Multi-project Scheduling Research Articles

A Benders decomposition algorithm for the multi-mode resource-constrained multi-project scheduling problem with uncertainty

A Benders decomposition algorithm for the multi-mode resource-constrained multi-project scheduling problem with uncertainty

A comprehensive mathematical model for quality integration in a project supply chain with concentrating on material flow and transportation

This study aims to investigate how quality integration affects a joint model of multi-project scheduling and material transportation. Therefore, the research problem not only considers the quality of project activities, but also focuses on quality in processes related to suppliers and customers. Accordingly, this study considers the quality of materials as a factor when determining the suppliers in a project-based organization. Furthermore, unlike the existing studies, it also assumes that the rework-time of projects depends on the quality of materials. The research problem is addressed using a mixed integer linear programming model, which minimizes the total normalized cost (TNC) as a measure of the balance between supply chain costs. Contrary to previous models, this model examines the simultaneous impacts of renewable and nonrenewable resources on project quality. Also, the material transportation is modeled based on the “multi-depot open vehicle routing problem” to increase the model’s applicability in real conditions. In order to solve the research problem, a tabu search algorithm is applied, which is verified based on instances of different sizes. Then, the proposed model is compared with one of the similar models in the literature to determine its efficiency. Additionally, a real-world case study is utilized to show the model application. The results show that the quality concept in the suggested model leads to more appropriate materials and shorter routes for transportation. Therefore, quality integration in this model reduces the total normalized cost by improving projects' delays and transportation costs.

Maximizing projects’ profitability, environmental score, and quality: a multi-project scheduling and material ordering problem

The proper trade-off between various project costs is often disregarded when planning projects. This leads to several detrimental effects, such as inaccurate planning and higher total cost, far more significant in a multi-project environment. To overcome this limitation, this study proposes a combined approach for the multi-project scheduling and material ordering problem (MPSMOP), which maintains the proper trade-off among various costs. Moreover, the environmental impact and project quality objectives are optimized alongside the economic criterion. The proposed methodology involves three stages: (a) quantifying the environmental performance of suppliers; (b) measuring the activities’ quality through the Construction Quality Assessment System approach; and (c) building and solving the mathematical model of the MPSMOP. The MPSMOP is modeled as a tri-objective optimization approach aiming to determine project scheduling and material ordering decisions so that the net present value, environmental score, and total quality of implemented projects are maximized simultaneously. As the proposed model comes into the nondeterministic polynomial optimization problem category, two powerful metaheuristics are customized and used to solve the problem. The efficiency of both algorithms was assessed on several datasets. The proposed framework is applied to railway construction projects in Iran as a case study, which presents the validity of the model and the decision-making options provided to managers.

Tabu search for dedicated resource-constrained multiproject scheduling to minimise the maximal cash flow gap under uncertainty

Maintaining a positive balance between cash outflow and inflow is very important for contractors to smoothly implement projects. Based on this fact, with the objective of minimising the maximal cash flow gap, this paper investigates a dedicated resource-constrained multiproject scheduling problem with stochastic activity duration where renewable resources are dedicated to individual projects in advance, and the schedule of each project is then arranged independently. First, we construct an optimisation model for the problem and propose properties for it. Second, for the NP-hardness of the problem, we design a tabu search algorithm consisting of two modules and develop the measures according to the proposed properties to improve the searching efficiency of the algorithm. Finally, we conduct a computational experiment on a randomly generated dataset to evaluate the performance of the developed algorithm and analyse the effects of key parameters on the objective function value. The conclusions are as follows. The developed tabu search algorithm with the improvement measures is the most promising algorithm for solving the studied problem, and the contribution of the improvement measures may vary as the project parameters change. The maximal cash flow gap increases along with the resource factor, required robustness of project schedule, and discount rate per period of cash flows, and decreases with the increase in the project deadline, resource strength, number of milestone activities, advance payment proportion, or progress payment proportion. The interactions among the resource strength, resource factor, and project deadline may also generate an effect on the maximal cash flow gap.

