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The scheduling of parallel machines is a well-known problem in many companies. Nevertheless, not always all the jobs can be manufactured in any machine and the eligibility appears. Based on a real-life problem, we present a model which has m parallel machines with different level of quality from the highest level for the first machine till the lowest level for the last machine. The set of jobs to be scheduled on these m parallel machines are also distributed among these m levels: one job from a level can be manufactured in a machine of the same or higher level but a penalty, depending on the level, appears when a job is manufactured in a machine different from the highest level i.e. different from the first machine. Besides, there are release dates and delivery times associated to each job. The tackled problem is bi-objective with the criteria: minimisation of the final date - i.e. the maximum for all the jobs of their completion time plus the delivery time - and the minimisation of the total penalty generated by the jobs. In a first step, we analyse the sub-problem of minimisation of the final date on a single machine for jobs with release dates and delivery times. Four heuristics and an improvement algorithm are proposed and compared on didactic examples and on a large set of instances. In a second step an algorithm is proposed to approximate the set of efficient solutions and the Pareto front of the bi-objective problem. This algorithm contains two phases: the first is a depth search phase and the second is a backtracking phase. The procedure is illustrated in detail on an instance with 20 jobs and 3 machines. Then extensive numerical experiments are realised on two different sets of instances, with 20, 30 and 50 jobs, 3 or 4 machines and various values of penalties. Except for the case of 50 jobs, the results are compared with the exact Pareto front. [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]

Biogeography-based optimisation (BBO) algorithm is a new evolutionary optimisation algorithm based on geographic distribution of biological organisms. With probabilistic operators, this algorithm is able to share more information from good solutions to poor ones. BBO prevents the good solutions to be demolished during the evolution. This feature leads to find the better solutions in a short time rather than other metaheuristics. This paper provides a mathematical model which integrates machine loading, part routing, sequencing and scheduling decision in flexible manufacturing systems (FMS). Moreover, it tackles the scheduling problem when various constraints are imposed on the system. Since this problem is considered to be NP-hard, BBO algorithm is developed to find the optimum /near optimum solution based on various constraints. In the proposed algorithm, different types of mutation operators are employed to enhance the diversity among the population. The proposed BBO has been applied to the instances with different size and degrees of complexity of problem adopted from the FMS literature. The experimental results demonstrate the effectiveness of the proposed algorithm to find optimum /near optimum solutions within reasonable time. Therefore, BBO algorithm can be used as a useful solution for optimisation in various industrial applications within a reasonable computation time. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to show how the results of an optimisation model that can be integrated with the decisions made within a simulation model to schedule back-end operations in a semiconductor assembly and test facility. The problem is defined by a set of resources that includes machines and tooling, process plans for each product and the following four hierarchical objectives: minimise the weighted sum of key device shortages, maximise weighted throughput, minimise the number of machines used and minimise the makespan for a given set of lots in queue. A mixed integer programming model is purposed and first solved with a greedy randomised adaptive search procedure (GRASP). The results associated with the prescribed facility configuration are then fed to the simulation model written in AutoSched AP. However, due to the inadequacy of the options built into AutoSched, three new rules were created: the first two are designed to capture the machine set-up profiles provided by the GRASP and the third to prioritise the processing of hot lots containing key devices. The computational analysis showed that incorporating the set-up from the GRASP in dynamic operations of the simulation greatly improved its performance with respect to the four objectives. [ABSTRACT FROM AUTHOR]

In this paper, the problem of scheduling commercial messages during the peak of viewing time of a TV channel is formulated as a combinatorial auction-based mathematical programming model. Through this model, a profitable and efficient mechanism for allocating the advertising time to advertisers is developed by which the revenue of TV channels is maximised while the effectiveness of advertising is increased. We developed a steady-state genetic algorithm to find an optimal or a near optimal solution for the proposed problem. A computational experiment was conducted for evaluating the efficiency of the proposed algorithm. A set of test problems with different sizes were generated, using the pseudo-random generation mechanism, and solved by the proposed genetic algorithm. The optimal solutions of the linear programming relaxation of these test problems were also obtained and were used for evaluating the quality of solutions obtained by the developed algorithm. The results of this computational experiment revealed the robustness of the solutions and acceptably low computational time for obtaining these solutions. The computational results also demonstrated that the proposed genetic algorithm had an appropriate ability to preserve population diversity during the search and was capable of obtaining high-quality solutions for the proposed problem. [ABSTRACT FROM PUBLISHER]

