An investigation into minimising total energy consumption and total weighted tardiness in job shops

Manufacturing enterprises nowadays face the challenge of increasing energy prices and requirements to reduce their emissions. Most reported work on reducing manufacturing energy consumption today focuses on the need to improve the efficiency of resources (machines) largely ignoring the potential for energy reducing on the system-level where the operational method can be employed as the energy saving approach. The advantage is clearly that the scheduling and planning approach can also be applied across existing legacy systems and does not require large investment. Therefore, a multi-objective scheduling method is developed in this paper with reducing energy consumption as one of the objectives. This research focuses on classical job shop environment which is widely used in the manufacturing industry. A model for the bi-objectives problem that minimises total electricity consumption and total weighted tardiness is developed and the Non-dominant Sorting Genetic Algorithm is employed as the solution to obtain the Pareto front. A case study based on a modified 10 × 10 job shop is presented to show the effectiveness of the algorithm and to prove the feasibility of the model

A multi-objective genetic algorithm for optimisation of energy consumption and shop floor production performance

Increasing energy price and requirements to reduce emission are new chal-lenges faced by manufacturing enterprises. A considerable amount of energy is wasted by machines due to their underutilisation. Consequently, energy saving can be achieved by turning off the machines when they lay idle for a comparatively long period. Otherwise, turning the machine off and back on will consume more energy than leave it stay idle. Thus, an effective way to reduce energy consumption at the system level is by employing intelligent scheduling techniques which are capable of integrating fragmented short idle periods on the machines into large ones. Such scheduling will create opportunities for switching off underutilised resources while at the same time maintaining the production performance. This paper introduces a model for the bi-objective optimisation problem that minimises the total non-processing electricity consumption and total weighted tardiness in a job shop. The Turn off/Turn on is applied as one of the electricity saving approaches. A novel multi-objective genetic algorithm based on NSGA-II is developed. Two new steps are introduced for the purpose of expanding the solution pool and then selecting the elite solutions. The research presented in this paper is focused on the classical job shop envi-ronment, which is widely used in the manufacturing industry and provides considerable opportunities for energy saving. The algorithm is validated on job shop problem instances to show its effectiveness. Keywords: Energy efficient production plannin

Production scheduling considering dynamic electricity price in energy-efficient factories

The 1st Virtual IFAC World Congress (IFAC-V 2020)Factories account for more than 42% of global energy consumption. In order to contribute to reduce carbon footprint and increase energy efficiency, it is important to optimize the tasks and time of product manufacturing according to the renewable generation and lower prices of the grid but without compromising production quality and output. This paper aims to develop flexible optimization platform for industrial production processes. The proposed production scheduling model is formulated as a 15-minute interval of one week time-span adopting mixed-integer linear optimization model and solved in TOMLAB. The model considers general production constraints for different products and takes into account with the photovoltaic generation of the factory as well as the dynamic price of the grid. The results are compared with a reference case without photovoltaic and where the dynamic price is not considered. The energy cost savings can amount up to 29% or 100 € in the considered example.This work has received funding from Portugal 2020 under SPEARproject (NORTE-01-0247-FEDER-040224) and from FEDER Fundsthrough COMPETE program and from National Funds through(FCT) under the project UIDB/00760/2020, Joao Soares is sup-ported by FCT under CEECIND/02814/2017 grant.info:eu-repo/semantics/publishedVersio

Distributed optimization under partial information using direct interaction: a methodology and applications

This research proposes a methodology to solve distributed optimization problems where quasi-autonomous decision entities directly interact with each other for partial information sharing. In the distributed system we study the quasi-autonomy arising from the assumption that each decision entity has complete and unique responsibility for a subset of decision variables. However, when solving a decision problem locally, consideration is given to how the local decisions affect overall system performance such that close-to-optimal solutions are obtained among all participating decision entities. Partial information sharing refers to the fact that no entity has the complete information access needed to solve the optimization problem globally. This condition hinders the direct application of traditional optimization solution methods. In this research, it is further assumed that direct interaction among the decision entities is allowed. This compensates for the lack of complete information access with the interactive exchange of non-private information. The methodology is tested in different application contexts: manufacturing capacity allocation, single machine scheduling, and jobshop scheduling. The experimental results show that the proposed method generates close-to optimal solutions in the tested problem settings

