Sequencing Mixed-model Production Systems by Modified Multi-objective Genetic Algorithms

As two independent problems,scheduling for parts fabrication line and sequencing for mixed-model assembly line have been addressed respectively by many researchers.However,these two problems should be considered simultaneously to improve the efficiency of the whole fabrication/assembly systems.By far,little research effort is devoted to sequencing problems for mixed-model fabrication/assembly systems.This paper is concerned about the sequencing problems in pull production systems which are composed of one mixed-model assembly line with limited intermediate buffers and two flexible parts fabrication flow lines with identical parallel machines and limited intermediate buffers.Two objectives are considered simultaneously：minimizing the total variation in parts consumption in the assembly line and minimizing the total makespan cost in the fabrication/assembly system.The integrated optimization framework,mathematical models and the method to construct the complete schedules for the fabrication lines according to the production sequences for the first stage in fabrication lines are presented.Since the above problems are non-deterministic polynomial-hard（NP-hard）,a modified multi-objective genetic algorithm is proposed for solving the models,in which a method to generate the production sequences for the fabrication lines from the production sequences for the assembly line and a method to generate the initial population are put forward,new selection,crossover and mutation operators are designed,and Pareto ranking method and sharing function method are employed to evaluate the individuals＇ fitness.The feasibility and efficiency of the multi-objective genetic algorithm is shown by computational comparison with a multi-objective simulated annealing algorithm.The sequencing problems for mixed-model production systems can be solved effectively by the proposed modified multi-objective genetic algorithm.

MULTI-OBJECTIVE OPTIMIZATION DESIGN FOR TRANSONIC TURBINE CASCADES USING SIMULATED ANNEALING ALGORITHM

On the basis of computational of transonic viscid flow, the simulated annealing algorithm, used in statistical mechanics to study solid cooling process, is adaptedfor soving multi-objective cascades design problem. The simulated annealing algorithmdraws an analogy between the energy minimization in physical system and the objectivefunction in the real design problem, To model the multi-objective functions, a minimumdeviation method is used. In this paper, the loss and work are considered as the objectivefunctions. Simulated results indicate that this algorithm can be effectively applied to theimprovement of the design for transonic turbine cascades.

Multi-Objective Optimization for Structure Crashworthiness Based on Kriging Surrogate Model and Simulated Annealing Algorithm

Multi-objective optimization of crashworthiness in automobile front-end structure was performed,and finite element model(FEM)was validated by experimental results to ensure that FEM can predict the response value with sufficient accuracy.Seven design variables and four crashworthiness indicators were defined.Through orthogonal design method,18 FEMs were established,and the response values of crashworthiness indicators were extracted.By using the variable-response specimen matrix,Kriging surrogate model(KSM)was constructed to replace FEM to refect the function correlation between variables and responses.The accuracy of KSM was also validated.Finally,the simulated annealing optimization algorithm was implemented in KSM to seek optimal and reliable solutions.Based on the optimal results and comparison analysis,the 9096-th iteration point was the optimal solution.Although the intrusion of firewall and the mass of optimal structure increased slightly,the vehicle acceleration of the optimal solution decreased by 6.9%,which fectively reduced the risk of occupant injury.

Overview of multi-objective optimization methods

To assist readers to have a comprehensive understanding, the classical and intelligent methods roundly based on precursory research achievements are summarized in this paper. First, basic conception and description about multi-objective (MO) optimization are introduced. Then some definitions and related terminologies are given. Furthermore several MO optimization methods including classical and current intelligent methods are discussed one by one succinctly. Finally evaluations on advantages and disadvantages about these methods are made at the end of the paper.

MULTI-OBJECTIVE PROGRAMMING FOR AIRPORT GATE REASSIGNMENT

To improve the efficiency of gate reassignment and optimize the plan of gate reassignment,the concept of disruption management is introduced,and a multi-objective programming model for airport gate reassignment is proposed.Considering the interests of passengers and the airport,the model minimizes the total flight delay,the total passengers′walking distance and the number of flights reassigned to other gates different from the planned ones.According to the characteristics of the gate reassignment,the model is simplified.As the multi-objective programming model is hard to reach the optimal solutions simultaneously,a threshold of satisfactory solutions of the model is set.Then a simulated annealing algorithm is designed for the model.Case studies show that the model decreases the total flight delay to the satisfactory solutions,and minimizes the total passengers′walking distance.The least change of planned assignment is also reached.The results achieve the goals of disruption management.Therefore,the model is verified to be effective.

双线束铁路集装箱中心站“列车-轨道”分配策略研究

为了满足铁路集装箱中心站日益增长的作业需求,部分中心站已建成或规划在建第二线束,多线束的建设会大幅提升中心站的作业能力,但相应的也可能会产生线束间的作业均衡和集装箱的场内转运问题。以最小化线束间轨道门吊的工作量差异和集装箱场内转运数量为目标,考虑轨道的时空容量、列车的进出站时间要求及装卸作业要求等约束以保证列车按时完成作业,建立双目标整数规划模型来研究双线束集装箱中心站内的“列车-轨道”分配问题。设计启发式规则生成初始可行解,并利用存档式多目标模拟退火算法处理双目标优化问题,以得到一组帕累托近似最优解。设计算例验证了算法的可行性和有效性,并研究了装卸线的数量和轨道门吊的配置对实验结果的影响。结果表明:本文提出的双线束铁路集装箱中心站“列车-轨道”分配策略能够有效均衡线束间的工作量并降低集装箱场内转运数,灵敏度分析结果能够为未来双线束集装箱中心站的规划建设提供借鉴。