The response time variability problem (RTVP) is a scheduling problem that has a wide range of real-world applications in mixed-model assembly lines, multi-threaded computer systems, machine maintenance, waste collection and others. The RTVP arises whenever products, clients or jobs need to be sequenced in such a way that the variability in the time between the points at which they receive the necessary resources is minimised. The RTVP has been demonstrated to be NP-hard and heuristic and metaheuristic techniques are needed to solve large, real-life instances. The best results, on average, in the literature for the RTVP were obtained with variable neighbourhood search hybidised with multi-start. Several algorithms based on simulated annealing are proposed to solve the RTVP. An extensive computational experiment is carried out and it is shown that the solution of the RTVP is clearly improved. [ABSTRACT FROM PUBLISHER]

Production systems are often managed to maximise their production rate (). In some cases, this leads to excessive work-in-process and, as a result, long production lead time (). This issue is of particular importance in systems with ‘unlimited’ buffer space, wheremay also become unlimited. The question addressed in this paper is: What should be the release rate of raw materials so that the desiredis obtained, whileis maximised? We answer this question for serial lines with infinite buffers and machines obeying the Bernoulli reliability model. Specifically, given a serial line, we quantify the set of attainables (feasible set), and for each point in the feasible set provide the release rate (as a function of machine parameters) that guarantees the desired lead time, while maximising the production rate. In addition, we offer a feedback control law that enforces the desired system behaviour. The development is based on a recursive aggregation procedure that leads to analytical estimates of. The accuracy of these estimates is evaluated by simulations and is shown to be acceptable for practical applications (well within 5%). The results obtained offer small- and mid-size enterprises the possibility of managing their production lead time, when neither limited buffers nor kanban/CONWIP systems are available, by releasing raw materials on, say, an hourly basis. [ABSTRACT FROM PUBLISHER]

This paper proposes a tabu search (TS) algorithm to solve an NP-hard cyclic robotic scheduling problem. The objective is to find a cyclic robot schedule that maximises the throughput. We first formulate the problem as a linear program, provided that the robot move sequence is given, and reduce the problem to searching for an optimal robot move sequence. We find that the solution space can be divided into some specific subspaces by the maximal number of works-in-process. Then, we propose a TS algorithm to synchronously perform local searches in each subspace. To speed up our algorithm, dominated subspaces are eliminated by lower and upper bounds of the cycle time during the iterations. In the TS, a constructive heuristic is developed to generate initial solutions for each subspace and a repairing procedure is proposed to maintain the feasibility of the solutions generated in the initialisation stage and the neighbours search process. Computational comparison both on benchmark instances and randomly generated instances indicates that our algorithm is efficient for the cyclic robotic scheduling problem. [ABSTRACT FROM PUBLISHER]

Owing to intense global competition, one of the main concerns of the production-scheduling literature is the modelling and the optimisation of the robotic cells. In this type of cell, as in a classical flowshop, each part follows the same processing order on all machines. The objective is the minimisation of the cycle time. To efficiently exploit the potential of such cells, an algorithmic framework to a variety of challenging NP-hard optimisation problems is required. In this paper we consider the problem of scheduling operations in a three-machine robotic cell producing multiple part types. Besides the part sequence, the sequence of robot moves must be optimised as well. The simultaneous optimisation of the sequences of part and of robot activities has rarely been tackled in the literature. In addition, most studies restrict the search of the best MPS cycle to CRM sequences or to the combination of one-unit cycles despite the fact that these restrictions may not lead to the optimality. This paper attempts to fill this gap by providing a constructive heuristic based on specific features of the problem. The reported computational tests provide clear evidence that this new way to tackle the problem is promising. [ABSTRACT FROM AUTHOR]

