基于AGV的智能仓库系统储位分配问题研究

研究了基于自动引导机器人(AGV)的"货到人"拣选模式下的智能仓库系统补货阶段的储位分配问题.根据待拣选订单信息计算出商品之间的关联度,考虑了货架上存放的物品信息、空余储位数量、待补货物品信息,以同一货架上的各种商品之间的关联度之和最大化为目标函数,建立了补货阶段储位分配问题的整数规划模型;设计了求解模型的贪婪算法,并分析了算法复杂度.利用一个具体实例进行模拟计算,分析了贪婪算法的求解效果.进一步利用不同规模算例进行模拟计算,分析了贪婪算法的计算时间和近似比,结果显示贪婪算法可以在很短的时间内得到近似最优解,近似比不超过1.15.设计的贪婪算法可以作为智能仓库管理信息系统的核心算法.     

用于寻源导热反问题的自适应蚁群算法研究

建立以蚁群算法(ant colony optimization,ACO)为基础的二维稳态导热反问题的求解模型.模型根据边界测点的测量信息与计算所得到的测点温度进行比较,将导热反问题转化为一个优化问题.对蚁群算法进行改进,利用不同路径构造方法的自适应蚁群算法对热源强度、热源位置进行反演,得到较为精确的反演结果.结果表明,所采用的蚁群算法和针对不同反演参数的路径构造方法具有较强的稳定性,能够较好反演热源强度及热源位置.     

震后应急物流设施不确定失效情景下的LRP研究

针对震后应急物流设施不确定失效情景下的定位-路径问题,综合考虑灾区路网存在部分损毁、应急物资多方式配送与运达时间限制等因素,以应急物流设施最坏失效情景下的需求点覆盖期望最大、应急物资总配送时间最短为目标,采用情景建模方法构建多目标定位-路径问题的数学模型,并根据模型特点设计基于分层序列法的两阶段混合启发式求解算法,第一阶段设计改进贪婪算法进行应急物流设施定位分配,第二阶段采用蚁群算法优化应急物资配送路线。最后,通过算例验证了本文模型和算法的可行性与有效性。     

基于“货到人”拣选模式的储位分配问题研究

研究了“货到人”拣选模式下的储位分配问题,以订单拣选过程中搬运货架总时间最短为目标建立了整数非线性规划模型,并证明其为NP-hard问题,分别设计了求解模型的贪婪算法和单亲进化遗传算法。首先根据订单和物品的关联关系对物品进行聚类,基于聚类结果设计了求解模型的贪婪算法。然后设计了直接求解模型的单亲进化遗传算法,遗传算法中采用了0-1矩阵编码、多点基因倒位算子、单点基因突变算子和精英保留等策略,通过合理选取参数,能够很快求解出问题的近似最优解。最后利用模拟算例和一个具体实例进行计算,并对贪婪算法和遗传算法的求解时间和求解效果进行了比较分析。结果显示,对于小规模问题,两种算法均能在较短的时间内以很高的概率得到问题的全局最优解,对于中等规模的实际问题,利用两种算法得到的储位分配方案均优于企业目前采取的基于出库频率的储位分配方案,遗传算法得到的储位分配方案对应的货架搬运次数、货架搬运总时间等均优于贪婪算法。本文设计的遗传算法可以作为智能仓库管理信息系统的核心算法。     

基于信息素的改进蚁群算法及其在TSP中的应用

针对基本蚁群算法收敛速度慢、易陷于局部最优从而导致搜索停滞的缺陷,提出了一种改进蚁群算法模型.改进算法引入信息素调节系数,避免算法初期各路径上信息素出现过大差异,导致算法"早熟".通过动态调整信息素挥发,在求解速度和寻找全局最优之间寻找平衡.对旅行商问题的仿真结果表明:改进算法的求解结果和求解效率都明显优于基本蚁群算法.     

求解复杂优化问题的基于信息熵的自适应蚁群算法

针对基本蚁群算法存在收敛速度慢、易陷入局部最优、计算复杂且不易求解连续优化问题等缺陷 ,提出了一种基于信息熵的改进自适应蚁群算法 ,采用由信息熵控制的路径选择及随机扰动策略实现了算法的自适应调节 ,克服了基本蚁群算法的不足 .典型的 NP-hard问题的计算实例表明 ,该方法具有较好的收敛性、稳定性和鲁棒性 ,可用于离散及连续的组合优化问题求解中 ,其不失为求解复杂组合优化问题的一种较好的方法 .     

