Cyclic and non-cyclic crew rostering problems in public bus transit

The crew rostering problem arises in public transport bus companies, and addresses the task of assigning a given set of anonymous duties and some other activities, such as standbys and days off, to drivers or groups of drivers, without violating any complex labor union rules. In addition, the preferences of drivers are considered during the assignment. The plan generated for each driver/group of drivers is called a roster. Optimal rosters are characterized by maximum satisfaction of drivers and minimal operational costs. To generate a personalized roster for each driver/group of drivers, the problem is formulated as a multi-commodity network flow problem in this paper. In each network layer, a roster is generated for each driver or driver group. The network model is very flexible and can accommodate a variety of constraints. In addition, with a minor modification, the network can formulate the cyclic and non-cyclic crew rostering problems. To the best of our knowledge, this is the first publication which solves both problems with one model. The main goal of this paper is to develop a mixed-integer mathematical optimization network model for both problems with sequential and integrated approaches and to solve this model using commercial solvers. Both problems are usually solved with the sequential approach. Therefore, another contribution of this paper is comparing the sequential approach with the integrated one. Our experiments on real-world instances show that the integrated approach outperforms the sequential one in terms of solution quality.     

Multi-objective analysis of an integrated supply chain scheduling problem

We study the problem of minimising the total weighted tardiness and total distribution costs in an integrated production and distribution environment. Orders are received by a manufacturer, processed on a single production line, and delivered to customers by capacitated vehicles. Each order (job) is associated with a customer, weight (priority), processing time, due time, and size (volume or storage space required in the transportation unit). A mathematical model is presented in which a number of weighted linear combinations of the objectives are used to aggregate both objectives into a single objective. Because even the single objective problem is NP-hard, different heuristics based on a genetic algorithm (GA) are developed to further approximate a Pareto-optimal set of solutions for our multi-objective problem.     

Multi-objective resource assignment problem in a product-driven supply chain using a Taguchi-based DNA algorithm

This paper conceptualises the integration of tangible and intangible factors into the design consideration of a resource assignment problem for a product-driven supply chain. The problem is formulated mathematically as a multi-objective optimisation model to maximise the broad objectives of profit, ahead of time of delivery, quality, and volume flexibility. Product characteristics are associated with the design requirements of a supply chain. Different types of resources are considered, each differing in its characteristics, thereby providing various alternatives during the design process. The aim is to design integrated supply chains that maximise the weighted sum of the objectives, the weights being decided by the desired product characteristics. The problem is solved through the proposed Taguchi-based DNA algorithm that draws its traits from random search optimisation and the statistical design of experiments. In order to minimise the effect of the causes of variations, the fundamental Taguchi method is integrated with the DNA-metaheuristic. The suggested methodology exhibits the global exploration capability to exploit the optimal or near-optimal DNA strands with a faster convergence rate. In order to authenticate the performance of the proposed solution methodology, a set of ten problem instances are considered and the results obtained are compared with that of the basic DNA, particle swarm optimisation (PSO) and its variant (PSO — time varying acceleration coefficients). The results demonstrate the benefits of the proposed algorithm for solving this type of problem.     

Solving vehicle assignment problems by process-network synthesis to minimize cost and environmental impact of transportation

A method and software are proposed for optimal assignment of vehicles to transportation tasks in terms of total cost and emission. The assignment problem is transformed into a process-network synthesis problem that can be algorithmically handled by the P-graph framework. In the proposed method, each task is given by a set of attributes to be taken account in the assignment; this is also the case for each vehicle. The overall mileage is calculated as the sum of the lengths of all the routes to be travelled during, before, after, and between the tasks (Desaulniers et al. 1998; Baita et al. 2000). Cost and emission are assigned to the mileages of each vehicle type. In addition to the globally optimal solution of the assignment problem, the P-graph framework provides the n-best suboptimal solutions that can be ranked according to multiple criteria. The viability of the proposed method is illustrated by an example.     

A coordinated algorithm for integrated production scheduling and vehicle routing problem

In this paper, the integrated production scheduling and vehicle routing problem is considered for a Make-to-Order manufacturer, who has a single machine for production and limited vehicles with capacity constraints for transportation. The objective is to determine production scheduling and vehicle routing, which are two interacted decisions, to minimise the maximum order delivery time. A property on optimal production sequence is proposed first, based on which backward and forward batching methods are developed and are embedded into a proposed genetic algorithm. The proposed genetic algorithm is capable of providing high-quality solutions by determining the two decisions simultaneously. For comparison purpose, a two-stage algorithm is developed, which decomposes the overall problem into two successively solved sub-problems. The experiments show that the proposed genetic algorithm can provide higher quality solutions than the proposed two-stage algorithm and two published algorithms studying related problems.     

