Multi-objective Optimal Scheduling Research Articles

Research of Cloud Manufacturing Technology in the Development of Digital Intelligent Production Control Platform

This paper focuses on the application of cloud manufacturing technology in the development of digital intelligent production control platform. Combined with the characteristics of NC machine shop, using the cloud manufacturing technology, the intelligent scheduling of production plan based on the theory of constraints, and multi-objective scheduling optimization technology, developed the target task balance optimization, manufacturing resource planning, equipment load balance module etc., achieved tracking and monitoring of the status of schedule, completion, production etc., ensure the statistics and analysis of production data correct and complete, and provide the basis for evaluation of production condition.

Enhanced Trade-Off of Construction Projects: Finance-Resource-Profit

AbstractThe parameters of finance requirements, resource leveling, and anticipated profit have significant influence on many aspects of project management. These parameters interact and occasionally conflict with each other. Accordingly, achievement of a balance between these three parameters is crucial to ensure the accomplishment of project objectives. A multi-objective multimode scheduling optimization algorithm is proposed to establish the optimal trade-off between these three parameters. The strength Pareto evolutionary algorithm (SPEA) was implemented to obtain the solutions comprising the Pareto-optimal trade-off. The developed SPEA was validated by reproducing identical results of a time/cost trade-off problem in the literature. The developed SPEA was used to obtain the Pareto-optimal trade-off of a network of nine multimode activities that comprised fifty solutions. The trade-off of fifty solutions allows decision makers explore the impact of finance upon the efficiency of resource utilization an...

Multi‐objective short‐term scheduling of thermoelectric power systems using a novel multi‐objective θ ‐improved cuckoo optimisation algorithm

This study proposes a multi-objective optimal static and dynamic scheduling of thermoelectric power systems considering the conflicting environmental and economical objectives. Meantime, some restrictions such as valve-point effects, prohibited operating zones, multi-fuel options, line flow limits as well as spinning reserve should be taken into account in order to ensure secure real-time power system operation. A novel multi-objective θ-improved cuckoo optimisation algorithm is projected to solve the optimisation problems by defining a set of nondominated points as the solutions. The suggested method moves forward the particles to the problem search space in the polar coordinates as a substitute of the Cartesian one. In addition, in order to achieve better performance and higher-convergence speed, several improvement strategies are utilised. This algorithm is equipped with a novel powerful mutation strategy in order to increase the population diversity and to amend the convergence criteria. Furthermore, a fuzzy-based clustering is used to control the size of the repository and a niching method is utilised to choose the best solution during the optimisation process and to ensure diversity among non-dominated solutions. Performance of the proposed algorithm is tested on 6-, 10-, 14-, 40- and 100-unit test systems and compared with those of other well-known methods.

Multiobjective Synergistic Scheduling Optimization Model for Wind Power and Plug-In Hybrid Electric Vehicles under Different Grid-Connected Modes

In order to promote grid’s wind power absorptive capacity and to overcome the adverse impacts of wind power on the stable operation of power system, this paper establishes benefit contrastive analysis models of wind power and plug-in hybrid electric vehicles (PHEVs) under the optimization goal of minimum coal consumption and pollutant emission considering multigrid connected modes. Then, a two-step adaptive solving algorithm is put forward to get the optimal system operation scheme with the highest membership degree based on the improvedεconstraints method and fuzzy decision theory. Thirdly, the IEEE36 nodes 10-unit system is used as the simulation system. Finally, the sensitive analysis for PHEV’s grid connected number is made. The result shows the proposed algorithm is feasible and effective to solve the model. PHEV’s grid connection could achieve load shifting effect and promote wind power grid connection. Especially, the optimization goals reach the optimum in fully optimal charging mode. As PHEV’s number increases, both abandoned wind and thermal power generation cost would decrease and the peak and valley difference of load curve would gradually be reduced.

Study on Multi-Objective Greenness Planning Model for Liners

According to the characteristics of high-speed liners, the multi-objective scheduling optimization mathematical model of high speed liners is established from the greenness angle. And the quantitative indicators and qualitative indicators were organic combined. The integer linear programming method was adopted to optimize analysis. Finally verify the feasibility of this optimal dispatch model through an example. Thus a scientific, reasonable way is provided for the high-speed liners scheduling arrangement.

