Production rates for various activities and overall construction project duration are significantly influenced by crew formation. Crews are composed of available renewable resources. Construction companies tend to reduce the number of permanent employees, which reduces fixed costs, but at the same time limits production capacity. Therefore, construction project planning must be carried out by means of scheduling methods which allow for resource constrains. Authors create a mathematical model for optimized scheduling of linear construction projects with consideration of resources and work continuity constraints. Proposed approach enables user to select optimal crew formation under limited resource supply. This minimizes project duration and improves renewable resource utilization in construction linear projects. This paper presents mixed integer linear programming to model this problem and uses a case study to illustrate it.
Construction risk assessment is the final and decisive stage of risk analysis. When highly changeable conditions of works execution are predicted, risk should be evaluated in the favorable, moderate, and difficult random conditions of construction. Given the random conditions, the schedule and cost estimate of the construction are developed. Based on these values, the risk of final deadline delay and the risk of total cost increase of construction completion are calculated. Next, the charts of the risks are elaborated. Risk changes are shown in the charts and are analyzed in the range [1, 0].
The basic element of a project organizing construction works is a schedule. The preparation of the data necessary to specify the timings of the construction completion as indicated in the schedule involves information that is uncertain and hard to quantify. The article presents the methods of building a schedule which includes a fuzzy amount of labour, time standards and number of workers. The proposed procedure allows determining the real deadline for project completion, taking into account variable factors affecting the duration of the individual works.
The paper present the concept of stability assessing the of solutions which are construction schedules. Rank have been obtained through the use of task scheduling rules and the application of the KASS software. The aim of the work is the choice of the equivalent solution in terms of the total time of the project. The selected solution optimization task should be characterized by the highest resistance to harmful environmental risk factors. To asses the stability of schedule simulation technique was used.
This paper presents a model of scheduling of multi unit construction project based on an NP-hard permutation flow shop problem, in which the considered criterion is the sum of the costs of the works' execution of the project considering the time of the project as a constraint. It is also assumed that each job in the units constituting the project may be realized in up to three different ways with specific time and cost of execution. The optimization task relies on solving the problem with two different decision variables: the order of execution of units (permutation) and a set of ways to carry out the works in units. The task presented in the paper is performed with the use of a created algorithm which searches the space of solutions in which metaheuristic simulated annealing algorithm is used. The paper presents a calculation example showing the applicability of the model in the optimization of sub-contractors' work in the construction project.
The In the paper, we investigate two single processor problems, which deal with the process of negotiation between a producer and a customer about delivery time of ﬁnal products. This process is modelled by a due interval, which is a generalization of well known classical due date and describes a time interval, in which a job should be ﬁnished. In this paper we consider two diffierent mathematical models of due intervals. In both considered problems we should ﬁnd such a schedule of jobs and such a determination of due intervals to each job, that the generalized cost function is minimized. The cost function is the maximum of the following three weighted parts: the maximum tardiness, the maximum earliness and the maximum due interval size. For the ﬁrst problem we proved several properties of its optimal solution and next we show the mirror image property for both of considered problems, which helps us to provide an optimal solution for the second problem.
The paper presents a new elastic scheduling task model which has been used in the uniprocessor node of a control measuring system. This model allows the selection of a new set of periods for the occurrence of tasks executed in the node of a system in the case when it is necessary to perform additional aperiodic tasks or there is a need to change the time parameters of existing tasks. Selection of periods is performed by heuristic algorithms. This paper presents the results of the experimental use of an elastic scheduling model with a GRASP heuristic algorithm.
Redundancy based methods are proactive scheduling methods for solving the Project Scheduling Problem (PSP) with non-deterministic activities duration. The fundamental strategy of these methods is to estimate the activities duration by adding extra time to the original duration. The extra time allows to consider the risks that may affect the activities durations and to reduce the number of adjustments to the baseline generated for the project. In this article, four methods based on redundancies were proposed and compared from two robustness indicators. These indicators were calculated after running a simulation process. On the other hand, linear programming was applied as the solution technique to generate the baselines of 480 projects analyzed. Finally, the results obtained allowed to identify the most adequate method to solve the PSP with probabilistic activity duration and generate robust baselines.