An Integrated Multi-project Scheduling, Materials Ordering and Suppliers Selection Problem

Conventionally, project managers schedule the activities first; later prepare for materials management, procurement, and supplier selection in project planning. This disjointed process leads in a loss of expected profit for the company owing to a lack of planning coordination. In this paper, a mixed integer programming model is developed for resource constrained multi-project scheduling, materials ordering (MO), and supplier selection (SS) problems to maximize the net profit of the organization. Since the resource constraint multi-project scheduling with MO and SS belongs to the class of problems that are NP-hard. Thus, a genetic algorithm-based memetic algorithm (MA) is proposed to solve the proposed model. The proposed MA utilizes the basic components of genetic algorithm (GA) like selection, crossover, and mutation with a local search. The proposed algorithm is tested on self-generated 16 instances with a range of 2 to 5 projects in a multi-project set with 30 to 90 activities in a project with 6 to 8 suppliers. The results revealed that the suggested MA generates better solutions than the GA by achieving projects higher net present value (NPV). The findings of this paper provide managerial insights for the project managers to secure organizations’ profitability.

A Two-Stage Algorithm Based on 12 Priority Rules for the Stochastic Distributed Resource-Constrained Multi-Project Scheduling Problem With Multi-Skilled Staff

In practical multi-skilled resource-constrained multi-project management, the activity duration is often affected by some factors (e.g., rework, increased workload), leading to uncertainty. Moreover, multiple projects are often managed under a distributed decision-making environment. To deal with uncertain activity durations in distributed multi-project management with multi-skilled staff, this paper studies a stochastic distributed resource-constrained multi-project scheduling problem with multi-skilled staff (MS-SDRCMPSP). In a distributed decision-making environment, a two-stage model with local scheduling and global coordination stages is established to describe MS-SDRCMPSP. A two-stage algorithm with 12 priority rules (TSA-12PRs) is proposed, these 12 priority rules are composed of 4 activity priority rules and 3 resource priority rules. In the local schedule stage, 4 activity priority rules (PRs) are applied to obtain the local schedule plan. In the global coordination phase, we develop 3 resource PRs based on variable neighborhood search (VNS), of which VNS is used to solve the execution order of conflicting projects, and 3 resource PRs are developed to formulate multi-skilled resource assignment strategies. Based on the multi-skilled instances adapted from benchmark instances, we evaluate the performance of the 12 PRs on different instances. The experiment results show that two PRs among 12 PRs perform better than other PRs in all-size instances. Comparing the two-stage algorithm with better two PRs with other approaches in literatures, we find that our method performs better than other approaches, especially in large-size instances. In addition, further experiments show that our method is more conducive to shortening the CPU runtime on distributed problems than centralized methods.

Multi-project scheduling: A benchmark analysis of metaheuristic algorithms on various optimisation criteria and due dates

This paper reviews a set of ten existing metaheuristic solution procedures for the resource-constrained multi-project scheduling problem. Algorithmic implementations are constructed based on the description of the original procedures in literature. Equivalence is verified on the original test instances for the original objective and parameters through a comparison with the reported results. An extensive benchmark analysis is performed on a novel, publicly available dataset for a variety of optimisation criteria and due date settings for which the original algorithms have not been tested earlier. The impact of the different objectives, due dates and test instance parameters is analysed and an overall ranking of the metaheuristic solution methods for different situations is discussed. Key insights into the structure of competitive solutions for disparate objectives and due date settings are presented and effective algorithmic components are revealed.

Dynamic multi-mode resource-constrained multi-project scheduling problem with weighted earliness and tardiness: a real-life boutique furniture implementation

Real-life project scheduling environments are often dynamic and subject to disruption. Early or late completion of interrupted projects can create costs for the business. At the same time, there is an alternative to producing multiple projects at multiple different costs. In this study, a new mixed integer linear programming model that minimizes the sum of weighted earliness and tardiness penalties and mode selection costs is proposed to solve the real-life problem faced by a boutique furniture company. A proposed dynamic model also considers the cost of deviation from the baseline schedule in case disruption scenarios corrupt the resulting baseline schedule. The problems are solved with the CPLEX solver using the GAMS program. The results show that the interruption scenarios partially change the baseline schedule and increase the total cost. In case of more than one interruption in the same schedule, the number of late completed activities and their delay times increased.

