In multi-project environments characterized by resource constraints and high uncertainty, traditional scheduling approaches often fail to respond effectively to dynamic project conditions. Fixed activity durations and rigid resource allocations limit adaptability, leading to inefficiencies and delays. To address this, the paper proposes a novel heuristic-based scheduling method that compresses and decompresses activity durations dynamically within the context of multi-project scheduling under uncertainty and resource flexibility—while preserving resource and precedence feasibility. The technique integrates Critical Path Method (CPM) calculations with heuristic rules to identify candidate activities whose durations can be reduced or extended based on slack availability and resource effort profiles. The objective is to enhance scheduling flexibility, improve resource utilization, and better align project execution with organizational priorities and sustainability goals. Validated through a case study at an automotive company in Portugal, the method demonstrates its practical effectiveness in recalibrating schedules and balancing resource loads. This contribution offers a timely and necessary innovation for companies aiming to enhance responsiveness and competitiveness in increasingly complex project landscapes. It provides an actionable framework for dynamic schedule adjustment in multi-project environments, helping companies to respond more effectively to uncertainty and resource fluctuations. Importantly, the proposed approach also supports sustainability objectives in new product development and supply chain operations. For practitioners, the method offers a responsive and sustainable planning tool that supports real-time adjustments in project portfolios, enhancing resource visibility and execution resilience. For researchers, the study contributes a reproducible, Python-based implementation grounded in Design Science Research (DSR), addressing gaps in stochastic multi-project scheduling and sustainability-aware planning.
This paper is concerned with an extension of the Resource-Constrained Project Scheduling Problem (RCPSP) which belongs to the class of the optimization scheduling problems with multi-level (or multi-mode) activities. We developed a practical tool, useful to represent multi-mode projects, and to find a solution for the problem on hand – select the best mode for each resource in each activity in order to minimize the total cost, considering the resource cost, a penalty for tardiness and a bonus for early completion. We implemented an adaptation of a filtered beam search (FBS) algorithm to this problem, using the C# programming language. A “filtered beam” search is a heuristic Branch and Bound (BaB) procedure that uses breadth first search but only the top “best” nodes are kept. We give some of the most important solution details and we report on further computational results, by testing the application for different problem sizes.
No abstract is provided for this article.
In the current business world setting, the strategi c directions are important not only for individual entities in the chain, but the complete chain itself should be cons idered for analysis. This helps developing competit ive advantages and retaining them. This article present s a methodology for analyzing a supply chain and de scribes how entities in the chain can be improved to materi alize the available and potential opportunities. Th e use of Planning-Link for mapping the cause and effect rela tionship between existing status of the chain to th e future or planned outputs is suggested here which makes the p rocess of strategic planning easier and logical.
No abstract is provided for this article.
Previous developments from the first author and other researchers were made on devising models for the total cost optimization of projects described by activity networks under stochastic conditions. Those models only covered the single resource case. The present paper will discuss the case of multiple resources. More precisely, we introduce a strategy of allocation of those resources in order to minimize the waste arising from their latent idleness on their consumption within the same activity. On this strategy we elect one resource as “pivot” and write equations that describe all the quantities for the other resources, on the same activity.
The economic activity of Small and Medium-Sized Enterprises (SMEs) has become increasingly project-oriented. Given SMEs’ significant role in promoting social and economic well-being, Project Management (PM) has emerged as a crucial tool for fostering innovation and sustainability within these companies and society as a whole. However, there is a gap in the existing literature concerning identifying practices that ensure innovation and sustainability throughout PM within SMEs and the associated benefits of adopting these practices. To address this gap, a comprehensive Systematic Literature Review (SLR) was conducted, gathering articles from reputable databases such as Scopus and Web of Science. In addition, this study seeks to explore the practical, political, and theoretical implications of these findings. The review identified 166 innovation practices, 86 sustainability practices, and 61 benefits, which were categorized based on their primary subject areas. The results of this study contribute to both the academic and practical understanding of the interplay between PM, innovation, and sustainability in SMEs. By identifying these practices and associated benefits, this research provides valuable insights for practitioners and policymakers, enabling them to foster innovation and sustainability within SMEs. In conclusion, this research fills a critical gap in the literature by providing an in-depth analysis of innovation and sustainability practices in the context of PM in SMEs. Also, it identifies that the benefits of adopting these practices extend well beyond economic considerations. The practical, political, and theoretical implications of these findings underscore the importance of integrating PM, innovation, and sustainability for the overall well-being of SMEs and society.
