In this paper, three-stage assembly flowshop scheduling is considered with respect to minimizing bi-objectives, namely mean flow time AND mean tardiness. This problem is a model of production systems, which several production operations are done simultaneously AND independently, AND then produced components are collected AND transferred to an assembly stage for the final operation. In this model, by considering sequence-dependent SETUP time AND components transformation TIMES in order to make a real situation for the considered model, a lower bound (LB) is introduced to completion TIMES of all the jobs. Further, due dates are generated rANDomly in a determined interval for some examples. To validate the proposed model, the Lingo 8.0 software AND an enumeration technique that is coded in MATLAB are used. Comparison between the results of the Lingo 8.0 AND enumeration technique shows that in larger problems (say n>8, where n is the number of jobs) the results obtained by Lingo do not have the good efficiency AND cannot be compared with the enumeration technique in terms of computational time AND deviation from the minimum objective function. For example, in some large problems, the objective function values obtained by the Lingo 8.0 software have 20% deviation from their minimum. Therefore, to solve such a hard problem, a meta-heuristic method is required as future research.

This paper presents a new mathematical programming model for an integrated production AND air TRANSPORTATION in supply chain management with sequence-dependent SETUP TIMES in order to design an applied procedure for the production AND distribution schedule. The aim of this model is to minimize the total supply chain cost consisting of the costs of distribution, production earliness AND tardiness, AND delivery. Because of the complexity AND NP-hardness of this problem, two meta-heuristics based on genetic algorithm (GA) AND variable neighborhood search (VNS) are proposed. The parameters of these algorithms AND their appropriate operators are set AND determined by the use of the Taguchi experimental design. Then, the quality of the results obtained by these algorithms is compared. The computational results show that the developed VNS outperforms the proposed GA.

In this paper, a new mathematical model for the problem of job scheduling in virtual manufacturing cells (VMC) is presented to minimizing the completion time of all jobs. Sequence dependent SETUP TIMES of machines is considered AND lot-streaming is possible. In Virtual manufacturing cells, each job has a different processing path AND there is a set of machines for processing each operation. There are multiple machine types with several identical machines in each type locating in different locations in the shop floor. In this type of system, the cells are not physical AND Machines can be shared between the cells. In Mixed-integer nonlinear programming model presented, the scheduling decisions involve assigning a machine to each operation, the start time at each operation, the start time of machines AND sub-lot sizes of each job. Some test problems have been generated to demonstrate the implementation of the model AND solved by Lingo.

In recent decade, many researches has been done on job shop scheduling problem with sequence dependent SETUP TIMES (SDSJSP), but with respect to the knowledge of authors in very few of them the assumption of existing inseparable SETUP has been considered. Also, in attracted metaheuristic algorithms to this problem the Particle Swarm Optimization has not been considered. In this paper, the ISDSJSP with makespan minimization is considered. For this problem an MILP model has been developed AND a PSO algorithm has been applied to solve it. In order to improve final solution obtained by PSO a simulated annealing algorithm has been applied. The proposed hybrid algorithm (HPSO) has been applied on some generated benchmark problems AND the results indicate the effectiveness AND efficiency of the proposed hybrid PSO with respect to two other algorithms.

The present paper, titled “optimizing the time of preparation, taking into account the timing of   repair AND maintenance using non-linear modeling”, has been implemented with the aim of designing AND presenting a mathematical model to get the best sequence of operations leading to minimum the total time of production. The main research question is related to the reduction of time AND production costs following the implementation of the presented model.The method of research’s data AND information collection, have been survey AND study AND library resources, referring to the documentations of repair, maintenance AND production segments in the company AND expert managers in the areas of planning, engineering production AND … Following, the model taking into account the constraints of cost AND time, as well as the limitations of the production system is presented. LINGO software is used to solve the models AND model outputs, will provide the local solutions that would show the best sequence. AND finally it can be extended the model to other industrial units with only smart alternations AND present the best operational sequence related to multifunctional machines.

This paper considers the problem of scheduling parallel machines for split jobs to minimize the total tardiness. Accepting a new job, each machine needs to be set up AND the SETUP TIMES depend on the sequence of jobs. To solve the above problem, a new approach is suggested AND a number of theorems are provided AND proved regarding resource planning AND job sequencing for the given problem in hAND. Then, the proposed algorithm is verified AND evaluated with a number of test problems. The associated results are analyzed AND compared with the results obtained by the Lingo software.

In this paper, the problem of job scheduling AND preventive maintenance activities is investigated simultaneously in an unrelated parallel machine’s environment with machine deterioration. Due to the existence of deterioration phenomenon AND its effect on the job scheduling, the aim of this paper is to find the ideal time, the optimum number of maintenance activities AND the sequence of the jobs, for minimizing the sum of job's completion TIMES. Preventive maintenance activities may be needed because of tool wears or machine deterioration in a job scheduling horizon.Here, two different deterioration time function AND tool wear are studied, in which the machines are reverted to the original condition after any preventive maintenance activity. The duration of each activity is considered as a function of its starting time AND its position on the sequence of job scheduling. A mathematical model is proposed AND due to its complexity, a simulated annealing Mehta-heuristic algorithm is used for solving the model.Finally, numerical examples are given AND sensitivity analysis with various parameters is applied to evaluate the performance of the proposed method.

This paper presents a novel, multi-objective model of a parallel machines scheduling problem that minimizes the number of tardy jobs AND total completion time of all jobs. In this model, machines are considered as unrelated parallel units with different speeds. In addition, there is some precedence, relating the jobs with non-identical due dates AND their ready TIMES. Sequence-dependent SETUP TIMES embedded in the proposed model may vary in different machines based on their characteristics. This paper proposes a two-level mixed-integer programming for the given problem. By solving the presented model, the associated promising results show the effectiveness of this model for small AND medium-sized problems, respectively.

This study presents two mixed integer programming models for scheduling hybrid flow shops with sequence dependent SETUP TIMES. In the first model, we assumed the machines at each stage are identical, but in the second model the machines at each stage are different. These models may be used to determine optimal solutions for hybrid flow shop problems of moderate size, AND these problems could then be used as benchmarks for testing new sequencing heuristics.