A double-layered optimisation approach for the integrated due date assignment and scheduling problem

As the market competition becomes fiercer, contemporary make-to-order firms are confronted with both due date quotation and production scheduling problems at the same time. On the one hand, in order to attract customers, the firm needs to quote a short lead time; on the other hand, once a due date has been promised, the firm must spare no effort to deliver the goods no later than this date. If due date assignment and shop scheduling are processed separately by two systems, the overall performance is unlikely to be satisfactory because the two tasks are actually interrelated (e.g. a tighter due date setting will increase the chances of tardiness despite its appeal for the incoming customer). Therefore, we consider the problem by integrating due date assignment and shop scheduling into one optimisation model. A double-layered heuristic optimisation algorithm is presented for solving this problem. In the upper-layer genetic algorithm which performs coarse-granularity optimisation, Bayesian networks are used to learn the distribution of optimal due date values. As the second-layer algorithm, a parameter perturbation method is applied for a finer-granularity neighbourhood search. Computational experiments prove the efficacy and efficiency of the proposed algorithm.     

铝和轨道交通运输

叙述了铝在轨道列车应用中的特点、应用开发的概况和前景。     

Models and algorithms for solving combined vehicle and crew scheduling problems with rest constraints : an application to road feeder service planning in air cargo transportation

We present a mathematical model and two algorithms for solving a complex combined vehicle and crew scheduling problem. The problem arises in the area of road feeder service (RFS) for air cargo transportation where cargo airlines engage specifically equipped RFS-carriers to serve so-called lines, i.e. regular weekly patterns of trips starting and ending at the central hub, respectively. The complexity of the problem stems from the time windows, the rest regulations for drivers and the highly heterogenous requirements with respect to the fleet. The model can be applied to different planning scenarios at the RFS-carrier as well as the airline. The model and method has been incorporated into a decision support system called block.buster where sequences of single trips are combined to feasible blocks starting and ending at the hub and then combined to feasible vehicle round trips.     

一种返回舱座椅水平缓冲杆设计

在对典型"联盟式"返回舱座椅局部改进的基础上,建立了能够吸收水平着陆冲击能量的水平缓冲杆支撑座椅模型。基于胀环式缓冲杆的缓冲原理和工作特性,定义了典型、极限水平着陆工况和水平缓冲杆设计要求;应用三部分设计方案和优化选择程序,分别设计了各段胀环长度相等和不相等的水平缓冲杆。座椅实例设计结果表明,综合了着陆工况、缓冲原理、工作特性和优化效率各个影响因素的设计方法,具有统一、全面和高效的特点;优化的水平缓冲杆支撑座椅,实现了同时缓冲头盆向和胸背向过载、适应更宽着陆工况的目的。     

Branching strategies to improve regularity of crew schedules in ex-urban public transit

We discuss timetables in ex-urban bus traffic that consist of many trips serviced every day together with some exceptions that do not repeat daily. Traditional optimization methods for vehicle and crew scheduling in such cases usually produce schedules that contain irregularities which are not desirable especially from the point of view of the bus drivers. We propose a solution method which improves regularity while partially integrating the vehicle and crew scheduling problems. The approach includes two phases: first we solve the LP relaxation of a set covering formulation, using column generation together with Lagrangean relaxation techniques. In a second phase, we generate integer solutions using a new combination of local branching and various versions of follow-on branching. Numerical tests with artificial and real instances show that regularity can be improved significantly with no or just a minor increase of costs.     

Cash transportation vehicle routing and scheduling under stochastic travel times

Stochastic disturbances occurring in real-world operations could have a significant influence on the planned routing and scheduling results of cash transportation vehicles. In this study, a time–space network flow technique is utilized to construct a cash transportation vehicle routing and scheduling model incorporating stochastic travel times. In addition, to help security carriers to formulate more flexible routes and schedules, a concept of the similarity of time and space for vehicle routing and scheduling is incorporated into the model. The test results show that the model could be useful for security carriers in actual practice.     

