Static target search path planning optimization with heterogeneous agents

Disruptions in airline operations can result in infeasibilities in aircraft and passenger schedules. Airlines typically recover aircraft schedules and disruptions in passenger itineraries sequentially. However, passengers are severely affected by disruptions and recovery decisions. In this paper, we present a mathematical formulation for the integrated aircraft and passenger recovery problem that considers aircraft and passenger related costs simultaneously. Using the superimposition of aircraft and passenger itinerary networks, passengers are explicitly modeled in order to use realistic passenger related costs. In addition to the common routing recovery actions, we integrate several passenger recovery actions and cruise speed control in our solution approach. Cruise speed control is a very beneficial action for mitigating delays. On the other hand, it adds complexity to the problem due to the nonlinearity in fuel cost function. The problem is formulated as a mixed integer nonlinear programming (MINLP) model. We show that the problem can be reformulated as conic quadratic mixed integer programming (CQMIP) problem which can be solved with commercial optimization software such as IBM ILOG CPLEX. Our computational experiments have shown that we could handle several simultaneous disruptions optimally on a four-hub network of a major U.S. airline within less than a minute on the average. We conclude that proposed approach is able to find optimal tradeoff between operating and passenger-related costs in real time.     

A rolling stock circulation model for combining and splitting of passenger trains

This paper addresses the railway rolling stock circulation problem. Given the departure and arrival times as well as the expected numbers of passengers, we have to assign the rolling stock to the timetable services. We consider several objective criteria that are related to operational costs, service quality and reliability of the railway system.Our model is an extension of an existing rolling stock model for routing train units along a number of connected train lines. The extended model can also handle underway combining and splitting of trains.We illustrate our model by computational experiments based on instances of NS Reizigers, the main Dutch operator of passenger trains.     

铁路客运网络列车开行方案优化模型的列生成算法

列车开行方案的设计是铁路旅客运输组织规划中的一个重要环节。本文首先给出了一个综合考虑铁路旅客运输的经济效益和公共服务性的优化模型,以铁路旅客运输的公共效益最大化为目标,对整个铁路客运网络上不同始发-终到和不同停站方式的列车开行方案进行优化。然后提出了一个求解此模型的启发式列生成算法,该算法与标准列生成算法相比,可以减少迭代次数并缩短收敛时间。最后给出一组利用随机生成的网络和需求进行求解的算例,验证本算法可以在较短时间内求解较大规模的铁路网络列车开行方案优化问题,并能有效缩小问题规模。     

Profit maximization in an inventory system with time-varying demand,partial backordering and discrete inventory cycle

In this paper, an inventory problem where the inventory cycle must be an integer multiple of a known basic period is considered. Furthermore, the demand rate in each basic period is a power time-dependent function. Shortages are allowed but, taking necessities or interests of the customers into account, only a fixed proportion of the demand during the stock-out period is satisfied with the arrival of the next replenishment. The costs related to the management of the inventory system are the ordering cost, the purchasing cost, the holding cost, the backordering cost and the lost sale cost. The problem is to determine the best inventory policy that maximizes the profit per unit time, which is the difference between the income obtained from the sales of the product and the sum of the previous costs. The modeling of the inventory problem leads to an integer nonlinear mathematical programming problem. To solve this problem, a new and efficient algorithm to calculate the optimal inventory cycle and the economic order quantity is proposed. Numerical examples are presented to illustrate how the algorithm works to determine the best inventory policies. A sensitivity analysis of the optimal policy with respect to some parameters of the inventory system is developed. Finally, conclusions and suggestions for future research lines are given.

     

Configuration of fully replicated distributed database system over wide area networks

The cost and performance of a distributed database system (DDS) depends on data distribution and database server configuration across the network. An inappropriate allocation of data and database severs could result in a DDS which is either too costly or unacceptably slow. This paper models the optimal configuration of fully replicated DDS. The problem is formulated as an integer linear programming problem and a solution procedure based on Lagrangian relaxation and subgradient optimization is proposed. The proposed solution procedure was computationally tested under various scenarios regarding communication, processor costs, and transaction characteristics.     