DRS: Dynamic Resource Scheduling for Real-Time Analytics over Fast Streams

In a data stream management system (DSMS), users register continuous queries, and receive result updates as data arrive and expire. We focus on applications with real-time constraints, in which the user must receive each result update within a given period after the update occurs. To handle fast data, the DSMS is commonly placed on top of a cloud infrastructure. Because stream properties such as arrival rates can fluctuate unpredictably, cloud resources must be dynamically provisioned and scheduled accordingly to ensure real-time response. It is quite essential, for the existing systems or future developments, to possess the ability of scheduling resources dynamically according to the current workload, in order to avoid wasting resources, or failing in delivering correct results on time. Motivated by this, we propose DRS, a novel dynamic resource scheduler for cloud-based DSMSs. DRS overcomes three fundamental challenges: (a) how to model the relationship between the provisioned resources and query response time (b) where to best place resources; and (c) how to measure system load with minimal overhead. In particular, DRS includes an accurate performance model based on the theory of \emph{Jackson open queueing networks} and is capable of handling \emph{arbitrary} operator topologies, possibly with loops, splits and joins. Extensive experiments with real data confirm that DRS achieves real-time response with close to optimal resource consumption.Comment: This is the our latest version with certain modificatio

Energy-aware coordination of machine scheduling and support device recharging in production systems

Electricity generation from renewable energy sources is crucial for achieving climate targets, including greenhouse gas neutrality. Germany has made significant progress in increasing renewable energy generation. However, feed-in management actions have led to losses of renewable electricity in the past years, primarily from wind energy. These actions aim to maintain grid stability but result in excess renewable energy that goes unused. The lost electricity could have powered a multitude of households and saved CO2 emissions. Moreover, feed-in management actions incurred compensation claims of around 807 million Euros in 2021. Wind-abundant regions like Schleswig-Holstein are particularly affected by these actions, resulting in substantial losses of renewable electricity production. Expanding the power grid infrastructure is a costly and time-consuming solution to avoid feed-in management actions. An alternative approach is to increase local electricity consumption during peak renewable generation periods, which can help balance electricity supply and demand and reduce feed-in management actions. The dissertation focuses on energy-aware manufacturing decision-making, exploring ways to counteract feed-in management actions by increasing local industrial consumption during renewable generation peaks. The research proposes to guide production management decisions, synchronizing a company's energy consumption profile with renewable energy availability for more environmentally friendly production and improved grid stability

COMBINATION OF ACO AND PSO TO MINIMIZE MAKESPAN IN ORDERED FLOWSHOP SCHEDULING PROBLEMS

The problem of scheduling flowshop production is one of the most versatile problems and is often encountered in many industries. Effective scheduling is important because it has a significant impact on reducing costs and increasing productivity. However, solving the ordered flowshop scheduling problem with the aim of minimizing makespan requires a difficult computation known as NP-hard. This research will contribute to the application of combination ACO and PSO to minimize makespan in the ordered flowshop scheduling problem. The performance of the proposed scheduling algorithm is evaluated by testing the data set of 600 ordered flowshop scheduling problems with various combinations of job and machine size combinations. The test results show that the ACO-PSO algorithm is able to provide a better scheduling solution for the scheduling group with small dimensions, namely 76 instances from a total of 600 inctances and is not good at obtaining makespan in the scheduling group with large dimensions. The ACO-PSO algorithm uses execution time which increases as the dimension size (multiple jobs and many machines) increases in a scheduled instanc