In this paper, multi-objective flow-shop scheduling with parallel machines is considered, and an algorithm for production-scheduling optimisation is proposed. The objective is to design an algorithm to optimise production scheduling, taking into consideration customer changes to delivery dates and quantities, materials requisitioned from the supplier and inventory management. Manufacturing lead time for the production order is minimised, and, finally, resource use is considered as an auxiliary criterion. Numerical examples, modelled with data from a Mexican enterprise, and computational results are reported. The paper indicates that the mathematical model, as well as the program that solves it, provides flexibility with minimum modifications. Thus it can provide useful information for multiple decision processes. [ABSTRACT FROM AUTHOR]

This paper proposes two new differential evolution algorithms (DE) for solving the job shop scheduling problem (JSP) that minimises two single objective functions: makespan and total weighted tardiness. The proposed algorithms aim to enhance the efficiency of the search by dynamically balancing exploration and exploitation ability in DE and avoiding the problem of premature convergence. The first algorithm allows DE population to simultaneously perform different mutation strategies in order to extract the strengths of various strategies and compensate for the weaknesses of each individual strategy to enhance the overall performance. The second algorithm allows the whole DE population to change the search behaviour whenever the solutions do not improve. This study also introduces a modified local mutation operation embedded in the two proposed DE algorithms to promote exploitation in different areas of the search space. In addition, a local search technique, called Critical Block (CB) neighbourhood, is applied to enhance the quality of solutions. The performances of the proposed algorithms are evaluated on a set of benchmark problems and compared with results obtained from an efficient existing Particle Swarm Optimisation (PSO) algorithm. The numerical results demonstrate that the proposed DE algorithms yield promising results while using shorter computing times and fewer numbers of function evaluations. [ABSTRACT FROM AUTHOR]

The spatial scheduling problem that arises in hull block assembly shops occurs when scheduling and spatial allocation of the blocks must be considered simultaneously. We present a two-stage approach to this type of problem. The first stage aims to reduce the number of blocks. The second stage optimises the scheduling and spatial allocation of blocks using nonlinear mixed integer programming (NMIP) methods. The procedure proposed in this paper uses an agglomeration algorithm (AA) for the blocks. This procedure is based on the space–time coupling mechanism. The AA is a three-dimensional classification used to cluster blocks linked closely in time and space into virtual blocks. Extensive computational results from real cases are presented to demonstrate the effectiveness of the proposed approach, demonstrating a significant improvement over results obtained from existing methods. [ABSTRACT FROM AUTHOR]

Wafer sorting is one of the most critical processes involved in semiconductor device fabrication. This study addresses the wafer sorting scheduling problem (WSSP), with minimisation of total setup time as the primary criterion and minimisation of the number of testers used as the secondary criterion. In view of the strongly NP-hard nature of this problem, a simple and effective iterated greedy heuristic is presented. The performance of the proposed heuristic is empirically evaluated by 480 simulation instances based on the characteristics of a real wafer testing shop-floor. The experimental results show that the proposed heuristic is effective and efficient as compared to the state-of-art algorithms developed for the same problem. It is believed that this study has developed an approach that is easy to comprehend and satisfies the practical needs of wafer sorting. [ABSTRACT FROM AUTHOR]

This paper addresses a two-machine no-wait job shop problem with makespan minimisation. It is well known that this problem is strongly NP-hard. A divide-and-conquer approach (DC for short) is adopted to calculate the optimal timetable of a given sequence. It decomposes the given sequences into several independent parts and conquers them separately. A timetable enhancing method is introduced to further improve the timetable obtained by DC. It constructs a set of flow shop type jobs based on the result from DC and calculates the best timetable for these newly constructed jobs by the well-known Gilmore and Gomory method (GG for short). An efficient greedy search is proposed by integrating DC with GG to search for the best sequence. Experimental results show that the proposed algorithm can find the optimal solutions for 96% of the randomly generated test instances on average. [ABSTRACT FROM AUTHOR]