Self-evolution Algorithm with Multi-objective for Scheduling a Job-shop-like Knowledgeable Manufacturing Cell

It is difficult to find the global optimal solution for the multi-objective job-shop scheduling problem and the solutions obtained usually belong to the Pareto optimal ones. A multi-objective scheduling optimization model is given for the Job-shop-like knowledgeable manufacturing cell and the relationship between the multi objectives is analyzed. The properties of key arcs of tasks are presented through the analysis of its disjunctive graph and the conclusion is obtained that it is helpless to improve the function value by changing the direction of the middle key arc of job. A simplified neighborhood is proposed based on the conclusion which can reduce greatly the number of feasible solutions to be searched. A self-evolution algorithm for multi-objective scheduling problem is proposed based on the properties of the simplified neighborhood by the use of adaptive heuristic critic method whose associate search module can find the best action for the concurrent solution to obtain a better solution by learning and training, and such ability of the module will be improved continuously with the training increasing. The numerical simulation results show that the algorithm proposed has the excellent ability to search the optimal solution for the proposed scheduling problem and possesses obvious evolution capacities through learning.

Developing a Multiobjective Optimization Scheduling System for a Screw Manufacturer: A Refined Genetic Algorithm Approach

Over time, the traditional single-objective job shop scheduling method has grown increasingly incapable of meeting the requirements of contemporary business models; thus, a multiobjective scheduling solution is required. Because of changing orders, understanding the schedule and output is a constant challenge when using a traditional manual schedule, particularly among manufacturers that produce various products. The multiobjective optimization genetic algorithm (MOGA) is a relatively superior method of solving multiobjective optimization problems; therefore, we used a MOGA to solve flexible job-shop problems for a middle-scale screw manufacturer in Taiwan. For solving the problems of incorrect jobs assign and diversity problem of traditional genetic algorithm (GA) caused by encoding method when applying traditional GA in the flexible manufacturing environment, a refined GA was proposed. Two-phase test has performed for proposed approach, using a classical benchmark of distributed and flexible jobs-shop scheduling problem, and 80 set of work orders, the empirical results indicated that the proposed model yielded substantial savings, regardless of the total order completion time, machine retooling rate, and average machine load rate.

Short-term multi-objective optimization scheduling for cascaded hydroelectric plants with dynamic generation flow limit based on EMA and DEA

A dynamic generation flow strategy using for dynamically coordinating water head of reservoir and water consumption volume for electric energy production is presented in this paper. A novel multi-objective scheduling model is proposed to achieve the optimal trade-off between water volume for generation and electric quantity. To realize the optimal power output the coordination condition is used to describe the relationship between the current water head level and generation flow rate. The constraints are presented in an efficient method based on characters of the cascaded hydroelectric plants. A hybrid global optimization method, which can overcome the shortcoming of weighted method, is proposed for solving the multi-objective problem efficiently. This is done by embedding the data envelopment analysis (DEA) into an electromagnetism-like algorithm (EMA). A test system with eight hydroelectric plants was used to verify this new method. Results show that this novel scheduling method can enhance the synthesis generation benefit of the cascaded hydroelectric plants and realize the optimal solution for the scheduling model.

The Research of Wind- thermal Power Random Multi-objective Scheduling Based on Combined MIPSO

There are strong randomness in wind power, its output is not controllable. Large scale wind power grid-connection to dispatch system brings new uncertainty. This paper constructs the wind-fire random optimal model based on combined MIPSO algorithm and achieves the multi-objective optimizations on the minimization of power cost and pollution emissions of thermal power units. Firstly, wind power outputs can be forecasted by utilizing BP neural network algorithm. Then random multi-objective optimal solution is obtained through MIPSO algorithm. Finally, multi-objective optimal scheduling is illustrated by using the IEEE-30 node. The results show that the combination arithmetic solution set uniform distribution, predict accuracy and qualified rate were 0.9962 and 0.7651, using MIPSO algorithm is out of the solution of the highest degree of coordination, get each time specific unit output of the plan, according to the calculation results make corresponding power generation plan, to access a fairly large wind power dispatch system has the actual reference value, so the proposed method is feasible and effective.

Multi-Objective Optimal Energy Consumption Scheduling in Smart Grids

A major source of inefficiency in power grids is the underutilization of generation capacity. This is mainly because load demand during peak hours is much larger than that during off-peak hours. Moreover, extra generation capacity is needed to maintain a security margin above peak load demand. As load demand keeps increasing and two-way communications are enabled by smart meters (SMs), demand response (DR) has been proposed as an alternative to installing new power plants in smart grids. DR makes use of real-time schemes to allow users to modify their load demand patterns according to their energy consumption costs. In particular, when load demand is high, energy consumption cost will be high and users may decide to postpone certain amount of their consumption needs. This strategy may effectively reduce the peak load demand and increases the off-peak demand, and hence could increase existing generation capacity utilization and reduce the need to install extra generation plants. In this paper, we consider a third-party managing the energy consumption of a group of users, and formulate the load scheduling problem as a constrained multi-objective optimization problem (CMOP). The optimization objectives are to minimize energy consumption cost and to maximize a certain utility, which can be conflicting and non-commensurable. We then develop two evolutionary algorithms (EAs) to obtain the Pareto-front solutions and the e-Pareto front solutions to the CMOP, respectively, which are validated by extensive simulation results.

