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Multi-mode resource and precedence-constrained project scheduling is a well-known challenging real-world optimisation problem. An important variant of the problem requires scheduling of activities for multiple projects considering availability of local and global resources while respecting a range of constraints. A critical aspect of the benchmarks addressed in this paper is that the primary objective is to minimise the sum of the project completion times, with the usual makespan minimisation as a secondary objective. We observe that this leads to an expected different overall structure of good solutions and discuss the effects this has on the algorithm design. This paper presents a carefully designed hybrid of Monte-Carlo tree search, novel neighbourhood moves, memetic algorithms, and hyper-heuristic methods. The implementation is also engineered to increase the speed with which iterations are performed, and to exploit the computing power of multicore machines. Empirical evaluation shows that the resulting information-sharing multi-component algorithm significantly outperforms other solvers on a set of "hidden" instances, i.e. instances not available at the algorithm design phase.

Introduction Agricultural production involves a series of tasks including tillage, planting, and harvesting, which must be done at the right time for each region and type of product. Failing to complete these tasks on time can lead to a decrease in yield. Farmers may wrongly attribute this to factors such as infertile land, pests, diseases, and uneven rainfall distribution. However, this decrease in yield may not always be evident or tangible. To avoid such losses and unforeseen expenses, it is crucial to plan agricultural mechanization projects using the principles of project control. Agricultural projects, like industrial projects, must be carried out in the correct order and at the right time to achieve optimal results. Given the limited availability of resources for mechanization projects, it is imperative to meticulously plan activities to ensure that they are carried out on time and with maximum utilization of resources. To address these challenges, researchers have used meta-heuristic methods in project control, such as the colonial competition algorithm, which has been proven effective in solving the issue of scheduling projects with limited resources. The algorithm has been tested across various industrial activities and projects, and its performance in scheduling the Resource-Constrained Project Scheduling Problem (RCPSP) has been validated by researchers globally. Materials and Methods There is a scheduling issue regarding limited resources in agriculture, and this study presents a novel approach using the imperialist competitive algorithm (ICA). The algorithm not only explores a wider solution space but also strives to minimize deviation from the optimal solution, thereby improving the success rate of the proposed method. This research focuses on two dominant products, wheat and rapeseed, produced in Moghan Agriculture and Industry located in Northwest Iran. To evaluate the effectiveness of ICA, we compared it with other well-known meta-heuristic algorithms. We successfully resolved the problem of project scheduling problem with limited resources by implementing the imperialist competitive algorithm. Our findings have shown that this approach not only significantly increased efficiency but also outperformed other algorithms. Results and Discussion In this study, we assessed the efficiency of meta-heuristic methods in solving the RCPSP, which can be useful in optimizing the timeliness of project execution, especially for large-scale projects. Some meta-heuristic methods are only useful for smaller problems, while others can provide near-optimal solutions for larger problems, making them suitable for RCPSP. The algorithm explores a wide range of solutions and avoids premature convergence and getting stuck in local optima, unlike other algorithms such as the genetic algorithm. Optimization reduced the required budget and shortened the duration by 42 days for wheat and 25 days for rapeseed. Conclusion We utilized the colonial competition algorithm to address the RCPSP problem in agricultural mechanization projects for two agricultural products in Moghan. Our results show that the proposed algorithm converged and reached the optimal solution. The proposed algorithm was compared with other algorithms and it outperformed them.

The field of project scheduling has received a great deal of study for many years with a steady evolution of problem complexity and solution methodologies. As solution methodologies and technologies improve, increasingly complex, real-world problems are addressed, presenting researchers a continuing challenge to find ever more effective means for approaching project scheduling. This dissertation introduces a project scheduling problem which is applicable across a broad spectrum of real-world situations. The problem is based on the well-known Resource-Constrained Project Scheduling Problem, extended to include multiple modes, generalized precedence, and expediting resources. The problem is further extended to include multiple projects which have generalized precedence, renewable and nonrenewable resources, and expediting resources at the program level. The problem presented is one not previously addressed in the literature nor is it one to which the existing specialized project scheduling methodologies can be directly applied. This dissertation presents a decomposition approach for solving the problem, including algorithms for solving the decomposed subproblems and the master problem. This dissertation also describes a methodology for generating instances of the new problem, extending the way existing problem generators describe and construct network structures and this class of problem. The methodologies presented are demonstrated through extensive empirical testing.

