Optimal Scheduling of Track Maintenance on a Railway Network

To ensure the safety and continued operation of the railway network system, many maintenance and renewal activities are performed on the track every month. Unplanned maintenance activities are expensive and would cause low service quality. Therefore, the track condition should be monitored, and when it has degraded beyond some acceptable limit, it should be scheduled for maintenance before failure. An optimal timetable of the maintenance activities is needed to be scheduled, planning the monthly workload, to reduce the effect on the transportation service and to reduce the potential costs. Considering the uncertainties of the deterioration process, the safety of transportation service, the lifetime loss of the replaced track, the maintenance cost and the travel cost, this article advances an optimisation model for the maintenance scheduling of a regional railway network. An enhanced genetic algorithm approach is proposed to search for a solution producing maintenance schedule such that the overall cost is minimised in a finite planning horizon. A case study is given to demonstrate the application of the method. The case study results were derived by using an enhanced genetic algorithm method, which is specifically developed to deal with the characteristics of the railway maintenance problem. Copyright © 2012 John Wiley & Sons, Ltd.     

Modelling and optimization of a bi-objective flow shop scheduling with diverse maintenance requirements

In real-world problems, machines cannot continuously operate and have to stop for maintenance before they fail. Lack of maintenance can also affect the performance of machines in processing jobs. In this paper, a permutation flow shop scheduling problem with multiple age-based maintenance requirements is modelled as a novel mixed-integer linear program in which the objectives are conflicting. In modelling the problem, we assume that infrequent maintenance can prolong job processing times. One of the objectives is to minimise the total maintenance cost by planning as few maintenance activities as possible to only meet the minimum requirements, and the other objective tries to minimise the total tardiness by sequencing the jobs and planning the maintenance activities in such a way that the processing times are not prolonged and unnecessary maintenance times are avoided. Because of this conflict, an interactive fuzzy, bi-objective model is introduced. Application of the model is illustrated through a case study for operations and maintenance scheduling of heavy construction machinery. An effective and efficient solution methodology is developed based on the structure of the problem and tested against commercial solvers and a standard GA. Computational results have verified the efficiency of the proposed solution methodology and show that unlike the proposed method, a generic metaheuristic that does not consider the unique structure of the problem can become ineffective for real-world problem sizes.     

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.     

Value-driven maintenance planning for a production plant

Maintenance involves maintaining and securing the equipment and systems in, or restoring them to, a state in which they can perform the required functions. The challenge for maintenance planning is to identify appropriate objects and tasks for preventive maintenance and ensure that there are adequate resources for the repair actions. In this paper we will present a maintenance planning approach, called value-driven maintenance planning (VDMP), to emphasise the fact that the objectives of the plant are the reference points for specifying functional requirements for the equipment locations and equipment.The three basic phases of the VDMP planning approach are:

(i)

the definition of the fundamental, strategic and maintenance (means) objectives of the production plant,

(ii)

the classification of equipment locations (including one or several equipment items) into maintenance classes, each associated with a functional requirement specification, and

(iii)

the selection of maintenance strategies and maintenance tasks (including timing) for equipment assigned to the equipment locations.

Feedback of the operational experience is crucial for the application of the maintenance planning approach. As a consequence, the approach is developed to support the continuous improvement of the cost effectiveness of maintenance rather than planning of the initial maintenance programme of a new plant. Analysis techniques are presented and challenges of application are discussed. The application of the VDMP approach to a gasification plant in Finland is also presented.     

Airline planning and scheduling: Models and solution methodologies

The airline industry is a representative industry with high cost and low profitability. Therefore, airlines should carefully plan their schedules to ensure that overall profit is maximized. We review the literature on airline planning and scheduling and focus on mathematical formulations and solution methodologies. Our research framework is anchored on three major problems in the airline scheduling, namely, fleet assignment, aircraft routing, and crew scheduling. General formulation, widely used solution approaches, and important extensions are presented for each problem and integrated problems. We conclude the review by identifying promising areas for further research.     

