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This research integrates maintenance planning and production scheduling from a green perspective to reduce the carbon footprint.

A mixed-integer nonlinear programming (MINLP) model is developed to study the relation between production makespan, energy consumption, maintenance actions and footprint, i.e. service level and sustainability measures. The speed scaling technique is used to control energy consumption, the capping policy is used to control CO2 footprint and preventive maintenance (PM) is used to keep the machine working in healthy conditions.

It was found that ignoring maintenance activities increases the schedule makespan by more than 21.80%, the total maintenance time required to keep the machine healthy by up to 75.33% and the CO2 footprint by 15%.

The proposed optimization model can simultaneously be used for maintenance planning, job scheduling and footprint minimization. Furthermore, it can be extended to consider other maintenance activities and production configurations, e.g. flow shop or job shop scheduling.

Maintenance planning, production scheduling and greenhouse gas (GHG) emissions are intertwined in the industry. The proposed model enhances the performance of the maintenance and production systems. Furthermore, it shows the value of conducting maintenance activities on the machine's availability and CO2 footprint.

This work contributes to the literature by combining maintenance planning, single-machine scheduling and environmental aspects in an integrated MINLP model. In addition, the model considers several practical features, such as machine-aging rate, speed scaling technique to control emissions, minimal repair (MR) and PM.

A real-time production scheduling method for semiconductor back-end manufacturing process becomes increasingly important in industry 4.0. Semiconductor back-end manufacturing process is always accompanied by order splitting and merging; besides, in each stage of the process, there are always multiple machine groups that have different production capabilities and capacities. This paper studies a multi-agent based scheduling architecture for the radio frequency identification (RFID)-enabled semiconductor back-end shopfloor, which integrates not only manufacturing resources but also human factors.

The architecture includes a task management (TM) agent, a staff instruction (SI) agent, a task scheduling (TS) agent, an information management center (IMC), machine group (MG) agent and a production monitoring (PM) agent. Then, based on the architecture, the authors developed a scheduling method consisting of capability & capacity planning and machine configuration modules in the TS agent.

The authors used greedy policy to assign each order to the appropriate machine groups based on the real-time utilization ration of each MG in the capability & capacity (C&C) planning module, and used a partial swarm optimization (PSO) algorithm to schedule each splitting job to the identified machine based on the C&C planning results. At last, we conducted a case study to demonstrate the proposed multi-agent based real-time production scheduling models and methods.

This paper proposes a multi-agent based real-time scheduling framework for semiconductor back-end industry. A C&C planning and a machine configuration algorithm are developed, respectively. The paper provides a feasible solution for semiconductor back-end manufacturing process to realize real-time scheduling.

Scheduling is defined by Baker as, “the allocation of resources over time to perform a collection of tasks”. The term facilities is often used instead of resources and the tasks to be performed may involve a variety of different operations.

Influenced by the constantly changing manufacturing environment, no single dispatching rule (SDR) can consistently obtain better scheduling results than other rules for the dynamic job-shop scheduling problem (DJSP). Although the dynamic SDR selection classifier (DSSC) mined by traditional data-mining-based scheduling method has shown some improvement in comparison to an SDR, the enhancement is not significant since the rule selected by DSSC is still an SDR.

This paper presents a novel data-mining-based scheduling method for the DJSP with machine failure aiming at minimizing the makespan. Firstly, a scheduling priority relation model (SPRM) is constructed to determine the appropriate priority relation between two operations based on the production system state and the difference between their priority values calculated using multiple SDRs. Subsequently, a training sample acquisition mechanism based on the optimal scheduling schemes is proposed to acquire training samples for the SPRM. Furthermore, feature selection and machine learning are conducted using the genetic algorithm and extreme learning machine to mine the SPRM.

Results from numerical experiments demonstrate that the SPRM, mined by the proposed method, not only achieves better scheduling results in most manufacturing environments but also maintains a higher level of stability in diverse manufacturing environments than an SDR and the DSSC.

This paper constructs a SPRM and mines it based on data mining technologies to obtain better results than an SDR and the DSSC in various manufacturing environments.

To propose a methodology based on genetic algorithm (GA) to solve the parallel machine scheduling problems with precedence constraints.

Workflow balancing helps to remove bottlenecks present in a shop floor yielding faster movements of components or jobs. Multiple machines are used in parallel for processing the jobs to meet the demand. In parallel machine scheduling with precedence constraints, there are m machines to which n jobs are assigned using suitable scheduling algorithms. Workflow of a machine is the sum of processing time of all jobs assigned. All the preceding jobs are allocated first to satisfy the constraints. GA is developed to solve parallel machine scheduling problems with precedence constraints based on the objective of workflow balancing. The GA was coded on IBM/PC compatible system in the C++ language for simulation to a standard manufacturing environment.

The relative percentage of imbalance (RPI) in workloads among the parallel machines is used to evaluate the performance of the GA developed. The proposed GA produces lesser RPI values against the RANDOM heuristic algorithm for a wider range of jobs and machines.

The performance of GA can be compared with the performance of other meta‐heuristic algorithms to find out the robustness of the results obtained by this research.