Fleet sizing and vehicle routing for container transportation in a static environment

Busan is one of the busiest seaports in the world where millions of containers are handled every year. The space of the container terminal at the port is so limited that several small container yards are scattered in the city. Containers are frequently transported between the container terminal and container yards, which may cause tremendous traffic problems. The competitiveness of the container terminal may seriously be aggravated due to the increase in logistics costs. Thus, there exist growing needs for developing an efficient fleet management tool to resolve this situation. This paper proposes a new fleet management procedure based on a heuristic tabu search algorithm in a container transportation system. The proposed procedure is aimed at simultaneously finding the minimum fleet size required and travel route for each vehicle while satisfying all the transportation requirements within the planning horizon. The transportation system under consideration is static in that all the transportation requirements are predetermined at the beginning of the planning horizon. The proposed procedure consists of two phases: In phase one, an optimization model is constructed to obtain a fleet planning with minimum vehicle travel time and to provide a lower bound on the fleet size. In phase two, a tabu search based procedure is presented to construct a vehicle routing with the least number of vehicles. The performance of the procedure is evaluated and compared with two existing methods through computational experiments.Correspondence to: Pyung Hoi KooThis work was supported by Korea Research Foundation Grant. KRF-2001-003-E00080.     

Heuristics for integrated job assignment and scheduling in the multi-plant remanufacturing system

We consider a multi-plant remanufacturing system where decisions have to be made on the choice of plant to perform the remanufacturing and the remanufacturing options. Each plant is in different geographical locations and differs in technological capability, labour cost, distance from customers, taxes and duties. There are three options of remanufacture: replacement, repair and recondition. Furthermore, the probability that each remanufacture job needs to be reworked depends on the remanufacturing option selected. We show the interdependencies among the plant selection, remanufacturing option and job scheduling when subject to resource constraints, which motivate the integrated solution proposed in this paper. The solution method is composed of the linear physical programming and the multi-level encoding genetic algorithm (GA). By performing a case study, we illustrate the use of the model and we present the resulting managerial insights. The results show that the proposed integrated approach performs better compared with the regular GA in terms of makespan.     

Storage assignment and order batching problem in Kiva mobile fulfilment system

This article studies the storage assignment and order batching problem in the Kiva mobile fulfilment system. The storage assignment model aims to decide which product to put in which pod to maximize the product similarity and the order batching model aims to minimize the number of visits of pods. To solve the order batching problem, a heuristic is proposed, where a batch schedule is initialized with the objective of maximizing the order association or minimizing order alienation and improved by variable neighbourhood search. Computational experiments are conducted to verify the performance of the proposed model and algorithm.     

Optimal rail container shipment planning problem in multimodal transportation

The optimal rail container shipment planning problem in multimodal transportation is studied in this article. The characteristics of the multi-period planning problem is presented and the problem is formulated as a large-scale 0-1 integer programming model, which maximizes the total profit generated by all freight bookings accepted in a multi-period planning horizon subject to the limited capacities. Two heuristic algorithms are proposed to obtain an approximate optimal solution of the problem. Finally, numerical experiments are conducted to demonstrate the proposed formulation and heuristic algorithms.     

The integrated machine allocation and layout problem

Cellular manufacturing has received a considerable amount of attention in the research literature as an approach for improving the performance of manufacturing facilities. However, recent studies have shown that cellular layouts are not always superior to the traditional functional machine layout. We propose a model that does not require the machines to be placed in a functional layout or in a cellular arrangement, but allows the material flow requirements to dictate the machine placement. The model is formulated as an aggregation of the quadratic assignment problem and several network flow problems coupled with linear side constraints. A mixed integer program is presented to find the optimal solution for small problems, and heuristics are developed to solve larger problems. Computational results evaluating the quality of the solution methodologies are also presented.     

Integrated channel assignment and data aggregation routing problem in wireless sensor networks

In wireless sensor networks, data aggregation routing could reduce the number of data transmission so as to achieve efficient total energy consumption. However, this kind of data aggregation introduces data retransmission that is caused by co-channel interference from neighbouring sensor nodes. Hence, more data aggregation leads to more extra energy consumption and significant retransmission delay from retransmission. This could jeopardise the benefits of data aggregation. One possible solution to circumvent retransmission caused by co-channel interference is to assign different channel to every sensor node that is within each other's interference range on the data aggregation tree. As the number of non-overlapping channels is limited in wireless networks, it is unlikely that we could assign a different channel to every sensor node on the data aggregation tree. Then, an interesting problem is to perform data aggregation routing in conjunction with channel assignment to minimise total transmission power under limited number of non-overlapping channels. This problem is an NP-complete problem. We devise heuristic algorithm, Iterative Channel Adjustment Data Aggregation Routing algorithm (ICADAR), and other three heuristics, to tackle this problem. From the simulation results, the ICADAR algorithm outperforms the other three algorithms under all experimental cases.