Grid workflow scheduling problem has been a research focus in grid computing in recent years. Various deterministic or meta-heuristic scheduling approaches have been proposed to solve this NP-complete problem. A perusal of published papers on the artificial immune system (AIS) reveals that most researchers use the clonal selection of B cells during the evolving processes and the affinity function of B cells to solve various optimisation problems. This research takes a different approach to the subject – firstly by applying a modified algorithm (Hu, T.C., 1961. Parallel sequencing and assembly line problem. Operations Research, 9 (6), 841–848) to sequence the job and this sequence is applied for further application. Secondly, the derived sequence is then used for machine allocations using the AIS approach. The proposed AIS apply B cells to reduce the antigens and then combining T helper cells and T suppressor cells to solve the grid scheduling problems. Our proposed methodology differs from other earlier approaches as follows: 1. A two-stage approach is applied using a fixed sequence derived from heuristic to allocate machine. 2. AIS apply B cells as bases and then T cells are employed next. T helper cells are used to help improve the solution and then T suppressor cells are generated to increase the diversity of the population. A new formula is proposed to calculate the affinity of the antibody with the antigen. The total difference of completion time of each job is applied instead of the difference of makespan of the schedule. This new AIS method can supplement the flaw of genetic algorithms (GA) using fitness as the basis and a new lifespan which will keep good diversified chromosomes within the population to extend the searching spaces. The experimental tests show that this novel AIS method is very effective when compared with other meta-heuristics such as GA, simulated annealing (SA), and ant colony optimisation (ACO). [ABSTRACT FROM AUTHOR]

Scheduling jobs with different processing times and distinct known due dates on parallel machines (which may be idle) so as to minimise the total weighted earliness and tardiness is NP-hard. It occurs frequently in industrial settings with a just-in-time production environment. Here, small instances of this problem are tackled via an exact mixed-integer program, whereas larger instances are approximately solved using hybrid algorithms. The computational investigation discerns what makes a difficult instance, and demonstrates the efficiency of the approach. [ABSTRACT FROM AUTHOR]

We study the problem of minimising the total weighted tardiness and total distribution costs in an integrated production and distribution environment. Orders are received by a manufacturer, processed on a single production line, and delivered to customers by capacitated vehicles. Each order (job) is associated with a customer, weight (priority), processing time, due time, and size (volume or storage space required in the transportation unit). A mathematical model is presented in which a number of weighted linear combinations of the objectives are used to aggregate both objectives into a single objective. Because even the single objective problem is NP-hard, different heuristics based on a genetic algorithm (GA) are developed to further approximate a Pareto-optimal set of solutions for our multi-objective problem. [ABSTRACT FROM AUTHOR]

In studies on automatic scheduling problems, processing times do not differ according to repetition of job or process sequences so it may also be necessary to consider processing times independent from setup times. While considering setup times, the human factor has an important effect on setup, so by the processing of similar tasks frequently worker skills improve and they are able to perform setup at a greater pace. This fact is known as the ‘learning effect’ in the literature. This paper deals with sequence-dependent setup times (SDSTs) hybrid flow shop scheduling with learning effect of setup times for minimising weighted sum of makespan and total tardiness. A mathematical programming model that incorporates these aspects of the problem is developed which belongs to the NP-hard class. Thus, because of the intensive computation, we propose a novel meta-heuristic approach called water flow-like algorithm (WFA) which has the feature of multiple and dynamic numbers of solution agents. Various parameters of the problem and the WFA are reviewed by means of Taguchi experimental design. For the evaluation of the proposed WFA, problem data was generated to compare it against a random key genetic algorithm (RKGA). The results demonstrate the high performance of the WFA with respect to the RKGA. [ABSTRACT FROM PUBLISHER]

