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In this paper, a new mathematical programming model is proposed for a partial flexible job shop scheduling problem with an integrated solution approach. The purpose of this model is the assignment of production operations to machines with the goal of simultaneously minimizing operating costs and penalties. These penalties include delayed delivery, deviation from a fixed time point for preventive maintenance, and deviation from the priorities of each machine. Considering the priorities for machines in partial flexible job shop scheduling problems can be a contribution in closer to the reality of production systems. For validation and evaluation of the effectiveness of the model, several numerical examples are solved by using the Baron solver in GAMS. Sensitivity analysis is performed for the model parameters. The results further indicate the relationship between scheduling according to priorities of each machine and production scheduling.

Nowadays, wireless sensor networks (WSNs) are widely used in different sectors. The problem in these networks is the non-rechargeable batteries of these sensors, which limit the lifetime of the network. Therefore, the optimal energy consumption of sensors is an open research topic. In this paper, a new algorithm with the Development of Genetic Algorithm with the Floyd Warshall (DGAFW) has been proposed. Using the proposed DGAFW algorithm, the number of clusters and nodes assigned to each cluster is first determined with the Floyd Warshall algorithm and then the Cluster Head (CH) is selected using fuzzy logic. Finally, the optimal placement of the base station is specified by the combination of the Genetic Algorithm and the Floyd Warshall. The DGAFW algorithm is based on minimizing the distance of sending multi-hop messages. The simulation is carried out in MATLAB 2023a online software. The simulation results obtained from the DGAFW algorithm have been compared based on the distance, the amount of remaining energy in each round, and the number of rounds of network activity in the case where the location of the base station is fixed or randomly determined in each round. The results obtained show that the DGAFW algorithm compared to the case of random base station and fixed station respectively, has 12.7% and 14.3% shorter average message-sending distance in each round, 14.7% and 19.1% more residual energy and also 36% and 48% more rounds of network activity.

This paper presents a Genetic algorithm for solving a multi-objective deterministic mixed integer nonlinear programming modeling of the workers scheduling problem in production logistics in a job shop production system. The proposed model integrates multiple decisions into the workers scheduling problem. In this model, production planning is independent of the influence of human resources management. In the other word, production planning is fixed and workers scheduling is based on it. This model explicitly taking into consideration the individual competencies and preferences of each worker, also the workers and competency requirements relate to each assembly activity. The objective of the introduced model is to allocate employees to assembly activities while minimizing human resources costs and workers dissatisfactions. To benchmark the performance of this Genetic algorithm, optimal solutions obtained through a mathematical model resolution using commercial solver CPLEX are used. Results show that this Genetic algorithm can produce high-quality and efficient solutions in a short computational time.

In this paper, a new mathematical programming model is proposed for a partial flexible job shop scheduling problem with an integrated solution approach. The purpose of this model is the assignment of production operations to machines with the goal of simultaneously minimizing operating costs and penalties. These penalties include delayed delivery, deviation from a fixed time point for preventive maintenance, and deviation from the priorities of each machine. Considering the priorities for machines in partial flexible job shop scheduling problems can be a contribution in closer to the reality of production systems. For validation and evaluation of the effectiveness of the model, several numerical examples are solved by using the Baron solver in GAMS. Sensitivity analysis is performed for the model parameters. The results further indicate the relationship between scheduling according to priorities of each machine and production scheduling.

This paper investigates the network design problem of a two-level supply chain (SC), which is applicable for industries such as automotive, fuel and tyre manufacturing. Models presented in this paper aim at locating retail facilities of an SC and identifying their required capacities in the presence of existing competing retailers of a rival SC. We consider feasible locating space of the retail facilities on the continuous plane with bounded constraints and static competition among the rivals of the markets with deterministic demands. The problem is used for both essential and luxury product cases; hence, we consider elastic and inelastic demands, both. The models discussed in this paper are non-linear and non-convex which are difficult to solve. We use interval branch-and-bound as optimization algorithm for small size single-retailer problems, but for large-scale, multi-retailer problems we need to have more efficient methods. Therefore, we apply a heuristic algorithm (H1), a simulated annealing (SA) algorithm, an interior point (IP) algorithm, a genetic algorithm (GA) and a pattern search algorithm for solving multi-retailer problem with elastic and inelastic demands. Computational results obtained from performing different solution approaches for both elastic and inelastic show that mostly IP, PS, and H1 methods outperform the other approaches. The computational results on a real-life case are also promising. Several extended mathematical models and an example of a typical case with details are presented in the appendices of the paper.

Background: Priority setting is one of the major issues in the health research system and no health system can afford to pay for every research they want to do, particularly in developing countries, so we decided to set the national main areas of the health research priorities. Methods: In this study, according to Essential National Health Research (ENHR) strategy and with cooperation of all the Iranian universities of medical sciences and other stakeholders, the national health research priorities were extracted. Results: The number of research priorities collected from the universities of medical sciences was 6723. Seventeen percent of the research priorities were related to basic science, 78 percent applied science, and 5 percent were related to developmental type. According to epidemiological classification, 50% of the research priorities were in descriptive form. In this process, 9 main extracted areas consist of communicable diseases, non-communicable disease, Health System Research, pharmaceutical sciences and Industry, basic science, traditional medicine and herbal medicine, nutrition, environmental health, and dentistry. And then for each area, five main projects were defined. Conclusions: In the Health Research System, the participatory priority setting is the main function based on needs assessment.