经过改进的求解TSP问题的蚁群算法

介绍了一种求解TSP问题的算法—改进的蚁群算法,算法通过模拟蚁群搜索食物的过程,可用于求解TSP问题,算法的主要特点是:正反馈、分布式计算、与某种启发式算法相结合.通过对传统蚁群算法的改进可以得到较好的结果.计算机仿真结果表明了该算法的有效性.     

集装箱翻箱问题的蚁群算法改进

翻箱问题属于NP难问题,基本蚁群算法在求解该问题上收敛困难且寻优能力低。因此,本文提出了一种适合于翻箱模型的改进型蚁群算法,在概率决策机制、解的重构、信息素更新机制三个方面对基本蚁群算法进行改进。最后通过与其他算法的分析比较,验证了该改进算法的可行性与有效性。     

不同风险偏好下虚拟企业风险规划研究

针对虚拟企业风险规划问题,在分析其各种风险具有随机性的特点的基础上,运用随机规划理论,分别建立风险规划的期望值模型和机会约束规划模型来描述决策者在不同风险偏好下的决策行为。针对所建立的模型,分别设计了基于蒙特卡罗模拟的粒子群优化算法、遗传算法和蚁群算法对其进行求解。仿真分析表明期望值模型较好地描述了风险中性决策者的决策行为,机会约束规划模型随着其偏好系数取值的不同描述了不同风险偏好(风险厌恶、风险中性、风险爱好)决策者的决策行为。通过对三种算法仿真结果的比较分析,表明基于蒙特卡罗模拟的粒子群优化算法在寻优能力、稳定性和收敛速度等方面优于其余两种算法,是解决此类风险规划问题的有效手段。     

大规模多设施Weber问题的改进Cooper算法

 多设施Weber问题（multi-source Weber problem,MWP）是设施选址中的重要模型之一,而Cooper算法是求解MWP最为常用的数值方法.Cooper算法包含选址步和分配步,两步交替进行直至达到局部最优解.本文对Cooper算法的选址步和分配步分别引入改进策略,提出改进Cooper算法：选址步中将Weiszfeld算法和adaptive Barzilai-Borwein （ABB）算法结合,提出收敛速度更快的ABB-Weiszfeld算法求解选址子问题;分配步中提出贪婪簇分割策略来处理退化设施,由此进一步提出具有更好性质的贪婪混合策略.数值实验表明本文提出的改进策略有效地提高了Cooper算法的计算效率,改进算法有着更好的数值表现.     

Cooperative advertising models in supply chain management: A review

This paper reviews articles on cooperative advertising, a topic which has gained substantial interest in the recent years. Thereby, we first briefly distinguish five different definitions of cooperative advertising which can be found in operations research literature. After that, we concentrate on vertical cooperative advertising, which is the most common object of investigation and is understood as a financial agreement where a manufacturer offers to pay a certain share of his retailer’s advertising expenditures. In total, we identified 58 scientific papers considering mathematical modeling of vertical cooperative advertising. These articles are then analyzed with regard to their general model setting (e.g., the underlying supply chain structure and design of the cooperative advertising program). After that, we explain the different demand and cost functions that are employed, whereupon we distinguish between static and dynamic models. The last dimension of our review is dedicated to the game-theoretic concepts which are mostly used to reflect different forms of distribution of power within the channel.     

转包且具有不同费用时间段的排序问题

本文研究制造商可以将工件转包给承包商加工的排序模型,承包商仅有一台机器,转包费用由分配给转包工件的不同时间段费用确定.本文分别研究制造商有一台单机及两台自由作业机器环境情形,需要确定被转包工件集及全部工件的加工顺序,使得工件最大完工时间与转包费用和最小.本文利用归约方法对制造商每个机器环境,证明问题NP困难性,并提出动态规划算法.     

