@article { author = {Lotfi, Mohammad Mehdi and Izadkhah, Leila}, title = {A chance-constrained multi-objective model for final assembly scheduling in ATO systems with uncertain sub-assembly availability}, journal = {Journal of Industrial and Systems Engineering}, volume = {10}, number = {special issue on scheduling}, pages = {1-16}, year = {2017}, publisher = {Iranian Institute of Industrial Engineering}, issn = {1735-8272}, eissn = {2717-3380}, doi = {}, abstract = {A chance-constraint multi-objective model under uncertainty in the availability of subassemblies is proposed for scheduling in ATO systems. The on-time delivery of customer orders as well as reducing the company's cost is crucial; therefore, a three-objective model is proposed including the minimization of1) overtime, idletime, change-over, and setup costs, 2) total dispersion of items’ delivery times in customers’ orders, and 3) tardiness and earliness costs.In order to reduce the involved risk,the uncertainty in the subassembly availabilities is addressed via a chance-constrained programming. The lexicographic method is employed to solve the model. The performance and validity is then evaluated using the real data from an electrical company. Notably, the decision maker can draw the appropriate results by a priority establishment between the costs and delivery time objectives. Moreover, formulating the existing uncertainty in the subassembly availabilities helps avoiding delay in the orders’ completion dates. Finally, applying joint lot size policy leads to a more proper scheduling of assembly sequence.}, keywords = {Assemble-to-order,Final assembly schedule,Joint lot size,Chance-constrained programming,Multi-Objective Optimization}, url = {https://www.jise.ir/article_50627.html}, eprint = {https://www.jise.ir/article_50627_f1731578db52b0430ad1132105554f94.pdf} } @article { author = {Mahmoudi, Reza and Alinaghian, Mahdi and Mogouie, Hamed}, title = {A mathematical model for sustainable probabilistic network design problem with construction scheduling considering social and environmental issues}, journal = {Journal of Industrial and Systems Engineering}, volume = {10}, number = {special issue on scheduling}, pages = {17-37}, year = {2017}, publisher = {Iranian Institute of Industrial Engineering}, issn = {1735-8272}, eissn = {2717-3380}, doi = {}, abstract = {Recent facility location allocation problems are engaged with social, environmental and many other aspects, besides cost objectives.Obtaining a sustainable solution for such problems requires development of new mathematical modeling and optimization algorithms. In this paper, an uncapacitated dynamic facility location-network design problem with random budget constraints is considered. Social issues such as public satisfaction as a function of construction time, number of missed jobs incurred in the regions under study and environmental considerations are incorporated in the model.Sincethe proposed model is expected to be capable of dealing with probabilistic network design, a new chance constraint formulation is proposed and manipulated to increase the applicability of the model in uncertain decisions. Moreover, the proposed method enables decision makers to determine the completion rate of projects through a time horizon while this notion is not achievable by applying other methods in the literature. The optimization of the proposed model is performed using anovel bi-section procedure in which a heuristic and a Simulated Annealing (SA) method are applied interactively. The efficiency of the proposed method is verified through a real world application of establishing a set of health care centers and the connecting links in MeshginShahr,Iran. The results of case study showed that all considered sustainable objectives come to a steady status in the fifth year. Also according to geographical data, the results about creating links are regional and the health centers have dispersed geographically in order to serve the demands of the whole under study region.}, keywords = {Facility location-allocation,network design,Sustainability,Healthcare,chance constraint,bi-section Optimization}, url = {https://www.jise.ir/article_50629.html}, eprint = {https://www.jise.ir/article_50629_97a39718f09092958855f13de3b3391f.pdf} } @article { author = {Rabani, Masoud and Niyazi, Mehrdad}, title = {Solving a nurse rostering problem considering nurses preferences by graph theory approach}, journal = {Journal of Industrial and Systems Engineering}, volume = {10}, number = {special issue on scheduling}, pages = {38-57}, year = {2017}, publisher = {Iranian Institute of Industrial Engineering}, issn = {1735-8272}, eissn = {2717-3380}, doi = {}, abstract = {Nurse Rostering Problem (NRP) or the Nurse Scheduling Problem (NSP) is a complex scheduling problem that affects hospital personnel on a daily basis all over the world and is known to be NP-hard.The problem is to decide which members of a team of nurses should be on duty at any time, during a rostering period of, typically, one month.It is very important to efficiently utilize time and effort, to evenly balance the workload among people and to attempt to satisfy personnel preferences.With demand ever fluctuating, designing a timetable to definea work schedule for each nurse is not an easy task.A NRP deals with a very high number of constraints.A lot of big healthcare organizations around the world still construct nurses’ duty roster manually.Many optimization algorithms have been proposedto solve NRPs such as exact algorithms and (Meta)heuristic algorithms. In this paper we propose an approach that use the graph theory concept to solve the problem. We use the graph coloring and bipartite graph concept. In our approach we first formulize the problem and solve it with exact algorithm and then by using the graph concept, the solution is improved. Finally by results obtained from the graph approaches the final timetable is available.In in order to validate the proposed approach some problems with different scales are solved. We solved the problems for 30, 40, 45 and 50 nurses. In all problems the proposed approach is efficient and for instance the relationship between the nurses are presented.