This paper presents a two-stage hybrid flow shop scheduling problem with setup and assembly operations. The proposed study of this paper considers one kind of product with a quantity of demand where each product is made by assembling a set of different parts. At first, the parts are manufactured in a two-stage hybrid flow-shop and then the parts are assembled into products on assembly stage. Setup operations are needed when a machine starts processing the parts or it changes items. The considered objective is minimizing the completion time of all products. Since the problem is classified as NP-hard class, a combinatorial algorithm is proposed. The proposed algorithm is a three-step procedure where we use heuristic, genetic algorithm (GA), simulated annealing (SA), NEH and Johnson’s algorithm. Three lower bounds are presented and improved to evaluate the proposed algorithms. An extensive computational experiment is conducted to compare the performances of the proposed algorithms.

The capacitated arc routing problem is one of the most important routing problems with many applications in real world situations such as snow removing, winter gritting, refuse collection, etc. Since this problem is NP-hard, many of researchers have been developed numerous heuristics and metaheuristics to solve it. In this paper, we propose a new constructive and improvement heuristic in which forming a vehicle’s tour is based on choosing an unserved edge randomly as current partial tour and then extending this partial tour from its both of end nodes base on four effective proposed criteria. When the vehicle load is near its capacity, it should come back to the depot immediately. Finally, the constructed tours are merged into more efficient and cheaper tours. The quality of this new approach was tested on three standard benchmark instances and the results were compared with some known existing heuristics and metaheuristics in the literature. The computational results show an excellent performance of our new method.

This article addresses multi-level lot sizing and scheduling problem in capacitated, dynamic and deterministic cases of a job shop manufacturing system with sequence-dependent setup times and costs assumptions. A new mixed-integer programing (MIP) model with big bucket time approach is provided to the problem formulation. It is well known that the capacitated lot sizing and scheduling problem (CLSP) is NP-hard. The problem of this paper that it is an extent of the CLSP is even more complicated; consequently, it necessitates the use of approximated methods to solve this problem. Hence, two new mixed integer programming-based approaches with rolling horizon framework have been used to solve this model. To evaluate the performance of the proposed model and algorithms, some numerical experiments are conducted. The comparative results indicate the superiority of the second heuristic.

Job shop scheduling (JSS) problem has been one of the most interesting research issues
in the literature during the recent years. JSS problem has been studied in different forms
of deterministic, fuzzy, and stochastic at different depths. The idea of robust
optimization (ROP), on the other hand, has earned a particular value to become a
popular subject of the breakthrough for problem solving affairs amongst the researchers.
Based on the emerged opportunity for illustrating a new area of search, a robust JSS
problem is proposed as a challenge to this boundary of knowledge. The proposed
method is capable of handling the perturbation which exists amongst the processing
times. In fact, in many real world job scheduling problems, a small change in the
processing times, not only causes a non-optimal solution, but also the infeasibility of the
final solution may also occur. The proposed robust method could guarantee that, a small
deviation of the processing times does not affect the feasibility. The implementation of
the proposed method is illustrated using some numerical examples and the outcomes of
the investigation are discussed

In this paper, we address the problem of scheduling jobs in a no-wait flowshop problem with sequence-dependent setup times with the objective of minimizing makespan. This problem is well-known for being nondeterministic polynomial-time hard, and small contribution to the problem has been made. We propose a new constructive heuristic named GAPH based on a structural property. The effectiveness of the structural property is crucial given that it is responsible for 100% of the success rate of the total problems tested. The computational results demonstrate that the proposed approach is superior than three of the best-know methods in the literature such as the twos by Bianco, Dell’Olmo and Giordani (INFOR Journal: 37 (1), 3-19, 1999) and TRIPS heuristic adapted for sequence-dependent setup times objective by Brown, Mcgarvey and Ventura (Journal of the Operational Research Society, 55 (6), 614-621, 2004) in terms of the solution quality and that it requires less computational effort.