Hamed Piroozfard

An efficient genetic algorithm for flexible job-shop scheduling problem

In this paper a genetic algorithm (GA) is developed to create a feasible and active schedule for the flexible job-shop scheduling problems with the aims of minimizing completion time of all jobs, i.e. makespan. In the proposed algorithm, an enhanced solution coding is used. To generate high quality initial populations, we designed an Operation order-based Global Selection (OGS), which is taken into account both the operation processing times and workload of machines while is assigning a machine to the operation which already is ordered randomly in chromosome `operation sequence part. The precedence preserving order-based crossover (POX) and uniform crossover are used appropriately and furthermore an intelligent mutation operator is carried out. The proposed algorithm is applied on the benchmark data set taken from literature. The results demonstrated efficiency and effectiveness of the algorithm for solving the flexible job shop scheduling problems.

A Hybrid Genetic Algorithm with a Knowledge-Based Operator for Solving the Job Shop Scheduling Problems

Scheduling is considered as an important topic in production management and combinatorial optimization in which it ubiquitously exists in most of the real-world applications. The attempts of finding optimal or near optimal solutions for the job shop scheduling problems are deemed important, because they are characterized as highly complex and -hard problems. This paper describes the development of a hybrid genetic algorithm for solving the nonpreemptive job shop scheduling problems with the objective of minimizing makespan. In order to solve the presented problem more effectively, an operation-based representation was used to enable the construction of feasible schedules. In addition, a new knowledge-based operator was designed based on the problem’s characteristics in order to use machines’ idle times to improve the solution quality, and it was developed in the context of function evaluation. A machine based precedence preserving order-based crossover was proposed to generate the offspring. Furthermore, a simulated annealing based neighborhood search technique was used to improve the local exploitation ability of the algorithm and to increase its population diversity. In order to prove the efficiency and effectiveness of the proposed algorithm, numerous benchmarked instances were collected from the Operations Research Library. Computational results of the proposed hybrid genetic algorithm demonstrate its effectiveness.

Job shop scheduling problem with late work criterion

Scheduling is considered as a key task in many industries, such as project based scheduling, crew scheduling, flight scheduling, machine scheduling, etc. In the machine scheduling area, the job shop scheduling problems are considered to be important and highly complex, in which they are characterized as NP-hard. The job shop scheduling problems with late work criterion and non-preemptive jobs are addressed in this paper. Late work criterion is a fairly new objective function. It is a qualitative measure and concerns with late parts of the jobs, unlike classical objective functions that are quantitative measures. In this work, simulated annealing was presented to solve the scheduling problem. In addition, operation based representation was used to encode the solution, and a neighbourhood search structure was employed to search for the new solutions. The case studies are Lawrence instances that were taken from the Operations Research Library. Computational results of this probabilistic meta-heuristic algori...

An imperialist competitive algorithm for the job shop scheduling problems

Scheduling is assigning a set of tasks on resources in a time period, taking into account the time, capability and capacity constraints. The job shop scheduling problems are the most important problems in management science and combinatorial optimization. These problems belong to the family of NP-hard, in which they cannot be solved in polynomial time (unless P=NP). In this paper, a meta-heuristic algorithm is proposed for solving the job shop scheduling problems with the objective of minimizing makespan. A meta-heuristic approach called imperialist competitive algorithm which imitates the behavior of imperialistic competition is presented. This algorithm is constructed with countries, colonies and imperialists in which colonies and imperialists make the empires. The algorithm starts with initializing the countries and empires. In addition, the algorithm continues the search process by applying assimilation and revolution operators. To further improve the solution quality obtained by the imperialist competitive algorithm, simulated annealing is applied. A set of well-studied benchmarking instances obtained from the OR-Library is used to evaluate the performance of the proposed algorithm, and the computational results indicate its efficiency.