}, keywords = {Nurse rostering,Graph theory,Graph Coloring,bipartite graph,DSATUR algorithm}, url = {https://www.jise.ir/article_49106.html}, eprint = {https://www.jise.ir/article_49106_c8aa51241f1ed28a7e6550759aa52a00.pdf} } @article { author = {Safari, Ghasem and Hafezalkotob, Ashkan and Khalilzadeh, Mohammad}, title = {A novel mathematical model for a hybrid flow shop scheduling problem under buffer and resource limitations-A case study}, journal = {Journal of Industrial and Systems Engineering}, volume = {10}, number = {special issue on scheduling}, pages = {58-77}, year = {2017}, publisher = {Iranian Institute of Industrial Engineering}, issn = {1735-8272}, eissn = {2717-3380}, doi = {}, abstract = {Scheduling problems play a big role in manufacturing and planning the production for increasing the production efficiency and assigning the resources to operations. Furthermore, in many manufacturing systems there is a physical space between stages that called intermediate buffers. In this study, a model is proposed for minimizing the makespan of a hybrid flow shop scheduling problem with intermediate buffers and resource constraints. These constraints exist in almost every realistic manufacturing system and have an imperative impact on improving the production cost, productivity, and sustainability. In this study, a hybrid algorithm based on genetic algorithm and variable neighborhood search was used, which in tuned with Taguchi’s method helped in solving the proposed model for a tire manufacturing company. The results show that the proposed mathematical model has a high ability for scheduling problems with resource and intermediate buffer constraints and is solvable by the hybrid genetic algorithm.}, keywords = {Hybrid flow shop scheduling,Buffer limits,Resource constraints,Hybrid genetic algorithm}, url = {https://www.jise.ir/article_53350.html}, eprint = {https://www.jise.ir/article_53350_6f179acba8fe77035a87a854e0c84d46.pdf} } @article { author = {Shojaie, Amir and Sajedi, Sina}, title = {Particle swarm optimization for minimizing total earliness/tardiness costs of two-stage assembly flowshop scheduling problem in a batched delivery system}, journal = {Journal of Industrial and Systems Engineering}, volume = {10}, number = {special issue on scheduling}, pages = {78-91}, year = {2017}, publisher = {Iranian Institute of Industrial Engineering}, issn = {1735-8272}, eissn = {2717-3380}, doi = {}, abstract = {This paper considers a two-stage assembly flow shop scheduling problem. When all parts of each product are completed in the first stage, they are assembled into a final product on an assembly machine in the second stage. In order to reduce the delivery cost, completed products can be held until completion of some other products to be delivered in a same batch. The proposed problem addresses scheduling a set of operation with specific due date in a batch delivery system. The aim is to minimize total weighted earliness/tardiness and delivery costs. As the problem is demonstrated to be NP-hard, a genetic algorithm (GA) and a particle swarm optimization (PSO) are presented to solve the problem in real scales. Number of problems are solved by them and the results are compared. The computational results illustrate that the proposed PSO has a qualifier performance than the GA.}, keywords = {Assembly system,flow shop scheduling,Particle Swarm Optimization,Batch scheduling,Genetic algorithm}, url = {https://www.jise.ir/article_50628.html}, eprint = {https://www.jise.ir/article_50628_303fed7a3393da79b2038ff0c9534abc.pdf} } @article { author = {Habibi, Farhad and Barzinpour, Farnaz and Sadjadi, Seyed Jafar}, title = {A Multi-objective optimization model for project scheduling with time-varying resource requirements and capacities}, journal = {Journal of Industrial and Systems Engineering}, volume = {10}, number = {special issue on scheduling}, pages = {92-118}, year = {2017}, publisher = {Iranian Institute of Industrial Engineering}, issn = {1735-8272}, eissn = {2717-3380}, doi = {}, abstract = {Proper and realistic scheduling is an important factor of success for every project. In reality, project scheduling often involves several objectives that must be realized simultaneously, and faces numerous uncertainties that may undermine the integrity of the devised schedule. Thus, the manner of dealing with such uncertainties is of particular importance for effective planning. A realistic schedule must also take account of the time-based variations in the capacity of renewable resources and the amount of resources needed to undertake the activities and the overall effect of such variations on the schedule. In this study, we propose a multi-objective project scheduling optimization model with time-varying resource requirements and capacities.This model, with the objectives of minimizing the project makespan, maximizing the schedule robustness, and maximizing the net present value, considers the interests of both project owner and contractor simultaneously. Two multi-objective solution algorithms, NSGA-II and MOPSO, are modified and adjusted with Taguchi method to be used for determination of the set of Pareto optimal solutions for the proposed problem. The proposed solution methods are evaluated by the use of fifteen problems of different sizes derived from Project Scheduling Problem Library (PSPLIB). Finally, solutions of the algorithms are evaluated in terms of five evaluation criteria. The comparisons show that NSGA-II yields better results than MOPSO algorithm. Also, we show that ignoring the time-based variations in consumption and availability of resources may lead to underestimation of project makespan and significant deviation from the optimal activity sequence.}, keywords = {Resource-constrained project scheduling,Net Present Value (NPV),Robusts cheduling,Resource variation,Multi-Objective Optimization}, url = {https://www.jise.ir/article_53352.html}, eprint = {https://www.jise.ir/article_53352_9042ef5d84a68e667971a9b64eb6985d.pdf} }