Machine Scheduling Research Articles

Pragmatic simultaneous scheduling of machines, AGVs, tool transporter and tools in a multi machine FMS using flower pollination algorithm

Pragmatic simultaneous scheduling of machines, AGVs, tool transporter and tools in a multi machine FMS using flower pollination algorithm

Metaheuristic Algorithms for Related Parallel Machines Scheduling Problem with Availability and Periodical Unavailability Constraints

Metaheuristic Algorithms for Related Parallel Machines Scheduling Problem with Availability and Periodical Unavailability Constraints

Scheduling parallel extrusion lines

This paper introduces the problem of scheduling jobs on parallel plastic extrusion lines where each line is composed of one or more than one extruder. Although there are some similarities between the introduced problem and the non-identical parallel machines scheduling problems with sequence-dependent setup times, limited additional resources and machine eligibility restrictions, the problem considered in this paper is a generalization of the parallel machine scheduling problem. This is because in parallel machines scheduling each job requires only one machine but in our case some jobs require more than one machine. Thus, our problem reduces to the parallel machine scheduling problem if all jobs require only one machine. This paper describes the problem of scheduling parallel extrusion lines, its industrial context, and develops a mixed-linear formulation to model the problem. This formulation allowed solving instances of up to 15 jobs. In addition, we developed four metaheuristics: a simulated annealing algorithm, a tabu search heuristic, a genetic algorithm, and a greedy randomized adaptive search procedure. These metaheuristics can be used to solve real-life instances of the problem. A numerical experiment shows that the proposed metaheuristics produce excellent solutions. Some of the proposed simulated annealing adaptations and of the tabu search heuristics obtained solutions with less than 2% deviation from the optimum.

Dynamical Artificial Bee Colony for Energy-Efficient Unrelated Parallel Machine Scheduling with Additional Resources and Maintenance

Dynamical Artificial Bee Colony for Energy-Efficient Unrelated Parallel Machine Scheduling with Additional Resources and Maintenance

Research on Agricultural Machine Scheduling in Hilly Areas Based on Improved Non-Dominated Sorting Genetic Algorithm-III

Research on Agricultural Machine Scheduling in Hilly Areas Based on Improved Non-Dominated Sorting Genetic Algorithm-III

A Job Sequence Optimization Approach for Parallel Machine Scheduling Problem in Printing Manufacturing Systems

A Job Sequence Optimization Approach for Parallel Machine Scheduling Problem in Printing Manufacturing Systems

Metaheuristic Optimization for Sustainable Unrelated Parallel Machine Scheduling: A Concise Overview With a Proof-of-Concept Study

Metaheuristic Optimization for Sustainable Unrelated Parallel Machine Scheduling: A Concise Overview With a Proof-of-Concept Study

A Cooperated Imperialist Competitive Algorithm for Unrelated Parallel Batch Machine Scheduling Problem

A Cooperated Imperialist Competitive Algorithm for Unrelated Parallel Batch Machine Scheduling Problem

Resource constraint scheduling on two dedicated machines: Application to avionics

In civil aircraft, two partially redundant hydraulic circuits typically power various systems. During assembly, a critical phase involves simultaneously rinsing and purging these hydraulic circuits using loops. Precedence constraints are necessary to prevent the recontamination of already rinsed loops, leading to increased rinsing time. This paper presents this problem as a unique instance of the Resource Constrained Parallel Machine Scheduling Problem, where each circuit represents a machine, pipe loops to be rinsed represent jobs, and machines share a hydraulic power source. For two dedicated processors and a single resource, an optimal schedule minimizing the makespan can be generated in polynomial time. However, due to the requirement of rinsing certain pipe loops on a circuit before others, there are precedence constraints between some jobs within the same circuit. By employing a reduction of the 3-partition problem, we demonstrate that this situation results in a problem that is NP-hard in the strong sense. We evaluate several Mixed-Integer Linear Programming and Constraint Programming formulations of the problem, using Cplex, CPO, Gurobi, and Z3, against several proposed heuristics. Given that the size of the instances we need to solve exceeds what can be solved in acceptable time by solvers, we propose a heuristic and compare its performance with the optimum.

