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In some industries as foundries, it is not technically feasible to interrupt a processor between jobs. This restriction gives rise to a scheduling problem called no-idle scheduling. This paper deals with scheduling of no-idle open shops to minimize maximum completion time of jobs, called makespan. The problem is first mathematically formulated by three different mixed integer linear programming models. Since open shop scheduling problems are NP-hard, only small instances can be solved to optimality using these models. Thus, to solve large instances, two meta-heuristics based on simulated annealing and genetic algorithms are developed. A complete numerical experiment is conducted and the developed models and algorithms are compared. The results show that genetic algorithm outperforms simulated annealing.

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Due to the considerable growth in the worldwide container transportation, optimization of container terminal operations is becoming highly needed to rationalize the use of logistics resources. For this reason, we focus our study on the Quay Crane Scheduling Problem (QCSP), which is a core task of managing maritime container terminals. From this planning problem arise two decisions to be made: The first one concerns tasks assignment to quay crane and the second one consists of finding the handling sequence of tasks such that the turnaround time of cargo vessels is minimized. In this paper, we provide a mixed-integer programming (MIP) model that takes into account non-crossing constraints, safety margin constraints and precedence constraints. The QCSP has been shown NP-complete, thus, we used the Ant Colony Optimization (ACO), a probabilistic technique inspired from ants’ behaviour, to find a feasible solution of such problem. The results obtained from the computational experiments indicate that the proposed method is able to produce good results while reducing the computational time.

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In this paper, the flexible job shop scheduling problem with machine flexibility and controllable process times is studied. The main idea is that the processing times of operations may be controlled by consumptions of additional resources. The purpose of this paper to find the best trade-off between processing cost and delay cost in order to minimize the total costs. The proposed model, flexible job shop scheduling with controllable processing times (FJCPT), is formulated as an integer non-linear programming (INLP) model and then it is converted into an integer linear programming (ILP) model. Due to NP-hardness of FJCPT, conventional analytic optimization methods are not efficient. Hence, in order to solve the problem, a Scatter Search (SS), as an efficient metaheuristic method, is developed. To show the effectiveness of the proposed method, numerical experiments are conducted. The efficiency of the proposed algorithm is compared with that of a genetic algorithm (GA) available in the literature for solving FJSP problem. The results showed that the proposed SS provide better solutions than the existing GA.

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In this paper, a new genetic algorithm (GA) is presented for solving the multi-mode resource-constrained project scheduling problem (MRCPSP) with minimization of project makespan as the objective subject to resource and precedence constraints. A random key and the related mode list (ML) representation scheme are used as encoding schemes and the multi-mode serial schedule generation scheme (MSSGS) is considered as the decoding procedure. In this paper, a simple, efficient fitness function is proposed which has better performance compared to the other fitness functions in the literature. Defining a new mutation operator for ML is the other contribution of the current study. Comparing the results of the proposed GA with other approaches using the well-known benchmark sets in PSPLIB validates the effectiveness of the proposed algorithm to solve the MRCPSP.

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Scheduling problem for the hybrid flow shop scheduling problem (HFSP) followed by an assembly stage considering aging effects additional preventive and maintenance activities is studied in this paper. In this production system, a number of products of different kinds are produced. Each product is assembled with a set of several parts. The first stage is a hybrid flow shop to produce parts. All machines can process all kinds of parts in this stage but each machine can process only one part at the same time. The second stage is a single assembly machine or a single assembly team of workers. The aim is to schedule the parts on the machines and assembly sequence and also determine when the preventive maintenance activities get done in order to minimize the completion time of all products (makespan). A mathematical modeling is presented and its validation is shown by solving an example in small scale. Since this problem has been proved strongly NP-hard, in order to solve the problem in medium and large scale, four heuristic algorithms is proposed based on the Johnson’s algorithm. The numerical experiments are used to run the mathematical model and evaluate the performance of the proposed algorithms.

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This paper presents the formulation and solution of the Combinatorial Multi-Mode Resource Constrained Multi-Project Scheduling Problem. The focus of the proposed method is not on finding a single optimal solution, instead on presenting multiple feasible solutions, with cost and duration information to the project manager. The motivation for developing such an approach is due in part to practical situations where the definition of optimal changes on a regular basis. The proposed approach empowers the project manager to determine what is optimal, on a given day, under the current constraints, such as, change of priorities, lack of skilled worker. The proposed method utilizes a simulation approach to determine feasible solutions, under the current constraints. Resources can be non-consumable, consumable, or doubly constrained. The paper also presents a real-life case study dealing with scheduling of ship repair activities.