The proposed GA also gives better solution for a case study of assembly scheduling.

The allocation of assembly operations to the operators is modeled into a parallel machine scheduling problem with precedence constraints using the objective of minimizing the workflow among the operators.

Describes flow‐shop scheduling problems and an interactive graphical flow‐shop manufacturing scheduling system (FSMS) developed to handle any number of jobs and machines. Outlines the methodical approach of using scheduling tools, such as lower bound, automatic generation of near‐optimal system sequences and schedule optimization in which the user is guided in determining optimal sequence, to cut scheduling time and make the scheduling system flexible. Outputs are in the form of Gantt charts. The graphical capability can be a very useful tool for decision makers such as production and operations managers who often encounter many day‐to‐day scheduling problems and challenges.

The purpose of this paper is to develop an effective and efficient approach to exploit meta‐heuristic in particle swarm optimization (PSO) for the job shop scheduling problem (JSP), a class of NP‐hard optimization problems. The approach is to be built on a PSO with multiple independent swarms. PSO was inspired by bird flocking and animal social behaviors. The particles operate collectively like a swarm that flies through the hyperdimensional space to search for possible optimal solutions. The behavior of the particles is influenced by their tendency to learn from their personal past experience and from the success of their peers to adjust their flying speed and direction. Research in fusing the multiple‐swarm concept into PSO is well‐established in solving single objective optimization problems and multimodal problems.

This study examines the optimization of the JSP via a search space division scheme and use of the meta‐heuristic method of PSO by assigning each machine in a JSP an independent swarm of particles. The use of multiple swarms in PSO is motivated by the idea of “divide and conquer” to reduce the computational complexity incurred through solving a NP‐hard combinatorial optimization problem. The resulted design, JSP/PSO algorithm, fully exploits the computing power presented by the multiple‐swarm PSO.

Simulation experiments show that the proposed JSP/PSO algorithm can effectively solve the JSP problems from small to median size. If certain mechanism of information sharing between swarms can be incorporated, it is believed that the new design could offer even more computing power to tackle the large‐sized problems.

The proposed JSP/PSO algorithm is effective in solving JSPs. The proposed algorithm shows considerable promise when searching the space of non‐delay schedules. It demands relatively lower number of function evaluations compared to other state‐of‐the‐art. The drawback to the JSP/PSO is that the GT scheduling adopted is too computationally expensive. Future works will address this concern.

This paper aims to propose a parallel automated assembly line system to produce multiple products in a semi-continuous system.

The control system developed in this research consists of a manufacturing system for two-level hierarchical dynamic decisions of autonomous/automated/automatic-guided vehicles (AGVs) dispatching/next station selection and machining schedules and a station control scheme for operational control of machines and components. In this proposed problem, the assignment of multiple AGVs to different assembly lines and the semi-continuous stations is a critical objective. AGVs and station scheduling decisions are made at the assembly line level. On the other hand, component and machining resource scheduling are made at the station level.

The proposed scheduler first decomposes the dynamic scheduling problems into a static AGV and machine assignment during each short-term rolling window. It optimizes weighted completion time of tasks for each short-term window by formulating the task and resource assignment problem as a minimum cost flow problem during each short-term scheduling window. A comprehensive decision making process and heuristics are developed for efficient implementation. A simulation study is worked out for validation.

Several assembly lines are configured to produce multiple products in which the technologies of machines are shared among the assembly lines when required. The sequence of stations is pre-specified in each assembly line and the components of a product are kept in machine magazine. The transportation between the stations in an assembly line (intra assembly line) and among stations in different assembly lines (inter assembly line) are performed using AGVs.

The purpose of this paper is to solve a flexible job shop scheduling problem where alternate machines are available to process the same job. The study considers the Flexible Job Shop Problem (FJSP) having n jobs and more than three machines for scheduling.

FJSP for n jobs and more than three machines is non polynomial (NP) hard in nature and hence a multi‐objective genetic algorithm (GA) based approach is presented for solving the scheduling problem. The two objective functions formulated are minimizations of the make‐span time and total machining time. The algorithm uses a unique method of generating initial populations and application of genetic operators.

The application of GA to the multi‐objective scheduling problem has given optimum solutions for allocation of jobs to the machines to achieve nearly equal utilisation of machine resources. Further, the make span as well as total machining time is also minimized.

The model can be extended to include more machines and constraints such as machine breakdown, inspection etc., to make it more realistic.

The paper presents a successful implementation of a meta‐heuristic approach to solve a NP‐hard problem of FJSP scheduling and can be useful to researchers and practitioners in the domain of production planning.

Reviews some new trends in parallel machine scheduling (PMS). PMS, as an area of research, is governed by questions that arise in production planning, flexible manufacture systems, computer control, etc. The main characteristic of these problems is to optimize an objective, with jobs to be finished on a series of machines with the same function. Gives a short review of new developments in PMS associated with the problems of just‐in‐time (JIT) production, pre‐emption with set‐up, and capacitated machine scheduling. Discusses non‐regular objectives oriented by the JIT concept; pre‐emption with set‐up; capacitated machine scheduling; and relationships between PMS and vehicle routeing problems.