constraints during a given fixed time period. To solve such a hard PJSS problem, we propose a hybrid algorithm, namely PSO-SA, based on particle swarm optimization (PSO) and simulated annealing (SA) algorithms. To evaluate this proposed PSO-SA, we carry out some randomly constructed instances by which the related results are compared with the proposed SA and PSO algorithms as well as a branch-and-bound algorithm. In addition, we compare the results with a hybrid algorithm embedded with electromagnetic-like mechanism and SA. Moreover, three lower bounds (LBs) are studied, and the gap between the found LBs and the best found solutions are reported. The outcomes prove that the proposed hybrid algorithm is an efficient and effective tool to solve the PJSSP. [ABSTRACT FROM AUTHOR]