Reduction of carbon emission and total late work criterion in job shop scheduling by applying a multi-objective imperialist competitive algorithm

New environmental regulations have driven companies to adopt low-carbon manufacturing. This research is aimed at considering carbon dioxide in the operational decision level where limited studies can be found, especially in the scheduling area. In particular, the purpose of this research is to simultaneously minimize carbon emission and total late work criterion as sustainability-based and classical-based objective functions, respectively, in the multiobjective job shop scheduling environment. In order to solve the presented problem more effectively, a new multiobjective imperialist competitive algorithm imitating the behavior of imperialistic competition is proposed to obtain a set of non-dominated schedules. In this work, a three-fold scientific contribution can be observed in the problem and solution method, that are: (1) integrating carbon dioxide into the operational decision level of job shop scheduling, (2) considering total late work criterion in multi-objective job shop scheduling, and (3) proposing a new multi-objective imperialist competitive algorithm for solving the extended multi-objective optimization problem. The elements of the proposed algorithm are elucidated and forty three small and large sized extended benchmarked data sets are solved by the algorithm. Numerical results are compared with two well-known and most representative metaheuristic approaches, which are multi-objective particle swarm optimization and non-dominated sorting genetic algorithm II, in order to evaluate the performance of the proposed algorithm. The obtained results reveal the effectiveness and efficiency of the proposed multi-objective imperialist competitive algorithm in finding high quality non-dominated schedules as compared to the other metaheuristic approache

Combining ability in elephant grass (Pennisetum purpureum Schum.) for energy biomass production.

The objective of this study was to estimate and evaluate the general and specific combining abilities of hybrid combinations for energy biomass production, adopting the methodology of Griffing as adapted by Geraldi and Miranda Filho in evaluation cuts referring to two periods, the wet and dry seasons, and in relation to total dry matter yield (TDMY). The experiment was conducted at Pesagro Rio, located in Campos dos Goytacazes, RJ, Brazil, in a randomized block design with three replicates. Crosses were performed in a partial diallel system. Treatments consisted of 10 parents, 25 hybrid combinations, and 1 control. Three evaluation cuts were made: two in the wet season and one in the dry season. The following traits were evaluated: plant height, stem diameter, leaf width, number of tillers, percentage of dry matter, dry matter yield, and TDMY. Regarding the general combining ability effects, the parents “Cameroon-Piracicaba” and “Mercker” showed outstanding positive values. Of the hybrid combinations, “Cubano Pinda”?“Mercker”, “Cameroon-Piracicaba”?“Três Rios”, “Cameroon-Piracicaba”?“Mercker 86-México”, “IAC-Campinas”?“Três Rios”, “IAC-Campinas”?“Mercker 86-México”, and “Guaçu IZ.2”?“Roxo” showed superior productive performance. Hybrids can be obtained with superior energy biomass production to that of the currently available elephant grass cultivars

An Agent-based Approach for Manufacturing Production Scheduling with Emission Consideration

In the current business climate with increasingly changing customer requirements and strong business competition, manufacturing organisations need to enhance their productivity and adaptability in order to survive in the current business environment and raise their competitiveness. As a result, the optimisation of production scheduling in manufacturing systems has attracted increasing attention by manufacturers. The optimisation of manufacturing scheduling can be simplified as an optimisation problem for minimising processing cost and time with a set of constraints reflecting the technical relationships between jobs or job features and the resource capability and capacity. Conventional optimisation approaches including mathematical approaches, dispatching rules, heuristics and meta-heuristics have been applied in this research area but optimal solutions cannot be achieved in a reasonable computational time. In this PhD research, an agent based approach is developed for solving the manufacturing production optimisation problem. There is an agent iterative bidding mechanism coordinated by a Genetic Algorithm (GA) which facilitates the search for optimal routing and sequencing solutions for processing an entire job with shared manufacturing resources. A shop agent in the system works as a mediator which announces bidding operations, collects bids and decides winner machines according to a weight-based function. Machine agents with specific technical capability calculate the total production cost and lead time for job operations according to the predesigned operational sequence, and decide whether to submit their bids based on local utility. Another agent self-adjusting mechanism is employed for resource agents updating the priorities of unprocessed jobs in their buffers. The objective of each machine agent is to maximise local utility, i.e., to increase individual profit. After genetic generations for updating parameters with agent self-adjusting, the near optimal schedule plans can be found. On the other hand, the use of energy in all organisations has become a key issue worldwide. Carbon emissions from manufacturing processes of a company are under the pressure of government and also affect the public opinion. In the previous works from the literature, however, economic and environmental issues are not considered simultaneously in manufacturing production scheduling. Based on the basic agent based optimisation mechanisms, two extensive models with the consideration of the carbon emission during production are built in this research work, where the emission factor is set to be a constraint and another objective respectively. Numerical tests are utilised in order to examine the effectiveness and efficiency of the proposed approaches. Furthermore, two previous approaches from the literature for solving the same problems are rebuilt and results are compared for testing the comparative performance of the proposed approaches. Test results show that near optimal schedule plans can be achieved in a reasonable computational time

