Comparing optimization modeling approaches for the multi-mode resource-constrained multi-project scheduling problem

Resource-constrained project scheduling (RCPS) aims to minimize project duration under limited resource availabilities. The heuristic methods that are often used to solve the RCPS problem make use of different priority rules. The comparative merits of different priority rules have not been discussed in the literature in sufficient detail. This study is a response to this research gap. It compares 17 heuristic priority rules and seeks the best performing heuristic priority rule. This is the first study ever that compares heuristic priority rules by considering combinations of variations in (1) resource allocation procedures, (2) number of activities, (3) number of resource constraints, and (4) resource supply levels. The objective is to understand the relative merits of heuristic rules used in solving the RCPS problem. The findings indicate that the “minimum late finish time” rule generates the shortest predicted project duration when used in parallel resource allocation, whereas the “minimum late start time”, “minimum late finish time”, and the “highest rank of positional weight 2” rules perform best in serial resource allocation. It was also found that parallel resource allocation is slightly superior to serial resource allocation in most instances.

This work deals with the multi-mode resource-constrained project scheduling problem. In this problem, activities of the project may be executed in more than one operating mode and renewable resource constraints exist. Each activity operation mode has a different duration and requires different amount of renewable resources. The objective function considered is the minimisation of the project completion time. Heuristics based on priority rule are considered as solution procedures for this problem. Both, parallel and serial scheduling generation schemes are described in the multi-mode context. On the basis of a well-known set of project instances, the new heuristics based on priority rules are evaluated against the best ones published. Finally, multi-pass heuristics based on priority rules that outperforms the deterministic multi-pass heuristic previously published are designed.

The Multi-mode Resource Constrained Project Scheduling Problem (MRCPSP) has been of research interest for over two decades. The problem is composed of two interacting sub problems: mode assignment and activity scheduling. These problems cannot be solved in isolation because of the interaction that exists between them. Many evolutionary algorithms have been applied to this problem most commonly the Genetic Algorithm (GA). It has been common practice to improve the performance of the GA with some local search techniques. The Bi-population Genetic Algorithm (BPGA) is one of the most competitive GAs for solving the MRCPSP. In this paper, we improve the BPGA by hybridising it with an Estimation of Distribution Algorithm that focuses on improving how modes are generated. We also suggest improvement to the existing experimental methodology.

We have used the ProGen instances from the project scheduling problem library (PSPLIB) (see Kolisch and Sprecher [91]) to create a test-bed for the presented methods. This library contains systematically generated instances for resource-constrained project scheduling. We have used the instances with $$ n\; \in \;\left\{ {30,\;60,\;90} \right\} $$.KeywordsFeasible SolutionDiscount RateCash FlowColumn GenerationProject ScheduleThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Most existing methods for scheduling projects are based on centralized or hierarchical decision making using global models. So far, these methods have not produced the desired results, especially in highly dynamic multi-projects environments. In this study, we investigate a new method based on multi-agent systems and a combinatorial auction mechanism to allocate resources for the projects tasks. In combinatorial auction the bidders demand a combination of dependent goods with a single bid. We consider basically two types of agents, projects and resources. Projects are the bidders and resources are the goods. An auction mechanism based on Lagrangian- based decomposition is designed to achieve efficient allocation resources. Our approach allows manage some traditional aspects of multi-project environments: dynamic addition of resources or projects, changes of the resources capabilities, allocation flexibility, etc.

This paper presents a hybrid genetic algorithm for the simple assembly line problem, SALBP-1. The chromosome representation of the problem is based on random keys. The assignment of the operations to the workstations is based on a heuristic priority rule in which the priorities of the operations are defined by the chromosomes. A local search is used to improve the solution. The approach is tested on a set of problems taken from the literature and compared with other approaches. The computation results validate the effectiveness of the algorithm.

Effective project management implies the use of advanced planning and scheduling methods that allow to determine feasible sequences of activities and to complete a project on time and on budget. Traditional scheduling tools like fundamental Critical Path Method (CPM) and various methods for Resource Constrained Project Scheduling Problem (RCPSP) and Time Constrained Project Scheduling Problem (TCPSP) have many shortcomings for construction projects where spatial factor plays a critically important role. Previous attempts to interpret space as a specific resource were successful for particular problems of line-of-balance scheduling, space scheduling, dynamic layout planning, horizontal and vertical logic scheduling, workspace congestion mitigating, scheduling multiple projects with movable resources, spatial scheduling of repeated and grouped activities and motion planning. However, none of these methods considers the spatio-temporal requirements in a holistic framework of generic RCPSP problem and provides feasible results accounting for workspace and workflow factors. In this paper we start with the classical RCPSP statement and then present mathematically strong formalisation of the extended generalised problem, taking into account workspace congestion and workflow disturbance constraints specified in practically meaningful and computationally constructive ways. For the generalised RCPSP problem an effective scheduling method is proposed. The method tends to minimise the project makespan while satisfying timing constraints and precedence relations, not exceeding resource utilisation limits, avoiding workspace congestions and keeping workflows continuous. The method reuses so-called serial scheduling scheme and provides for additional computational routines and heuristic priority rules to generate feasible schedules satisfying all the imposed requirements. Advantages of the method and prospects for its application to industrial needs are outlined in the paper too.

ABSTRACTUnpredictable uncertainties cause delays and additional costs for projects. Often, when using traditional approaches, the optimizing procedure of the baseline project plan fails and leads to delays. In this study, a two-stage multi-objective buffer allocation approach is applied for robust project scheduling. In the first stage, some decisions are made on buffer sizes and allocation to the project activities. A set of Pareto-optimal robust schedules is designed using the meta-heuristic non-dominated sorting genetic algorithm (NSGA-II) based on the decisions made in the buffer allocation step. In the second stage, the Pareto solutions are evaluated in terms of the deviation from the initial start time and due dates. The proposed approach was implemented on a real dam construction project. The outcomes indicated that the obtained buffered schedule reduces the cost of disruptions by 17.7% compared with the baseline plan, with an increase of about 0.3% in the project completion time.

