A new heuristic solution method in resource-constrained project scheduling

Dynamic reserve site selection under contagion risk of deforestation

The management of resources dedicated for the appraisal of critical product quality attributes is a matter of increasing importance in multistage manufacturing systems. An interesting quality planning problem lies in the operational implementation of a quality inspection policy to select specific locations and machines for inspection activities. In this research, a mathematical model is developed for solving the inspection allocation and assignment problems in a multistage manufacturing system. The multistage system here considered processes part types with distinctive machine visitation sequences, and inspections can be performed on one of the several inspection stations with possible inspection errors. The computational process shows that as the problem size increases the number of inspection allocation possibilities and the computer system memory required becomes prohibitively large. Thus, three heuristic solution methods have been developed and are found to be effective in arriving at a near optimal solution with considerable savings in computational effort when compared with the optimization method based on complete enumeration. The heuristic solution methods are further compared with each other to determine their relative strength and weakness. A computer simulation is conducted to evaluate the methods developed.

Project scheduling is an important management task in many companies across different industries. Generally, projects require resources, such as personnel or funds, whose availabilities are limited, giving rise to the challenging problem of resource-constrained project scheduling. In this paper, we consider the scheduling of a project consisting of precedence-related activities that require time and two types of resources for execution: storage resources representing, e.g., the project budget; and renewable resources representing, e.g., personnel or equipment. Storage resources are consumed by activities at their start or produced upon their completion, while renewable resources are allocated to activities at their start and released upon their completion. The resource-constrained project scheduling problem with consumption and production of resources (RCPSP-CPR) consists of determining a minimum-makespan schedule such that all precedence relations are respected, the demand for each renewable resource never exceeds its capacity, and the stock level of each storage resource never falls below a prescribed minimum. Due to the consideration of storage resources, the feasibility variant of this problem is NP-complete. We propose a novel compact mixed-integer linear programming (MILP) model based on a novel type of sequencing variables. These variables enable us to identify which activities are processed in parallel and whether a sequencing of activities is necessary to respect the resource capacities. Our computational results indicate that our novel model significantly outperforms state-of-the-art MILP models for all considered scarcity settings of the storage resources. Additionally, our results indicate a superior performance for instances of the well-known resource-constrained project scheduling problem (RCPSP).

A few analytical and heuristic solution methods were developed for some problems in Protean systems. The aim of this paper is to provide guidelines for using computer software developed in an IBM-PC/AT compatible environment to implement these solution methods. The problems and models are briefly stated to facilitate the use of this software.

Influence maximization problems aim to identify key players in (social) networks and are typically motivated from viral marketing. In this work, we introduce and study the Generalized Least Cost Influence Problem (GLCIP) that generalizes many previously considered problem variants and allows to overcome some of their limitations. A formulation that is based on the concept of activation functions is proposed together with strengthening inequalities. Exact and heuristic solution methods are developed and compared for the new problem. Our computational results also show that our approaches outperform the state-of-the-art on relevant, special cases of the GLCIP.

The multiple facility location problem (MFLP) is to locate certain facilities so as to serve optimally a given set of customers, whose locations and requirements are known. When the facility locations have to be selected from a given set of candidate locations, the corresponding location problem becomes a discrete multiple facility location problem (DMFLP). In this study we consider a special case of DMFLP where multiple facilities that are of different type are to be placed (location decision) and assign customers to these facilities (allocation or assignment). The case will be discussed based on with and without interactions among new facilities. We then propose new heuristic solution methods and branch-and-bound algorithms. Computational results on randomly generated data in comparison with optimal solutions indicate that the new methods are both accurate and efficient.

Course scheduling and classroom assignment problem is a common problem for all educational fields. It is an NP Hard problem. Especially, universities should handle this problem while preparing course timetabling for each level due to elective courses and students taking upper/lower-level courses. There is a vast literature on this problem both for modelling and solution approaches. Although the essence of the problems is similar for the most, each problem has some unique restriction and/or parameters. We study the timetabling of the courses of Industrial Engineering program at Yaşar University. We develop a mathematical model to maximize the minimum lecturer satisfaction. Due to the computational complexity of the problem, we proposed a heuristic solution method, namely the Genetic Algorithm. Gene structure we use ensures the feasibility of many constraints of the mathematical model. Computational results of optimal and heuristic method are compared. As a real-life implementation, a university in Turkey Industrial Engineering Department data is used and results were reported. KeywordsCourse schedulingMathematical modelling0–1 Integer modellingTimetablingGenetic algorithm

Postboxes generally represent a cost for a postal service provider since they require to be maintained and daily visited by an operator to collect the letters. On the other hand, for the users, they are the main access points to the postal network and so the reduction of their number could result in a deterioration of the quality of the service for them. This work addresses a real problem involving the reduction of the number of postboxes located in rural areas to reorganize the collection operations of a postal service provider. We formulate this problem as a location-routing problem where the number of postmen employed is minimized guaranteeing the user accessibility. Exact and heuristic solution methods have been developed, tested and compared to existing solution methods on instances based on real data of rural instances showing the effectiveness of the proposed methodologies.