This paper addresses the scheduling problems in a hybrid flowshop with two objectives of minimising the makespan and total tardiness. Since this problem is NP-hard, evolutionary algorithms based on the genetic algorithm (GA) namely; BOGAW, BOGAC, BOGAT, and BOGAS are proposed for searching the Pareto-optimal frontier. In these algorithms, we propose to generate a section of solutions for the next generation using a neighbourhood search structure on the best individual in each generation. The selection procedure selects the best chromosome based on an evaluation mechanism used in the algorithm (i.e., weighted sum, crowding distance, TOPSIS and single-objective). The aim of this paper is to clarify that the cited characteristic is efficient and it enhances the efficiency of algorithms. Therefore, we perform a comparison between the proposed algorithms to find the best alternative. Data envelopment analysis is used to evaluate the performance of approximation methods. The obtained result from the comparison shows that, BOGAC is the more efficient. To continue, since the efficiency of our idea is not clear, we compare our efficient algorithm with other efficient algorithms in the literature (namely PGA-ALS and MOGLS). The final persuasive results support the idea that BOGAC in comparison with PGA-ALS and MOGLS is more effective and efficient. [ABSTRACT FROM AUTHOR]

This article studies multi-objective no-wait flowshop scheduling problems to minimise both makespan and total tardiness. This problem is commonly denoted by . This article mathematically formulates the problem using two effective multi-objective mixed integer linear programming models. The multi-objective mathematical models are then solved by a multiple criteria decision making approach, called Fuzzy programming method. Moreover, the article proposes a novel multi-objective iterated local search algorithm incorporating three types of local search engine: greedy, moderate and curtailed fashions. The algorithm is carefully evaluated for its performance against two available algorithms by means of multi-objective performance measures and statistical tools. The results show that the proposed solution method outperforms the others. [ABSTRACT FROM PUBLISHER]

Fuzziness and flexibility are the features of most production processes; however, the scheduling problems with both flexible process plans and fuzzy processing conditions are not investigated fully for high complexity. This paper proposes an efficient swarm-based neighbourhood search algorithm (SNSA) for the fuzzy flexible job shop scheduling problem. In SNSA, ordered operation-based representation is used to indicate the solution of operation sequence sub-problem and machine assignment sub-problem is converted into a cell formation one, in which machines are regarded as cells and operations are allocated into cells. In each generation, two swaps, an insertion and tournament selection are applied to update swarms. Some numerical experiments are conducted by using some instances to show the effectiveness of SNSA. Computational results show that SNSA performs better than the existing methods from literature. [ABSTRACT FROM PUBLISHER]

This research considers a hybrid flowshop scheduling problem where jobs are organised in families according to their machine settings and tools. The family setup time arises when a machine shifts from processing one job family to another. The problem is compounded by the challenges that the formation of job families is different in different stages and only a limited number of jobs can be processed within one setup. This type of problem is common in the production process of standard metal components. This paper aims to propose two approaches to solve this problem. One is a metaheuristic in the form of a genetic algorithm and the other is a heuristic. The proposed approaches are compared and contrasted against the two relevant metaheuristic and heuristic adapted from solving a generalised sequence-dependent setup flowshop problem. Comparative results indicate that the proposed genetic algorithm has better performance on minimising makespan and the heuristic is more effective on reducing family setup time. [ABSTRACT FROM PUBLISHER]

The capacitated lot sizing problem with setup carryover deals with the issue of planning multiple products on a single machine. A setup can be carried over from one period to the next by incorporating the partial sequencing of the first and last product. This study proposes a novel hybrid approach by combining Genetic Algorithms (GAs) and a Fix-and-Optimise heuristic to solve the capacitated lot sizing problem with setup carryover. Besides this, a new initialisation scheme is suggested to reduce the solution space and to ensure a feasible solution. A comparative experimental study is carried out using some benchmark problem instances. The results indicate that the performance of the pure GAs improves when hybridised with the Fix-and-Optimise heuristic. Moreover, in terms of solution quality, promising results are obtained when compared with the recent results in the literature. [ABSTRACT FROM PUBLISHER]

