Passenger railway **scheduling** **problem** has received considerable attention from both practitioners and scholars. However, most of the work is concerned with the use of mathematical programming such as linear programming, which fails to incorporate real- life characteristics and is not easy to apply in real situations. There is limited literature regarding the adoption of a practical **approach** which capitalizes on the merits of mathematical modeling and heuristics search algorithms to obtain optimized outcomes in an effective way. This paper contributes to the literature by **using** novel heuristic optimization approaches in the multi-objective passenger railway **scheduling** **problem**. Well-developed optimization techniques, standard genetic algorithm (GA) method and **hybrid** fuzzy GA method, which focus on finding the nearly optimal solutions based on a stochastic search technique are adopted to optimize the total operational costs while maintaining the customer service level based on a list of known constraints. In this research, we proposed a pragmatic **approach**, taking advantage of readily available and easy to use solutions to put our suggested **approach** in practice, with specific skills and training. The guidelines and steps we have provided in this paper can be adopted by practitioners to incorporate in **solving** their **scheduling** **problem** with going through complex computation.

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Abstract: Multi objective Job Shop **scheduling** is a difficult task in both theoretical and practical **solving** issues. **Problem** statement: In the present scenario the modern Engineering and Industrial manufacturing units are facing lot of problems in many aspects such as machining time, raw material movement, man power requirement, electricity demand and customer satisfaction. **Approach**: A triangular fuzzy membership function is used to represent customer priority and due date. Results: A fuzzy rule-based system is developed which determines the study to be allocated to N number of machines with M number of Jobs in the following premise variables: size of the job, workload on the shop floor and the priority of the job. Multi objective fuzzy job shop **scheduling** problems are formulated as three-objective ones which not only maximize the minimum agreement index but also maximize the average agreement index and minimize the maximum fuzzy completion time. Conclusion/Recommendations: The study is analyzed on real-world data obtained from a printing company and the results are found satisfactory.

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In the literature, many exact and heuristics algorithms have been proposed to solve the **problem** of **scheduling** jobs on a single machine with a common due date [1, 4, 11–13, 33]. Biskup and Feldmann [1] proposed a mixed integer programming model for this **problem**, and also designed a **problem** generator to solve 280 instances **using** two heuristics for identifying the upper bounds on the optimal function value. A comprehensive survey, applying polynomial or pseudo-polynomial time solvable algorithms on special cases, for the common due date assignment and **scheduling** problems can be found in [4]. In [11], Liaw proposed a branch-and-bound algorithm to find optimal solutions for problems that jobs have distinct due dates. Mondal and Sen [12] developed a dynamic programming for **solving** this **problem**. In [13], a sequential exchange **approach** is proposed for minimizing earliness-tardiness penalties of single- machine **scheduling** with a common due date. Due to the complexity of this **problem**, it is difficult for above approaches to obtain the optimal solution when the **problem** size is large [14, 15].

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The use of opimizaion based on mathemaical models has been infeasible for the VCSP, since even for instances with few trips (about 20 or 30 trips), the computaional imes become prohibiive. Such complexity consists of the fact of aiming at **solving**, in an integrated way, diicult problems such as the mulicommodity network low and the set pariioning **problem**/set covering **problem**. Thus, it is essenial to search for a simpler mathemaical model which even though is able to relect appropriately the complexity of the VCSP.

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Grid computing system includes a set of programs and resources that are distributed in grid machines. The heterogeneous and dynamic nature of the grid, as well as the differing demands of applications run on the grid, makes grid **scheduling** complicated, so the deterministic algorithms will not necessarily be efficient to solve these kinds of problems. Hence, various researches have focused on heuristic algorithms such as Genetic Algorithm (GA). The simplicity and parallel nature of genetic algorithm and also searching the **problem** environment in different ways leads to **using** it for resolving several optimization problems. But since genetic algorithm is an inherent algorithm which searches the **problem** space globally and does not have the efficiency required for local searching, combining it with local search algorithms can compensate for this shortcomings. In this research, it has proposed a **hybrid** **scheduling** algorithm for **solving** the independent task **scheduling** **problem** in grid which is composed of GA with Firefly algorithm considering factor of time. The results of simulation show that the proposed algorithm can decrease Makespan of 10% as compared to the best processed method.

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Resource-Constrained Project **Scheduling** **Problem** (RCPSP) is considered as an important project **scheduling** **problem**. However, increasing dimensions of a project, whether in number of activities or resource availability, cause unused resources through the planning horizon. Such phenomena may increase makespan of a project and also decline resource-usage efficiency. To solve this **problem**, many methods have been proposed before. In this article, an effective backward-forward search method (BFSM) is proposed **using** Greedy algorithm that is employed as a part of a **hybrid** with a two-stage genetic algorithm (BFSM-GA). The proposed method is explained **using** some related examples from literature and the results are then compared with a forward serial programming method. In addition, the performance of the proposed method is measured **using** a mathematical metric. Our findings show that the proposed **approach** can provide schedules with good quality for both small and large scale problems.