Mission Track Coordination Based Multi-Objective Optimization for Multi-Satellite Imaging Scheduling

Satellite imaging scheduling is a complicated task been developed to ideally satisfy all requests under various constraints. A considerable amount of theoretical work has been carried out on single- or multi-satellite scheduling problems based on single-objective optimization. On the other hand, concerning multi-objective optimization in a multi-satellite scheduling scenario, few theoretical analyses have been performed. In this paper, we propose a novel imaging scheduling algorithm of multi-objective optimization for multi-satellite (MOO-MS). The concept of orbit coordination is introduced and the geometry analysis is incorporated to obtain the Pareto front as a set of trade-off solutions. Moreover, a truncated Cauchy probability distribution function (TCPDF) search algorithm is developed to efficiently obtain a sequence of tasking areas with imaging time. Simulation results show that the proposed algorithm avoids large amount of calculation and provides high quality solution within a short period of time by comparing with the single- or multi-satellite optimization scheme.

Multiobjective Optimization Problem of Multireservoir System in Semiarid Areas

With the increasing scarcity of water resources, the growing importance of the optimization operation of the multireservoir system in water resources development, utilization, and management is increasingly evident. Some of the existing optimization methods are inadequate in applicability and effectiveness. Therefore, we need further research in how to enhance the applicability and effectiveness of the algorithm. On the basis of the research of the multireservoir system’s operating parameters in the Urumqi River basin, we establish a multiobjective optimization problem (MOP) model of water resources development, which meets the requirements of water resources development. In the mathematical model, the domestic water consumption is the biggest, the production of industry and agricultural is the largest, the gross output value of industry and agricultural is the highest, and the investment of the water development is the minimum. We use the weighted variable-step shuffled frog leaping algorithm (SFLA) to resolve it, which satisfies the constraints. Through establishing the test function and performance metrics, we deduce the evolutionary algorithms, which suit for solving MOP of the scheduling, and realize the multiobjective optimization of the multireservoir system. After that, using the fuzzy theory, we convert the competitive multiobjective function into single objective problem of maximum satisfaction, which is the only solution. A feasible solution is provided to resolve the multiobjective scheduling optimization of multireservoir system in the Urumqi River basin. It is the significance of the layout of production, the regional protection of ecological environment, and the sufficient and rational use of natural resources, in Urumqi and the surrounding areas.

Erratum to: Multi-objective optimization for draft scheduling of hot strip mill

Erratum to: Multi-objective optimization for draft scheduling of hot strip mill

Multi-objective optimization for draft scheduling of hot strip mill

A multi-objective optimization model for draft scheduling of hot strip mill was presented, rolling power minimizing, rolling force ratio distribution and good strip shape as the objective functions. A multi-objective differential evolution algorithm based on decomposition (MODE/D). The two-objective and three-objective optimization experiments were performed respectively to demonstrate the optimal solutions of trade-off. The simulation results show that MODE/D can obtain a good Pareto-optimal front, which suggests a series of alternative solutions to draft scheduling. The extreme Pareto solutions are found feasible and the centres of the Pareto fronts give a good compromise. The conflict exists between each two ones of three objectives. The final optimal solution is selected from the Pareto-optimal front by the importance of objectives, and it can achieve a better performance in all objective dimensions than the empirical solutions. Finally, the practical application cases confirm the feasibility of the multi-objective approach, and the optimal solutions can gain a better rolling stability than the empirical solutions, and strip flatness decreases from (0±163) IU to (0±45) IU in industrial production.

Multi-objective optimization with fuzzy measures and its application to flow-shop scheduling

Most of the research in multi-objective scheduling optimization uses the classical weighted arithmetic mean operator to aggregate the various optimization criteria. However, there are scheduling problems where criteria are considered interact and thus a different operator should be adopted. This paper is devoted to the search of Pareto-optimal solutions in a tri-criterion flow-shop scheduling problem (FSSP) considering the interactions among the objectives. A new hybrid meta-heuristic is proposed to solve the problem which combines a genetic algorithm (GA) for solutions evolution and a reduced variable neighborhood search (RVNS) technique for fast solution improvement. To deal with the interactions among the three criteria the discrete Choquet integral method is adopted as a means to aggregate the criteria in the fitness function of each individual solution. Experimental comparisons (over public available FSSP test instances) with five existing multi-objective evolutionary algorithms (including the well known SPEA2 and NSGAII algorithms as well as the recently published L-NSGA algorithm) showed a superior performance for the developed approach in terms of diversity and domination of solutions.