We develop an optimisation model to minimise costs for work-in-process (WIP), finished goods inventory (FGI), backorders, and capacity for a multi-stage production system applying a work-ahead window work release policy. Customers arrive to a make-to-order production system and have stochastic due dates. The parameters to optimise are the capacity invested at each stage, consisting of the number of machines and the processing rate (defined by a set of possible processing rates), as well as the work-ahead window. An optimality condition is provided showing when it is optimal to invest into a single machine at each stage if processing rates are continuous decision variables. The optimality of increasing capacity towards the customer end of the production line is proven under certain conditions. For a special case consisting of two stages with M/M/s queues with exponentially distributed customer required lead time, explicit expressions for WIP, FGI and backorders are developed. A set of numerical examples illustrates the influence of predefined processing rates on the optimal number and selection of machines. A simple solution heuristic for the problem with a predefined set of processing rates is proposed and its performance is shown in a numerical example. Additionally, the influence of uncertain input rates into the production system is discussed. [ABSTRACT FROM AUTHOR]

One of the operating decisions involved in the scheduling of flexible manufacturing systems (FMS) is that of loading the FMS. Given a pool of jobs, which can be processed on alternate machines and alternate tools, the scheduler has to decide on the allocation of tools and machines to the different jobs. Various versions of this problem have appeared in the literature. We consider the version where jobs are selected for processing in a FMS in a planning horizon, operations for these jobs are assigned to machines, and corresponding tools are allocated to the slots in the machines. The objective is to minimise system unbalance. A hybrid genetic algorithm is presented that addresses this problem. Computational comparison between the genetic algorithm and previous algorithms is presented. [ABSTRACT FROM AUTHOR]

In this paper we propose the GAPN (genetic algorithms and Petri nets) approach, which combines the modelling power of Petri nets with the optimisation capability of genetic algorithms (GAs) for manufacturing systems scheduling. This approach uses both Petri nets to formulate the scheduling problem and GAs for scheduling. Its primary advantage is its ability to model a wide variety of manufacturing systems with no modifications either in the net structure or in the chromosomal representation. In this paper we tested the performance on both classical scheduling problems and on a real life setting of a manufacturer of car seat covers. In particular, such a manufacturing system involves features such as complex project-like routings, assembly operations, and workstations with unrelated parallel machines. The implementation of the algorithm at the company is also discussed. Experiments show the validity of the proposed approach. [ABSTRACT FROM AUTHOR]

In order to overcome the myopia problem, routing strategies must be based on formal representations of flow that automatically account for modifications in the values of parameters of interest and in the model itself. This work addresses this problem and discusses how to automatically incorporate resources (e.g. workstations/transportation devices/storage) in a Petri-net-derived model of flow that is modifiable at runtime to reflect and influence the routing in a manufacturing line. The modelling approach takes into consideration scalability needs and was experimentally validated. The applicability of the models is shown for PN-based dynamic scheduling. [ABSTRACT FROM AUTHOR]

In this competitive world cost and lead time reduction are of prime concern for manufacturing firms. To achieve this objective manufacturing entities are adopting several management philosophies such as Total Quality Management (TQM), just-in-time (JIT), and theory of constraints (TOC). The present paper addresses the advanced computer-aided process planning (ACAPP) problem in a distributed manufacturing supply chain environment and aims at cost and lead times reduction under several technological constraints. To deal with the complexity of the problem the constraint based fast simulated annealing (CBFSA) algorithm has been explored in this article. Extensive computations have been performed over the well-known benchmarks of advanced planning problems and the results obtained prove the superiority of the proposed algorithm over the prior approaches. [ABSTRACT FROM AUTHOR]

This study focuses on the dynamic scheduling problem of a re-entrant production line, in which all of the parts are assumed to have the same processing routes and be processed on every machine. A two-layer dynamic scheduling method is proposed for the dynamic scheduling of the re-entrant line with the objective of minimising total earliness and tardiness. This method consists of two layers. The top layer is to select the appropriate scheduling policy, and the bottom layer is to generate the scheduling by using the policy selected in the top layer. In the top layer, three different rules are constructed for selecting scheduling policies, namely the lateness comparison rule, the lateness variation comparison rule, and the equal interval switching rule. In the bottom layer, three different scheduling policies are proposed to generate the real-time scheduling for manufacturing, namely the FCFS (first come first service) scheduling policy, the PI (proportional-integral) scheduling policy, and the fuzzy PI scheduling policy. Considering that the real-time status of manufacturing changes constantly, it is necessary to switch among different scheduling policies to adapt to this change. Numerical experiments are performed in the situations with and without urgent jobs. The results show that the proposed two-layer dynamic scheduling method outperforms any single scheduling policy (e.g., the FCFS policy, the PI policy and the fuzzy PI policy) for the dynamic scheduling of a re-entrant production line. [ABSTRACT FROM AUTHOR]