The paper concerns the two-machine non-preemptive flow shop scheduling problem with a total late work criterion and a common due date (F2|dj = d|Y ). The late work performance measure estimates the quality of a solution with regard to the duration of late parts of activities performed in the system, not taking into account the quantity of this delay. In the paper, a few theorems are formulated and proven, describing features of an optimal solution for the problem mentioned, which is NP-hard. These theorems can be used in exact exponential algorithms (as dominance relations reducing the number of solutions enumerated explicitly), as well as in heuristic and metaheuristic methods (supporting the construction of sub-optimal schedules of a good quality).
The problem considered in the paper is motivated by production planning in a foundry equipped with the furnace and casting line, which provides a variety of castings in various grades of cast iron/steel for a large number of customers. The quantity of molten metal does not exceed the capacity of the furnace, the load is a particular type of metal from which the products are made. The goal is to create the order of the melted metal loads to prevent delays in delivery of goods to customers. This problem is generally considered as a lot-sizing and scheduling problem. The paper describes a mathematical programming model that formally defines the optimization problem and its relaxed version that is based on the conception of rolling-horizon planning
The problem considered in the paper is motivated by production planning in a foundry equipped with the furnace and casting line, which provides a variety of castings in various grades of cast iron/steel for a large number of customers. The quantity of molten metal does not exceed the capacity of the furnace, the load is a particular type of metal from which the products are made in the automatic casting lines. The goal is to create the order of the melted metal loads to prevent delays in delivery of goods to customers. This problem is generally considered as a lot-sizing and scheduling problem. The paper describes two computational intelligence algorithms for simultaneous grouping and scheduling tasks and presents the results achieved by these algorithms for example test problems.
Mathematical programming, constraint programming and computational intelligence techniques, presented in the literature in the field of operations research and production management, are generally inadequate for planning real-life production process. These methods are in fact dedicated to solving the standard problems such as shop floor scheduling or lot-sizing, or their simple combinations such as scheduling with batching. Whereas many real-world production planning problems require the simultaneous solution of several problems (in addition to task scheduling and lot-sizing, the problems such as cutting, workforce scheduling, packing and transport issues), including the problems that are difficult to structure. The article presents examples and classification of production planning and scheduling systems in the foundry industry described in the literature, and also outlines the possible development directions of models and algorithms used in such systems.
In this article we present an industrial application of our mathematical model that integrates planning and scheduling. Our main objective is to concretize our model and compare the reel results with the theoretical ones. Our application is realized on a conditioning line of pharmaceutical products at the ECAM EPMI production laboratory. For this reason and to save time, we used Witness simulation tool. It gives an overall idea of how the line works, the Makespan of each simulation and it highlights areas for improvement. We looked for the best resulting sequence which corresponds to the minest Makespan and total production cost. Then this sequence is applied on the conditioning line of pharmaceutical products for simulation. On the other hand, we program our mathematical model with the parameters of the conditioning line under python in version 3.6 and we adopt a simulation/optimization coupling approach to verify our model.
The article presents the problem of scheduling a multi-stage project with limited availability of resources with the discounted cash flow maximization criterion from the perspective of a contractor. The contractor’s cash outflows are associated with the execution of activities. The client’s payments (cash inflows for the contractor) are performed after completing the agreed project’s stages. The proposed solution for this problem is the use of insertion algorithms. Schedules are generated using forward and backward schedule generation schemes and modified justification techniques. The effectiveness of the proposed procedures is the subject of the examination with the use of standard test instances with additionally defined financial settlements of a project.
The paper refers to planning deliveries of food products (especially those available in certain seasons) to the recipients: supermarket networks. The paper presents two approaches to solving problems of simultaneous selection of suppliers and transportation modes and construction of product flow schedules with these transportation modes. Linear mathematical models have been built for the presented solution approaches. The cost criterion has been taken into consideration in them. The following costs have been taken into account: purchase of products by individual recipients, transport services, storing of products supplied before the planned deadlines and penalties for delays in supply of products. Two solution approaches (used for transportation planning and selection of suppliers and selection of transportation modes) have been compared. The monolithic approach calls for simultaneous solutions for the problems of supplier selection and selection of transportation modes. In the alternative (hierarchical) solution approach, suppliers are selected first, and then transportation companies and their relevant transportation modes are selected. The results of computational experiments are used for comparison of the hierarchical and monolithic solution approaches.
