To better meet the qualitative and quantitative requirements of customers or relevant sector managers, workshop environments are implementing increasingly complex task management systems. The job shop scheduling problem (JSSP) involves assigning each task to a single machine while scheduling many tasks on different machines. Finding the best scheduling for machines is one of the challenging optimizations of difficult non-deterministic polynomial (NP) time problems. The fundamental goal of optimization is to shorten the makespan (total execution time of all tasks). This paper is interested in the joint resolution of scheduling and transport problems and more particularly the Job-shop problem with Routing (JSSPR) as opposed to the Job-shop problem with Transport (JSSPT). These two problems are modeled in the form of a disjunctive graph. For the JSSPT, the solution to the transport problem is not linked to any quality of service (QoS) criterion and the solution is therefore often semi-active. The Job-shop with Routing explicitly considers transport operations and uses algorithms from the transport community to solve the transport problem. It is shown that the routing part of the JSSPR is a problem of the vehicle routing family and of the Pickup and Delivery Problem family. QoS in the JSSPR is defined by the duration of tours, the duration of transport of parts and the waiting time for them. A new evaluation function – named Time-Lag Insertion Heuristic (TLH) – is proposed to evaluate a disjunctive graph by simultaneously minimizing the makespan and maximizing the quality of service. Thus, the solution obtained is not semi-active, but a compromise between the different criteria. This evaluation function is included in a metaheuristic. Our numerical evaluations demonstrate that, on the one hand, the TLH evaluation can find almost optimal solutions regarding the QoS criterion; and on the other hand, the TLH evaluation is not very sensitive to the order of insertion of the maximum time-lags during the different minimization steps.

Job shop has been considered as one of the most challenging scheduling problems and there are literally tremendous efforts on reducing the complexity of solution procedure for solving job shop problem. This paper presents a heuristic method to minimize makespan for different jobs in a job shop scheduling. The proposed model is based on a constructive procedure to obtain good quality schedules, very quickly. The performance of the proposed model of this paper is examined on standard benchmarks from the literature in order to evaluate its performance. Computational results show that, despite its simplicity, the proposed heuristic is computationally efficient and practical approach for the problem.

Cell formation process is one of the first and the most important steps in designing cellular manufacturing systems. It consists of identifying part families according to the similarities in the design, shape, and presses of parts and dedicating machines to each part family based on the operations required by the parts. In this study, a hybrid method based on a combination of simulated annealing algorithm and dynamic programming was developed to solve a bi-objective cell formation problem with duplicate machines. In the proposed hybrid method, each solution was represented as a permutation of parts, which is created by simulated annealing algorithm, and dynamic programming was used to partition this permutation into part families and determine the number of machines in each cell such that the total dissimilarity between the parts and the total machine investment cost are minimized. The performance of the algorithm was evaluated by performing numerical experiments in different sizes. Our computational experiments indicated that the results were very encouraging in terms of computational time and solution quality.

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.