With ever-increasing concerns about climate change, and in the context of diminishing fossil energy resources, energy consumption has become a subject of great importance. In this framework, our study is concerned with the reduction of the energy consumed by a manufacturing system composed of several unrelated parallel machines. We investigate the problem of scheduling a set of n deteriorating jobs on m parallel unrelated machines subject to a deterioration effect, where the processing time of each job is a linear deterioration function of the starting time. The objective is to determine the best scheduling of the jobs, minimizing the makespan and the total energy consumption. We implement a nondominated sorting-based multiobjective algorithm (NSGA-II)-based approaches with a given scheduling rules to solve the developed mathematical model of the considered problem. The performance of the proposed approaches are compared with an exact algorithm and evaluated through computational experiments. Then, we give a multiobjective decision-making method based on the TOPSIS (technique for order of preference by similarity to ideal solution) technique to determine the best solution from the Pareto front according to the decision-maker’s preferences.

Multiobjective approach for deteriorating jobs scheduling for a sustainable manufacturing system

This paper presents an automated model for improving job shop scheduling by incorporating Industry 4.0 and project management. The proposed model develops dynamic and adaptive schedules to incorpor...

Automated job shop scheduling with dynamic processing times and due dates using project management and industry 4.0

Nowadays, the manufacturing industry is constantly changing. Production systems must operate in a highly dynamic environment where unexpected events could occur and create disruption, making rescheduling inevitable for manufacturing companies. Rescheduling models are fundamental to the robustness of production processes. This paper proposes a model to address rescheduling caused by unexpected events, aiming to achieve the zero-defect manufacturing (ZDM) concept. The goal of the model is to incorporate traditional and ZDM–oriented events into one methodology to calculate when the next rescheduling will be performed to effectively react to unexpected events. The methodology relies on the definition of two key time parameters for each event type: event response time (RT) and event delay response time (DRT). Based on these parameters, an event management algorithm is designed to identify the optimal rescheduling solution. The DRT parameter is calculated based on a multi-parametric dynamic formula to capture the dynamics of production. Moreover, ANOM, and ANOVA methods are used to analyse the behaviour of the developed method and to assess the level of robustness of the proposed approach. Finally, a case study based on real production scenarios is conducted, a series of simulation experiments are performed, and comparisons with other rescheduling policies are presented. The results demonstrate the effectiveness of the proposed event management algorithm for managing rescheduling.

/pdf/a-generic-methodology-for-calculating-rescheduling-time-for-3x95x1b3ns.pdf

A Generic Methodology for Calculating Rescheduling Time for Multiple Unexpected Events in the Era of Zero Defect Manufacturing

Predictive-reactive strategy for identical parallel machine rescheduling

There is a manufacturing system where several parts are processed through machining workstations and later assembled to form final products. In the event of disruptions such as machine failure, the original flow-shop schedule needs to be revised and/or rescheduled. In such a scenario, rescheduling methods based on right-shift rescheduling and affected operations rescheduling work very well. Here in this study, the deviation of the make-span of the revised schedule from the original schedule is used as a performance measure. We have proposed three rescheduling methods. There are multiple factors that influence the performance of the rescheduling methodology. One of them is the make-span deviation of the schedule, and the factors influencing it are optimality of the initial solution, failure duration, deviation of make-span, rescheduling method, size, and instant of failure. The initial schedule and problem size depend on the flow-shop manufacturing system for which scheduling is performed, but the method of rescheduling depends on the decision as to which rescheduling methodology is to be selected. Computations are performed using full factorial experimentation. We also observed that right-shift rescheduling is the preferred rescheduling method in the majority of situations. In contrast, the affected operation rescheduling method is also equally suitable when the initial solution is created using modified bottleneck minimum idleness.

Managing Disruptions in a Flow-Shop Manufacturing System

Advance in applied scheduling is a source of innovation in the manufacturing field, where new results help industrial practitioners in production management. The literature of rescheduling problems for single-objective optimization is well study, while there is a lack of extensive studies for the case of rescheduling for multi-objective optimization, especially for energy aware scheduling. This paper extends a previous work over an energy aware scheduling problem, modelled from a real industrial case study, extending its manufacturing environment to a dynamic one. To this end, two rescheduling techniques are developed to tackle machines disruptions (greedy-heuristic and meta-heuristic). They are compared to existing approach thought manufacturing environment simulations. The results give insight to improve the production management in terms of rescheduling quality and computational time.

/pdf/dynamic-rescheduling-in-energy-aware-unrelated-parallel-3j1eqxedtk.pdf

Dynamic Rescheduling in Energy-Aware Unrelated Parallel Machine Problems.

Nowadays,the manufacturing industry consumes almost half of the energy produced on the planet. Within this industry, sustainable scheduling represents a main issue due to the energy efficiency that it is able to achieve. Centralized or decentralized approaches to sustainable scheduling can be found in state-of- the-art techniques using both optimal or heuristic methods. In this paper, a distributed system modelled as a multi-agent framework is proposed to solve energy-aware scheduling problems by applying it to a real case of the injection air moulding industry, which has intense energy consumption. In this proposal, a set of agents collaborate to reach an agreement in order to minimize several objective functions based on: total job weighted tardiness, total setup time, and energy consumption. The proposed system is compared to centralized and distributed approaches, highlighting advantages and drawbacks and providing an interesting point of view about how to tackle energy-aware scheduling with new agreement techniques in a distributed environment.

/pdf/a-multi-agent-framework-to-solve-energy-aware-unrelated-1y8guxae40.pdf

A Multi-Agent Framework to Solve Energy-Aware Unrelated Parallel Machine Scheduling Problems with Machine-Dependent Energy Consumption and Sequence-Dependent Setup Time

Sergio Ferrer

Papers

Dynamic Rescheduling in Energy-Aware Unrelated Parallel Machine Problems.

A Multi-Agent Framework to Solve Energy-Aware Unrelated Parallel Machine Scheduling Problems with Machine-Dependent Energy Consumption and Sequence-Dependent Setup Time