基于模拟退火的混合差分进化算法及其在联合补货-配送集成优化中的应用

针对差分进化 (Differential evolution, DE)算法搜索效率较低和容易陷入局部最优的缺点,设计了基于SA的混合差分进化算法(SA-based Hybrid DE, SAHDE),以提高DE算法的全局寻优能力。该算法采用自适应变异算子和交叉算子,并结合模拟退火(Simulated Annealing, SA)算法的Metropolis 准则。首先通过标准测试函数对改进的SAHDE进行性能测试,证明了该算法比DE、自适应混合DE (Adaptive Hybrid DE, AHDE)和遗传算法(Genetic Algorithm, GA)更有效。进而将该算法运用到联合补货-配送集成优化(典型NP-hard)问题的求解中,通过大规模的算例分析,证实SAHDE在解决联合补货-配送优化问题比DE、AHDE和GA更有效。     

差分进化算法在模糊多资源约束联合补货模型中的应用

本文用三角模糊数表示不确定的资金约束,用梯形模糊数表示不确定的存储空间约束,构建了模糊规划联合补货模型,目标函数为最小化订货成本、库存持有成本和运输成本,决策变量为基本补充周期和每种产品的补充周期。通过对变异算子与选择操作进行变化,设计了改进的差分进化算法对模型进行求解,并通过实例证实了模型与算法的科学合理性。     

基于鲁棒优化的供应链联合补货策略

供应链管理中的客户需求不确定性会导致需求信息偏差逐级放大的"牛鞭效应",但目前常用的供应链管理策略为供应商管理库存,没有考虑需求不确定的影响。为此,在单个供应商、多个零售商需求不确定的情况下,结合鲁棒优化法提出一种联合补货策略进行库存管理。构建一个非线性混合整数规划模型以计算两级供应链的总成本,通过总成本的变化来反映供应链系统的性能,采用鲁棒优化法求解供应链系统的最小总成本,并使用外部和内部两层迭代算法获得供应商和零售商的补货周期及补货数目。实验结果表明,与传统的供应链策略ERI和AR相比,该策略可有效降低供应链系统的总成本。     

航班滑行路径规划和停机位分配联合优化

针对机场的航班滑行路径规划和停机位分配的联合优化调度问题,构建基于冲突回避的滑行道与停机位联合调度模型,并提出改进的自适应差分进化算法求解问题.以最小化航班的滑行时间和旅客转机的行走时间为优化目标,建立非线性混合整数规划联合调度模型.设计考虑了滑行冲突的路径规划算法,完成航班的滑行路径分配,并通过自适应动态调整差分进化...     

改进差分进化算法在可靠性冗余分配问题中的应用

提出一种改进差分进化算法(IDE),以解决系统可靠性冗余分配问题.在罚函数法的基础上,对约束处理方法进行改进. 新约束处理方法在搜索过程中不需要在每一步都计算惩罚函数值,加快了寻优速度.具有良好的通用性,可以引入到其他智能优化算法中.将改进的算法用于求解4类典型的系统可靠性冗余分配问题,实验结果表明了所提出的改进算法具有很好的寻优精度和收敛速度.     

需求依赖于变质时间的非立即变质品联合补货优化

基于产品非立即变质的特征,构建需求依赖于变质时间的多品种联合补货库存模型,目标是使单位时间内的总成本最小.由于联合补货问题是NP难题,且考虑变质使问题变得更加复杂,针对这一难点,采用一种截断泰勒级数的方法对目标函数的指数项进行简化,提出一种基于定界的启发式算法求解模型,并通过数值案例验证算法的有效性和实用性.最后对主要参数的敏感性进行分析,为非立即变质品的零售商在实施联合补货时提供有益的管理建议.     

基于VMI&TPL 供应链的单供应商两制造商补货策略

供应商与第三方物流合作实施供应商管理库存,采用共同补货转运策略( , , )为地理位置相邻的两制造商供应零部件,并通过两制造商初期库存之差 来分配库存,允许制造商相互转运.首先分析随机需求下两制造商总库存的变化情况,求解共同补货转运策略( , , )下的库存、缺货数量和转运数量及其随各控制值变化的性质;然后,通过算例将提出的共同补货转运策略与供应商单独为各制造商补货的策略进行比较;最后,考察不同参数对共同补货转运策略效果的影响.     

