Iranian Institute of Industrial EngineeringJournal of Industrial and Systems Engineering1735-82724320101101Modern Computational Applications of Dynamic Programming1521554027ENStuart DreyfusDepartment of Industrial Engineering and Operations Research, University of California at Berkeley,
CA 94720, USAJournal Article20090505Computational dynamic programming, while of some use for situations typically encountered in industrial and systems engineering, has proved to be of much greater significance in many areas of computer science. We review some of these applications here.Iranian Institute of Industrial EngineeringJournal of Industrial and Systems Engineering1735-82724320101101A Set of Algorithms for Solving the Generalized Tardiness Flowshop Problems1561664029ENFarhad Ghassemi-TariDepartment of Industrial Engineering, Sharif University of Technology, Tehran, IranLaya OlfatSchool of Management, Alameh-Tabatabie University, Tehran, IranJournal Article20090704This paper considers the problem of scheduling n jobs in the generalized tardiness flow shop problem with m machines. Seven algorithms are developed for finding a schedule with minimum total tardiness of jobs in the generalized flow shop problem. Two simple rules, the shortest processing time (SPT), and the earliest due date (EDD) sequencing rules, are modified and employed as the core of sequencing determination for developing these seven algorithms. We then evaluated the effectiveness of the modified rules through an extensive computational experiment.Iranian Institute of Industrial EngineeringJournal of Industrial and Systems Engineering1735-82724320101101A Genetic Based Scheduling Algorithm for the PHSP with Unequal Batch Size Inbound Trailers1671824030ENDouglas L. McWilliamsMississippi State University-Meridian, 1000 Highway 19 North, Meridian, MS 39307-5799Journal Article20090503This paper considers the parcel hub scheduling problem (PHSP) with unequal batch size inbound trailers, which is a combinatorial optimization problem commonly found in a parcel consolidation terminal in the parcel delivery industry (PDI). The problem consists of processing a large number of inbound trailers at a much smaller number of unload docks. The parcels in the inbound trailers must be unloaded, sorted and transferred to the load docks, and loaded onto the outbound trailers. Because the transfer operation is labor intensive and the PDI operates in a time-sensitive environment, the unloading, sorting, transferring, and loading of the parcels must be done in such a way as to minimize the timespan of the transfer operation. A genetic algorithm is used to solve the PHSP. An experimental analysis shows that the algorithm is able to produce solution results that are within 17% of the lower bound, 16% better than a competing heuristic, and 24% better than random scheduling.Iranian Institute of Industrial EngineeringJournal of Industrial and Systems Engineering1735-82724320101101Learning Curve Consideration in Makespan Computation Using Artificial Neural Network Approach1831924031ENSuresh KumarDepartment of Electrical and Electronics Engineering Technology, Yanbu Industrial College, Yanbu Al Sinaiyah, Kingdom of Saudi ArabiaA. ArunagiriDepartment of Electrical and Electronics Engineering Technology, Yanbu Industrial College, Yanbu Al Sinaiyah, Kingdom of Saudi ArabiaJournal Article20090806This paper presents an alternative method using artificial neural network (ANN) to develop a scheduling scheme which is used to determine the makespan or cycle time of a group of jobs going through a series of stages or workstations. The common conventional method uses mathematical programming techniques and presented in Gantt charts forms. The contribution of this paper is in three fold. Firstly, the learning curve which is characterized by a coefficient is considered in the computation work. Secondly, this work is limited to small number of jobs and is useful for project based pilot runs which involve learning. Lastly, the scheduling scheme is developed in ANN as an alternate method. Extensive and successful training using the input and output vector pairs were done to establish the proposed method. Comparison was done for the tested outputs and results produced seem reliable.Iranian Institute of Industrial EngineeringJournal of Industrial and Systems Engineering1735-82724320101101A Mushy State Simulated Annealing1932084032ENKambiz ShojaeeLow-Power High-Performance Nanosystems Laboratory, School of Electrical and Computer Engineering,
University of TehranHamed ShakouriIndustrial Engineering Department, University of Tehran, Tehran, IranMohammad Bagher MenhajElectrical Engineering Department, Amirkabir University of TechnologyJournal Article20090101It is a long time that the Simulated Annealing (SA) procedure is introduced as a model-free optimization for solving NP-hard problems. Improvements from the standard SA in the recent decade mostly concentrate on combining its original algorithm with some heuristic methods. These modifications are rarely happened to the initial condition selection methods from which the annealing schedules starts or the time schedule itself. There are several parameters in the process of annealing, the adjustment of which affects the overall performance. This paper focuses on the importance of initial temperature and then proposes a lower temperature with low energy to speed up the process, using an auxiliary memory to buffer the best solution. Such an annealing indeed starts from a “mushy state” rather than a quite liquid molten material. The mushy state characteristics depends on the problems that SA is being applied to solve for. In this paper, the Mushy State Simulated Annealing (MSSA) is fully developed and then applied to the popular Traveling Salesman Problem (TSP). The mushy state may be obtained by some simple methods like crossover elimination. A very fast version of a Wise Traveling Salesman, who starts from a randomly chosen city and seeks for the nearest one as the next, is also applied to initiate SA by a low-energy, low-temperature state. This fast method results in quite accurate solutions compared to the methods recently cited in the literature.