Jointly solving the group scheduling and machining speed selection problems: A hybrid tabu search and simulated annealing approach

In a cellular manufacturing environment, once the machines and parts have been grouped the remaining tasks are sequencing part families and scheduling operations for the parts within each part family so that some planning goals such as minimization of tardiness can be achieved. This type of problem is called group scheduling and will be analysed in this paper. The solution of the group scheduling is affected by the machining speed specified for each operation since the completion time of each operation is a function of machining speed. As such, the group scheduling and machining speed selection problems should be simultaneously solved to provide meaningful solutions. This, however, further complicates the solution procedure. In view of this, a hybrid tabu-simulated annealing approach is proposed to solve the group scheduling problem. The main advantage of this approach is that a short term memory provided by the tabu list can be used to avoid solution re-visits while preserving the stochastic nature of the simulated annealing method. The performance of this new method has been tested and favourably compared with two other algorithms using tabu search and simulated annealing alone.     

A review of methods,techniques and tools for project planning and control

The purpose of this article is to provide a brief review of methods and techniques developed for the most commonly studied decision-making problems in project planning and control over the last decade. These problems involve project representation, project scheduling, resource allocation, risk analysis, time and cost performance evaluation, time, cost, and cash flow forecasting, optimal timing of control points, and corrective action decision-making. We also review recent tools developed for project planning and control. The emphasis is on recent contributions, but several older yet important works are also cited. Our analysis shows an increasing attention to the stochastic nature of projects in planning and control decision and processes. Recent attention has also been put at improvements in existing project control techniques as well as developing new methods to automate data collection, process, and generate more integrated project plan. More importantly, our review highlights an important shift in the project planning and control research field, which has been largely dominated by the project scheduling literature in the past, as short term and reactive decision-making bring new challenges and opportunities to project organisations and researchers.     

Risk-based maintenance planning of subsea pipelines through fatigue crack growth monitoring

Research and development in the field of risk-based maintenance of offshore structures has recently attracted large attention due to the significant level of accident risk and the cost associated with maintenance in such remote facilities. The uncertainties associated with the deterioration of these facilities require a sound decision making methodology for maintenance planning. This paper presents a dynamic risk-based methodology for maintenance scheduling of subsea pipelines subjected to fatigue cracks. The developed method can assist the asset managers to select the optimum approach for mitigating the consequences of failure while minimizing the maintenance costs. A Bayesian network is developed to model the probabilistic deterioration process and then it is extended to an influence diagram for estimating the expected utility of each decision alternative. Observation of damage state is included in the model to enhance decision making capacity. To demonstrate the applicability of the methodology, three cases with different fatigue crack incidents on a pipeline are considered. Based on the monitoring results, the model is able to determine whether the maintenance should be performed or not. The economic risk associated with maintenance is also minimized by suggesting the optimum maintenance technique among multiple possible methods such as welding or major repair.     

The model of maintenance planning and production scheduling for maximising robustness

The accuracy of prediction and detection capability have a strong influence over the efficiency of the bottleneck, all equipment and the production system. The function of predictive scheduling is to obtain stable and robust schedules for a shop floor. The first objective is to present an innovative maintenance planning and production scheduling method. The approach consists of four modules: a database to collect information about failure-free times, a prediction module of failure-free times, predictive scheduling and rescheduling module, a module for evaluating the accuracy of prediction and maintenance performance. The second objective is to apply the proposed methods for a job shop scheduling problem. Usually, researchers who are concerned about maintenance scheduling do not take unexpected disturbances into account. They assume that machines are always available for processing tasks during the future-planned production time. Moreover, researches use the criteria that are not effective to deal with the situation of unpredicted failures. In this paper, a method based on probability theory is proposed for maintenance scheduling. For unpredicted failures, a rescheduling method is also proposed. The evaluation module which gives information about the degradation of each performance measure and the stability of a schedule is proposed.     