The three main objectives in construction projects are completing the project on time, within budget, and with good quality. Each construction project is unique and unpredictable making it beneficial to model the project before executing it. There are many ways to model a construction project; however, computer models are ideal. It is very costly and time consuming to experiment with the actual system. Therefore, by using a computer simulation, accurate data can be collected from the project without the time and cost drawbacks. The specific construction project researched is based on a real project from Fort Mcmurray Alberta, Canada. The construction project involved the delivery and erection of three different types of steel in a construction site. Once the steel has been delivered, it needs to be stored and then carried by forklift to one of two cranes to be erected. A schedule was provided for which days each type of material was expected to be delivered and erected, however this schedule did not account for the 20% chance that any delivery could be delayed by one day or the 10% chance that deliveries could be delayed by two days. A model project was created on Simphony.NET with the assumptions that work could commence the entire day (24 hours), the site has unlimited storage, and a delay in one delivery does not delay all the deliveries after it. The schedule for the project was then modified to reflect the results of the simulation. The modified schedule showed that several deliveries of materials were delayed. However, due to the model’s assumptions and the time for erection being relatively short, the planned schedule for the erection of the materials was not delayed. By using the data collected from the computer simulation it was possible to more accurately plan the schedule for this construction project.

In software development projects, it is desired to complete multiple projects at minimum cost and time while ensuring that the completion date is the same for all projects to meet certain operational and strategic objectives. Also, full-time employees assigned to projects should be reallocated smoothly to other tasks without any idle time during project execution to minimize costs even further. This study describes a model that enables the use of efficient continuous variable nonlinear solvers for finding the optimal schedule for possibly a large number of multiple software projects that make use of shared resources. The study validates the proposed solution using a random generator of multiple software project instances while interfacing to online optimization solvers to find a solution. Our continuous variable model was solved in the cloud for optimality for large instances of upto 40 different software projects and 100 employees in less than 21 min using nonlinear programming algorithms.•A continuous variable nonlinear model is developed to efficiently schedule large-scale software projects.•The model enables scheduling for multiple projects with identical completion times while ensuring work continuity.•A cloud-based program architecture is designed to facilitate the testing of multiple solvers online.

In this paper, we study the project staffing problem with discrete time/resource trade-offs to minimise the personnel staffing budget. This staffing problem embeds activity scheduling flexibility by incorporating the project scheduling problem into the personnel staffing problem to improve the quality of the staffing plan. In addition, we introduce extra demand scheduling flexibility resulting from the design of alternative execution modes for the activities, modelled via discrete time/resource trade-offs. In this way, the project manager is able to decide on the team size and duration for every activity. We propose a two-stage methodology to first design specific alternative activity modes using heuristic rules-of-thumb and subsequently we assess the resulting quality, i.e. the staffing cost, via the integrated composition of the project schedule and associated staffing plan. The heuristic mode generation rules determine the selection of a limited set of relevant activities and modes. The computational results show that the impact of these heuristic generation rules on the staffing budget is dependent on the defined relation between different activity alternatives for a particular activity and on the estimated characteristics of the activity base modes. We show that by focusing on a particular well-chosen subset of activity alternatives or on a particular subset of activities, high-quality solutions realising most of the potential cost improvements resulting from the discrete time/resource trade-offs can be derived with a reduced effort.

Effective risk management is essential to the success of real estate projects. However, in fact most of statistics emerging in the process of risk management are either ambiguous or unquantifiable, which makes it very hard to fathom. Fuzzy mathematics, to the contrary, is superior and feasible in the way to enable assessment by means of expressive variables. This article constructs a risk index system for real estate enterprise's construction project by means of Delphi method, and then quantifies and analyzes the risk factors by the method of entropy weight fuzzy synthetic evaluation. The case study demonstrates that the fuzzy synthetic evaluation model is practical for estate enterprise's agent construction project prior to the investment and during the contract negotiation.

Modular construction offers several advantages, such as safe working conditions, fast construction, and high-quality end products. The project scheduling of modular construction should consider the time involved in the factory production, transportation, and erection processes of the units given limited resources (e.g., the number of modular units, available transportation, and workers) and project restrictions (e.g., the sequences of manufacturing production and on-site installation). However, current modular construction project scheduling techniques and previous academic approaches cannot deal with the overall project processes and production/on-site installation sequences simultaneously. This is one reason few mass-production multiple projects exist in the manufacturing industry. Moreover, it can lead to inappropriate project management guidelines and fail to minimize the overall project duration. This paper suggests a model for modular construction projects scheduling using a genetic algorithm. The results show that implementing both overall project processes and project requirements is an efficient way to develop a modular construction project schedule. The proposed optimization tool will help project managers allocate resources efficiently and reduce the completion time of multiple projects.

The Multi-mode Resource Constrained Project Scheduling Problem (MRCPSP) is composed of two interacting sub- problems which are the mode assignment problem and the scheduling problem. Solving these sub-problems in isola- tion pose a challenge due to the interaction that exists be- tween them. We present a unified approach for applying a combination of algorithms to the sub-problems of the MR- CPSP. The use of Genetic algorithms and Estimation of Distribution Algorithms aims to exploit efficiently and syn- chronously the distinct search spaces presented by the two sub-problems.

An efficient hybrid genetic algorithm for scheduling projects with resource constraints and multiple execution modes