Solving multi-objective job shop scheduling problems using a non-dominated sorting genetic algorithm

The efforts of finding optimal schedules for the job shop scheduling problems are highly important for many real-world industrial applications. In this paper, a multi-objective based job shop scheduling problem by simultaneously minimizing makespan and tardiness is taken into account. The problem is considered to be more complex due to the multiple business criteria that must be satisfied. To solve the problem more efficiently and to obtain a set of non-dominated solutions, a meta-heuristic based non-dominated sorting genetic algorithm is presented. In addition, task based representation is used for solution encoding, and tournament selection that is based on rank and crowding distance is applied for offspring selection. Swapping and insertion mutations are employed to increase diversity of population and to perform intensive search. To evaluate the modified non-dominated sorting genetic algorithm, a set of modified benchmarking job shop problems obtained from the OR-Library is used, and the results are c...

A Hybrid Harmony Search Algorithm for the Job Shop Scheduling Problems

Machine scheduling is assigning a set of operations of jobs on machines during a time period, taking into account the time, capability, and capacity constraints. In machine scheduling and management science, job shop scheduling is considered as an important problem due to many real-world applications. The job shop scheduling problems are numerically intractable that cannot be solved in polynomial time, unless P = NP, and they are classified as NP-hard. Harmony search algorithm has been successfully implemented in many optimization problems, particularly in scheduling problems, and hybridization is an effective approach for improving the solution quality of the algorithm. This paper proposes an effective hybrid harmony search algorithm for solving the job shop scheduling problems with the objective of minimizing makespan. A set of well-studied benchmarked problems is used to prove the effectiveness and efficiency of the proposed algorithm. The results indicate that the proposed hybrid harmony search algorithm improves the efficiency.

Solving an Industrial Shop Scheduling Problem Using Genetic Algorithm

Spool fabrication shop is an intermediate phase in the piping process for construction projects. The delivery of pipe spools at the right time in order to be installed in the site is very important. Therefore, effective scheduling and controlling of the fabrication shop has a direct effect on the productivity and successfulness of the whole construction projects. In this paper, a genetic algorithm (GA) is developed to create an active schedule for the operational level of pipe spool fabrication. In the proposed algorithm, an enhanced solution coding is used to suitably represent a schedule for the fabrication shop. The initial population is generated randomly in the initialization stage and precedence preserving order-based crossover (POX) and uniform crossover are used appropriately. In addition, different mutation operators are used. The proposed algorithm is applied with the collected data that consist of operations processing time from an industrial fabrication shop. The results showed that by using GA for scheduling the fabrication processes, the productivity of the spool fabrication shop has increased by 88 percent.

A Hybrid Genetic Algorithm for Solving Job Shop Scheduling Problems

Job shop scheduling problems are immensely complicated problems in machine scheduling area, and they are classified as NP-hard problems. Finding optimal solutions for job shop scheduling problems with exact methods incur high cost, therefore, looking for approximate solutions with meta-heuristics are favored instead. In this paper, a hybrid framework which is based on a combination of genetic algorithm and simulated annealing is proposed in order to minimize maximum completion time i.e. makespan. In the proposed algorithm, precedence preserving order-based crossover is applied which is able to generate feasible offspring. Two types of mutation operators namely swapping and insertion mutation are used in order to maintain diversity of population and to perform intensive search. Furthermore, a new approach is applied for arranging operations on machines, which improved solution quality and decreased computational time. The proposed hybrid genetic algorithm is tested with a set of benchmarking problems, and simulation results revealed efficiency of the proposed hybrid genetic algorithm compared to conventional genetic based algorithm.

Solving a Capacitated p-Median Location Allocation Problem Using Genetic Algorithm: A Case Study

Facility location-allocation problems have various applications in private and public sectors. A capacitated p-median problem is considered in this work which is computationally NP-Hard. The primary goal of this paper was to determine a set of p-facilities location in which all demand points are allocated and its average distance traveled from the customers’ location to the selected p-facilities is minimized. In addition, the model also considered supplier’s allocation for p facilities. A real world case study has been addressed, and genetic algorithm which consists of crossover and mutation operators was proposed in order to solve the problem. Computational results for different values of p were generated, and finally the optimum solution based on minimum cost was reported.