Research on Optimization Model of Supply Chain Robot Task Allocation Based on Genetic Algorithm and Software Practice

In the logistics robot picking system, how to assign tasks to each robot reasonably determines the running time and cost of the whole system. A robust optimization model for task allocation based on logistics robot picking system is presented in this paper. In the model, the task completion time is taken as the objective function, and the actual walking distance of the robot is used as the uncertainty factor. With the robust optimization theory, the robust peer-to-peer transformation was performed on the task allocation model with uncertain parameters. Then, the task allocation model is solved by the Hungary algorithm. Finally, the effectiveness of the model is verified by an example simulation. The optimized task completion time is not sensitive to the uncertainty distance and is relatively stable.

Integrating robotics in construction works has the potential to address persistent challenges such as labor shortages, health risks, and low productivity. This paper proposes a systematic task planning approach for human-robot collaboration (HRC) in structure assembly, comprising a robotic potential scoring-based task classification system and a constraint programming-based task allocation and sequencing optimization model. Based on key factors such as component properties, connection methods, robotic payload capacities, material storage layouts, and workspace safety, the task classification system provides a systematic approach to classifying construction tasks. To obtain optimal task allocation and sequencing, an extended flexible job shop scheduling problem (FJSSP) based optimization model integrates human fatigue into the objective function to balance operational efficiency and worker well-being. A timber frame assembly case study with different HRC configurations and storage arrangements was conducted to evaluate the performance in enhancing makespan and reducing human fatigue. This work establishes a robust foundation for optimizing HRC in structure assembly, paving the way for more effective and human-centric practices in the industry. • Proposed a task classification system based on robotic potential evaluation • Developed a constraint programming model optimizing efficiency and human fatigue • Found high-capability robots enhance flexibility in collaboration with human worker • Guided design and application of robot deployment in structural assembly • Provided task sequencing and allocation combinations with varying emphases

Optimal water utilization and allocation in industrial sectors based on water footprint accounting in Dalian City, China

The model of the multi-objective optimal allocation of water resources with government system of water rights is a bi-level programming model. The function of water trade price is difficult to determine, and some factors are interactive such as water price, coefficient of water-saving and objective function, whose relationships are rather complex, so that it is attractive to study the model solution. In this paper, the solution to the bi-level programming model of the optimal allocation of water resources is put forward, in which the upper-level programming is solved by genetic algorithm and the lower-level programming is solved by simplex method, the values about water trade price and coefficient of water-saving are assigned via random-points in a certain range. The results show that the solution is reasonable, and it has been made great progress in ideas and methods to solve this model.

After a discussion of the task allocation problem in multi-core processor based parallel system, this paper gives the task allocation model, and proposes an iteration-based heuristic algorithm, which is composed of two rounds of operations, in which the processes are assigned to processing nodes in the first round and threads in process are assigned to processor cores in the second round respectively. Each round of operation partitions the task interaction graph by iterations with backtracking. Evaluation result shows that the algorithm can find near-optimal solutions in reasonable time, and behaves better than genetic algorithm when the number of threads increases, since it can find solutions in much less time than genetic algorithm.

Task allocation and scheduling models for distributed digital signal processing are presented. The notions of block-type and stream-type tasks in signal processing application are introduced, and models for sequential and parallel I/O are presented. By extending the traditional models, more accurate schedules can be obtained. Those models can be further enhanced by allowing additional restrictions on the number of parallel I/O ports and the amount of parallelism on memory access. The deterministic nature of digital signal processing algorithms allows for more computationally intensive and accurate task allocation techniques to be performed at compile time. By applying a branch and bound algorithm, the task allocation problem can easily be solved for a variety of scheduling models and various system restrictions.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Dynamic task allocation is an essential aspect in modeling ant behaviour [1],[2]. In this paper, we propose a new (abstract) model for dynamic task allocation of agents that combines a nested layered architecture with a threshold mechanism. We apply this new model to achieve flexible ant behaviour.

In order to control the discharge of regional total pollutants in the region and improve the ability of water environment management and decision making, a multi-objective decision-making optimization model of water pollution load allocation was constructed, which took into account economy and fairness. The model takes the maximum environmental benefit and the minimum weighted comprehensive Gini coefficient as the objective function and takes into account the uncertainty and multi-objectives of the model, which is conducive to promoting economic development and ensuring the fairness of regional water pollutant discharge. A method based on Monte Carlo simulation coupled with a genetic algorithm was designed to obtain the optimal solution set through multiple simulation optimization. This model is applied to Anhui Province to solve the allocation optimization problem of total pollutant reduction in the 13th Five-Year Energy Conservation and Emission Reduction Plan. After the optimization of water pollution load distribution, the comprehensive Gini coefficients of COD and NH3-N are reduced by different ranges. The comprehensive Gini coefficient after COD optimization decreased by 2.4–4.6%, and the comprehensive Gini coefficient after NH3-N optimization decreased by 25.1–32.5%, which verified the feasibility and rationality of the model in the optimal allocation of the total discharge of regional water pollutants. The model takes into account uncertain subjective and objective factors that have an important impact on water pollutant discharge targets and decision variables, thus optimizing the total emissions of the entire regional control unit in both space and time.