Maintenance schedule optimisation for a railway power supply system

High reliability is the crucial requirement in railway operation and a power supply system is one of the key components of electrified railways. The cost-effectiveness of the maintenance works is also the concern of the railway operators while the time window on trackside maintenance is often limited. Maintenance scheduling is thus essential to uphold reliability and to reduce operation cost. It is however difficult to formulate the optimal schedule to meet both reliability and maintenance cost for a railway power supply system as a whole because of its functional complexity and demanding operation conditions. Maintenance scheduling models to achieve reliability and maintenance cost are proposed in this study. Optimisation algorithms are then developed to attain the solutions of the model. The applicability of the models and efficiency of the solution algorithms are demonstrated in an example. The proposed methods are vitally important for the railway engineers and operators to assure the service quality in the increasing demands of the modern electrified railways.     

港口业船舶优化调度模型设计

以港口船舶计划调度为研究背景,分析了港口计划调度的特征,提出了生产计划调度的系统框架,并在此基础上建立了以船舶拖期惩罚费用为最小,多种因素约束下的调度模型,将人工智能技术应用到实际生产调度中,实现分层次,为分研究多种资源约束条件下的计划调度和优化。     

A simulated annealing-based optimization algorithm for process planning

Computer-aided process planning (CAPP) in the past typically employed knowledge-based approaches, which are only capable of generating a feasible plan for a given part based on invariable machining resources. In the field of concurrent engineering, there is a great need for process planning optimization. This paper describes an approach that models the constraints of process planning problems in a concurrent manner. It is able to generate the entire solution space by considering multiple planning tasks, i.e. operations (machine, tool and tool approach direction), selection and operations sequencing simultaneously. Precedence relationships among all the operations required for a given part are used as the constraints for the solution space. The relationship between an actual sequence and the feasibility of applying an operation is also considered. An algorithm based on simulated annealing (SA) has been developed to search for the optimal solution. Several cost factors including machine cost, tool cost, machine change cost, tool change cost and set-up change cost can be used flexibly as the objective function. The case study shows that the algorithm can generate highly satisfying results.     

Bi-objective scheduling on uniform parallel machines considering electricity cost

This article investigates a bi-objective scheduling problem on uniform parallel machines considering electricity cost under time-dependent or time-of-use electricity tariffs, where electricity price changes with the hours within a day. The aim is to minimize simultaneously the total electricity cost and the number of machines actually used. A bi-objective mixed-integer linear programming model is first formulated for the problem. An insertion algorithm is then proposed for the single-objective scheduling problem of minimizing the total electricity cost for a given number of machines. To obtain the whole Pareto front of the problem, an iterative search framework is developed based on the proposed insertion algorithm. Computational results on real-life and randomly generated instances demonstrate that the proposed approach is quite efficient and can find high-quality Pareto fronts for large-size problems with up to 5000 jobs.     

Integrated project task and manpower scheduling

The objective of project task scheduling is to determine task start dates and durations to complete a project on time with the minimum cost of performing tasks plus overhead. By altering task start dates and durations, the daily labor-demand profile can be changed. The objective of personnel scheduling is to determine how many workers must be assigned to each feasible days-off tour to satisfy a given labor-demand profile with minimum labor cost. Integrating these two problems permits the simultaneous determination of start dates, durations, labor levels and required tours for a minimum-cost and on-time schedule. Both integer programming and heuristic solution procedures to solve the integrated problem are presented. In a series of 20 test problems, the heuristic procedure outperformed the traditional two-step scheduling procedure by reducing the cost of labor and overhead by 8.6%.     