Multi-objective optimisation methods for minimising total weighted tardiness, electricity consumption and electricity cost in job shops through scheduling

Manufacturing enterprises nowadays face the challenge of increasing energy prices and requirements to reduce their emissions. Most reported work on reducing manufacturing energy consumption focuses on the need to improve the efficiency of resources (machines). The potential for energy reducing at the system-level has been largely ignored. At this level, operational research methods can be employed as the energy saving approach. The advantage is clearly that the scheduling and planning approach can be applied across existing legacy systems and does not require a large investment. For the emission reduction purpose, some electricity usage control policies and tariffs (EPTs) have been promulgated by many governments. The Rolling Blackout policy in China is one of the typical EPTs, which means the government electricity will be cut off several days in every week for a specific manufacturing enterprise. The application of the Rolling Blackout policy results in increasing the manufacturing enterprises’ costs since they choose to start to use much more expensive private electricity to maintain their production. Therefore, this thesis develops operational research methods for the minimisation of electricity consumption and the electricity cost of job shop type of manufacturing systems. The job shop is selected as the research environment for the following reasons. From the academic perspective, energy consumption and energy cost reduction have not been well investigated in the multi-objective scheduling approaches to a typical job shop type of manufacturing system. Most of the current energy-conscious scheduling research is focused on single machine, parallel machine and flow shop environments. From the practical perspective, job shops are widely used in the manufacturing industry, especially in the small and medium enterprises (SMEs). Thus, the innovative electricity-conscious scheduling techniques delivered in this research can provide for plant managers a new way to achieve cost reduction. In this thesis, mathematical models are proposed for two multi-objective job shop scheduling optimisation problems. One of the problems is a bi-objective problem with one objective to minimise the total electricity consumption and the other to minimise the total weighted tardiness (the ECT problem). The other problem is a tri-objective problem which considers reducing total electricity consumption, total electricity cost and total weighted tardiness in a job shop when the Rolling Blackout policy is applied (the EC2T problem). Meta-heuristics are developed to approximate the Pareto front for ECT job shop scheduling problem including NSGA-II and a new Multi-objective Genetic Algorithm (GAEJP) based on the NSGA-II. A new heuristic is proposed to adjust scheduling plans when the Rolling Blackout policy is applied, and to help to understand how the policy will influence the performance of existing scheduling plans. NSGA-II is applied to solve the EC2T problem. Six scenarios have been proposed to prove the effectiveness of the aforementioned algorithms. The performance of all the aforementioned heuristics have been tested on Fisher and Thompson 10×10, Lawrence 15×10, 20×10 and 15×15 job shop scenarios which were extended to incorporate electrical consumption profiles for the machine tools. Based on the tests and comparison experiments, it has been found that by applying NSGA-II, the total non-processing electricity consumption in a job shop can decrease considerably at the expense of the schedules’ performance on the total weighted tardiness objective when there are tight due dates for jobs. When the due dates become less tight, the sacrifice of the total weighted tardiness becomes much smaller. By comparing the Pareto fronts obtained by GAEJP and by NSGA-II, it can be observed that GAEJP is more effective in reducing the total non-processing electricity consumption than NSGA-II, while not necessarily sacrificing its performance on total weighted tardiness. Thus, the superiority of the GAEJP in solving the ECT problem has been demonstrated. The scheduling plan adjustment heuristic has been proved to be effective in reducing the total weighted tardiness when the Rolling Blackout policy is applied. Finally, NSGA-II is proved to be effective to generate compromised scheduling plans for using the private electricity. This can help to realise the trade-off between the total weighted tardiness and the total electricity cost. Finally, the effectiveness of GAJEP in reducing the total non-processing electricity consumption has been validated in a real-world job shop case

Multi-objective optimisation methods for minimising total weighted tardiness, electricity consumption and electricity cost in job shops through scheduling