The Bulletin of the Polish Academy of Sciences: Technical Sciences (Bull.Pol. Ac.: Tech.) is published bimonthly by the Division IV Engineering Sciences of the Polish Academy of Sciences, since the beginning of the existence of the PAS in 1952. The journal is peer‐reviewed and is published both in printed and electronic form. It is established for the publication of original high quality papers from multidisciplinary Engineering sciences with the following topics preferred: Artificial and Computational Intelligence, Biomedical Engineering and Biotechnology, Civil Engineering, Control, Informatics and Robotics, Electronics, Telecommunication and Optoelectronics, Mechanical and Aeronautical Engineering, Thermodynamics, Material Science and Nanotechnology, Power Systems and Power Electronics. Journal Metrics: JCR Impact Factor 2018: 1.361, 5 Year Impact Factor: 1.323, SCImago Journal Rank (SJR) 2017: 0.319, Source Normalized Impact per Paper (SNIP) 2017: 1.005, CiteScore 2017: 1.27, The Polish Ministry of Science and Higher Education 2017: 25 points. Abbreviations/Acronym: Journal citation: Bull. Pol. Ac.: Tech., ISO: Bull. Pol. Acad. Sci.-Tech. Sci., JCR Abbrev: B POL ACAD SCI-TECH Acronym in the Editorial System: BPASTS.
Vibrating plates have been recently used for a number of active noise control applications. They are resistant to difficult environmental conditions including dust, humidity, and even precipitation. However, their properties significantly depend on temperature. The plate temperature changes, caused by ambient temperature changes or plate heating due to internal friction, result in varying response of the plate, and may make it significantly different than response of a fixed model. Such mismatch may deteriorate performance of an active noise control system or even lead to divergence of a model-based adaptation algorithm. In this paper effects of vibrating plate temperature variation on a feedforward adaptive active noise reduction system with the multichannel Filtered-reference LMS algorithm are examined. For that purpose, a thin aluminum plate is excited with multiple Macro-Fiber Composite actuators. The plate temperature is forced by a set of Peltier cells, what allows for both cooling and heating the plate. The noise is generated at one side of the plate, and a major part of it is transmitted through the plate. The goal of the control system is to reduce sound pressure level at a specified area on the other side of the plate. To guarantee successful operation of the control system in face of plate temperature variation, a gain-scheduling scheme is proposed to support the Filtered-reference LMS algorithm.
Orthogonal frequency division multiple access (OFDMA) in Long Term Evolution (LTE) can effectively eliminate intra-cell interferences between the subcarriers in a single serving cell. But, there is more critical issue that, OFDMA cannot accomplish to decrease the inter-cell interference. In our proposed method, we aimed to increase signal to interference plus noise ratio (SINR) by dividing the cells as cell center and cell edge. While decreasing the interference between cells, we also aimed to increase overall system throughput. For this reason, we proposed a dynamic resource allocation technique that is called Experience-Based Dynamic Soft Frequency Reuse (EBDSFR). We compared our proposed scheme with different resource allocation schemes that are Dynamic Inter-cellular Bandwidth Fair Sharing FFR (FFRDIBFS) and Dynamic Inter-cellular Bandwidth Fair Sharing Reuse-3 (Reuse3DIBFS). Simulation results indicate that, proposed EBDSFR benefits from overall cell throughput and obtains higher user fairness than the reference schemes.