Multi-objective Flexible Scheduling Optimization Scheme base on Improved DNA Genetic Algorithm

In this paper, established a mathematical model for multi-objective flexible scheduling problems, combined Pareto non-dominated sorting method, put forward a hybrid genetic algorithm based on improved DNA computation. To ensure the diversity of the optimal solution sets, designed RNA quaternary encoder mode and genetic operator based on improved DNA computation, adopted sub-area crossover and dynamic mutation, imposed on manipulation of the molecular level. Through simulation, tested the performance of the designed algorithm, compared it with the standard genetic algorithm test results. Simulation results showed the proposed algorithm can provide an optimum searching, owned better seeking abilities; the obtained scheduling results were fairly reasonable. This algorithm can effectively solve the multi-objective flexible scheduling optimization problems.

Bat intelligence search with application to multi-objective multiprocessor scheduling optimization

In this paper, we present the bat intelligence search for the first time. Bat intelligence is a novel and unique heuristic that models two major prey hunting behaviors of bats: (a) utilization of echolocation to observe the environment and (b) employment of constant absolute target direction approach to pursue preys. In order to illustrate the performance of bat intelligence, we implement this heuristic to solve two types of multiprocessor scheduling problems (MSP): single objective MSP and multi-objective MSP. In single objective MSP, we independently solve for minimization of makespan and minimization of tardiness. In multiple objective MSP, these two objectives are optimized simultaneously. In the single objective MSP, on average, the bat intelligence outperformed the list algorithm and the genetic algorithm by 11.12% when solving for minimization of makespan and by 23.97% when solving for the minimization of tardiness. In comparison to the genetic algorithm, the bat intelligence produces better results for the same computational effort. In multiple objective MSP, bat intelligence is combined with normalized weighted additive utility function to generate a set of efficient solutions by varying the weights of importance. The results demonstrate that the bat intelligence finds a set of Pareto optimal solutions on bi-objective optimization of MSP.

A Genetic Algorithm for Multiobjective Hard Scheduling Optimization

<p>This paper proposes a genetic algorithm for multiobjective scheduling optimization based in the object oriented design with constrains on delivery times, process precedence and resource availability. Initially, the programming algorithm (PA) was designed and implemented, taking into account all constraints mentioned. This algorithm’s main objective is, given a sequence of production orders, products and processes, calculate its total programming cost and time.<br /> Once the programming algorithm was defined, the genetic algorithm (GA) was developed for minimizing two objectives: delivery times and total programming cost. The stages defined for this algorithm were: selection, crossover and mutation. During the first stage, the individuals composing the next generation are selected using a strong dominance test. Given the strong restrictions on the model, the crossover stage utilizes a process level structure (PLS) where processes are grouped by its levels in the product tree. Finally during the mutation stage, the solutions are modified in two different ways (selected in a random fashion): changing the selection of the resources of one process and organizing the processes by its execution time by level.<br /> In order to obtain more variability in the found solutions, the production orders and the products are organized with activity planning rules such as EDD, SPT and LPT. For each level of processes, the processes are organized by its processing time from lower to higher (PLU), from higher to lower (PUL), randomly (PR), and by local search (LS). As strategies for local search, three algorithms were implemented: Tabu Search (TS), Simulated Annealing (SA) and Exchange Deterministic Algorithm (EDA). The purpose of the local search is to organize the processes in such a way that minimizes the total execution time of the level.<br /> Finally, Pareto fronts are used to show the obtained results of applying each of the specified strategies. Results are analyzed and compared.</p>

Multi-objective differential evolution with adaptive Cauchy mutation for short-term multi-objective optimal hydro-thermal scheduling

Abstract A new multi-objective optimization method based on differential evolution with adaptive Cauchy mutation (MODE-ACM) is presented to solve short-term multi-objective optimal hydro-thermal scheduling (MOOHS) problem. Besides fuel cost, the pollutant gas emission is also optimized as an objective. The water transport delay between connected reservoirs and the effect of valve-point loading of thermal units are also taken into account in the presented problem formulation. The proposed algorithm adopts an elitist archive to retain non-dominated solutions obtained during the evolutionary process. It modifies the DE’s operators to make it suit for multi-objective optimization (MOO) problems and improve its performance. Furthermore, to avoid premature convergence, an adaptive Cauchy mutation is proposed to preserve the diversity of population. An effective constraints handling method is utilized to handle the complex equality and inequality constraints. The effectiveness of the proposed algorithm is tested on a hydro-thermal system consisting of four cascaded hydro plants and three thermal units. The results obtained by MODE-ACM are compared with several previous studies. It is found that the results obtained by MODE-ACM are superior in terms of fuel cost as well as emission output, consuming a shorter time. Thus it can be a viable alternative to generate optimal trade-offs for short-term MOOHS problem.