基于遗传算法和状态信息的多设备混联系统维护调度优化

针对多设备混联系统维护优化的建模复杂性,系统分析了设备间的相互依赖性,建立了混联系统的维护调度模型。首先利用威布尔分布模拟设备的衰退过程;定义小修、大修和更换3种维护方式,以及3种维护方式对设备故障率的影响;考虑故障成本、维护成本、资源成本和停机成本,建立了系统一次维护活动的费用模型。其次,基于每次维护活动的费用模型,建立了系统维护多阶段的总费用率模型。最后,通过算例证明了提出的多设备混联系统维护调度优化模型的有效性     

Human Factors Effects and Analysis in Maintenance: A Power Plant Case Study

This paper proposes a methodical approach for identifying and reducing human error in maintenance activities, the human factors effect and analysis. Human factors effect and analysis presents a roadmap for selecting significant human factors affecting maintenance management as well as the most effective solutions using cost–benefit analysis. Safety and operational consequences of each human factor are compared to preventive and recovery risk controls to select the preferred risk control method. Because human factor programs are not implemented in many maintenance departments, quantitative data are rare. Thus, expert judgment may help to compare potential solutions. In order to show the applicability of the proposed approach a power plant in Kenya is selected as a case study. Procedure usage, fatigue, knowledge and experience, and time pressure are identified as the most important human factors. Training, task planning /shift management, knowledge management, scheduling as well as incident report programs are the most cost‐effective solutions for performing human factors program. The proposed approach would improve system reliability by recognizing human related failures. Furthermore, unexpected incident and accident may be reduced having knowledge about potential risk factors. Copyright © 2016 John Wiley & Sons, Ltd.     

Integrated maintenance and mission planning using remaining useful life information

The modern world requires high reliability and availability with minimum ownership cost for complex industrial systems (high-value assets). Maintenance and mission planning are two major interrelated tasks affecting availability and ownership cost. Both tasks play critical roles in cost savings and effective utilization of the assets, and cannot be performed without taking each other into consideration. Maintenance schedule may make an asset unavailable or too risky to use for a mission. Mission type and duration affect the health of the system, which affects the maintenance schedule. This article presents a mathematical formulation for integrated maintenance and mission planning for a fleet of high-value assets, using their current and forecast health information. An illustrative example for a fleet of unmanned aerial vehicles is demonstrated and evolutionary-based solutions are presented.     

A cost minimisation model for joint production and maintenance planning under quality constraints

In this paper, integrated planning of production, imperfect maintenance and process inspections in a multi-machine system is investigated. This system consists of parallel machines which deteriorate with time and they may shift from a primarily in-control state to a degraded state with a higher defective rate or to a failed state. Maintenance scheduling corresponds to a discrete time age-based imperfect maintenance with a large number of maintenance alternatives. Process inspections are considered to detect the current state of the system. Detecting a deteriorated condition initiates the quality check of the related sub-lots, rework of defective items and a process adjustment that brings the machine in its normal conditions. Production planning includes a capacitated lot-sizing problem with multiple products. We propose a joint approach that coordinates the decisions of the three functions, where the objective function minimises the total cost. Evaluation of costs and interacting factors is presented and two heuristic methods are proposed to solve the problem. The results of the joint model are compared to a non-integrated method and a sensitivity analysis is conducted.     

A new approach to finite scheduling

This paper discusses the general framework of a methodology for material planning and operations scheduling. The methodology is designed to individually schedule customer orders according to the relative priority values assigned to each order, based on various factors including but not limited to due-dates. Resource constraints are considered during the scheduling process to ensure the feasibility of the finite production schedule, while attempting to ensure a reduced production lead time. The methodology is exemplified through test problems compared with the normal approaches. The preliminary results are quite promising, although further mathematical proofs are required to establish the potential advantages of the method in a generic form.     

面向供应链的MC计划调度功能模块运作研究

指出了面向供应链的MC生产计划调度过程是由若干协同化的主导子过程共同实现的;同时,每一主导子过程的实现对应于一个主导功能模块的合理设计与运作.分析了各主导功能模块的基本特征及其模式框架;阐述了基于各主导功能模块协同运作的面向供应链的MC计划调度过程实施模式.将对该计划调度问题在理论探索与实践运用方面提供了基础框架,起到了导向性作用.