A model for setting services on auxiliary bus lines under congestion

In this paper, a mathematical programming model and a heuristically derived solution is described to assist with the efficient planning of services for a set of auxiliary bus lines (a bus-bridging system) during disruptions of metro and rapid transit lines. The model can be considered static and takes into account the average flows of passengers over a given period of time (i.e., the peak morning traffic hour). Auxiliary bus services must accommodate very high demand levels, and the model presented is able to take into account the operation of a bus-bridging system under congested conditions. A general analysis of the congestion in public transportation lines is presented, and the results are applied to the design of a bus-bridging system. A nonlinear integer mathematical programming model and a suitable approximation of this model are then formulated. This approximated model can be solved by a heuristic procedure that has been shown to be computationally viable. The output of the model is as follows: (a) the number of bus units to assign to each of the candidate lines of the bus-bridging system; (b) the routes to be followed by users passengers of each of the origin–destination pairs; (c) the operational conditions of the components of the bus-bridging system, including the passenger load of each of the line segments, the degree of saturation of the bus stops relative to their bus input flows, the bus service times at bus stops and the passenger waiting times at bus stops. The model is able to take into account bounds with regard to the maximum number of passengers waiting at bus stops and the space available at bus stops for the queueing of bus units. This paper demonstrates the applicability of the model with two realistic test cases: a railway corridor in Madrid and a metro line in Barcelona.     

A fast algorithm for near cost optimal line plans

We consider the design of line plans in public transport at a minimal total cost. Both, linear and nonlinear integer programming are adequate and intuitive modeling approaches for this problem. We present a heuristic variable fixing procedure which builds on problem knowledge from both techniques. We derive and compare lower bounds from different linearizations in order to assess the quality of our solutions. The involved integer linear programs are strengthened by means of problem specific valid inequalities. Computational results with practical data from the Dutch Railways indicate that our algorithm gives excellent solutions within minutes of computation time.     

A heuristic approach to a single stage assembly problem with transportation allocation

Determining assembly scheduling and transportation allocation are two practical problems that industries face, such as the electronics and health products industries. Problems associated with assembly scheduling mainly focus on how to determine the orders’ processing sequence on the assembly line in order to minimize the waiting times before they are flown to their destinations. Problems associated with transportation allocation arise in the system of assigning processed orders to transport modes with the purpose of minimizing penalties from earliness and tardiness. To minimize overall delivery costs, businesses should decide on assembly scheduling and transportation allocation decision simultaneously. However, since simultaneously making these two decisions is not an easy task, most of the works done on them usually deal with these two problems separately. Apart from previous works, this paper establishes a mixed integer programming model that deals with these problems simultaneously. Due to the computational complexity of the problem, this paper develops a hybrid heuristic algorithm to solve this problem, and we evaluate the performance of the presented heuristic algorithm with the well-known GAMS/BARON and Lingo commercial software, which tests the heuristic algorithm on randomly-generated problems. The presented heuristic algorithm is shown to perform well compared with well-known commercial software.     

The freight consolidation and containerization problem

In today’s global free market, third-party logistics providers (3PLs) are becoming increasingly important. This paper studies a problem faced by a 3PL operating a warehouse in Shanghai, China, under contract with a major manufacturer of children’s clothing based in the United States. At the warehouse, the 3PL receives textile parcel shipments from the suppliers located in China; each shipment is destined for different retail stores located across the United Sates. These shipments must be consolidated and loaded into containers of varying sizes and costs, and then sent along shipping routes to different destination ports. An express company, such as UPS and FedEx, unloads the shipments from the containers at the destination ports and distributes them to their corresponding stores or retailers by parcel delivery. The objective is to find an allocation that minimizes the total container transportation and parcel delivery costs. We formulate the problem into an integer programming model, and also propose a memetic algorithm approach to solve the problem practically. A demonstration of a good solution to this problem was a decisive factor in the awarding of the contract to the 3PL in question.     