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Flexible job shop **scheduling** has been noticed as an effective manufacturing system to cope with rapid development in today’s competitive environment. Flexible job shop **scheduling** **problem** (FJSSP) is known as a NP-hard **problem** in the field of optimization. Considering the dynamic state of the real world makes this **problem** more and more complicated. Most studies in the field of FJSSP have only focused on minimizing the total makespan. In this paper, a mathematical model for FJSSP has been developed. The objective function is maximizing the total profit while meeting some constraints. Time-varying raw material costs and selling prices and dissimilar demands for each period, have been considered to decrease gaps between reality and the model. A manufacturer that produces various parts of gas valves has been used as a case study. Its **scheduling** **problem** for multi-part, multi-period, and multi-operation with parallel machines has been solved by **using** genetic algorithm (GA). The best obtained answer determines the economic amount of production by different machines that belong to predefined operations for each part to satisfy customer demand in each period.

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U.BasaranFilik and M.Kurban [3] have utilized a Fuzzy Logic (FL) method in **solving** the UC **problem** of the four-unit Tuncbilek thermal plant of Turkey for an optimum schedule of the generating units subjected to load data constraints forecasted **using** conventional ANN (ANN) and an improved method which is a combination of ANN and Weighted Frequency Bin Blocks (WFBB). Kaveh Abookazemi et al [8] have presented and identified the alternative strategies with the advantages of Genetic Algorithm for **solving** the Thermal Unit Commitment (UC) **problem**. A Parallel Structure has been developed to handle the infeasibility **problem** in a structured and improved Genetic Algorithm (GA) which provides an effective search and therefore greater economy. Their proposed method leads us to obtain better performance by **using** both computational methods and classification of unit characteristics. Typical constraints such as system power balance, minimum up and down times, start up and shut-down ramps have been considered. A number of effective parameters related to UC **problem** have been identified.

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The use of methods for decision aid based on a single criterion decision quickly showed its limitations in **solving** problems of multiple choices. Indeed, the optimization of economic function is difficult when the **problem** under consideration involves factors not easily quantifiable. The use of methods of multi-criteria decision aid was therefore widely used in recent years [17], [6]. They allow to takes into account various viewpoints in the decision process which are expressed through the importance given by each actor (makers) to judgment criteria considered. This allows managing conflicts between several actors. Finally, the methods of multi-criteria decision aid are used to guide discussion towards a set of possible solutions or options.

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There are job **scheduling** policies which can make use of history events. The job **scheduling** in volunteer grid compu- ting environment can be aided with container stowage consid- ering the jobs as containers and resources as ships or vessels. A job **scheduling** algorithm **using** the container stowage has been proposed for volunteer grid computing environment. The design and evaluation has been discussed in details by making comparisons with the other job **scheduling** algorithms includ- ing EDF, RM, RR, LLF and FCFS. The proposed algorithm considers job reassignments dynamically that’s why it is named as onCSJS. The effect of not including reassignments has also been discussed. The onCSJS takes history events into account at time of assigning jobs to volunteer resources. If the history events are not taken in considerations, it will increase the number of reassignments and we call it as CSJS.

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Despite its computational burden, the integration of all or some of these problems is expected to outperform the corresponding sequential **approach**. Efficient algorithms have been developed to solve the integrated vehicle-crew **scheduling** **problem** (Borndörfer et al. (2006), Huisman et al. (2005), Hollis et al. (2006), Mesquita and Paias (2008)). Crew- rostering integration has been devised by Caprara et al. (2001), Ernst et al. (2001), Freling et al. (2004) and Lee and Chen (2003) albeit within other transport contexts (railway and air crews) and by Chu (2007) for airport staff. In Mesquita et al. (2008) advantages of integrating the three problems for public transit companies were pointed out. For an overview of problems arising in the transport domain, see Barnhart and Laporte (2007).

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Ahead the fact that the vehicle **scheduling** in Fortaleza requires a generation of a set of unfeasible duties, an itemizer process, as the Depth-First Search, it could incur in prohibitive computer costs in the optimization process. Therefore, it was taken as an option to generate duties heuristically, according to the procedure described as follows: the algorithm seeks to generate duties with a meal break, allocating the other trips with the smallest break possible (if possible, with no break). This way, the heuristic seeks to generate good duties. The matrix A was generated with breaks of [30;60], while matrix B was generated with breaks of more than 60 minutes. With the stop of the algorithm criterion, it was used a maximum number of iterations w equal to 20n (in which n is the number of trips), because, this way, some thousands of duties would be already generated to the optimization stage.

