Ibrahim M. Alharkan

A chance-constrained multi-period model for a special multi-reservoir system

We develop a model for optimal multi-period operation of a multi-reservoir system for a basin operating under a conjunctive use of ground and surface Water. The inflows to the main reservoir as well as the irrigation demands are stochastic. The ground stock suffers from severe overdrafts increasing the risk of the total depletion of the aquifer in addition to the quality degradation and the threat of seawater intrusion. We treat the uncertainties in the inflows through chance constraints and penalties of failure to release the planned amounts of surface water from the main reservoir. However, we reflect uncertainties in irrigation demands by opting for deficit irrigation and using adequate production functions to estimate the expected crop yields. We attempt in the model to avoid large deficits except perhaps for periods where crop yields are relatively insensitive to water shortage. The objective is to maximize the total expected profit of the entire region. We illustrate the model through an example partially based on some hypothetical data.

Flow-shop scheduling for three serial stations with the last two duplicate

This paper addresses the problem of scheduling jobs for three serial stations with the last two duplicate, i.e., identical. The performance measure considered is the makespan, and a Branch and Bound and two heuristic procedures are proposed. Two dominance criteria are developed to produce the optimal schedule for the jobs sequenced so far. Extensive computational experiments reveal that the Genetic Algorithm can be used to obtain a group of effective solutions for any realistic size problem within small computation time.

Modeling and simulation of Queuing Systems using arena software: A case study

This paper includes a simulation model for KSU Main Student Restaurant that built using Arena simulation software. We proposed some performance measures to be evaluated for our case study, which is the average waiting time in system and the average number of students in queues. The system faced a big pressure in the first hour of serving the lunch, a slightly pressure in the second hour and we can say there is no pressure on the system in the last hour. We used Arena simulation software to build a simulation model and after that, we analyzed the output from the simulation program results and applying “what if” analysis to produce a group of alternatives (scenarios). We ranked these alternatives to choose the best alternative to improve the efficiency of our system to get better service quality during rush hours. We used Arena Process Analyzer to rank and select the best scenario beside the D&D procedures for ranking and selecting the best alternative, which we will explain it latter in details. At the end, we describe some recommendations and future work as the last part of our study.

Metaheuristics for two-stage no-wait flexible flow shop scheduling problem

The flexible flow-shop scheduling problem (FFSSP) is an important branch of production scheduling, the flexible flow shop is a combination of two well-known machine environments, which are flow shops and parallel machines, and it is well known that this problem is NP-hard. The no-wait requirement is a phenomenon that may occur in flow shops, and its prevent the jobs to wait between two successive machines (or stages), jobs must be processed from the start to finish, without any interruption on machines (or stages) or between them. Many different approaches have been applied to FFSSP with no-wait. In this research, two-stage no-wait flexible flow shop scheduling problem (NWFFSSP) has been solved using two meta-heuristics, which are Tabu Search (TS) and Particle Swarm Optimization (PSO). We solved the NWFFSSP with minimum makespan as a performance measure. The performance of the proposed algorithms are studied and compared with previous research results using the same problem data. The results of the study proposes the effective algorithm.

An Order Effect of Neighborhood Structures in Variable Neighborhood Search Algorithm for Minimizing the Makespan in an Identical Parallel Machine Scheduling

Variable neighborhood search (VNS) algorithm is proposed for scheduling identical parallel machine. The objective is to study the effect of adding a new neighborhood structure and changing the order of the neighborhood structures on minimizing the makespan. To enhance the quality of the final solution, a machine based encoding method and five neighborhood structures are used in VNS. Two initial solution methods which were used in two versions of improved VNS (IVNS) are employed, namely, longest processing time (LPT) initial solution, denoted as HIVNS, and random initial solution, denoted as RIVNS. The proposed versions are compared with LPT, simulated annealing (SA), genetic algorithm (GA), modified variable neighborhood search (MVNS), and improved variable neighborhood search (IVNS) algorithms from the literature. Computational results show that changing the order of neighborhood structures and adding a new neighborhood structure can yield a better solution in terms of average makespan.

Minimization of earliness & Tardiness penalties with common due dates problem using Tabu search

This research relates to a single machine scheduling problem with objective is to minimize earliness & Tardiness penalties with common due dates. We proposed a heuristic method for finding initial solution as an input to the Tabu search algorithm for finding a near optimal solution for this problem. In our research study the due dates are restrictive with due date parameter h, ranges from 0.2, 0.4, 0.6 to 0.8. Our study is a paper base in which we have applied the Tabu search algorithm for the prescribe problem. The base paper of (Ronconi and Kawamura 2010) studied single machine earliness and tardiness scheduling problem: lower bounds and a branch-and-bound algorithm) obtaining optimal solution. We compared the results of Tabu search algorithms with the base paper, Heuristic & benchmark results of D. Biskup and M. Feldmann. Our algorithm shows near optimal solution to the branch & bound algorithm.

Metaheuristics for identical parallel machines scheduling to minimize mean tardiness

Scheduling of jobs has been a challenging task in manufacturing and the most real life scheduling problems, which involves multi-objectives and multi-machine environments. This paper presents tabu search and simulated annealing approaches for scheduling jobs on identical parallel machines. The identical parallel machine scheduling problem has been considered to minimize the mean tardiness for the jobs. Initially, an initial solution has been obtained using EDD dispatching rule then, simulated annealing and tabu search have been applied to reach a near optimal solution. Computational experiments are performed on problems with up to 10 machines and 150 jobs. The computational results indicate that the two proposed approaches are capable of obtaining better solutions for the given scheduling problem. Moreover, the tabu search approach provides better solution then simulated annealing approach.

Scheduling of Jobs on Identical Parallel Machines to Minimize Total Flow Time Subject to Optimal Makespan

Algorithms based on Simulated Annealing and Tabu search has been proposed and implemented on scheduling a problem of parallel machines. The identical parallel machine scheduling problem has been considered to minimize the total flow-time subject to optimal makespan. The proposed algorithms have two phases. In the first phase, an initial solution has been obtained using Longest Processing Time (LPT) dispatching rule and in the second phase, simulated annealing and tabu search have been applied to reach a near optimal solution. The performance of the both proposed algorithms have been evaluated by comparing their results for different number of jobs and processing times. The computational results indicate that the proposed Tabu Search algorithm is capable of obtaining better solutions for the given scheduling problem as compared to the Simulated Annealing algorithm. Although both of these algorithms provide the best solutions as compared to the other heuristic algorithms but in comparison of these two; Tabu Search provides the better solutions for the given problem.