一种基于差分进化和免疫克隆选择算法的混合优化方法

为了提高免疫克隆选择算法的搜索能力,提出了一种基于差分进化和免疫克隆选择算法的混合优化方法。该方法采用差分进化提高免疫克隆选择算法的抗体亲和度,并对该算法的收敛性进行了分析。为了测试该算法的有效性,将该算法应用于函数优化问题中。仿真结果表明,该方法具有更高的收敛速度和收敛精度。     

基于精英多策略的货位分配优化方法

针对智能立体仓库货位分配问题,提出一种基于精英多策略的货位分配优化方法。首先,考虑货物重量、出入库频率和出入库时间等因素,以货架重心低、出入库频率高、货物离出入库口近等为原则建立货位分配优化模型;然后,提出一种精英多策略差分进化算法,通过提取部分精英个体的信息指导变异,并根据精英个体的拥挤度变化对不同的阶段使用不同的策略,从而产生高质量的解,同时加快算法的收敛速度;最后,通过10个经典测试函数验证了所提算法的有效性,并基于该方法对某智能制造企业的成品库进行了优化,得到了满意的货位分配方案。     

采用改进差分进化算法的防空导弹火力分配

针对目前采用差分进化算法求解防空导弹火力分配问题需要人工确定惩罚系数,从而增大模型建立复杂性的问题。采用一种处理约束条件的改进差分进化算法求解该问题;该方法在解是否可行的基础上采用三种选择准则用于搜索可行解区域,并增加了一个整数变量用于保存整数解。实例结果表明,与采用惩罚函数的方法相比,该方法在同等的求解效率下,能够获得较好的最优值。由于该方法不用人为确定惩罚系数,减少了模型的确定难度和时间,可用于求解火力分配问题。     

The joint replenishment problem involving multiple suppliers offering quantity discounts

In this article, we deal with the problem of determining the economic operating policy when a number of items are to be procured from a number of suppliers offering different quantity discounts schedules. In such inventory problems, a fixed cost is incurred with each replenishment order, independent of the suppliers as well as the items involved in the order. Further, the item involves a minor fixed cost. In such a system, it includes the supplier selection problem when considering the quantity discounts as well as the general joint replenishment problem. We develop a hybrid genetic algorithm for this NP-hard decision problem and extend it to systems with resource restrictions.     

逆向物流供应链最佳供应商选择及订单量分配*

为解决逆向物流供应链中,供应商选择、订单量分配和提货点位置等不确定问题,建立了一个新的模糊多目标数学模型来确定最佳供应商选择、供应量及提货点位置,为避免在解决多目标模型时人为主观赋权,运用基于模糊目标规划的蒙特卡罗仿真模型来求解帕累托(pareto)理想解,采用遗传算法进行求解,并给出了相应优化方案,在此基础上研究讨论了不同权重分配下结果的优劣性及供应商选择风险,最后,针对不同权重分配,比较了遗传算法和Gurobi求解,实验表明,对于该问题模型遗传算法在解的优劣性上优于Gurobi。     

Supplier selection and order allocation with green criteria: An MCDM and multi-objective optimization approach

This research provides a decision-making tool to solve a multi-period green supplier selection and order allocation problem. The tool contains three integrated components. First, fuzzy TOPSIS (technique for order of preference by similarity to ideal solution) is used to assign two preference weights to each potential supplier according to two sets of criteria taken separately: traditional and green. Second, top management uses an analytic hierarchy process (AHP) to assign a global importance weight to each of the two sets of criteria based on the strategy of the company and independently of the potential suppliers. Third, for each supplier, the preference weight obtained from fuzzy TOPSIS regarding the traditional criteria is then multiplied by the global importance weight of the set of traditional criteria. The same is done for the green criteria. The two combined preference weights obtained for each supplier are then used in addition to total cost to select the best suppliers and to allocate orders using multi-period bi-objective and multi-objective optimization. The mathematical models are solved using the weighted comprehensive criterion method and the branch-and-cut algorithm. The approach of this research has a major advantage: it provides top management with flexibility in giving more or less importance weight to green or traditional criteria regardless of the number of criteria in each category through the use of AHP, which reduces the effect of the number of criteria on the preference weight of the suppliers. Contrary to the case in which each supplier is evaluated on the basis of all criteria at the same time, the proposed approach would not necessarily result in a supplier with poor green performance being ranked among the best for a situation in which the number of green criteria is smaller than the number of traditional criteria. In this case, the final ranking would mainly depend on the global weight of the green criteria set given by the top management using AHP as well as on the ranking of the supplier in terms of green criteria obtained from fuzzy TOPSIS. Extensive numerical experiments are conducted in which the bi-objective and multi-objective models are compared and the effect of the separation between green and traditional criteria is investigated. The results show that the two optimization approaches provide very close solutions, which leads to a preference for the bi-objective approach because of its lower computation time. Moreover, the results confirm the flexibility of the proposed approach and show that combining all criteria in one set is a special case. Finally, a time study is performed, which shows that the bi-objective integer linear programming model has a polynomial computation time.     