Industrial systems are constantly subject to random events with inevitable uncertainties in production factors, especially in processing times. Due to this stochastic nature, selecting appropriate dispatching rules has become a major issue in practical problems. However, previous research implies that using one dispatching rule does not necessarily yield an optimal schedule. Therefore, a new algorithm is proposed based on computer simulation and artificial neural networks (ANNs) to select the optimal dispatching rule for each machine from a set of rules in order to minimise the makespan in stochastic job shop scheduling problems (SJSSPs). The algorithm contributes to the previous work on job shop scheduling in three significant ways: (1) to the best of our knowledge it is the first time that an approach based on computer simulation and ANNs is proposed to select dispatching rules; (2) non-identical dispatching rules are considered for machines under stochastic environment; and (3) the algorithm is capable of finding the optimal solution of SJSSPs since it evaluates all possible solutions. The performance of the proposed algorithm is compared with computer simulation methods by replicating comprehensive simulation experiments. Extensive computational results for job shops with five and six machines indicate the superiority of the new algorithm compared to previous studies in the literature. [ABSTRACT FROM AUTHOR]

In this paper, a distributed multi-agent scheduling system (MASS) based on co-operative approach is proposed to solve static and dynamic job shop scheduling problems (JSSP). The proposed system is composed of two kinds of agents, Supervisor agents and Resource agents. The Supervisor agent decomposes JSSP into interrelated sub-problems and the Resource agents co-operate, through a distributed approach of local idle time minimisation, to solve this problem which is known as one of the most difficult NP-hard problems. Computational results are presented to show the efficiency of MASS in static job shop scheduling. Then, a comparison of the computational results between MASS and some common dispatching rules, on dynamic job arrivals, is studied in terms of effectiveness and stability. Finally, the developed system is validated within an illustrative example, to demonstrate the feasibility of MASS. [ABSTRACT FROM AUTHOR]

In this study, we consider the multi resource agent bottleneck generalised assignment problem. Our aim is to minimise the maximum load over all agents. We take our motivation from an assignment problem faced in heating, ventilating and air conditioning sector. We study the linear programming (LP) relaxation of the problem. We use the optimal LP relaxation solutions in our branch and bound algorithm while defining bounding and branching schemes. We find that our branch and bound algorithm returns optimal solutions to the problems with up to 60 jobs when the number of agents is 5, and up to 30 jobs when the number of agents is 10, in less than 20 minutes. To find approximate solutions, we define a tabu search algorithm and α approximation algorithm. Our computational results have revealed that both algorithms can find high quality solutions to large sized instances very quickly. To the best of our knowledge our study is the first reported attempt to solve the problem. We hope our study fills an important gap in the literature. [ABSTRACT FROM AUTHOR]

In order to solve the multi-objective job shop scheduling problem effectively, this paper proposes a greedy strategy to establish the mathematical model of minimizing the maximum completion time, the average flow time and the machine idle time. The algorithm sets up a dual-population structure: leading-population and searching-population. Firstly, the algorithm calculates the fitness values of every particle for each population for the three objectives, and the optimize solutions will be incorporated in the individual optimal value set and the global optimal value set accordingly. Then the algorithm performs a crossover operation which includes two types: one is the leading-cross to lead the searching-population to convergence to the optimal solution quickly, and the other is intro-population crossover of each population in order to increase the diversity of individuals. The mutation operation of each population is followed to further enhance the diversity of individuals. And the simulated annealing algorithm is employed to avoid the algorithm falling into the local optimum effectively. The simulation experiments of various scale instances are carried out and compared with the simulation results of the other two popular algorithms to verify the effectiveness of the algorithm. The simulation results show that the proposed algorithm is superior to the other two algorithms in both the quality of the solution and the time-consuming. [ABSTRACT FROM AUTHOR]