The problem of sequencing jobs on a single machine to minimize total cost (earliness and tardiness) is nowadays not just important due to traditional concerns but also due to its importance in the context of Collaborative Networked Organizations and Virtual Enterprises, where precision about promptly responses to customers’ requests, along with other important requirements, assume a crucial role. In order to provide a contribution in this direction, in this paper the authors contribute with an applied constructive heuristics that tries to find appropriate solutions for single machine scheduling problems under different processing times and due dates, and without preemption allowed. In this paper, two different approaches for single-machine scheduling problems, based on external and internal performance measures are applied to the problem and a comparative analysis is performed. Computational results are presented for the problem under Just-in-Time and agile conditions on which each job has a due date, and the objective is to minimize the sum of holding costs for jobs completed before their due date and tardiness costs for jobs completed after their due date. Additional computational tests were developed based on different customer and enterprise oriented performance criteria, although preference is given to customer-oriented measures, namely the total number of tardy jobs and the maximum tardiness.
The paper presents a novel Iterated Local Search (ILS) algorithm to solve multi-item multi-family capacitated lot-sizing problem with setup costs independent of the family sequence. The model has a direct application to real production planning in foundry industry, where the goal is to create the batches of manufactured castings and the sequence of the melted metal loads to prevent delays in delivery of goods to clients. We extended existing models by introducing minimal utilization of furnace capacity during preparing melted alloy. We developed simple and fast ILS algorithm with problem-specific operators that are responsible for the local search procedure. The computational experiments on ten instances of the problem showed that the presence of minimum furnace utilization constraint has great impact on economic and technological conditions of castings production. For all test instances the proposed heuristic is able to provide the results that are comparable to state-of-the art commercial solver.
In the paper, we present a coordinated production planning and scheduling problem for three major shops in a typical alloy casting foundry, i.e. a melting shop, molding shop with automatic line and a core shop. The castings, prepared from different metal, have different weight and different number of cores. Although core preparation does not required as strict coordination with molding plan as metal preparation in furnaces, some cores may have limited shelf life, depending on the material used, or at least it is usually not the best organizational practice to prepare them long in advance. Core shop have limited capacity, so the cores for castings that require multiple cores should be prepared earlier. We present a mixed integer programming model for the coordinated production planning and scheduling problem of the shops. Then we propose a simple Lagrangian relaxation heuristic and evolutionary based heuristic to solve the coordinated problem. The applicability of the proposed solution in industrial practice is verified on large instances of the problem with the data simulating actual production parameters in one of the medium size foundry.
A novel approach for treating the uncertainty about the real levels of finished products during production planning and scheduling process is presented in the paper. Interval arithmetic is used to describe uncertainty concerning the production that was planned to cover potential defective products, but meets customer’s quality requirement and can be delivered as fully valuable products. Interval lot sizing and scheduling model to solve this problem is proposed, then a dedicated version of genetic algorithm that is able to deal with interval arithmetic is used to solve the test problems taken from a real-world example described in the literature. The achieved results are compared with a standard approach in which no uncertainty about real production of valuable castings is considered. It has been shown that interval arithmetic can be a valuable method for modeling uncertainty, and proposed approach can provide more accurate information to the planners allowing them to take more tailored decisions.
The problem considered in the paper is motivated by production planning in a foundry equipped with a furnace and a casting line, which provides a variety of castings in various grades of cast iron/steel for a large number of customers. The goal is to create the order of the melted metal loads to prevent delays in delivery of goods to customers. This problem is generally considered as a lot-sizing and scheduling problem. However, contrary to the classic approach, we assumed the fuzzy nature of the demand set for a given day. The paper describes a genetic algorithm adapted to take into account the fuzzy parameters of simultaneous grouping and scheduling tasks and presents the results achieved by the algorithm for example test problem.
The objective of the milk-run design problem considered in this paper is to minimize transportation and inventory costs by manipulating fleet size and the capacity of vehicles and storage areas. Just as in the case of an inventory routing problem, the goal is to find a periodic distribution policy with a plan on whom to serve, and how much to deliver by what fleet of tugger trains travelling regularly on which routes. This problem boils down to determining the trade-off between fleet size and storage capacity, i.e. the size of replenishment batches that can minimize fleet size and storage capacity. A solution obtained in the declarative model of the milk-run system under discussion allows to determine the routes for each tugger train and the associated delivery times. In this context, the main contribution of the present study is the identification of the relationship between takt time and the size of replenishment batches, which allows to determine the delivery time windows for milkrun delivery and, ultimately, the positioning of trade-off points. The results show that this relationship is non-linear.