A supplier selection and order allocation problem with stochastic demands

We consider a system comprising a retailer and a set of candidate suppliers that operates within a finite planning horizon of multiple periods. The retailer replenishes its inventory from the suppliers and satisfies stochastic customer demands. At the beginning of each period, the retailer makes decisions on the replenishment quantity, supplier selection and order allocation among the selected suppliers. An optimisation problem is formulated to minimise the total expected system cost, which includes an outer level stochastic dynamic program for the optimal replenishment quantity and an inner level integer program for supplier selection and order allocation with a given replenishment quantity. For the inner level subproblem, we develop a polynomial algorithm to obtain optimal decisions. For the outer level subproblem, we propose an efficient heuristic for the system with integer-valued inventory, based on the structural properties of the system with real-valued inventory. We investigate the efficiency of the proposed solution approach, as well as the impact of parameters on the optimal replenishment decision with numerical experiments.     

Decision-making for the best selection of suppliers by using minor ANP

There are many studies on the application examples and the definitions of the super-matrix in the Analytic Network Process (ANP). The ANP applies network structures with the alternatives and the criteria to clarify a complex decision-making. It is important to obtain the consent from both the criteria and the alternatives because the evaluation is relative and reciprocal. In addition, there are very few studies on the interaction between the criteria and the alternatives. There is no realistic interpretation of the maximum eigenvalue in the ANP and the negative elements of the criteria matrix. Based on our study, the evaluation with only the alternatives consists of the missing values or the non-square matrix, it is not easy to make. The ANP that contains these irregular alternatives is defined as ??Minor ANP??. Then, we describe these methods of making the priority of only the alternative??s matrix values by using the ANP. This study has proven that the missing values in the alternative matrix can be replaced by zeros such that the criteria matrix can be derived without using the inverse alternative matrix. Then, the priority of alternative selection can be obtained through the calculation of eigenvector. An empirical case of the best selection of supplier illustrates our proposed methods for applying to the manufacturing industry. The result reveals the methods can be well adopted in the real world.     

随机时变需求下有阶段和总量约束的多源采购优化

针对有阶段采购量区间约束及最小总量要求的多供应商多阶段订单分配问题,以采购方采购、库存、缺货成本总和与期末库存残值之差的期望值最小为目标,建立随机非线性规划模型.考虑采购量约束及多供应商情形,扩展报童模型得出单阶段最优采购策略.以此为基础建立多阶段启发式算法,通过仿真分析各参数对最优采购策略及其总成本的影响.将最小总量承诺契约扩展到时变需求和多供应商情形,可为采购方降低成本.     

Cooperation and profit allocation in two-echelon logistics joint distribution network optimization

Collaborative two-echelon logistics joint distribution network can be organized through a negotiation process via logistics service providers or participants existing in the logistics system, which can effectively reduce the crisscross transportation phenomenon and improve the efficiency of the urban freight transportation system. This study establishes a linear optimization model to minimize the total cost of two-echelon logistics joint distribution network. An improved ant colony optimization algorithm integrated with genetic algorithm is presented to serve customer clustering units and resolve the model formulation by assigning logistics facilities. A two-dimensional colony encoding method is adopted to generate the initial ant colonies. Improved ant colony optimization combines the merits of ant colony optimization algorithm and genetic algorithm with both global and local search capabilities. Finally, an improved Shapley value model based on cooperative game theory and a cooperative mechanism strategy are presented to obtain the optimal profit allocation scheme and sequential coalitions respectively in two-echelon logistics joint distribution network. An empirical study in Guiyang City, China, reveals that the improved ant colony optimization algorithm is superior to the other three methods in terms of the total cost. The improved Shapley value model and monotonic path selection strategy are applied to calculate the best sequential coalition selection strategy. The proposed cooperation and profit allocation approaches provide an effective paradigm for logistics companies to share benefit, achieve win–win situations through the horizontal cooperation, and improve the negotiation power for logistics network optimization.     

Beer game order policy optimization under changing customer demand

The present work analyses the optimal Beer Game order policy when customers demand increases. The optimal policy is found by means of a Genetic Algorithms (GAs) technique. GAs are specially suited for this problem because of the high dimension of the search space, and because the objective function i.e. the global score of the chain, has many local minima. Our results show that the best performance of the chain is obtained when the sectors have different order policies. The advantage increases with the increasing change in the customer demand.     

单船岸桥分配与调度集成优化模型

针对集装箱码头泊位确定条件下的单船岸桥(QC)分配和调度问题,建立了线性规划模型.模型以船舶在泊作业时间最短为目标,考虑多岸桥作业过程中的干扰等待时间与岸桥间的作业量均衡,并设计了嵌入解空间切割策略的改进蚁群优化(IACO)算法进行模型求解.实验结果表明:与可用岸桥全部投放使用的方法相比,所提模型与算法求得结果平均能够节省31.86%的岸桥资源;IACO算法与Lingo求得的结果相比,船舶在泊作业时间的平均偏差仅为5.23%,但CPU处理时间平均降低了78.7%,表明了所提模型与算法的可行性和有效性.