Manufacturing enterprises nowadays face the challenge of increasing energy prices and requirements to reduce their emissions. Most reported work on reducing manufacturing energy consumption focuses on the need to improve the efficiency of resources (machines). The potential for energy reducing at the system-level has been largely ignored. At this level, operational research methods can be employed as the energy saving approach. The advantage is clearly that the scheduling and planning approach can be applied across existing legacy systems and does not require a large investment. For the emission reduction purpose, some electricity usage control policies and tariffs (EPTs) have been promulgated by many governments. The Rolling Blackout policy in China is one of the typical EPTs, which means the government electricity will be cut off several days in every week for a specific manufacturing enterprise. The application of the Rolling Blackout policy results in increasing the manufacturing enterprises’ costs since they choose to start to use much more expensive private electricity to maintain their production. Therefore, this thesis develops operational research methods for the minimisation of electricity consumption and the electricity cost of job shop type of manufacturing systems. The job shop is selected as the research environment for the following reasons. From the academic perspective, energy consumption and energy cost reduction have not been well investigated in the multi-objective scheduling approaches to a typical job shop type of manufacturing system. Most of the current energy-conscious scheduling research is focused on single machine, parallel machine and flow shop environments. From the practical perspective, job shops are widely used in the manufacturing industry, especially in the small and medium enterprises (SMEs). Thus, the innovative electricity-conscious scheduling techniques delivered in this research can provide for plant managers a new way to achieve cost reduction. In this thesis, mathematical models are proposed for two multi-objective job shop scheduling optimisation problems. One of the problems is a bi-objective problem with one objective to minimise the total electricity consumption and the other to minimise the total weighted tardiness (the ECT problem). The other problem is a tri-objective problem which considers reducing total electricity consumption, total electricity cost and total weighted tardiness in a job shop when the Rolling Blackout policy is applied (the EC2T problem). Meta-heuristics are developed to approximate the Pareto front for ECT job shop scheduling problem including NSGA-II and a new Multi-objective Genetic Algorithm (GAEJP) based on the NSGA-II. A new heuristic is proposed to adjust scheduling plans when the Rolling Blackout policy is applied, and to help to understand how the policy will influence the performance of existing scheduling plans. NSGA-II is applied to solve the EC2T problem. Six scenarios have been proposed to prove the effectiveness of the aforementioned algorithms. The performance of all the aforementioned heuristics have been tested on Fisher and Thompson 10×10, Lawrence 15×10, 20×10 and 15×15 job shop scenarios which were extended to incorporate electrical consumption profiles for the machine tools. Based on the tests and comparison experiments, it has been found that by applying NSGA-II, the total non-processing electricity consumption in a job shop can decrease considerably at the expense of the schedules’ performance on the total weighted tardiness objective when there are tight due dates for jobs. When the due dates become less tight, the sacrifice of the total weighted tardiness becomes much smaller. By comparing the Pareto fronts obtained by GAEJP and by NSGA-II, it can be observed that GAEJP is more effective in reducing the total non-processing electricity consumption than NSGA-II, while not necessarily sacrificing its performance on total weighted tardiness. Thus, the superiority of the GAEJP in solving the ECT problem has been demonstrated. The scheduling plan adjustment heuristic has been proved to be effective in reducing the total weighted tardiness when the Rolling Blackout policy is applied. Finally, NSGA-II is proved to be effective to generate compromised scheduling plans for using the private electricity. This can help to realise the trade-off between the total weighted tardiness and the total electricity cost. Finally, the effectiveness of GAJEP in reducing the total non-processing electricity consumption has been validated in a real-world job shop case

Modelling and Determining Inventory Decisions for Improved Sustainability in Perishable Food Supply Chains

Since the introduction of sustainable development, industries have witnessed significant sustainability challenges. Literature shows that the food industry is concerned about its need for efficient and effective management practices in dealing with perishability and the requirements for conditioned storage and transport of food products that effect the environment. Hence, the environmental part of sustainability demonstrates its significance in this industrial sector. Despite this, there has been little research into environmentally sustainable inventory management of deteriorating items. This thesis presents mathematical modelling based research for production inventory systems in perishable food supply chains. In this study, multi-objective mixed-integer linear programming models are developed to determine economically and environmentally optimal production and inventory decisions for a two-echelon supply chain. The supply chain consists of single sourcing suppliers for raw materials and a producer who operates under a make-to-stock or make-to-order strategy. The demand facing the producer is non-stationary stochastic in nature and has requirements in terms of service level and the remaining shelf life of the marketed products. Using data from the literature, numerical examples are given in order to test and analyse these models. The computational experiments show that operational adjustments in cases where emission and cost parameters were not strongly correlated with supply chain collaboration (where suppliers and a producer operate under centralised control), emissions are effectively reduced without a significant increase in cost. The findings show that assigning a high disposal cost, limit or high weight of importance to perished goods leads to appropriate reduction of expected waste in the supply chain with no major cost increase. The research has made contributions to the literature on sustainable production and inventory management; providing formal models that can be used as an aid to understanding and as a tool for planning and improving sustainable production and inventory control in supply chains involving deteriorating items, in particular with perishable food supply chains.the Ministry of Science and Technology, the Royal Thai Government

Theoretical and Computational Research in Various Scheduling Models

Nine manuscripts were published in this Special Issue on “Theoretical and Computational Research in Various Scheduling Models, 2021” of the MDPI Mathematics journal, covering a wide range of topics connected to the theory and applications of various scheduling models and their extensions/generalizations. These topics include a road network maintenance project, cost reduction of the subcontracted resources, a variant of the relocation problem, a network of activities with generally distributed durations through a Markov chain, idea on how to improve the return loading rate problem by integrating the sub-tour reversal approach with the method of the theory of constraints, an extended solution method for optimizing the bi-objective no-idle permutation flowshop scheduling problem, the burn-in (B/I) procedure, the Pareto-scheduling problem with two competing agents, and three preemptive Pareto-scheduling problems with two competing agents, among others. We hope that the book will be of interest to those working in the area of various scheduling problems and provide a bridge to facilitate the interaction between researchers and practitioners in scheduling questions. Although discrete mathematics is a common method to solve scheduling problems, the further development of this method is limited due to the lack of general principles, which poses a major challenge in this research field