An algorithm is proposed for scheduling project networks having a single constraining resource and a constant level of available resources. The algorithm seeks to generate a minimum length schedule indirectly by maximizing the average resource utilization over the two time intervals represented by the current decision point and its successor. An attempt is made to schedule all of the activities whose predecessors have been completed, failing this all feasible subsets are considered. Where possible, the algorithm considers only those subsets which introduce new activities at the subsequent decision point.

An algorithm is proposed for scheduling project networks having a single constraining resource and a constant level of available resources. The algorithm seeks to generate a minimum length schedule indirectly by maximizing the average resource utilization over the two time intervals represented by the current decision point and its successor. An attempt is made to schedule all of the activities whose predecessors have been completed; failing this, all feasible subsets are considered. Where possible, the algorithm considers only those subsets which introduce new activities at the subsequent decision point.

We study a Lagrangian decomposition algorithm recently proposed by Dan Bienstock and Mark Zuckerberg for solving the LP relaxation of a class of open pit mine project scheduling problems. In this study we show that the Bienstock-Zuckerberg (BZ) algorithm can be used to solve LP relaxations corresponding to a much broader class of scheduling problems, including the well-known Resource Constrained Project Scheduling Problem (RCPSP), and multi-modal variants of the RCPSP that consider batch processing of jobs. We present a new, intuitive proof of correctness for the BZ algorithm that works by casting the BZ algorithm as a column generation algorithm. This analysis allows us to draw parallels with the well-known Dantzig-Wolfe (DW) algorithm. We discuss practical computational techniques for speeding up the performance of the BZ and DW algorithms on project scheduling problems. Finally, we present computational experiments independently testing the effectiveness of the BZ and DW algorithms on different sets of publicly available test instances. Our computational experiments confirm that the BZ algorithm significantly outperforms the DW algorithm for the problems considered. Our computational experiments also show that the proposed speed-up techniques can have a significant impact on solution time. We provide some insights on what might be explaining this significant difference in performance.

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This research aims to develop new optimization models and algorithms for project scheduling under both resource constraints and uncertainties, a problem known as the stochastic resource-constrained project scheduling problem (SRCPSP) in the operations research (OR) and scheduling literature. In a typical SRCPSP, a decision-maker attempts to obtain a feasible schedule of project tasks such that: (i) their temporal/precedence relationships are satisfied; (ii) the available resource capacity is not exceeded in each time period; and (iii) the expected project makespan is minimized. Using the developed modeling and solution methodologies, it is our goal to enhance the quality of decision support for scheduling complex large-scale projects in military, manufacturing, construction and professional service sectors.

This research aims to develop new optimization models and algorithms for project scheduling under both resource constraints and uncertainties, a problem known as the stochastic resource-constrained project scheduling problem (SRCPSP) in the operations research (OR) and scheduling literature. In a typical SRCPSP, a decision-maker attempts to obtain a feasible schedule of project tasks such that: (i) their temporal/precedence relationships are satisfied; (ii) the available resource capacity is not exceeded in each time period; and (iii) the expected project makespan is minimized. Using the developed modeling and solution methodologies, it is our goal to enhance the quality of decision support for scheduling complex large-scale projects in military, manufacturing, construction and professional service sectors.

In this paper Hybrid Ant Colony Optimization (HAntCO) approach in solving Multi--Skill Resource Constrained Project Scheduling Problem (MS--RCPSP) has been presented. We have proposed hybrid approach that links classical heuristic priority rules for project scheduling with Ant Colony Optimization (ACO). Furthermore, a novel approach for updating pheromone value has been proposed, based on both the best and worst solutions stored by ants. The objective of this paper is to research the usability and robustness of ACO and its hybrids with priority rules in solving MS--RCPSP. Experiments have been performed using artificially created dataset instances, based on real--world ones. We published those instances that can be used as a benchmark. Presented results show that ACO--based hybrid method is an efficient approach. More directed search process by hybrids makes this approach more stable and provides mostly better results than classical ACO.