Portfolio Planning: a Comprehensive Approach to Limited Resources and Project Prioritization

The main issue for portfolio managers is to set a realistic schedule that ensures that the resources needed for operations are readily available on deadline dates, which is the classic goal of the Resource-Constrained Multi-Project scheduling problem (RCMPSP). This article presents an algorithm that provides solution to RCMPSP, where each project has a different priority for the organization. The key feature of this algorithm is that it includes project prioritization during the planning process. Before planning, the portfolio manager determines which projects are relevant for the company. With this in mind, the algorithm uses market mechanisms to determine which activities should be prioritized in the planning process. Based on this, the algorithm creates an artificial market and enables to plan those projects that are of higher priority. Thus, out of a number of possible schedules corresponding to resource constraints (i.e., the solution provided by classic approaches to RCMPSP), the approach considered in the article allows obtaining an optimal portfolio schedule. This schedule, in addition to having the shortest possible duration of the portfolio, allows planning activities in accordance with priorities of the portfolio manager and, consequently, priorities of the company.

Time and finance optimization model for multiple construction projects using genetic algorithm

Abstract Construction contractors usually undertake multiple construction projects simultaneously. Such a situation involves sharing different types of resources, including monetary, equipment, and manpower, which may become a major challenge in many cases. In this study, the financial aspects of working on multiple projects at a time are addressed and investigated. The study considers dealing with financial shortages by proposing a multi-project scheduling optimization model for profit maximization, while minimizing the total project duration. Optimization genetic algorithm and finance-based scheduling are used to produce feasible schedules that balance the finance of activities at any time with the available funds. The model has been tested in multi scenarios, and the results are analyzed. The results show that negative cash flow is minimized from −693,784 to −634,514 in enterprise I and from −2,646,408 to −2,529,324 in enterprise II in the first scenario and also results show that negative cash flow is minimized to −612,768 with a profit of +200,116 in enterprise I and to −2,597,290 with a profit of +1,537,632 in enterprise II in the second scenario.

A two-stage genetic programming framework for Stochastic Resource Constrained Multi-Project Scheduling Problem under New Project Insertions

This study proposes a novel hyper-heuristic based two-stage genetic programming framework (HH-TGP) to solve the Stochastic Resource Constrained Multi-Project Scheduling Problem under New Project Insertions (SRCMPSP-NPI). It divides the evolution of genetic programming into generation and selection stages, and then establishes a multi-state combination scheduling mode with multiple priority rules (PRs) for the first time to realize resource constrained project scheduling under both stochastic activity duration and new project insertion. In the generation stage, based on a modified attribute set for multi-project scheduling, NSGA-II is hybridized to evolve a non-dominated PR set for forming a selectable PR set. While in the selection stage, the whole decision-making process is divided into multiple states based on the completion activity duration, and a weighted normalized evolution process with two crossovers, two mutations and four local search operators to match the optimal PR for each state from the PR set. Under the existing benchmark, HH-TGP is compared with the existing methods to verify its effectiveness.

Multi-project scheduling problem under shared multi-skill resource constraints

Multi-project scheduling problem under shared multi-skill resource constraints

Simultaneous scheduling of multiple construction projects considering supplier selection and material transportation routing

In a construction supply chain, supply-related decisions can affect project scheduling because the project activities require essential resources to start. This study wants to show how the integration of project scheduling with supplier selection and transportation routing creates value in the entire project supply chain. Therefore, an integrated problem including the three mentioned concepts is considered in an organization managing multiple construction projects. Moreover, the following conditions are assumed in the problem to make it closer to the real situations of construction projects: (1) the quality of all projects is inspected by a single committee; (2) suppliers sell the resources with incremental discounts; (3) generalized precedence relations are considered between the project activities. In order to address the research problem, a mathematical model is proposed based on mixed-integer linear programming. The model is hard to solve because it simultaneously involves multi-project scheduling and vehicle routing. Therefore, a solution method based on the tabu search (TS) algorithm is suggested, validated with small-scale and big-scale examples. This method is used to compare the suggested model with two related models. The comparisons show that the proposed model creates a better balance between costs of different sectors in the project supply chain. Achieving this balance decreases the total cost associated with the problem and increases customer satisfaction. As a result of these findings, project managers and related researchers should pay more attention to supply chain integration in their problems.