Sun, G.; Chen, H., and Mao, Y., 2020. The efficiency analysis model of technological innovation resource allocation of coastal enterprises based on genetic algorithm. In: Yang, Y.; Mi, C.; Zhao, L., and Lam, S. (eds.), Global Topics and New Trends in Coastal Research: Port, Coastal and Ocean Engineering. Journal of Coastal Research, Special Issue No. 103, pp. 691–695. Coconut Creek (Florida), ISSN 0749-0208.With the continuous development of marine technology, the tasks of marine survey, exploration, navigation, transportation and development are becoming more and more serious. It is necessary to pay attention to the problem of planning resource allocation in coastal areas, so as to design a more reasonable resource allocation scheme. The traditional analysis model can only converge locally, resulting in the deviation of the analysis results of resource allocation efficiency. Therefore, the construction method of the efficiency analysis model of technological innovation resource allocation of coastal enterprises based on genetic algorithm is proposed. According to the relationship between fuzzy operator and fuzzy scale, this method calculates the carrying capacity of coastal environmental resources. In light of the prediction results of the dynamic demand forecast function of emergency resources, it sets the evaluation weight of the efficiency of technological innovation resource allocation of coastal enterprises. Based on the global convergence characteristics of genetic algorithm, it realizes the construction of the efficiency analysis model of technological innovation resource allocation of coastal enterprises. The experimental results show that compared with the traditional method, the model based on genetic algorithm has stronger convergence and can achieve global data convergence. It can be seen that the analysis results of this model accord with the reality and can be applied to the analysis of resource allocation efficiency at this stage.

Pallets are the most common equipment for transporting and storing goods. More and more companies are willing to rent pallets. Pallet rental companies need to transport pallets from their pallet rental service stations to customers and take these pallets back when they are unloaded. Hence, managers should scientifically configure vehicles for their pallet rental service stations. The fleet size, which indicates the amount and types of vehicles, can significantly affect the efficiency and costs of empty pallet allocation. Therefore, an optimization model for fleet sizing and empty pallet allocation is proposed using the methods of mixed-integer programming and stochastic programming. The objectives of this model are to maximize the profits of pallet rental companies and minimize carbon dioxide (CO2) emissions from vehicles. A particle swarm optimization algorithm with inertia weight (IPSO) is developed to solve the proposed model because IPSO can avoid becoming trapped in local optima and is able to find a globally optimal solution within a reasonable number of iterations. A numerical example proves the effectiveness of the proposed model and IPSO. The results of numerical tests show that the amount of CO2 emissions from vehicles can affect the decision on fleet sizing and empty pallet allocation. However, if the price, rental fees, or idle costs of the vehicles with low CO2 emissions are too high, managers would not choose them.

A stochastic model for joint optimal congestion control and power allocation in multi-channel multi-radio wireless mesh networks is proposed. The design problem characterizes the stochastic traffic of the network data flows and the capacity of radio channels in multi-channel multi-radio wireless mesh networks as some random variables. The corresponding design problem is formulated as the stochastic network utility maximization problem subject to some stochastic constrains, which is a chance constrained programming. The genetic algorithm is used to solve this stochastic maximization problem. The simulation is conducted in the Hyacinth network, and the result shows the quantitative relationship between the network performance and the confidence level.

A model for regional optimal allocation of irrigation water resources under deficit irrigation and its applications

This paper proposes a probabilistic model for optimal joint allocation of energy and spinning reserve to determine energy and spinning reserve capacities. The model takes into consideration both the hourly changes of load demands and the probability of generators' contingencies. The objective function aims at minimizing not only fuel costs caused by power generation but also the costs associated with spinning reserve supply. The proposed model is tackled using the Primal-Dual Interior Point Method (PDIPM), which is capable of solving optimization problems with nonlinear equality and inequality constraints efficiently. This paper reports on the simulation results obtained by using a benchmark IEEE Reliability Test System (IEEE-RTS). The simulation studies also include a comparison between the proposed probabilistic model and the conventional deterministic model, which suggests that the joint allocation of energy and spinning reserve can be optimized by the proposed model for economical and reliable purposes. Consequently, it can be applied to minimize the costs of running the power system with adequate spinning reserve.

To solve the problem of the shortage of the higher education resources, the limited resources have to be allocated more efficiently and reasonably. In view of this point, the optimization allocation method of higher education resources is investigated in this paper. An optimization model of higher education resources allocation resources is designed according to the social contribution capability of higher education. And to achieve the optimal solution of the model, the resources allocation algorithm based on genetic algorithm is proposed. By this optimization model, the allocation of higher education resources can be optimized and the social contribution capability of higher can be improved. The proposed optimization model can provide a reference scheme to take full use of the limited higher education resources.

In this paper, how to design the layout of transit hub terminals is discussed, and an optimized allocation model about bus lines and bus terminals is established. In order to address the slow convergence of adaptive genetic algorithm, an index that indicates population diversity degree is introduced to adjust the individual crossover and mutation rate. This improved adaptive genetic algorithm is applied for the allocation model and an example is used to validate its efficiency. Results show that it is a promising approach and can improve the convergence speed.