单向航道船舶进港次序与泊位分配协同优化

为提高单向航道离散泊位港口的服务水平,研究船舶进港次序和泊位分配的协同优化。考虑船舶进出港及泊位作业的实际约束,以计划期内所有船舶的锚地、泊位等待成本、滞期成本和偏离成本之和最小为目标,构建了一个混合整数规划模型,结合问题特征设计了引入禁忌搜索算法的和声搜索算法进行求解。算例结果给出了计划期内每艘船舶的进港次序和靠泊泊位,并通过与单独优化方案的对比和不同规模算例求解效果的分析,验证了模型和算法的有效性;分析进出港时段变动对船舶作业成本的影响,确定不同船舶抵港规模下的最佳进出港时段长度,为单向航道港口时长设置提供借鉴。     

带固定轴线成本的轴辐式网络设计问题

带固定轴线成本的轴辐式网络设计问题广泛应用于第三方物流、邮政和航空运输等领域. 现有研究主要考虑了枢纽站的节点成本, 本研究则强调合并运输的固定轴线成本. 固定轴线成本的必要性在于：轴辐式网络中的轴线运输需要借助更大型的运输工具, 因此必须支付固定成本. 建立了该问题的混合整数规划模型, 探讨了最优解特征, 并构造了求解问题的拉格朗日松驰算法, 实验显示算法具有非常好的求解效率与求解质量. 同时, 还讨论了一个重要的扩展问题：增加O-D流的绕道约束, 绕道约束常常应用于快递运输和应急物流等领域. 在局部修改原算法的基础上提供了扩展问题的求解方案.     

Incorporating inventory and routing costs in strategic location models

We consider a supply chain design problem where the decision maker needs to decide the number and locations of the distribution centers (DCs). Customers face random demand, and each DC maintains a certain amount of safety stock in order to achieve a certain service level for the customers it serves. The objective is to minimize the total cost that includes location costs and inventory costs at the DCs, and distribution costs in the supply chain. We show that this problem can be formulated as a nonlinear integer programming model, for which we propose a Lagrangian relaxation based solution algorithm. By exploring the structure of the problem, we find a low-order polynomial algorithm for the nonlinear integer programming problem that must be solved in solving the Lagrangian relaxation sub-problems. We present computational results for several instances of the problem with sizes ranging from 40 to 320 customers. Our results show the benefits of having an integrated supply chain design framework that includes location, inventory, and routing decisions in the same optimization model.     

物流服务供应链订单分配优化及其遗传算法

针对物流服务供应链订单分配问题中,物流服务集成商通常会按照所分配的订单价值向分包商收取一定比例交易费用的特点,设定交易费用为交易额的线性函数,构建了新的物流服务供应链订单分配优化混合整数规划模型,其优化目标为最小化交易费用、采购费用、短缺服务与延迟供给的物流能力数量。鉴于问题的NP-hard特性,设计了相应的遗传算法,并结合基于优先权的启发式规则避免了大量非法初始解的出现。实验算例表明所建立的模型能够反映物流服务供应链订单分配过程中的线性交易费用因素,其所设计的算法能够在可接受的时间内获得质量较高的满意解,并且对于大规模订单分配优化问题,遗传算法的求解时间与求解结果要优于LINGO软件。     

An Integer Programming Heuristic for Component Allocation in Printed Circuit Card Assembly Systems

Component allocation is an important element of process planning for printed circuit card assembly systems. The component allocation problem directly impacts the productivity and cost of a circuit card assembly system. Many companies have recognized the importance of component allocation and have started to develop a better decision process. Also, a few commercial software packages have been developed that provide environments to support process planning. However, optimization methods are not yet widely used. We demonstrate that component allocation is amenable to improvement using optimization methods. We present an integer programming heuristic for the component allocation problem and report on several case studies that have been conducted and that demonstrate its effectiveness. The heuristic is based on a mixed integer programming formulation of the component allocation problem that incorporates estimates of downstream process planning decisions.     

