The relentless growth of global energy consumption poses a multitude of complex challenges, including the depletion of finite energy resources and the exacerbation of greenhouse gas emissions, which contribute to climate change. In the face of these pressing environmental concerns, the manufacturing sector, a significant energy consumer, is under immense pressure to adopt sustainable practices. The critical intersection of energy consumption management and production Operation Scheduling emerges as a pivotal domain for addressing these challenges. The Scheduling of Common Operations, exemplified by the cutting stock problem in industries like furniture and apparel, represents a prevalent challenge in production environments. For the first time, this paper pioneers an investigation into an Identical Parallel Machine Scheduling problem, taking into account Common Operations to minimize total energy consumption and total completion time concurrently. For this purpose, two bi-objective mixed integer linear programming models are presented, and an augmented ε – constraint method is used to obtain the Pareto optimal front for small-scale instances. Considering the NP-hardness of this problem, a non-dominated sorting genetic algorithm (NSGA-II) and a hybrid non-dominated sorting genetic algorithm with particle swarm optimization (HNSGAII-PSO) are developed to solve medium- and large-scale instances to achieve good approximate Pareto fronts. The performance of the proposed algorithms is assessed by conducting computational experiments on test problems. The results demonstrate that the proposed HNSGAII-PSO performs better than the suggested NSGA-II in solving the test problems.

This paper presents a novel, integer-linear programming (ILP) model for an Identical Parallel-Machine Scheduling problem with family setup times that minimizes the total weighted flow time (TWFT). Some researchers have addressed Parallel-Machine Scheduling problems in the literature over the last three decades. However, the existing studies have been limited to the research of independent jobs, and most classical optimization methods are focused on Parallel-Machine Scheduling problems without considering setup times and relationship between jobs. This problem is shown to be NP-hard one in the strong sense. Obtaining an optimal solution for this type of complex, large-sized problems in reasonable computational time is extremely difficult. A meta-heuristic method, based on genetic algorithms, is thus proposed and applied to the given problem in order to obtain a good and near-optimal solution, especially for large sizes. Further, the efficiency of the proposed algorithm, based on various test problems, is compared with the Lingo 8.0 software.

Considering maintenance strategy in models which schedule and allocate jobs to Machines, will make the proposed models compatible with production environments. Furthermore, this will cause higher model efficiency in optimizing the production systems. To this end, a mathematical model for Scheduling unrelated Parallel Machines is developed to minimize total weighted completion times. Also in this approach, availability constraints have been considered, and preemption is allowed. Due to executing preventive maintenance and emergency maintenance programs, Machine inaccessible times have been added to job completion times. Since the proposed model has high complexity, in order to solve the problem, two meta-heuristic methods including simulated annealing and genetic algorithm are used. In addition, their performances are compared to each other. The results indicate the superiority of simulated annealing over genetic algorithm for this particular problem.

We study the order acceptance, Scheduling, and pricing problem (OASP) in a Parallel Machine environment. Each order is characterized by due date, release date, deadline, controllable processing time, sequence-dependent setup time, and price in MTO system. An MILP formulation is used to maximize the net profit. Then, under a joint optimization approach, the pricing decisions are set for the unrelated Parallel Machine environment. The results show that the proposed model can solve the Scheduling decision problems based on different levels of products’ prices. Thus, the model solves these two categories of decisions, simultaneously. Moreover, the changes in accepted orders in pricing levels can be analyzed regarding their dependency on price elasticity of items for future research.

A multi-product system is one of the different types of manufacturing systems, in which a large number of products are produced that complement each other and have interdependence. These types of systems have recently been widely used in various industries. In some types of multi-product manufacturing industries that offer their products as a package, the Scheduling of the production of components of each package affects the time it takes to complete the package. Therefore, a new problem has been defined that the primary purpose of its production Scheduling, in addition to reducing the completion time of the products, is to make various items forming a package, get ready over a short interval of time and be supplied to the sales unit so that the package can be delivered to the final consumer. The purpose of this paper is to express the problem of production Scheduling of multi-product production systems in the form of linear programming. For this purpose, two mathematical models are presented, and their functions are compared. Besides, an efficient genetic algorithm is proposed to solve the problem, which is able to solve the problem in a reasonable time, with acceptable accuracy.

Predicting unexpected incidents and energy consumption decline is one of the current problems in the industry. The extant study addressed Parallel Machine Scheduling by consideration of failures and energy consumption decline. Moreover, the present paper aimed at minimizing early and late delivery penalties, and enhancing tasks. This research designed a mathematical model for this problem that considered processing times, delivery time, rotation speed and torque, failure time, and Machine availability after repair and maintenance. Failure times have been predicated on using Machine learning algorithms. The results indicated that the proposed model can be suitably solved for the size of 10 jobs or tasks and five Machines. This research addresses the problem in two parts: the first part predicts failures, and the second part includes the sequence of Parallel Machine Scheduling Operations. After the previous data were received in the first step, Machine failure was predicted by using Machine learning algorithms, and a set of rules were obtained to correct the process. The obtained rules were used in the model to improve the machining process. In the second step, Scheduling mode was used to determine Operations sequence by consideration of these failures and Machinery unavailability to achieve the optimal sequence. Moreover, it is supposed to reduce energy consumption and failures. This study used the Light GBM algorithm and achieved 85% precision in failure prediction. The rules obtained from this algorithm contributed to cost reduction.

This paper considers the problem of Scheduling Parallel Machines for split jobs to minimize the total tardiness. Accepting a new job, each Machine needs to be set up and the setup times depend on the sequence of jobs. To solve the above problem, a new approach is suggested and a number of theorems are provided and proved regarding resource planning and job sequencing for the given problem in hand. Then, the proposed algorithm is verified and evaluated with a number of test problems. The associated results are analyzed and compared with the results obtained by the Lingo software.

The paper proposes a new real life model and the main aim is to examine the cost benefit analysis of Textile Industry model subject to different failure and repair strategies. The reliability model comprises of three units i, e Spinning Machine (S), Weaving Machine (W), Colouring and Finishing Machine(Cf). The working principal of the model starts with spinning Machine (S) where in unit S is in operative state while as weaving Machine, Colouring and Finishing Machine are in ideal state. Complete failure of system is observed when all three units of system i. e. S, W and Cf are in down state. Repairperson is always available to carry out the repair activities in the system in which first priority in repair is given to Colouring and Finishing Machine followed by Spinning and weaving Machine. The proposed model attempts to maximize the reliability of a real life system. Reliability measures such as Mean Sojourn time, Mean time to system failure, Profit analysis of system are examined to define the performance of the reliability characteristics. For concluding the study of such model, different stochastic measures are analyzed in steady state using regenerative point technique. The tables are prepared for arbitrary values of the parameters to show the performance of some important reliability measures and to check the efficiency of the model under such situations.