A machine learning-based selection approach for solving the single machine scheduling problem with early/tardy jobs

A machine learning-based selection approach for solving the single machine scheduling problem with early/tardy jobs

Renewable Energy-Aware Machine Scheduling Under Intermittent Energy Supply

Renewable Energy-Aware Machine Scheduling Under Intermittent Energy Supply

Solving Preemptive Single Machine Scheduling with Lovebird Algorithm and Constructive Heuristics for Equal-Length Jobs

Scheduling is essential in manufacturing to maintain machinery and increase revenue. Machines that are routinely maintained will boost production, whereas scheduling based on total weighted completion time (TWCT) can improve the performance of production equipment. The Lovebird Algorithm (LBA), Constructive Heuristics (CH), and combination of LBA-CH are used in this study to schedule a single machine with equal processing times. LBA, a population-based algorithm, represents job parts as birds for jobs that can be preempted. The experiments, which used ten jobs divided into dozens of job parts, show that LBA-CH can effectively reduce TWCT compared to CH by 18.16%.

Optimized resource allocation in cloud computing for enhanced performance with modified particle swarm optimization

Cloud Computing (CC) offers abundant resources and diverse services for running a wide range of consumer applications, although it faces specific issues that need attention. Cloud customers aim to choose the most suitable resource that fulfills the requirements of consumers at a fair cost and within an acceptable timeframe; however, at times, they wind up paying more for a shorter duration. Many advanced algorithms focus on optimizing a single variable individually. Hence, an Optimized Resource Allocation in Cloud Computing (ORA-CC) Model is required to achieve equilibrium between opposing aims in Cloud Computing. The ORA-CC study aims to create a task processing structure with the decision-making ability to choose the best resource in real-time for handling diverse and complicated uses on Virtual Computers (VC). It will utilize a Modified Particle Swarm Optimization (MoPSO) method to meet a deadline set by the user. The fitness value is calculated by combining a base value with the enhanced estimation of resources based on the ORA-CC algorithm to create a robust arrangement. The ORA-CC technique's effectiveness is evaluated by contrasting it with a few current multi-objective restrictions applied to machine scheduling strategies utilizing the Cloudsim simulation. The comparison demonstrates that the suggested ORA-CC strategy offers more efficient resource allocation than other techniques.

An Accurate and Robust Genetic Algorithm to Minimize the Total Tardiness in Parallel Machine Scheduling Problems

This paper uses a Genetic Algorithm (GA) to reduce total tardiness in an identical parallel machine scheduling problem. The proposed GA is a crossover-free (vegetative reproduction) GA but used for four types of mutations (Two Genes Exchange mutation, Number of Jobs mutation, Flip Ends mutation, and Flip Middle mutation) to make the required balance between the exploration and exploitation functions of the crossover and mutation operators. The results showed that use of these strategies positively affects the accuracy and robustness of the proposed GA in minimizing the total tardiness. The results of the proposed GA are compared to the mathematical model in terms of the time required to tackle the proposed problem. The findings illustrate the ability of the propounded GA to acquire the results in a short time compared to the mathematical model. On the other hand, increasing the number of machines degraded the performance of the proposed GA.

Using Local Search Methods for Solving Two Multi-Criteria Machine Scheduling Problems

In this paper, we have improved solutions for two of the Multi-Criteria Machine Scheduling Problems (MCMSP). These problems are to maximize early jobs time and range of lateness jobs times (1//(E_max,R_L ), and the second problem is maximum tardy jobs time and range of lateness jobs times (1//(T_max,R_L ) in a single machine with Multi-Objective Machine Scheduling Problems (MOMSP) 1//(E_max+R_L ) and 1//(T_max+R_L ) which are derived from the main problems respectively. The Local Search Methods (LSMs), Bees Algorithm (BA), and a Simulated Annealing (SA) are applied to solve all suggested problems. Finally, the experimental results of the LSMs are compared with the results of the Branch and Bound (BAB) method for a reasonable time. These results are ensuring the efficiency of LSMs.

The stability of dynamic [formula omitted]-person Cournot games with strategic adjustment costs

In certain Cournot games, firms have to incur additional costs when adjusting production plans, such as scheduling of employees and machines, which is usually neglected. Therefore, by designing a quadratic cost function, these strategic adjustment costs are introduced in a dynamic n-person Cournot game with adaptive expectation. And both theoretical and numerical analyses demonstrate that the costs improve the stability of the original Cournot–Nash equilibrium. This implies that the Cournot–Nash equilibria with over three firms are not as fragile as previously believed.

