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This paper considers the no-wait flow shop scheduling problem with due date constraints. In the no-wait flow shop problem, waiting time is not allowed between successive operations of jobs. Moreover, a due date is associated with the completion of each job. The considered objective function is makespan. This problem is proved to be strongly NP-Hard. In this paper, a particle swarm optimisation (PSO) is developed to deal with the problem. Moreover, the effect of some dispatching rules for generating initial solutions are studied. A Taguchi-based design of experience approach has been followed to determine the effect of the different values of the parameters on the performance of the algorithm. To evaluate the performance of the proposed PSO, a large number of benchmark problems are selected from the literature and solved with different due date and penalty settings. Computational results confirm that the proposed PSO is efficient and competitive; the developed framework is able to improve many of the best-known solutions of the test problems available in the literature. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of scheduling the no-wait flow-shop system with sequence-dependent set-up times and server side-constraints. No-wait constraints state that there should be no waiting time between consecutive operations of jobs. In addition, sequence-dependent set-up times are considered for each operation. This means that the set-up time of an operation on its respective machine is dependent on the previous operation on the same machine. Moreover, the problem consists of server side-constraints i.e. not all machines have a dedicated server to prepare them for an operation. In other words, several machines share a common server. The considered performance measure is makespan. This problem is proved to be strongly NP-Hard. To deal with the problem, two genetic algorithms are developed. In order to evaluate the performance of the developed frameworks, a large number of benchmark problems are selected and solved with different server limitation scenarios. Computational results confirm that both of the proposed algorithms are efficient and competitive. The developed algorithms are able to improve many of the best-known solutions of the test problems from the literature. Moreover, the effect of the server side-constraints on the makespan of the test problems is explained using the computational results. [ABSTRACT FROM AUTHOR]

This paper considers the no-wait job shop (NWJS) problem with makespan minimisation criteria. It is well known that this problem is strongly NP-hard. Most of the previous studies decompose the problem into a timetabling sub-problem and a sequencing sub-problem. Each study proposes a different sequencing and timetabling algorithm to solve the problem. In this research, this important question is aimed to be answered: is the timetabling or the sequencing algorithm more important to the effectiveness of the developed algorithm? In order to find the answer, three different sequencing algorithms are developed; a tabu search (TS), a hybrid of tabu search with variable neighbourhood search (TSVNS), and a hybrid of tabu search with particle swarm optimisation (TSPSO). Afterwards, the sequencing algorithms are combined with four different timetabling methods. All the approaches are applied to a large number of test problems available in the literature. Statistical analysis reveals that although some of the sequencing and timetabling algorithms are more complicated than the others, they are not necessarily superior to simpler algorithms. In fact, some of the simpler algorithms prove to be more effective than complicated and time-consuming methods. [ABSTRACT FROM PUBLISHER]

No-wait flow-shop scheduling problems refer to the set of problems in which a number of jobs are available for processing on a number of machines in a flow-shop context with the added constraint that there should be no waiting time between consecutive operations of the jobs. The problem is strongly NP-hard. In this paper, the considered performance measure is the makespan. In order to explore the feasible region of the problem, a hybrid algorithm of Tabu Search and Particle Swarm Optimisation (PSO) is proposed. In the proposed approach, PSO algorithm is used in order to move from one solution to a neighbourhood solution. We first employ a new coding and decoding technique to efficiently map the discrete feasible space to the set of integer numbers. The proposed PSO will further use this coding technique to explore the solution space and move from one solution to a neighbourhood solution. Afterwards, the algorithm decodes the solutions to its respective feasible solution in the discrete feasible space and returns the new solutions to the TS. The algorithm is tested by solving a large number of problems available in the literature. Computational results show that the proposed algorithm is able to outperform competitive methods and improves some of the best-known solutions of the considered test problems. [ABSTRACT FROM PUBLISHER]

Undesirable delays in construction projects impose excessive costs and precipitate exacerbated durations. Investigating Iran, a developing Middle Eastern country, this paper focuses on the reasons for construction project delays. We conducted several interviews with owners, contractors, consultants, industry experts and regulatory bodies to accurately ascertain specific delay factors. Based on the results of our industry surveys, a statistical model was developed to quantitatively determine each delay factor's importance in construction project management. The statistical model categorises the delay factors under four major classes and determines the most significant delay factors in each class: owner defects, contractor defects, consultant defects and law, regulation and other general defects. The most significant delay factors in the owner defects category are lack of attention to inflation and inefficient budgeting schedule. In the contractor defects category, the most significant delay factors are inaccurate budgeting and resource planning, weak cash flow and inaccurate pricing and bidding. As for the consultant defects delay factors such as inaccurate first draft and inaccuracies in technical documents have the most contribution to the defects. On the other hand, outdated standard mandatory items in cost lists, outdated mandatory terms in contracts and weak governmental budgeting are the most important delay factors in the law, regulation and other general defects. Moreover, regression models demonstrate that a significant difference exists between the initial and final project duration and cost. According to the models, the average delay per year is 5.9 months and the overall cost overrun is 15.4%. Our findings can be useful in at least two ways: first, resolving the root causes of particularly important delay factors would significantly streamline project performance and second, the regression models could assist project managers and companies with revising initial timelines and estimated costs. This study does not consider all types of construction projects in Iran: the scope is limited to certain types of private and publicly funded projects as will be described. The data for this study has been gathered through a detailed questionnaire survey. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of scheduling a no-wait flow-shop system with sequence-dependent set-up times. As a result of having no-wait constraints, each operation of a job should start as soon as the previous operation is processed. In addition, sequence-dependent set-up times are considered for each operation. This means that the set-up time of an operation on a machine is dependent on the previous operation on the same machine. The considered performance measure is makespan. This problem is proven to be strongly NP-hard. In order to deal with the problem, an encoding system called Matrix Coding (MC) is used to code job sequences. The developed particle swarm optimisation (PSO) is applied on the encoded sequences for further exploration of the solution space. In order to evaluate the performance of the developed framework, a large number of benchmark problems are selected. Computational results confirm that the proposed algorithm outperforms competitive methods, by improving many of the best-known solutions of the test problems. [ABSTRACT FROM PUBLISHER]