Multi-project scheduling with multi-skilled workforce assignment considering uncertainty and learning effect for large-scale equipment manufacturer

This study addresses a resource-constrained multi-project scheduling and multi-skilled workforce assignment problem for the large-scale equipment manufacturing industry. The goal is to help the production manager perform effective planning and decrease the production cost under delivery constraints. The deterministic duration is used in most past investigations in resource-constrained multi-project scheduling problems. Uncertainty in project execution is neglected. Additional related factors are included in the current study to be close to practical circumstances. The required processing duration and material arrival time are modeled as stochastic variables. The heterogeneous skill efficiency of the internal workforce is considered with the learning effect. Skill efficiency grows with the accumulation of experience. Actual processing time is calculated on the basis of skill efficiency and stochastic process duration. The external workforce is hired when the internal workforce fails to satisfy processing demand. The objective is to minimize the expected integrated cost, which is the sum of tardiness penalty and external workforce cost. A genetic algorithm with a heuristic workforce assignment method is also developed, and a case study is illustrated to explain the scheduling result. The proposed approach with uniform crossover and local search mechanism outperforms other comparative methods by approximately 10.34% considering total costs. Furthermore, the Taguchi method of design of the experiment is conducted to find the optimal parameter setting efficiently. Afterward, the sensitivity analysis of production-related parameters is discussed.

Hybrid differential evolution particle swarm optimization (DEA-DCWPSO) for resource-constrained multi-project scheduling problem

In this paper, we studied the resource constrained project scheduling problem, and the research object is extended to the multi-project environment. On the basis of multi-project priority evaluation, with the goal of minimizing the weighted duration of multi-project, a multi-project schedule planning model is constructed. Through reasonable scheduling of multiple parallel projects sharing resources under resource constraints, it provides a decision-making basis for project managers to allocate resources reasonably under resource constraints to meet the duration requirements of each project and to shorten the weighted total duration of multiple projects as much as possible. A two-stage hybrid differential evolution particle swarm optimization algorithm is used to solve the model. In the first stage, differential evolution algorithm is used to produce new individuals, and in the second stage, particle swarm optimization algorithm uses a new speed update formula. In the first stage, in order to ensure that the optimal individual will not be destroyed by crossover and mutation, and to maintain the convergence of differential evolution algorithm, we try to introduce Elitist Retention (ER) strategy into differential evolution algorithm. In the second stage, we use a kind of particle swarm optimization algorithm with dynamically changing inertia weight. Through the dynamic changing inertia weight, the global search and local search ability of the algorithm can be adjusted flexibly. The case verification shows that the hybrid differential evolution particle swarm optimization algorithm can be used to solve the RCMPSP model more effectively.

A two-stage approach with softmax scoring mechanism for a multi-project scheduling problem sharing multi-skilled staff

This paper studies a multi-project scheduling problem sharing multi-skilled staff (MSMPSP). In a distributed decision-making environment, each project is independently scheduled by its project managers, while multiple projects compete for limited staff with multiple skills. A two-stage decomposition model, including an initial local scheduling stage and a global coordination stage, is established to describe this problem. Then, a two-stage approach with softmax scoring mechanism (TSA-SSM) is proposed to solve the local schedule of minimizing the single-project makespan and the global coordination decision of minimizing the total tardiness cost (TTC). According to the local scheduling plan obtained by the forward–backward scheduling genetic algorithm (FBSGA), the softmax scoring mechanism uses a greedy assignment strategy to solve the resource conflicts in the global decision stage, which is combined with the characteristics of multi-skilled staff. Based on the collection of multi-project instances, some numerical experiments are carried out. The results show that our TSA-SSM gets better solutions to large-size and strong conflict instances than some distributed and centralized methods, which proves that our method can effectively coordinate the allocation of multi-skilled staff among multiple projects. In addition, further experiments show that our method is also suitable for solving de-coupled problems, and satisfactory results are obtained.