In this paper, integrated production and batch delivery scheduling problem for make to order production system and one customer in supply chain has been addressed. One manufacture received n orders from one customer. Orders must be processed by single machine and sent in batches to customer. Sending several jobs as a batch leads to less transportation cost but may increase the cost of tardiness jobs. The objective is determining the production and delivery scheduling so that the related costs is minimized. The problem is strongly NP-hard. In this paper, one new math programming model including Mixed Integer Programming (MIP) model, Ant Colony System (ACS) and Elastic Ant System (EAS) are presented for solving it. In order to evaluate the efficiency of these two methods computational tests based on full factorial experimental design has been conducted. Computational test is performed for evaluation of these methods. The obtained results show that the heuristic algorithm is efficient which has been verified by using. Analysis of variance (ANOVA) technique. The results showed that the ACS is the most efficient method. [ABSTRACT FROM AUTHOR]

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We consider the problem of scheduling a given set of n jobs with equal processing times on m parallel machines so as to minimize the makespan. Each job has a given release date and is compatible to only a subset of the machines. The machines are ordered and indexed in such a way that a higher-indexed machine can process all the jobs that a lower-indexed machine can process. We present a solution procedure to solve this problem in O(n2+mnlogn) time. We also extend our results to the tree-hierarchical processing sets case and the uniform machine case. [ABSTRACT FROM AUTHOR]

The factory-based modular construction process has proven to increase the speed of construction, and improve quality and safety, while providing value to the customer and a rapid return on investment to the builder and owner. However, onsite module assembly creates new schedule demands, as activities are scheduled on a minute-by-minute basis; therefore simulation of the process becomes essential at early stages of a project. Although simulation proves to be an effective tool for project engineers to assess complex construction operations, it remains a symbolic base model with no visual link to the actual physical shape and look of the project's activities. This paper presents the application of integrated simulation and post simulation visualization as a tool to assist the modular construction industry in scheduling onsite installation of prefabricated modules. The proposed methodology uses simulation model output as an ASCII file in a binary format and imports this ASCII file to 3D Studio Max to perform the animation. The output from the high level simulation model is transformed into frames/second in 3D Studio Max. The proposed methodology was tested on the planned construction of a 34-storey building in Brooklyn, New York, USA. Simulation visualization of the process proved to be effective in communicating the value and simplicity of a minute-by-minute schedule. Based on the output information, the most efficient solutions were generated. The use of post simulation visualization was effective in analyzing the construction methods of the case study which consisted of 950 structural steel modules. Issues related to construction activities' productivity were synchronized to achieve onsite installation of the project in only 56 working days. [ABSTRACT FROM AUTHOR]

This paper presents the results of a research project conducted to solve a production scheduling problem observed in a system in which meeting customer due dates is the primary objective. Each job to be scheduled is defined by its processing time, ready time, due date, weight and size. The Batch Processing Machine (BPM) can process several jobs simultaneously as a batch as long as its capacity is not violated. The processing time of a batch is the largest processing time among the jobs in the batch, and the batch ready time is the largest ready time among the jobs in the batch. Given that the problem is NP-hard we propose a Variable Neighborhood Search (VNS) heuristic to minimize the total weighted tardiness on a single BPM. The computational experiments conducted to assess the quality of the solutions found show that in a computational time restricted to a maximum of 30 minutes, the proposed heuristic finds solutions considerably better than the solutions obtained after implementing a mixed integer programming model available in the literature in a commercial solver. [ABSTRACT FROM AUTHOR]

An introduction to cloud computing is presented which includes evaluation of distributed storage system, job scheduling by game theory based approach, and data warehouse systems in cloud.

The authors present their response to comments on their article about the use of the software tool called Virtual Construction Simulator (VCS) in creating and reviewing construction schedules. They describes the mechanism by which three-dimensional model information is integrated into VCS and the process by which computer-aided design (CAD) model data is used to create an interactive scheduling system. They explains the educational value and utility of VCS in a classroom setting.