An efficient computational approach for railway booking problems

This paper deals with the determination of seat allocations for a rail booking system. It is assumed that demand for each trip in the network can be divided into two segments, namely a full fare segment and a discounted fare segment. A constrained nonlinear integer programming model is formulated to deal with this problem. The purpose of this paper is to develop an efficient heuristic approach to develop the booking limits for all ticket types in the railway network. The solutions obtained by the heuristic approach are compared with those found by the Lingo software and the DICOPT solver. Numerical results show that the proposed heuristic approach only require a small number of CPU time to obtain superior solutions.     

Variable neighborhood search for minimum cost berth allocation

The berth allocation problem is to allocate space along the quayside to incoming ships at a container terminal in order to minimize some objective function. We consider minimization of total costs for waiting and handling as well as earliness or tardiness of completion, for all ships. We assume ships can arrive at any given time, i.e., before or after the berths become available. The resulting problem, which subsumes several previous ones, is expressed as a linear mixed 0–1 program. As it turns out to be too time-consuming for exact solution of instances of realistic size, a Variable Neighborhood Search (VNS) heuristic is proposed, and compared with Multi-Start (MS), a Genetic Search algorithm (GA) and a Memetic Search algorithm (MA). VNS provides optimal solutions for all instances solved to optimality in a previous paper of the first two authors and outperforms MS, MA and GA on large instances.     

一类变质性物品在考虑信用期时的补货策略

随着人们物质生活的不断丰富,库存控制系统所处的环境变得既复杂又多变,光靠规模化的经营已经不能满足顾客的需要,经典的EOQ模型的订购批量解也与实际的最优值之间具有很大的差距.由次,本文修改了EOQ模型中的一些假设,在考虑信用期的情况下,研究了需求率为常数、物品存贮寿命为两参数维布尔分布的单一变质性物品在有限计划期内的补货策略,证明了最优补货策略的存在性及惟一性,同时给出了求解最优补货策略的算例,得到了应用更为广泛的结论.     

在预报更新的环境下具有两种配送方式的供应链模型

本文研究在预报更新环境下具有快、慢两种配送方式和需求预报更新的库存系统，为了得到更多关于费用参数和预报改进对最优定货量以及最优的平均费用的影响，我们考虑两个周期的情形．以动态规划为工具我们得到了系统的最优策略．对于需求预报服从均匀分布情形，本文得到了最优定货量和最优的平均总费用的精确表达式．我们通过一些数值例子来说明库存费用、罚金、需求的预报改进和预报误差对最优定货量和最优的     

博弈联盟模糊收益的分配

提出了联盟模糊收益合理分配的一种新方法.首先,在模糊收益α截集上定义了α合理分配集,分析了该分配集与模糊收益Shapley值的关系.接着,给出了模糊收益的α合理Shapley分配函数,对其性质进行了讨论.然后,构造了模糊合理Shapley分配,证明其连续性,得到了联盟模糊收益与模糊合理Shapley分配具有包含关系的结论.     

On the tradeoff between optimal order-base-stock levels and demand lead-times

We investigate the tradeoff between finished-goods inventory and advance demand information for a model of a single-stage make-to-stock supplier who uses an order-base-stock replenishment policy to meet customer orders that arrive a fixed demand lead-time in advance of their due-dates. We show that if the replenishment orders arrive in the order that they are placed, then the tradeoff between the optimal order-base-stock level and the demand lead-time is “exhaustive”, in the sense that the optimal order-base-stock level drops all the way to zero if the demand lead-time is sufficiently long. We then provide a sufficient condition under which this tradeoff is linear. We verify that this condition is satisfied for the case where the supply process is modeled as an M/M/1 queue. We also show that the tradeoff between the optimal order-base-stock level and the demand lead-time is linear for the case where the supply process is modeled as an M/D/1 queue. More specifically, for this case, we show that the optimal order-base-stock level decreases by one unit if the demand lead-time increases by an amount equal to the supplier’s constant processing time. Finally, we show that the tradeoff between the optimal order-base-stock level and the demand lead-time is exhaustive but not linear in the case where the supply process is modeled as an M/D/∞ queue. We illustrate these results with a numerical example.     

Solving multistage asset investment problems by the sample average approximation method

The vast size of real world stochastic programming instances requires sampling to make them practically solvable. In this paper we extend the understanding of how sampling affects the solution quality of multistage stochastic programming problems. We present a new heuristic for determining good feasible solutions for a multistage decision problem. For power and log-utility functions we address the question of how tree structures, number of stages, number of outcomes and number of assets affect the solution quality. We also present a new method for evaluating the quality of first stage decisions.     

基于可控提前期和服务水平约束的(Q,r)库存问题

本文研究了存在顾客流失情况下，基于可控提前期和服务水平约束的连续盘点（Q，r）库存管理同题。当市场需求分布未知时采用MiniMax方法建立年总成本函数表达式，修正以往研究中对安全系数取值范围错误判断，推导出合理的安全系数取值范围，并给出最优解的计算方法。     

A multi-objective wholesaler–retailers inventory-distribution model with controllable lead-time based on probabilistic fuzzy set and triangular fuzzy number

This paper develops a single wholesaler and multi retailers mixture inventory distribution model for a single item involving controllable lead-time with backorder and lost sales. The retailers purchase their items from the wholesaler in lots at some intervals throughout the year to meet the customers’ demand. Not to loose the demands, the retailers offer a price discount to the customers on the stock-out items. Here, it is assumed that the lead-time demands of retailers are uncertain in both stochastic and fuzzy sense, i.e., these are simultaneously random and imprecise. To implement this behavior of the lead-time demands, at first, these demands are assumed to be random, say following a normal distribution. With these random demands, the expected total cost for each retailer is obtained. Now, the mean lead-time demands (which are crisp ones) of the retailers are fuzzified. This fuzzy nature of the lead-time demands implies that the annual average demands of the retailers must be fuzzy numbers, suppose these are triangular fuzzy numbers. Using signed distance technique for defuzzification, the estimate of total costs for each retailer is derived. Therefore, the problem is reduced to optimize the crisp annual costs of wholesaler and retailers separately. The multi-objective model is solved using Global Criteria method. Numerical illustrations have been made with the help of an example taking two retailers into consideration. Mathematical analyses have been made for global pareto-optimal solutions of the multi-objective optimization problem. Sensitivity analyses have been made on backorder ratio and pareto-optimal solutions for wholesaler and different retailers are compared graphically.     

混沌量子克隆算法求解认知无线网络频谱分配问题

对认知无线网络中的频谱进行有效分配是实现动态频谱接入的关键技术.考虑3次用户对频谱的需求和分配的公平性,给出了频谱分配的数学模型,并将其转换为以最大化网络收益为目标的带约束优化问题,进而提出一种采用混沌量子克隆优化求解的认知无线网络频谱分配算法, 并证明了该算法以概率1收敛.最后,通过仿真实验比较了本文算法与颜色敏感图着色算法、基于遗传算法的频谱分配、基于量子遗传算法的频谱分配的性能.结果表明:本文算法性能较优, 能更好地实现网络收益最大化. 关键词： 混沌量子克隆算法 认知无线网络 频谱分配