A community detection approach for the resource leveling problem in a multi-project scheduling environment

This paper develops an integrated mathematical formulation that embraces the Resource Leveling Problem (RLP) and the Multi-Project Scheduling Problem (MPSP). To incorporate both aspects into one formulation, the proposed model is bi-objective that seeks to minimize projects’ durations and resource usage, simultaneously. Resources are not available in all time periods because of sickness, failure, maintenance, holidays, training, laying off, etc. This assumption complicates the scheduling process for multiple concurrent projects that share a limited number of resources with finite capacities. To tackle this intricacy, a coherent approach is required that not only schedules activities of multiple projects, but also levels resource consumptions as efficient as possible. Therefore, this study offers an approach based on the Community Detection problem to identify homogeneous communities of activities that have common resource requirements. These communities are obtained by the Vibration Damping Optimization (VDO) method through modularity maximization. The identified communities help the projects’ planner to avoid simultaneous scheduling of the activities within a community; hence, resource consumptions are minimized. A Multi-Objective Gravitational Search Algorithm (MOGSA) is developed to solve the proposed bi-objective problem. The MOGSA uses the communities detected by the VDO and schedules the projects. The MOGSA has been invigorated by using two Cellular Automata (CA), namely “Seeds” and “Wolfram’s elementary cellular automaton” in its procedures. A set of test problems have been examined to compare the efficacy of the MOGSA with some of the best existing algorithms. The results demonstrate that the MOGSA is highly competitive and yields proper solutions comparing to the outputs of well-known optimizers. Besides, a real construction case study has been presented to demonstrate that the proposed model and algorithm can deliver practical solutions.

A sealed bid auction-based two-stage approach for a decentralized multiproject scheduling problem with resource transfers

A sealed bid auction-based two-stage approach for a decentralized multiproject scheduling problem with resource transfers

An efficient hybrid integer and categorical particle swarm optimization algorithm for the multi-mode multi-project inverse scheduling problem in turbine assembly workshop

This article deals with the multi-mode multi-project inverse scheduling problem of the turbine assembly workshop in a Chinese electric power station equipment manufacturing firm considering some unexpected disturbances in the assembly process, such as materials delay, equipment failure and parts rework, etc. The objective is to optimize the assembly cost in inverse scheduling under the constraints of due date, worker modes, cranes, etc. A modified integer and categorical particle swarm optimization algorithm combined with Tabu search (MICPSO-TS) is proposed. In the proposed MICPSO-TS, double-vector encoding is presented to show the execution modes of activities and overtime schedule of projects which are optimized by ICPSO and TS respectively. A hybrid heuristic decoding algorithm (HHDA) including project order selection rules, crane scheduling rules, resource reservation mechanism and overtime determination rules is proposed. Eventually, the feasibility and effectiveness of the proposed MICPSO-TS are verified by the experimental test data and a real-world engineering case.

A filtering genetic programming framework for stochastic resource constrained multi-project scheduling problem under new project insertions

Multi-project management and uncertain environment are very common factors, and they bring greater challenges to scheduling due to the increase of problem complexity and response efficiency requirements. In this paper, a novel hyper-heuristic based filtering genetic programming (HH-FGP) framework is proposed for evolving priority rules (PRs) to deal with a multi-project scheduling problem considering stochastic activity duration and new project insertion together, namely the Stochastic Resource Constrained Multi-Project Scheduling Problem under New Project Insertions (SRCMPSP-NPI), within heuristic computation time. HH-FGP is designed to divide traditional evolution into sampling and filtering evolution for simultaneously filtering two kinds of parameters constituting PRs, namely depth range and attribute, to obtain more effective PRs. Based on this, the existing genetic search and local search are improved to meet the depth constraints, and a multi-objective evaluation mechanism is designed to achieve effective filtering. Under the existing benchmark, HH-FGP is compared and analysed with the existing methods to verify its effectiveness.