集中通勤接送服务的最小化成本模型及算法

本文针对集中通勤接送服务业务流程中的车次分配与调度问题,建立了以最小化成本为目标的0-1混合整数规划模型,刻画了该问题所具有的多目标抵达地、多车协作、多行程车次等特征事实,同时考虑顾客满意度,以获得不同满意度下的近似最小成本。根据问题的复杂性和大规模性,设计开发了一种基于kNN思想的类标签启发式算法求解模型,拓展了构造路径的方法。算例数值结果表明,本文所设计模型和算法是有效性、可行的。     

Capacity allocation problem with random demands for the rail container carrier

In this paper, we consider the formulation and heuristic algorithm for the capacity allocation problem with random demands in the rail container transportation. The problem is formulated as the stochastic integer programming model taking into account matches in supply and demand of rail container transportation. A heuristic algorithm for the stochastic integer programming model is proposed. The solution to the model is found by maximizing the expected total profit over the possible control decisions under the uncertainty of demands. Finally, we give numerical experiments to demonstrate the efficiency of the heuristic algorithm.     

A GRASP for simultaneously assigning and sequencing product families on flexible assembly lines

This paper introduces a new model and solution methodology for a real-world production scheduling problem arising in the electronics industry. The production environment is a high volume, just-in-time, make-to-order facility with volatile demand over many product families that are assembled on flexible lines. A distinguishing characteristic of the problem is the presence of non-traditional sequence-dependant setup costs, which complicate our ability to find high-quality solutions. The scheduling problem arose when product variety exceeded the mix that the existing lines could accommodate. A nonlinear integer programming formulation is presented for the problem of minimizing setup costs, and a greedy randomized adaptive search procedure (GRASP) is developed to find solutions. To select the GRASP parameter values, an efficient, space-filling experimental design method is used based on nearly orthogonal Latin hypercubes. The proposed methodology is tested on actual factory data and compared to a prior heuristic presented in the literature; our heuristic provides a cost savings in 7 out of the 10 cases examined, and an average improvement of 17.39 % which is shown to be highly statistically significant. This improvement is due in part to the introduction of a pre-processing step to determine preferential and non-preferential line assignment information.     

The Solution of a Railway Locomotive Scheduling Problem

A method is described for finding a minimum cost set of schedules for railway locomotives to work a given set of trains. The times at which the trains start may be fixed or variable. A heuristic method, based on a linear programming model, is described. This gives good integer solutions to the problem.     

A fuzzy shortest path with the highest reliability

This paper concentrates on a shortest path problem on a network where arc lengths (costs) are not deterministic numbers, but imprecise ones. Here, costs of the shortest path problem are fuzzy intervals with increasing membership functions, whereas the membership function of the total cost of the shortest path is a fuzzy interval with a decreasing linear membership function. By the max–min criterion suggested in [R.E. Bellman, L.A. Zade, Decision-making in a fuzzy environment, Management Science 17B (1970) 141–164], the fuzzy shortest path problem can be treated as a mixed integer nonlinear programming problem. We show that this problem can be simplified into a bi-level programming problem that is very solvable. Here, we propose an efficient algorithm, based on the parametric shortest path problem for solving the bi-level programming problem. An illustrative example is given to demonstrate our proposed algorithm.     

周期列车运行图的多目标模型及基于Job-shop的遗传算法研究

计算机编制客运专线周期列车运行图问题已成为国内外研究的热点问题之一.在充分研究国内外周期与非周期列车运行图的规划理论与方法的基础上,构建了我国客运专线周期列车运行图的多目标模型,并将之转化为具有优先级结构的单目标模型;然后结合运行图的数学本质与周期性,设计了基于Job-shop的遗传算法,弥补了国外基于PESP理论所开发的周期列车运行图的算法不足.最后结合京津客运专线实例来验证算法的有效性.