Cost-based Virtual Machine Scheduling for Data-as-a-Service

Data-as-a-Service (DaaS) is a branch of cloud computing that supports “querying the Web”. Due to its ultrahigh scale, it is essential to establish rules when defining resources’ costs and guidelines for infrastructure investments. Those decisions should prioritize minimizing the incidence of agreement breaches that compromise the performance of cloud services and optimize resources’ usage and services’ cost. This article aims to address the cost problem of DaaS by developing a model that optimizes the costs of querying distributed data sources over virtual machines spread across multisite data centers. We have designed and analyzed a cost model for DaaS, besides implementing a scheduling system to perform a cost-based VM assignment. To validate our model, we have studied and characterized a real-world DaaS system’s network and processing workloads. On average, our cost-based scheduling performs at least twice as well as the traditional round-robin approach. Our model also supports load balancing and infrastructure scalability when combined with an adaptive cost scheme that prioritizes VM allocation within the underutilized data centers and avoids sending VMs to data centers in the eminence of becoming over-utilized.

Solving an Unrelated Parallel Machines Scheduling Problem with machine- and job-dependent setups and precedence constraints considering Support Machines

Single-stage production planning problems are common in the academic literature and in real-world environments. One of the least studied scenarios in the literature for this environment is that with Unrelated Parallel Machines. In this work, this type of problem is inspired by a real station within the wind tower production process. The problem presents characteristics that have already been studied, such as mandatory precedences and setup times that depend on the machine and the job. However, a new and real feature is presented: the existence of “support machines”, which are machines that can continue to work but cannot complete jobs due to reduced capacity for some operational reason. In this case, instead of abandoning their work, support machines can still be used to assist other machines by performing partial jobs before handing them over to full-capacity machines for completion. This innovative concept of support machines, never before presented in this context of production planning, introduces a unique approach to dealing with reduced-capacity machines without sacrificing their operational potential. A Mixed-Integer Linear Programming (MILP) model is formulated to mathematically represent this problem.This work explores the impact of these support machines on production planning. For this purpose, Tabu Search and Simulated Annealing metaheuristics have been adapted for their solution, and a novel Constructive Heuristic has been developed based on the real and manual process currently performed in the aforementioned factory. These three algorithms are run and compared on a real database in order to minimise the makespan. Their analysis shows that although the use of support machines generally gives positive results, an improvement is not achieved in all cases. Furthermore, contrary to what might be expected, the use of full-capacity machines in the role of support machines sometimes improves completion times.

Learning-based two-phase cooperative optimizer for distributed machine scheduling with heterogeneous factories and order priorities

In the realm of customized manufacturing, production cycles are often compressed to capture market opportunities swiftly. The blanking system stands as the inaugural and pivotal phase in the realm of large equipment customized manufacturing. This study abstracts a novel problem from real-world blanking systems, as the distributed unrelated parallel machine scheduling with heterogeneous factories and order priorities (DUPMS-HP). The presented work formulates the bi-objective DUPMS-HP, aiming to minimize both the total weighted tardiness and the workload gap of each machine. A learning-based two-phase cooperative optimizer (LCTPO) is introduced to address this NP-hard problem, featuring: i) a cooperative evolutionary algorithm during the first stage for global search to ensure diversity; ii) the incorporation of five problem-specific local search strategies in the first stage to balance priority and due date constraints. Additionally, reinforcement learning is applied to learn and select the best neighborhood search operator for each elite solution, further enhancing diversity. The effectiveness of the proposed algorithm is validated through a comparative analysis with five state-of-the-art algorithms on 20 instances. Experimental results affirm that LCTPO is more adept at solving DUPMS-HP compared to the alternative algorithms.

A fix-and-optimize heuristic for the Unrelated Parallel Machine Scheduling Problem

This paper proposes and evaluates a matheuristic approach for the Unrelated Parallel Machine Scheduling Problem (UPMSP). The UPMSP consists of assigning jobs to unrelated parallel machines considering different processing times for the same job in different machines. Additionally, a setup time is considered between the execution of jobs in the same machine. The problem is addressed by a fix-and-optimize matheuristic that iteratively selects a subset of variables to be fixed to their current values so that the remaining variables will compose a subproblem to be optimized by a mathematical programming solver. In the proposed approach, each subproblem consists of a subset of jobs that are assigned to a subset of machines in the incumbent solution. The subproblems are solved by the state-of-the-art exact algorithm for the UPMSP. In the experiments conducted on benchmark instances, the proposed fix-and-optimize algorithm achieved remarkable results. It outperformed the standalone exact algorithm by a large margin and resulted in competitive solutions when compared to the literature’s best-performing heuristic method for the problem. The proposed algorithm obtained the best solution for 669 out of the 1000 instances addressed in this work. Among them, 338 are new best-known solutions. In general, the proposed approach excels at solving instances with a high number of jobs per machine — it resulted in the best solution for 89% of the instances with a ratio of 10 or more jobs per machine in total.