不同推介模式下网络零售商广告策略研究

研究了不同推介模式下网络零售商的广告策略问题,通过Nash静态博弈方法求解无推介、单向推介以及双向推介三种模式下零售商均衡零售价格、广告努力水平及利润。研究发现:无推介情况下,两零售商均衡广告策略为同时投入广告。单向推介情况下,若利润分享比例较小,则两零售商同时投入广告;若利润分享比例较大,则随着广告成本系数的增大,两零售商均衡广告策略从仅有被推介零售商投入广告转变为同时投入广告。双向推介情况下,若广告成本系数较小,则两家零售商都不投入广告;若广告成本系数较大,则两家零售商同时投入广告。此外,还分析了零售商均衡广告策略下的推介动力问题。     

A linear programming approach for determining optimal advertising policy

^** Email: wkc{at}maths.hku.hk In this paper, we propose a new advertising model which cancapture the advertising wear out phenomenon. The objective hereis to maximize overall sales. We show how to derive the optimalpulsation advertising strategy. The optimization problem canbe formulated as a linear programming problem. Closed-form optimalsolution can also be obtained under some conditions. We presentnumerical examples to illustrate the proposed model and applythe model to practical sales data.     

Advertising and production of a seasonal good for a heterogeneous market

We bring some concepts from market segmentation, which is a fundamental topic of marketing theory and practice, into the statement of an advertising and production problem for a seasonal product with Nerlove–Arrow’s linear goodwill dynamics. We consider two kinds of situations. In the first one, the advertising process can reach selectively each segment. In the second one, one advertising medium is available which has a known effectiveness spectrum for a non-trivial set of segments. In both cases we solve, using the Pontryagin’s Maximum Principle conditions, the optimal control problems in which goodwill productivity of advertising is concave and good production cost is convex. Two special cases are discussed in detail.     

Coordinating advertising and pricing in a manufacturer–retailer channel

Cooperative advertising is a practice that a manufacturer pays retailers a portion of the local advertising cost in order to induce sales. Cooperative advertising plays a significant role in marketing programs of channel members. Nevertheless, most studies to date on cooperative advertising have assumed that the market demand is only influenced by advertising expenditures but not by retail price. This paper addresses channel coordination by seeking optimal cooperative advertising strategies and equilibrium pricing in a two-member distribution channel. We establish and compare two models: a non-cooperative, leader–follower game and a cooperative game. We develop propositions and insights from the comparison of these models. The cooperative model achieves better coordination by generating higher channel-wide profits than the non-cooperative model with these features: (a) the retailer price is lower to consumers; and (b) the advertising efforts are higher for all channel members. We identify the feasible solutions to a bargaining problem where the channel members can determine how to divide the extra profits.     

Advertising decisions for a segmented market

We consider the problem of choosing the levels of a set of advertising media in order to maximize the firm profit when the market is heterogeneous. Advertising efforts affect the demand of the different segments variably and we assume that the advertising effects on demand over time are mediated by a vector goodwill variable. A first general advertising decision problem is stated and solved in the non-linear programming framework. A preference index is then obtained for the medium selection problem when each segment demand function is linear in goodwill and each medium advertising cost function is quadratic in its level. Finally the theoretical case of disjoint advertising media is discussed.     

Advertising for a new product introduction: A stochastic approach

We formulate a stochastic extension of the Nerlove and Arrow’s advertising model in order to analyze the problem of a new product introduction. The main idea is to introduce some uncertainty aspects in connection both with the advertising action and the goodwill decay, in order to represent the random consequences of the advertising messages and of the word-of-mouth publicity, respectively. The model is stated in terms of the stochastic optimal control theory and a general study is attempted using the stochastic Maximum Principle. Closed form solutions are obtained under linear quadratic assumptions for the cost and the reward functions. Such optimal policies suggest that the decision-maker considers both the above mentioned phenomena as opportunities to increase her/his final reward. After stating some general features of the optimal solutions, we analyze in detail three extreme cases, namely the deterministic model and the stochastic models with either the word-of-mouth effect only, or the lure/repulsion effect only. The optimal policies provide us with some insight on the general effects of the advertising action. Supported by MIUR and University of Padua.     

On a cooperative advertising model for a supply chain with one manufacturer and one retailer

, , ,  and  recently studied a game-theoretic model for cooperative advertising in a supply chain consisting of one manufacturer and one retailer. However, the sales-volume (demand) function considered in this model can become negative for some values of the decision variables, and in fact, this does happen for the proposed Stackelberg and Nash equilibrium solutions. Yue et al. (2006) acknowledge the negativity problem and suggest two constraints to fix it; however, they do not incorporate these constraints into their mathematical analysis. In this paper, we show that the results obtained by analyzing the advertising model under the constraints suggested by Yue et al. can differ significantly from those obtained in the previous papers.