In a telecommunication network, routing and wavelength assignment (RWA) is the problem of finding lightpaths for incoming connection requests. When facing a dynamic traffic, greedy assignment of lightpaths to incoming requests based on predefined deterministic policies leads to a fragmented network that cannot make use of its full capacity because of stranded bandwidth. At this point, service providers try to recover the capacity via a defragmentation process. We study this setting from two perspectives: (i) while granting the connection requests via the RWA problem and (ii) during the defragmentation process by lightpath rerouting. For both problems, we present the first two-stage stochastic integer programming model incorporating incoming request uncertainty to maximize the expected grade of service. We develop a decomposition-based solution approach, which uses various relaxations of the problem and a newly developed problem-specific cut family. Simulation of two-stage policies for a variety of instances in a rolling-horizon framework of 52 stages shows that our stochastic models provide high-quality solutions when compared with traditionally used deterministic ones. Specifically, the proposed provisioning policies yield improvements of up to 19% in overall grade of service and 20% in spectrum saving, while the stochastic lightpath rerouting policies grant up to 36% more requests, using up to just 4% more bandwidth spectrum.Summary of Contribution: For handling the intrinsic uncertainty of demand in the telecommunications industry, this paper proposes novel stochastic models and solution methodology for two fundamental problems in telecommunications at operational level: (i) routing and wavelength assignment (RWA) and (ii) lightpath rerouting problem. Despite the vast literature on the RWA problem, stochastic optimization has not been considered as a viable solution for resource allocation in optical networks. We propose two-stage stochastic programming models for both problems and design efficient decomposition-based solution methods that use various relaxations of the models and a new family of cutting planes. Our extensive and rigorous numerical experiments show the significant merit of incorporating uncertainty into decision making, as well as the effectiveness of the decomposition framework and our newly designed family of cuts in enhancing the solvability of both models. This work opens new avenues to explore where the powerful stochastic programming literature can be leveraged to make operational decisions in telecommunications problems, a field that currently relies mostly on deterministic and heuristic solution methods.

This paper studies an integrated decision problem on assigning and sequencing tasks to service providers. By considering the uncertain available time of the service providers, a stochastic programming model is proposed on the basis of a finite set of scenarios. Some realistic factors are also taken into account and formulated as some non-linear cost functions, which are linearised in this study. Moreover, a heuristic solution method is designed for solving some extremely large-scale problem cases within a reasonable period of time. Numerical experiments are performed to validate the effectiveness of the proposed model and the efficiency of the proposed solution method.

Supply Chain Optimization in Pulp Distribution Using a Rolling Horizon Solution Approach

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.

In this paper we present different exact and heuristic optimization methods for scheduling planes which want to land (and start) at an airport - the Aircraft Sequencing Problem (ASP). We compare two known integer programming formulations with four new exact and heuristic solution methods regarding quality, speed and flexibility.KeywordsSimulated Annealing AlgorithmConstraint ProgrammingValid SolutionMixed Integer Programming FormulationPlane LandThese 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.

Project scheduling consists of defining and assigning resources to a set of time-scattered activities. Several economic, business and engineering disciplines are considered for practical application: project management, operations research, software development, systems engineering. In Resource-Constrained Project Scheduling (RCPS), task allocation is determined by resource distribution based on a set of constraints. Poor constraint evaluation and its impact on the project schedule, may lead to project baseline disruptions, therefore to cost failover and company loss. The aim of the current research is to provide a solution for the standard Resource Constrained Project Scheduling Problem (RCPSP) through a hybrid heuristic algorithm based on priority rule scheduling (priority rules, schedule generation schemes). We present a conceptual framework for a software engineering context, where activities of concurrent projects are allocated based on employees' skills to optimize a regular objective of estimated activity duration.

The planning of purchasing and production lot-size for multi-item multi-period multi-workstation capacitated production (MLCLSP-M) is discussed. Since the model is a NP-hard problem, this research proposes a heuristic method (A-LS), which decomposes the model into two sub-problems and iteratively solves them. The two sub-problems consisting of an assignment sub-problem with given lot size and a lot sizing sub-problem with given assignment. Solution method was developed, tested and compared to illustrate the solution quality and computational time. Mathematical models for both sub-problems were solved with AMPL/CPLEX 8.0.0 solver. The computational results indicate that the proposed heuristic (A-LS) gives satisfactory solution with faster solving time comparing with the original mathematical model.