In this paper, with emphasis on SCHEDULING of head of CREWs for coach trains, a CREW SCHEDULING problem is presented in a network form with task arcs. To solve the problem, a meta heuristic algorithm based on grouping evolutionary algorithm is developed. The grouping evolutionary algorithm contains two search methods which generate offspring from a parent chromosome; one of these methods is based on a logic constraints heuristic algorithm, and the other one is relied on branch and bound approach. Computational results showed that combining grouping evolutionary algorithm and branch and bound approach generates good solutions, and for the problems that we knew their optimal solutions accomplish optimal outcomes. It is also shown that the algorithm provides goods results for large scale real world problems.

This paper aims to solve railway CREW SCHEDULING problem that is concerned with building the work schedules of CREWs needed to cover a planned timetable. The proposed model is a two-phase procedure. For the first phase, the pairing generation, a depth first search approach is employed to generate a set of round trips starting and ending at home base. The second phase deals with the selection of a subset of the generated pairings with minimal cost, covering all trips that have to carry out during a predefined time period. This problem, which is modeled by set covering formulation, is solved with a genetic algorithm. In order to check for quality and validity of the suggested method, we compare its results with the final solutions that are produced over the 6 test problems of Beasley and Cao (1996). Comparison of the results indicates good quality and solutions.

Railway CREW SCHEDULING problem is a substantial part of the railway transportation planning, which aims to find the optimal combination of the trip sequences (pairings), and assign them to the CREW complements. In this problem, each trip must be covered by at least one pairing. The multiple-covered trips lead to impose useless transfers called “transitions”.In this study, a new mathematical model to simultaneously minimize both costs of trips and transitions is proposed. Moreover, a new mathematical model is suggested to find the optimal solution of railway CREW assignment problem. This model minimizes the total cost, including cost of assigning CREW complements, fixed cost of employing CREW complements and penalty cost for short workloads. To evaluate the proposed models, several random examples, based on the railway network of Iran are investigated.The results demonstrated the capability of the proposed models to decrease total costs of the CREW SCHEDULING problem.

In this paper we address the railway CREW-SCHEDULING problem, for which we have solved it for head of CREW (train master) in passenger trains with real data of Iranian I.R. Railways. All possible activities of CREW during a period of time are classified into two groups of activities, tasks and transfers. First, these activities and the precedence relationships among them are shown graphically by a multi depot network containing tasks and transfers arcs. According to the designed network, a new mathematical model, is, then, presented which is able to optimally solve small and medium size problems. In order to solve large size or real world problems, a heuristic algorithm which is called constraints logic pattern is developed. Based on real data taken from passenger train SCHEDULING of Iranian Railways, using branch and bound method, several problems in different sizes are solved. These problems are, also, solved and compared by a simplified method and the constraints logic pattern method. By comparing these two latter heuristic solution approaches with the present approach at Iranian Railways, it is concluded that constraints logic pattern, while improving quality of solutions, can solve real world problems and provide noticeable savings.

Airline CREW SCHEDULING problem is one of the important problems in the field of the operation research and generally assign CREW member to trips that should be covered with minimum cost. Airline flights are usually faced with some of uncertainty events, some of these are: unpredicted bad weather, absent of airCREWs without any reason etc… that indicates using of fuzzy set theory in the modeling of the problem. Airline CREW SCHEDULING problem are divide in two phase: in the first phase, find all trips that origin and destination, ending where the CREWs are located. trips obtained in the first phase called pairings. In the second phase the CREW SCHEDULING problem modeled by using fuzzy set theory for assigning each CREW member to trips with optimized mode. and then for solving the airline CREW SCHEDULING problem which categorized in NP-hard problems the Particle Swarm Optimization Approach is proposed.

Increasing competition in the air transport market has intensified active airlines’ efforts to keep their market share by attaching due importance to cost management aimed at reduced final prices. CREW costs are second only to fuel costs on the cost list of airline companies. So, this paper attempts to investigate the cockpit CREW pairing problem. The set partitioning problem has been used for modelling the problem at hand and, because it is classified in large scale problems, the column generation approach has been used to solve LP relaxation of the set partitioning model. Our focus will be on solving the column generation sub-problem. For this purpose, two algorithms, named SPRCF and SPRCD, have been developed based on the shortest path with resource constraint algorithms. Their efficiency in solving some problem instances has been tested and the results have been compared with those of an algorithm for CREW pairing problem reported in the literature. Results indicate the high efficiency of the proposed algorithms in solving problem instances with up to 632 flight legs in a reasonable time.