A hybrid genetic algorithm for multi-objective flexible job shop scheduling problem considering transportation time

Purpose–Flexible job-shop scheduling is significant for different manufacturing industries nowadays.Moreover,consideration of transportation time during scheduling makes it more practical and useful.The purpose of this paper is to investigate multi-objective flexible job-shop scheduling problem(MOFJSP)considering transportation time.Design/methodology/approach–A hybrid genetic algorithm(GA)approach is integrated with simulated annealing to solve the MOFJSP considering transportation time,and an external elitism memory library is employed as a knowledge library to direct GA search into the region of better performance.Findings–The performance of the proposed algorithm is tested on different MOFJSP taken from literature.Experimental results show that proposed algorithm performs better than the original GA in terms of quality of solution and distribution of the solution,especially when the number of jobs and the flexibility of the machine increase.Originality/value–Most of existing studies have not considered the transportation time during scheduling of jobs.The transportation time is significantly desired to be included in the FJSP when the time of transportation of jobs has significant impact on the completion time of jobs.Meanwhile,GA is one of primary algorithms extensively used to address MOFJSP in literature.However,to solve the MOFJSP,the original GA has a possibility to get a premature convergence and it has a slow convergence speed.To overcome these problems,a new hybrid GA is developed in this paper.

具有局部和全局QoS约束的Web服务选择

提出一种具有局部和全局QoS约束的Web服务选择算法。将基于QoS的Web服务选择问题建模为带QoS约束的多目标组合优化问题,使用局部QoS约束过滤不满足约束条件的候选服务,通过归档式多目标模拟退火(AMOSA)算法同时优化多个QoS目标函数以产生一组Pareto优化解,利用全局QoS约束筛选出满足约束条件的Pareto最优解集。实验结果证明了该算法的有效性。

Design of Water Network with Multiple Contaminants and Zero Discharge

The paper presents a procedure to design water network. First of all, water reuse system, water regeneration reuse system (including regeneration recycle) and wastewater treatment system are designed separately. But the interaction between different parts demands that each part is designed iteratively to optimize the whole water network. Therefore, on the basis of the separated design a water netvrork superstructure including reuse, regeneration and wastewater treatment is established from the system engineering point of view. And a multi-objective adaptive simulated annealing genetic algorithm is adopted to simultaneously integrate the overall water netvrork to balance the economic and environmental effects. The algorithm overcomes the defect of local optimum of simulated annealing (SA), avoids the pre-maturation of genetic algorithm (GA) and finds a set of solutions (pareto front) in acceptable computer time. Prom the pareto front, a point with minimum fresh water consumption will be extended to zero discharge as our ultimate goal.

基于模拟退火和历史存档融合的风驱动优化算法

为了解决风驱动优化算法处理多峰值问题时陷入局部最优而无法成功脱困,并且不能取得全局最优解,提出基于模拟退火和历史存档的算法融合改进策略。当风驱动优化算法陷入局部停滞状态时,采用模拟退火改进策略通过以概率作为接受新状态的方法,可以有效避免搜索陷入局部最优的问题,提高算法取得全局最优解的能力。当算法陷入长时间的周期迭代且无法进一步取得最优解时,采用历史存档改进策略变异一定百分比的种群,使算法改进后具有跳出局部最优解的能力。通过实验结果表明,基于模拟退火和历史存档的风驱动优化算法与粒子群算法、标准风驱动算法相比,全局搜索能力更强、收敛速度更快、精度更高、寻优能力更佳。

A Class of Optimization Method for Bilevel Multi-objective Decision Making Problem with the Help of Satisfactoriness

In the paper, it is discussed that the method on how to transform the multi-person bilevel multi-objective decision making problem into the equivalent generalized multi-objective decision making problem by using Kuhn-Tucker sufficient and necessary condition. In order to embody the decision maker′s hope and transform it into single-objective decision making problem with the help of ε-constraint method. Then we can obtain the global optimal solution by means of simulated annealing algorithm.

A Multi-Criteria Decision Making for the Unrelated Parallel Machines Scheduling Problem

In this paper, we propose a multi-criteria machine-schedules decision making method that can be applied to a produc-tion environment involving several unrelated parallel machines and we will focus on three objectives: minimizing makespan, total flow time, and total number of tardy jobs. The decision making method consists of three phases. In the first phase, a mathematical model of a single machine scheduling problem, of which the objective is a weighted sum of the three objectives, is constructed. Such a model will be repeatedly solved by the CPLEX in the proposed Multi-Objective Simulated Annealing (MOSA) algorithm. In the second phase, the MOSA that integrates job clustering method, job group scheduling method, and job group – machine assignment method, is employed to obtain a set of non-dominated group schedules. During this phase, CPLEX software and the bipartite weighted matching algorithm are used repeatedly as parts of the MOSA algorithm. In the last phase, the technique of data envelopment analysis is applied to determine the most preferable schedule. A practical example is then presented in order to demonstrate the applicability of the proposed decision making method.

GASA Hybird Algorithm Applied in Airline Crew Rostering System

Crew rostering system is a daily grind in the management of both corporation and enterprise. A fair and reasonable rostering method plays a very important role in the arousing worker’s enthusiasm and improving the work efficiency. This paper presents a method of building models for automatic crew rostering mode with computer and advancing the multi-objective optimum scheme. The method to build models for crew rostering system is also discussed. The question to crew rostering system model is solved by genetic algorithms and simulated annealing algorithms. Simulation results show the correctness of algorithms. The actual data of the airways have justified its reasonability and efficiency.