Antonio Lova

A Competitive Heuristic Solution Technique for Resource-Constrained Project Scheduling

In this work a new heuristic solution technique for the Resource-Constrained Project Scheduling Problem (RCPSP) is proposed. This technique is a hybrid multi-pass method that combines random sampling procedures with a backward–forward method. The impact of each component of the algorithm is evaluated through a step-wise computational analysis which in addition permits the value of their parameters to be specified. Furthermore, the performance of the new technique is evaluated against the best currently available heuristics using a well known set of instances. The results obtained point out that the new technique greatly outperforms both the heuristics and metaheuristics currently available for the RCPSP being thus competitive with the best heuristic solution techniques for this problem.

A MULTICRITERIA HEURISTIC METHOD TO IMPROVE RESOURCE ALLOCATION IN MULTIPROJECT SCHEDULING

Abstract Many works published in the area of project management make reference to the scheduling of single projects and time objectives like minimising project duration. Nevertheless, frequently companies manage various projects which share a pool of constrained resources, taking into account other objectives in addition to time. In order to add flexibility in using project scheduling tools, we have developed a multicriteria heuristic that improves lexicographicly two criteria: one time type – mean project delay or multiproject duration increase – and one no time type – project splitting, in-process inventory, resource levelling or idle resources – that can be chosen by the user. The multicriteria heuristic algorithm consists of several algorithms based on the improvement of multiproject feasible schedules. Through an extensive computational study, we have shown that this method improves the feasible multiproject schedule obtained from heuristic methods based on the priority rules coded Maximum Total Work Content (MAXTWK) and Minimum Latest Finish Time (MINLFT) as well as project management software – Microsoft Project, CA-SuperProject, Time Line and Project Scheduler.

Analysis of Scheduling Schemes and Heuristic Rules Performance in Resource-Constrained Multiproject Scheduling

Frequently, the availability of resources assigned to a project is limited and not sufficient to execute all the concurrent activities. In this situation, decision making about their schedule is necessary. Many times this schedule supposes an increase in the project completion time. Additionally, companies commonly manage various projects simultaneously, sharing a pool of renewable resources. Given these resource constraints, we often can only apply heuristic methods to solve the scheduling problem. In this work the effect of the schedule generation schemes – serial or parallel – and priority rules – MINLFT, MINSLK, MAXTWK, SASP or FCFS – with two approaches – multi-project and single-project – are analysed. The time criteria considered are the mean project delay and the multiproject duration increase. Through an extensive computational study, results show that with the parallel scheduling generation scheme and the multi-project approach the project manager can obtain a good multiproject schedule with the time criterion selected: minimising mean project delay or minimising multiproject duration increase. New heuristics – based on priority rules with a two-phase approach – that outperform classical ones are proposed to minimise mean project delay with a multi-project approach. Finally, the best heuristics analysed are evaluated together with a representative sample of commercial project management software.

An efficient multi-pass heuristic for project scheduling with constrained resources

In this work an efficient heuristic solution technique for the Resource-Constrained Project Scheduling Problem (RCPSP) is proposed. This technique is a hybrid multi-pass method that combines random sampling procedures with a Backward-Forward scheduling method applied in a selective way. The performance of the new technique is evaluated against the best currently available heuristics using a well-known set of instances. The results obtained point out the interest of the selective use of the Backward-Forward scheduling methods as improving procedure. The resultant technique outperforms both the heuristics and metaheuristics currently available for the RCPSP, thus it is competitive with the best heuristic solution techniques for this problem.

A Genetic Algorithm for Railway Scheduling Problems

This work is focused on the application of evolutionary algorithms to solve very complex real-world problems. For this purpose a Genetic Algorithm is designed to solve the Train Timetabling Problem. Optimizing train timetables on a single line track is known to be NP-hard with respect to the number of conflicts in the schedule. This makes it difficult to obtain good solutions to real life problems in a reasonable computational time and raises the need for good heuristic scheduling techniques. The railway scheduling problem considered in this work implies the optimization of trains on a railway line that is occupied (or not) by other trains with fixed timetables. The timetable for the new trains is obtained with a Genetic Algorithm (GA) that includes a guided process to build the initial population. The proposed GA is tested using real instances obtained from the Spanish Manager of Railway Infrastructure (ADIF). The results of the computational experience, point out that GA is an appropriate method to explore the search space of this complex problems and able to lead to good solutions in a short amount of time.

Domain-dependent distributed models for railway scheduling

Many combinatorial problems can be modelled as Constraint Satisfaction Problems (CSPs). Solving a general CSP is known to be NP-complete, so closure and heuristic search are usually used. However, many problems are inherently distributed and the problem complexity can be reduced by dividing the problem into a set of subproblems. Nevertheless, general distributed techniques are not always appropriate to distribute real-life problems. In this work, we model the railway scheduling problem by means of domain-dependent distributed constraint models, and we show that these models maintained better behaviors than general distributed models based on graph partitioning. The evaluation is focused on the railway scheduling problem, where domain-dependent models carry out a problem distribution by means of trains and contiguous sets of stations.

The Evolution of Software Quality in Project Scheduling

Since the end of the 50’s when CPM -Critical Path Method- and PERT -Program Evaluation and Review Technique- were developed, the evolution of the use in practice of these and other methods of project scheduling has been closely linked to the evolution of the software and hardware which has made it possible from the start. We can divide its four decades of history into three principle periods. The first until the 70’s, during which large machines, mainframes, were necessary. Therefore, project scheduling in that period was quite costly, reserving it in practice for large projects and big companies. The PC’s are the protagonists of the second period, taking place in approximately the decade of the 80’s. In 1981 the IBM PC came out and in 1983 Harvard Project Manager software appeared. Later, tens of packages for the PC flooded the market, making it possible for any company to have access to software at a low cost and with few hardware requirements (Maroto and Tormos, 1994; Burke, 1997). Finally, the third period corresponds to the decade of the 90’s, in which software and hardware have made high quality and low price compatible. The improved quality of some programs has been achieved in successive versions by correcting flaws and deficiencies detected in previous ones, parallel to the improvements in the capabilities of the hardware and software in general. The programs are becoming more and more interactive and user-friendly, offering greater ease in connecting with other tools and more and better capabilities in resource-constrained project scheduling.

An Interactive Train Scheduling Tool for Solving and Plotting Running Maps

We present a tool for solving and plotting train schedules which has been developed in collaboration with the National Network of Spanish Railways (RENFE). This tool transforms railway problems into formal mathematical models that can be solved and then plots the best possible solution available. Due to the complexity of problems of this kind, the use of preprocessing steps and heuristics become necessary. The results are plotted and interactively filtered by the human user.

New heuristics to solve the “CSOP” railway timetabling problem

The efficient use of infrastructures is a hard requirement for railway companies. Thus, the scheduling of trains should aim toward optimality, which is an NP-hard problem. The paper presents a friendly and flexible computer-based decision support system for railway timetabling. It implements an efficient method, based on meta-heuristic techniques, which provides railway timetables that satisfy a realistic set of constraints and, that optimize a multi-criteria objective function.

An Efficient Method to Schedule New Trains on a Heavily Loaded Railway Network

With the aim of supporting the process of adapting railway infrastructure to present and future traffic needs, we have developed a method to build train timetables efficiently. In this work, we describe the problem in terms of constraints derived from railway infrastructure, user requirements and traffic constraints, and we propose a method to solve it efficiently. This method carries out the search by assigning values to variables in a given order and verifying the satisfaction of constraints where these are involved. When a constraint is not satisfied, a guided backtracking is done. The technique reduces the search space allowing us to solve real and complex problems efficiently.