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Algorithms
Special Issues
Algorithms and Optimization for Project Management and Supply Chain Management
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Algorithms and Optimization for Project Management and Supply Chain Management

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Combinatorial Optimization, Graph, and Network Algorithms".

Deadline for manuscript submissions: closed (15 February 2024) | Viewed by 3447

Special Issue Editor

Dr. Ripon K. Chakrabortty

E-Mail Website
Guest Editor
School of Systems & Computing, University of New South Wales-Canberra, Canberra, ACT 2610, Australia
Interests: machine learning with optimization algorithms and applications; sustainable smart scheduling; logistics and supply chain management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

It is my pleasure to invite you to submit your cutting-edge and contemporary research on optimization algorithms to solve different operation management problems. The key application areas of this Special Issue are project management and supply chain management. Fundamentally, project scheduling has been considered one of the critical tasks in project management, which predominantly assumes scheduling project activities by satisfying precedence and resource constraints. By virtue of proper project scheduling, a project manager can determine the timeline, allocate resources, plan for the budget, and most importantly get a sense of the reality of delivering the project (Chakrabortty et al., 2019). Nevertheless, due to the COVID-19 pandemic and its subsequent impacts, traditional project management and scheduling are now characterized by volatility, uncertainty, complexity, and ambiguity. Thus, to sustain a nation’s economic growth and competitiveness in the new reality, it is vital not to cancel but to optimize affected or vulnerable project portfolios (Chakrabortty and Ryan, 2020).

To do so, embedding business reforms, accelerating the digital transformation of classical projects, and implementing advanced technologies and automation programs (e.g., artificial intelligence-based approaches) in supply chain and project management problems can encourage better business agility, which is potentially a shortcoming in the available literature (Rahman et al., 2021). Moreover, in practice, while scheduling activities for multiple projects in dynamic environments, project managers face challenges typically due to the lack of timely, accurate, and consistent information; finite resource transfer times and interdependencies among activities of different projects; and uncertain activity interruptions. Therefore, to avoid these issues, an integrated framework considering both project management features and supply chain drivers, advanced solution approaches, and better ways of dealing with uncertainties (e.g., artificial-intelligence-based) can lead to an optimal decision support system for the whole business.

This Special Issue aims to bring together optimization algorithms to optimize supply chain drivers (e.g., supplier selection, make or buy decision, subcontracting or overtime option, project compression or project crashing, inventory handling, number of orders, lead times, supply uncertainty, lead time uncertainty, product availability) while simultaneously managing projects.

References

CHAKRABORTTY, R., ABBASI, A. & RYAN, M. J. 2019. A risk assessment framework for scheduling projects with resource and duration uncertainties. IEEE Transactions on Engineering Management.

CHAKRABORTTY, R. K. & RYAN, M. J. Robust Optimization Based Heuristic Approach for Solving Stochastic Multi-Mode Resource Constrained Project Scheduling Problem.  2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), 2020. IEEE, 1157-1161.

RAHMAN, H. F., CHAKRABORTTY, R. K., ELSAWAH, S. & RYAN, M. J. 2021. Energy-Efficient Project Scheduling with Supplier Selection in Manufacturing Projects. Expert Systems with Applications, 116446.

Dr. Ripon Kumar Chakrabortty
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • resource-constrained project scheduling problems (RCPSPs) 
  • project management 
  • scheduling problems in segments of a supply chain 
  • scheduling problems in logistics, transport, timetabling, sports, healthcare, engineering, energy management, etc. 
  • vehicle routing 
  • scheduling under uncertainty 
  • scheduling under resource constraints 
  • agent based scheduling 
  • real-time scheduling 
  • scheduling heuristics and metaheuristics 
  • evolutionary algorithms 
  • approximation algorithms 
  • enumerative algorithms 
  • complexity issues 
  • artificial intelligence 
  • decision making and analytics 
  • supply chain risk management 
  • project risk management

Published Papers (3 papers)

