Prediction of unexpected incidents and energy consumption are some industry issues and problems. Single machine scheduling with preemption and considering failures has been pointed out in this study. Its aim is to minimize earliness and tardiness penalties by using job expansion or compression methods. The present study solves this problem in two parts. The first part predicts failures and obtains some rules to correct the process, and the second includes the sequence of Single-machine scheduling operations. The failure time is predicted using some machine learning algorithms includes: Logistic Regression, Decision Tree, Random Forest, Support Vector machine (SVM), Naïve Bayes, and k-nearest neighbors. Results of comparing the algorithms, indicate that the decision tree algorithm outperformed other algorithms with a probability of 70% in predicting failure. In the second part, the problem is scheduled considering these failures and machine idleness in a Single-machine scheduling manner to achieve an optimal sequence, minimize energy consumption, and reduce failures. The mathematical model for this problem has been presented by considering processing time, machine idleness, release time, rotational speed and torque, failure time, and machine availability after repair and maintenance. The results of the model solving, concluded that the relevant mathematical model could schedule up to 8 jobs within a reasonable time and achieve an optimal sequence, which could reduce costs, energy consumption, and failures. Moreover, it is suggested that further studies use this approach for other types of scheduling, including parallel machine scheduling and flow job shop scheduling. Metaheuristic algorithms can be used for larger dimensions.

This article investigates a JIT Single machine scheduling problem with a periodic preventive maintenance. Also to maintain the quality of the products, there is a limitation on the maximum number of allowable jobs in each period. The proposed bi-objective mixed integer model minimizes total earliness-tardiness and makespan simultaneously. Due to the computational complexity of the problem, multi-objective particle swarm optimization (MOPSO) algorithm is implemented. Also, as well as MOPSO, two other optimization algorithms are used for comparing the results. Eventually, Taguchi method with metrics analysis is presented to tune the algorithms’ parameters and a multiple criterion decision making technique based on the technique for order of preference by similarity to ideal solution is applied to choose the best algorithm. Comparison results confirmed the supremacy of MOPSO to the other algorithms.

A multi-agent Single machine scheduling problem with transportation constraints is studied. We assume that there are several independent agents placed in different geographical locations, each of them has several orders and each order includes different types of products. We use a simple and effective model to obtain maximum profit of the products. To have desired on-time deliveries, the minimization of the transportation costs and total tardiness costs are considered as objective functions. The main idea of this research is to develop a simple and integrated scheduling and transportation model which can be applied in many factories, chain stores, and so on. In order to solve this problem, a mixed integer linear programming (MILP) model is presented. Moreover, since solving large instances of the proposed MILP model is very time-consuming, a heuristic algorithm is presented. Implementing of two approaches on a variety of datasets show that the heuristic algorithm can provide good-quality solutions in very short time.

This paper considers the Single machine scheduling problem with precedence constraints and deteriorating jobs and a mathematical model based on binary integer programming (BIP) is developed. Since the precedence constraints exist, a job cannot start before completion of its all predecessors. The proposed model consists of two steps: in the first step, the earliest starting time of each job is computed, then the results are used in the second step in which an optimal sequence between jobs is determined with the aim of minimizing the total completion time. Finally, a numerical example is presented and solved using optimization software LINGO 8.0.

Preventive maintenance is the essential part of many maintenance plans. From the production point of view, the flexibility of the maintenance intervals enhances the manufacturing efficiency. On the contrary, the maintenance departments tend to know the timing of the long term maintenance plans as certain as possible. In a Single-machine production environment, this paper proposes a simulation– optimization approach which establishes periodic flexible maintenance plans by determining the time between the maintenance intervals and the flexibility (i. e., length) of each interval. The objective is the minimization of the estimated total costs of the corrective and preventive maintenance, the undesirability of the flexibility (i. e., uncertainty) in maintenance timing, and the tardiness and long due date costs of jobs. Two mixed continuous-discrete variations of the ant colony optimization algorithm and the particle swarm optimization algorithm are developed as the solution approaches. Numerical studies are used to compare the performance of these algorithms. Further, the average reduction of the total costs gained from the flexibility of maintenance intervals on a wide range of parameters is reported.