This paper presents an innovative approach to compressing and decompressing activity durations within the context of multi-project scheduling under uncertainty and resource flexibility. In dynamic project environments, fixed activity durations often restrict optimal scheduling outcomes. To address this challenge, the proposed method systematically adjusts activity durations—through compression and decompression—while maintaining resource and precedence feasibility. By integrating Critical Path Method (CPM) analysis with heuristic rules, the approach identifies candidate activities whose durations can be reduced or extended based on slack availability and resource effort profiles. The objective is to enhance scheduling flexibility, improve resource utilization, and better align project execution with organizational priorities. Validated through a case study at an automotive company in Portugal, the method demonstrates its practical effectiveness in recalibrating schedules and balancing resource loads. This contribution offers a timely and necessary innovation for companies aiming to enhance responsiveness and competitiveness in increasingly complex project landscapes. It provides an actionable framework for dynamic schedule adjustment in multi-project environments, helping companies to respond more effectively to uncertainty and resource fluctuations. Importantly, the proposed approach also supports sustainability objectives in new product development and supply chain operations. By optimizing resource usage, reducing idle time and overuse, and improving responsiveness to real-world conditions, it contributes to minimizing waste, increasing operational resilience, and aligning project execution with strategic sustainability goals.
In the past several years, various optimization algorithms had been implemented on the project total cost minimization problem. Lately, it was developed an application that serves as a central platform that integrates all those previous implementations. Currently, such platform allows the access and execution of each one of the other utilities as modules/plugins. Each of which can be configured to execute optimizations over different projects. Although the platform already saves the optimization results, it lacks a suitable processing mechanism that would ease the cross analysis over all the utilities. Therefore, we want to include capture and analysis of results into the platform in order to properly aggregate them in a results database. Such database could then be queried for numerous purposes being the performance evaluation, across the optimization utilities, one of the first. In this project we analyze a total of five heterogeneous optimization utilities, all coded in Java, using algorithms based on dynamic programming and global optimization. The heterogeneity poses a challenge which we overcame by establishing a common results storage language, by means of XML files.
The main objective of this research was to integrate scheduling techniques, documented in the literature, with Microsoft Project 2010, in order to help project managers’ deal with scheduling tasks in an easier and controlled way, and at the same time achieve better results. As far as we know, there is no information about the techniques or algorithms used by Microsoft Project to do the scheduling tasks so the possibility to select the algorithms to use gives a higher degree of control to the project manager. The initial hypothesis was that it is possible to get better results, concerning the project’s duration, using the studied techniques rather than the default scheduling technique used by Microsoft Project 2010, with an increased control.
In this paper we describe an extension of the Resource-Constrained Project Scheduling Problem (RCPSP). A literature review is presented to place our research in its proper context. The problem presented here belongs to the class of the optimization scheduling problems with multi-level (or multi-mode) activities. This means that the activities can be scheduled at different modes, each mode using a different resource level, implying different costs and durations. Each activity must be allocated exactly one unit of each required resource and the resource unit may be used at any of its specified levels. The processing time of an activity is given by the maximum of the durations that would result from a specific allocation of resources. The objective is to find the optimal solution that minimizes the overall project cost, while respecting a delivery date. A penalty is included for tardiness beyond the specified delivery date. We present a formal description of the problem and a mathematical model for it. We also introduce the implementation algorithm for the problem. The implementation was designed using the JAVA language, and the algorithm proposed is based on a branch and bound procedure, using breadth-first search (BFS) project network traversing, among some heuristic rules to filter large subsets of fruitless candidates relative to resource levels combinations.
No abstract is provided for this article.
No abstract is provided for this article.
Earned Value Management (EVM) and Project Risk Management are normally considered separately in Project Management. Referring PMBoK®, EVM is part of Project Time Management and Project Cost Management knowledge areas, while Project Risk Management is a different knowledge area with no apparent relation with the previous ones, although the fundamentals of EVM are fully influenced by risk management. This paper seeks to clarify the mechanisms of this relation, and in which points they meet or affect each other. The interconnection between the two methods was explored in this study and a framework of integration between them was proposed. The framework was implemented in Excel resulting in a set of tools that can be useful in monitoring and controlling a project.
No abstract is provided for this article.
To support the assessment of R&D project portfolios and to establish a systemic model to carry multiple evaluations using the decision-maker knowledge, preferences and purposes we have developed an evaluation matrix and a new procedure based on the PROV exponential decision method which uses multiple utility functions modeled to establish a common framework from which we can determine the projects relative value. The presentation of this new procedure is the main focus of this article and numerical examples are presented to illustrate the proposed approaches to attain comprehensible results and to discover the most valuable R&D projects to support investment decisions.
ICOPEV2018: 4th International Conference on Production Economics and Project Evaluation | 20 Set. - 21 Set. 2018