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Research

28 pages, 758 KiB  
Article
Maximizing Net Present Value for Resource Constraint Project Scheduling Problems with Payments at Event Occurrences Using Approximate Dynamic Programming
by Tshewang Phuntsho and Tad Gonsalves
Algorithms 2024, 17(5), 180; https://doi.org/10.3390/a17050180 (registering DOI) - 28 Apr 2024
Viewed by 130
Abstract
Resource Constraint Project Scheduling Problems with Discounted Cash Flows (RCPSPDC) focuses on maximizing the net present value by summing the discounted cash flows of project activities. An extension of this problem is the Payment at Event Occurrences (PEO) scheme, where the client makes [...] Read more.
Resource Constraint Project Scheduling Problems with Discounted Cash Flows (RCPSPDC) focuses on maximizing the net present value by summing the discounted cash flows of project activities. An extension of this problem is the Payment at Event Occurrences (PEO) scheme, where the client makes multiple payments to the contractor upon completion of predefined activities, with additional final settlement at project completion. Numerous approximation methods such as metaheuristics have been proposed to solve this NP-hard problem. However, these methods suffer from parameter control and/or the computational cost of correcting infeasible solutions. Alternatively, approximate dynamic programming (ADP) sequentially generates a schedule based on strategies computed via Monte Carlo (MC) simulations. This saves the computations required for solution corrections, but its performance is highly dependent on its strategy. In this study, we propose the hybridization of ADP with three different metaheuristics to take advantage of their combined strengths, resulting in six different models. The Estimation of Distribution Algorithm (EDA) and Ant Colony Optimization (ACO) were used to recommend policies for ADP. A Discrete cCuckoo Search (DCS) further improved the schedules generated by ADP. Our experimental analysis performed on the j30, j60, and j90 datasets of PSPLIB has shown that ADP–DCS is better than ADP alone. Implementing the EDA and ACO as prioritization strategies for Monte Carlo simulations greatly improved the solutions with high statistical significance. In addition, models with the EDA showed better performance than those with ACO and random priority, especially when the number of events increased. Full article
(This article belongs to the Special Issue Algorithms and Optimization for Project Management and Supply Chain Management)
18 pages, 1419 KiB  
Article
Fast Algorithm for High-Throughput Screening Scheduling Based on the PERT/CPM Project Management Technique
by Eugene Levner, Vladimir Kats, Pengyu Yan and Ada Che
Algorithms 2024, 17(3), 127; https://doi.org/10.3390/a17030127 - 19 Mar 2024
Viewed by 752
Abstract
High-throughput screening systems are robotic cells that automatically scan and analyze thousands of biochemical samples and reagents in real time. The problem under consideration is to find an optimal cyclic schedule of robot moves that ensures maximum cell performance. To address this issue, [...] Read more.
High-throughput screening systems are robotic cells that automatically scan and analyze thousands of biochemical samples and reagents in real time. The problem under consideration is to find an optimal cyclic schedule of robot moves that ensures maximum cell performance. To address this issue, we proposed a new efficient version of the parametric PERT/CPM project management method that works in conjunction with a combinatorial subalgorithm capable of rejecting unfeasible schedules. The main result obtained is that the new fast PERT/CPM method finds optimal robust schedules for solving large size problems in strongly polynomial time, which cannot be achieved using existing algorithms. Full article
(This article belongs to the Special Issue Algorithms and Optimization for Project Management and Supply Chain Management)
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16 pages, 3396 KiB  
Article
Data-Driven Deployment of Cargo Drones: A U.S. Case Study Identifying Key Markets and Routes
by Raj Bridgelall
Algorithms 2023, 16(8), 373; https://doi.org/10.3390/a16080373 - 03 Aug 2023
Viewed by 1192
Abstract
Electric and autonomous aircraft (EAA) are set to disrupt current cargo-shipping models. To maximize the benefits of this technology, investors and logistics managers need information on target commodities, service location establishment, and the distribution of origin–destination pairs within EAA’s range limitations. This research [...] Read more.
Electric and autonomous aircraft (EAA) are set to disrupt current cargo-shipping models. To maximize the benefits of this technology, investors and logistics managers need information on target commodities, service location establishment, and the distribution of origin–destination pairs within EAA’s range limitations. This research introduces a three-phase data-mining and geographic information system (GIS) algorithm to support data-driven decision-making under uncertainty. Analysts can modify and expand this workflow to scrutinize origin–destination commodity flow datasets representing various locations. The algorithm identifies four commodity categories contributing to more than one-third of the value transported by aircraft across the contiguous United States, yet only 5% of the weight. The workflow highlights 8 out of 129 regional locations that moved more than 20% of the weight of those four commodity categories. A distance band of 400 miles among these eight locations accounts for more than 80% of the transported weight. This study addresses a literature gap, identifying opportunities for supply chain redesign using EAA. The presented methodology can guide planners and investors in identifying prime target markets for emerging EAA technologies using regional datasets. Full article
(This article belongs to the Special Issue Algorithms and Optimization for Project Management and Supply Chain Management)
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