We study an inverse counterpart of the two machine flow-shop scheduling problem that arises in the context of inverse optimization. While in the forward scheduling problem all parameters are given and the objective is to find job sequence(s) for which the value of the makespan is minimum, in the inverse scheduling the exact values of processing times are unknown and they should be selected with...

The assumption of classical shop scheduling problems that each job visits each machine only once (Baker, 1974) is often violated in practice. A new type of manufacturing shop, the reentrant shop has recently attracted attention. The basic characteristic of a re-entrant shop is that a job visits certain machines more than once. For example, in semiconductor manufacturing, consequently, each wafe...

We survey solution methods for the job shop scheduling problem with an emphasis on local search. Both deterministic and randomized local search methods as well as the proposed neighborhoods are discussed. We compare the computational performance of the various methods in terms of their eeectiveness and eeciency on a standard set of problem instances. In the job shop scheduling problem we are gi...

Scheduling problems have the standard consideration in the field of manufacturing. Among the various types of scheduling problems, the job shop scheduling problem is one of the most interesting NP-hard problems. As the job shop scheduling is an optimization problem, Genetic algorithm was selected to solve it In this study. Selection scheme is one of the important operators of Genetic algorithm....

This paper proposes a new and efficient hybrid heuristic scheme for solving job-shop scheduling problems (JSP). A new and efficient population initialization and local search concept, based on genetic algorithms, is introduced to search the solution space and to determine the global minimum solution to the JSP problem. Simulated results imply that the proposed novel JSP method (called the PLGA ...

This paper represents the metaheuristics proposed for solving a class of Shop Scheduling problem. The Bacterial Foraging Optimization algorithm is featured with Ant Colony Optimization algorithm and proposed as a natural inspired computing approach to solve the Mixed Shop Scheduling problem. The Mixed Shop is the combination of Job Shop, Flow Shop and Open Shop scheduling problems. The sample i...

Scheduling is a combinatorial problem with important impact on both industry and commerce. If it is performed well it yields time and efficiency benefits and hence reduces costs. Genetic Algorithms have been applied to solve several types of scheduling problems; Flow Shop, Resource, Staff and Line Balancing have all been tackled. However Jobshop Scheduling is the most common problem of interest...

This paper introduces a modified shifting bottleneck approach to solve train scheduling and rescheduling problems. The problem is formulated as a job shop scheduling model and a mixed integer linear programming model is also presented. The shifting bottleneck procedure is a wellestablished heuristic method for obtaining solutions to the job shop and other machine scheduling problems. We modify ...

The optimization of job-shop scheduling is very important because of its theoretical and practical significance. In this paper, a computationally effective approach of combining bacterial foraging strategy with particle swarm optimization for solving the minimum makespan problem of job shop scheduling is proposed. In the artificial bacterial foraging system, a novel chemotactic model is designe...

Multi Objective Job Shop Scheduling Problem (MOJSP) is a problem for finding optimal operation sequences of some jobs according to more than one goal to achieve. The problem gets harder as its complexity increases. The development of optimization method has led many new methods to solve this problem. This paper offers Cross Entropy-Genetic Algorithm (CEGA) method to solve job shop scheduling pr...