Iranian Institute of Industrial Engineering
Journal of Industrial and Systems Engineering
1735-8272
2717-3380
4
3
2010
11
01
Modern Computational Applications of Dynamic Programming
152
155
EN
Stuart
Dreyfus
Department of Industrial Engineering and Operations Research, University of California at Berkeley,
CA 94720, USA
Computational 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.
Dynamic programming applications
https://www.jise.ir/article_4027.html
https://www.jise.ir/article_4027_4987424e2245cecf6c971d9eb641deef.pdf
Iranian Institute of Industrial Engineering
Journal of Industrial and Systems Engineering
1735-8272
2717-3380
4
3
2010
11
01
A Set of Algorithms for Solving the Generalized Tardiness Flowshop Problems
156
166
EN
Farhad
Ghassemi-Tari
Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran
Laya
Olfat
School of Management, Alameh-Tabatabie University, Tehran, Iran
This 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.
Flowshop,Total tardiness,Intermediate due dates
https://www.jise.ir/article_4029.html
https://www.jise.ir/article_4029_221a99a8828aafb50b121de62f29ab9b.pdf
Iranian Institute of Industrial Engineering
Journal of Industrial and Systems Engineering
1735-8272
2717-3380
4
3
2010
11
01
A Genetic Based Scheduling Algorithm for the PHSP with Unequal Batch Size Inbound Trailers
167
182
EN
Douglas L.
McWilliams
Mississippi State University-Meridian, 1000 Highway 19 North, Meridian, MS 39307-5799
This 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.
Distribution,Cross dock,Sortation,Genetic algorithm,Work load balancing received
https://www.jise.ir/article_4030.html
https://www.jise.ir/article_4030_b9f14d64f99016cc7e7dd7a0248c15dd.pdf
Iranian Institute of Industrial Engineering
Journal of Industrial and Systems Engineering
1735-8272
2717-3380
4
3
2010
11
01
Learning Curve Consideration in Makespan Computation Using Artificial Neural Network Approach
183
192
EN
Suresh
Kumar
Department of Electrical and Electronics Engineering Technology, Yanbu Industrial College, Yanbu Al Sinaiyah, Kingdom of Saudi Arabia
A.
Arunagiri
Department of Electrical and Electronics Engineering Technology, Yanbu Industrial College, Yanbu Al Sinaiyah, Kingdom of Saudi Arabia
This 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.
Learning curve,scheduling,Back Propagation Network,Gantt chart
https://www.jise.ir/article_4031.html
https://www.jise.ir/article_4031_bf5b6ad8bf3cbc51385baf2acc84fcde.pdf
Iranian Institute of Industrial Engineering
Journal of Industrial and Systems Engineering
1735-8272
2717-3380
4
3
2010
11
01
A Mushy State Simulated Annealing
193
208
EN
Kambiz
Shojaee
Low-Power High-Performance Nanosystems Laboratory, School of Electrical and Computer Engineering,
University of Tehran
Hamed
Shakouri
Industrial Engineering Department, University of Tehran, Tehran, Iran
Mohammad Bagher
Menhaj
Electrical Engineering Department, Amirkabir University of Technology
It 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.
Combinatorial Optimization,Traveling Salesman,Initial Condition
https://www.jise.ir/article_4032.html
https://www.jise.ir/article_4032_38c7411d1c4c900db3e1e74ab7f47e4b.pdf