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Este trabalho trata do problema de Flow Shop **Scheduling** **Problem** (PFSP), onde um conjunto de tarefas devem ser sequenciadas numa quantidade de máquinas, objetivando a minimização do Total Completion Time (TCT) do processo. A proposta é, portanto, solucionar o problema através da aplicação das meta- heurísticas Variable Neighborhood Descent (VND) e Iterated Local Search (ILS). Para a construção da solução inicial, utilizou-se um algoritmo aleatorizado e o NEH, proposto por (Newaz Enscore e Ham 1983), e as buscas locais foram incorporadas nas meta-heurísticas para refinamento de resultados. Os métodos foram aplicados ao conjunto de instâncias proposto por (Taillard 1993) e tiveram seus respectivos desempenhos comparados às melhores soluções conhecidas na literatura a fim de conferir sua eficácia. Verificou-se, então, que o método NEH garante uma solução inicial de alta qualidade e, associado ao ILS, gera melhores resultados finais com tempo computacional razoável.

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Categories of **hybrid** genetic algorithm: When a local search method is added within a genetic algorithm, the performance of the Genetic Algorithm increases. There are several issues which should be taken care of when designing a **hybrid** genetic algorithm. The way by which information through local search is utilized within a **hybrid** genetic algorithm has a great impact on the performance of the search process. Two basic approaches based on biological learning models have been adopted to utilize local information: (a) The Lamarckian **approach** (Ei-Mihoub et al., 2006) (b) The Baldwinian **Approach**. There in an opportunity in **hybrid** optimization to achieve to capture the best of both schemes (Lobo and Goldberg, 1997). Both of these schemes are described below.

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The integrated vehicle and crew **scheduling** **problem** is a hard Combinatorial Optimization **problem** widely studied over the years. Taking into consideration the range of variables related to the planning process of vehicles and drivers, there are several practical characteristics of the **problem** that are not reflected in the solutions generated computationally. Among these characteristics, that were not found in the consulted literature, the most important is the existence of multiple objectives. This paper aims at presenting a multiobjective **approach** for the integrated vehicle and crew **scheduling** **problem** based on Genetic Algorithms. A case study in Portimão (Portugal) is presented and discussed. Were applied: (i) a Pareto Envelope-based Selection Algorithm II (PESA-II), and (ii) a hybridization between PESA-II and Integer Programming, which were summarized in a table. These results indicate that this new **approach** has a considerable potential for achieving significant gains in terms of operation costs and reduction in planning times.

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The one stage, one processor and one stage, multiple processors problems require one processing step that must be performed on a single resource or multiple resources respectively. In the multistage, flow shop **problem** each job consists of several tasks, which require processing by distinct resources; but there is a common route for all jobs. Finally, in the multistage, job shop situation, alternative resource sets and routes can be chosen, possibly for the same job, allowing the production of different part types. (c) **Scheduling** criteria, states the desired objectives to be met. “They are numerous, complex, and often conflicting”. Some commonly used **scheduling** criteria include the following: Minimize total tardiness, Minimize the number of late jobs, Maximize system/resource utilization, Minimize in- process inventory, Balance resource usage, Maximize production rate etc.

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2. Ignatov V.P., Ignatova E.V. Analiz napravleniy issledovaniy, osnovannykh na kontseptsii infor- matsionnogo modelirovaniya stroitel’nykh ob”ektov [Analysis of Lines of Research Based on the Concept of Information Modeling of Buildings]. Vestnik MGSU [Proceedings of Moscow State **University** of Civil Engineering]. 2011, no. 1, vol.1, pp. 325 — 330.

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Operating in textual domains, filtering systems or recommender systems evaluate and filter the great amount of information available on the Web, stored in XML documents (or HTML) to assist people in their search processes. The use of fuzzy linguistic modelling can be very useful to help users in the expression of their information needs. The permanent aim is the **problem** of improving the query language of search engines. A fundamental **problem** with the existing Web is that the data is machine-readable but not machine-understandable. The semantic web appears to create a new form of Web content meaningful to computers as well as human. The others most known systems are Information Retrieval Systems (on the Web, the search engine), and systems for electronic commerce.

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For a better presentation of the results, it is important to recall the organizational context in the period before the intervention, summarizing the existing representations and perceptions of the various subjects involved. The perceptions expressed by management, concerning the operation of the Centre, were anchored in the absence of a united leadership and a unifying strategy that could bring a clear direction to the organization, and the mutual perception that the other side did not collaborate in the building of a common project. The goal initially selected for the intervention reflected the need to find answers to the two central issues identified: non-involvement of the board and the difficulty of interaction between the technical and the administrative areas. The Centre management was made based on a context where difficulties in teamwork, time management, and communication, emerged as obstacles that reinforced the coexistence of two different realities within the same organization. The vision of the **problem**, expressed by management before the intervention, was coincident with the existing view of the other layers of the organization, as evidenced in the interviews: the employees of a department accused the ones of the other department of not doing their job, in a daily conflict between individuals and groups, reinforcing the hypothesis of a widespread perception of operation in silos, one department being seen as of low importance than the other.

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