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The major suppliers of commercially available project management software were identified. These suppliers were surveyed to ascertain the nature of the time constrained and resource constrained scheduling effectiveness provided by the software. The survey also identifies the major features of the software as well as the minimum and recommended computer hardware requirements for the software. The software suppliers were all provided with the same sample network for time constrained and resource constrained scheduling. The results of the scheduling calculations are analyzed to evaluate the effectiveness of the software's procedures. No specific recommendations or opinions are given concerning any of the individual software products or suppliers

The common presuppositions and limitations regarding the Resource Constrained Project Scheduling Problem (RCPSP) were investigated in addition to their reliability in modeling in order to investigate the possibility of   availability of renewable resources using a new attitude. The objective of modeling RCPSP was the quantification of total costs and minimization of delays in projects. Hence, in order to mathematically model RCPSP, the first non-linear complex integer math programming was transformed into a linear programming model using the features of exponential functions. To solve the final linear math problem, some experimental examples were designed in different dimensions aiming to study the performance and efficiency of the designed model. For solving low-dimension problems, the exact (epsilon) constraint multi-objective optimization method was used in the Lingo software. A meta-heuristic algorithm called NSGA-II was employed to find solutions for high-dimension problems that the Exact method could not solve. The results of using these algorithms and the statistical analysis (with 95% reliability) indicated that the performance was suitable for the Genetic Algorithm (GA). The calculation error between the Exact method and the meta-heuristic method for the three target categories of total cost, time delay, and reliability was calculated based on the obtained results. The number of errors in calculating the total cost was 26%, 19%, and 5%, respectively. Also, the delay objective function error was equal to 28%, 24%, 12 %, and 14%, respectively. Finally, the reliability objective function error value was equal to 8%, 3%, 29%, and 36%, respectively. Accordingly, it can be concluded that this meta-heuristic algorithm (GA) has more efficiency and more apposite performance for the recommended model compared with the Exact optimization software. The use of the math model designed in this study can result in decreasing the time delays in projects and the costs of scheduling problems, as well as increasing the reliability in multi-mode activities.

The technical report presents a generic exact solution approach for minimizing the project duration of the resource-constrained project scheduling problem with generalized precedences (Rcpsp/max). The approach uses lazy clause generation, i.e., a hybrid of finite domain and Boolean satisfiability solving, in order to apply nogood learning and conflict-driven search on the solution generation. Our experiments show the benefit of lazy clause generation for finding an optimal solutions and proving its optimality in comparison to other state-of-the-art exact and non-exact methods. The method is highly robust: it matched or bettered the best known results on all of the 2340 instances we examined except 3, according to the currently available data on the PSPLib. Of the 631 open instances in this set it closed 573 and improved the bounds of 51 of the remaining 58 instances.

The technical report presents a generic exact solution approach for minimizing the project duration of the resource-constrained project scheduling problem with generalized precedences (Rcpsp/max). The approach uses lazy clause generation, i.e., a hybrid of finite domain and Boolean satisfiability solving, in order to apply nogood learning and conflict-driven search on the solution generation. Our experiments show the benefit of lazy clause generation for finding an optimal solutions and proving its optimality in comparison to other state-of-the-art exact and non-exact methods. The method is highly robust: it matched or bettered the best known results on all of the 2340 instances we examined except 3, according to the currently available data on the PSPLib. Of the 631 open instances in this set it closed 573 and improved the bounds of 51 of the remaining 58 instances.

The major suppliers of commercially available project management software were identified. These suppliers were surveyed to ascertain the nature of the time constrained and resource constrained scheduling effectiveness provided by the software. The survey also identifies the major features of the software as well as the minimum and recommended computer hardware requirements for the software. The software suppliers were all provided with the same sample network for time constrained and resource constrained scheduling. The results of the scheduling calculations are analyzed to evaluate the effectiveness of the software's procedures. No specific recommendations or opinions are given concerning any of the individual software products or suppliers.

The major suppliers of commercially available project management software were identified. These suppliers were surveyed to ascertain the nature of the time constrained and resource constrained scheduling effectiveness provided by the software. The survey also identifies the major features of the software as well as the minimum and recommended computer hardware requirements for the software. The software suppliers were all provided with the same sample network for time constrained and resource constrained scheduling. The results of the scheduling calculations are analyzed to evaluate the effectiveness of the software's procedures. No specific recommendations or opinions are given concerning any of the individual software products or suppliers.