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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.

The purpose of this study is to propose a new mathematical model that integrates strategic decision-making with tactical-operational decision-making in order to optimize production and scheduling decisions.

This study presents a multi-objective optimization framework to make production planning, scheduling and maintenance decisions. An epsilon-constraint method is used to solve small instances of the model, while new hybrid optimization algorithms, including multi-objective particle swarm optimization (MOPSO), non-dominated sorting genetic algorithm, multi-objective harmony search and improved multi-objective harmony search (IMOHS) are developed to address the high complexity of large-scale problems.

The computational results demonstrate that the metaheuristic algorithms are effective in obtaining economic solutions within a reasonable computational time. In particular, the results show that the IMOHS algorithm is able to provide optimal Pareto solutions for the proposed model compared to the other three algorithms.

This study presents a new mathematical model that simultaneously determines green production planning and scheduling decisions by minimizing the sum of the total cost, makespan, lateness and energy consumption criteria. Integrating production and scheduling of a shop floor is critical for achieving optimal operational performance in production planning. To the best of the authors' knowledge, the integration of production planning and maintenance has not been adequately addressed.

The purposes of this paper are to: investigate schedule performance of new and retrofitting green building projects; identify the critical factors that influence the schedule performance of new and retrofitting green building projects; and provide solutions to improve schedule performance of new and retrofitting green building projects.

A questionnaire survey were conducted and responses were received from 34 firms experienced in green building projects in Singapore. After the data from the survey had been analyzed, face-to-face interviews were conducted with two senior project managers to solicit comments on the survey results.

This study identified the degree of project delay in 98 new green building projects and 51 retrofitting green building projects in Singapore. The result indicated that 22 percent of the Singaporean green building projects were plagued with delay and retrofitting projects had a significantly higher likelihood of delay and significantly longer extension than new projects. In addition, “consultant cooperation to solve problems” was the most influential to schedule performance of both new and retrofitting green building projects, and the two project groups agreed on the overall ranking of the factors affecting schedule performance.

There may be geographical limitation on the conclusions drawn from the findings. Also, the sample size was still small, despite a relatively high response rate. In addition, the majority of the respondents were contractors as other project players were reluctant to respond to the survey.

This study provides a clear understanding of the schedule performance of green building projects as well as the critical factors that should be highlighted when constructing green building projects. Also, strategies to overcome the negative impact of these factors allow practitioners to better deal with the potential causes of delay and to attain the schedule performance.

Although construction delays have been widely investigated in previous studies relating to construction management, few have attempted to analyze the schedule performance of new and retrofitting green buildings. Thus, this study adds significantly to the existing research on both green building and construction delay.

Occupants are one of the most impacting factors in the overall energy performance of buildings, according to literature. Occupants’ behaviours and actions may impact the overall use of energy in more than 50%. In order to quantify the impact that occupant behaviour has in the use of energy, this study simulated interactions between occupants and the systems present in two actual buildings. The main aim was to compare the deviations due to occupant behaviour with the actual conditions and energy use of the two buildings.

The buildings used as a case study in this research were green buildings, rated according to the Australian Green Star certification system as a 6-star and a non-rated building. The two buildings are university buildings with similar characteristics, from Western Sydney University, in Sydney, Australia. A comparison was performed by means of building simulations among the use of energy in both buildings, aiming to understand if the green rating had any impact on the energy related to occupant behaviour. Therefore, to represent the actual buildings' conditions, the actual data related with climate, geometry, systems, internal loads, etc. were used as input variables in the simulation models of the green and the non-rated buildings. Both models were calibrated and validated, having as target the actual monitored use of electricity.

Occupants were categorized according to their levels of energy use as follows: saving, real and intensive energy users. Building simulations were performed to each building, with varying parameters related with lighting, plug loads, windows/doors opening, shading and air conditioning set points. Results show that occupant behaviour may impact the buildings' energy performance in a range of 72% between the two extremes. There is no significant relationship between the green rating and the way occupants behave in terms of the energy use.

This study intends to show the impact of different categories of occupant behaviour in the overall energy performance of two university buildings, a non-rated and a green-rated building, having as reference an actual representation of the buildings. Additionally, the study aims to understand the main differences between a green-rated and a non-rated building when accounting with the previous categories.

Data centres evolve constantly in size, complexity and power consumption. Energy-efficient scheduling in a cloud data centre is a critical and challenging research problem. It becomes essential to minimize the overall operational costs as well as environmental impact and to guarantee the service-level agreements for the services provided by the cloud data centres. Resource scheduling in cloud data centres is NP-hard and often requires substantial computational resources.

To overcome these problems, the authors propose a novel model that leads to nominal operational cost and energy consumption in cloud data centres. The authors propose an effective approach, parallel hybrid Jaya algorithm, that performs parallel processing of Jaya algorithm and genetic algorithm using multi-threading and shared memory for interchanging the information to enhance convergence premature rate and global exploration.

Experimental results reveal that the proposed approach reduces the power consumption in cloud data centres up to 38% and premature convergence rate up to 60% compared to other algorithms.

Experimental results reveals that our proposed approach reduces the power consumption in cloud data centres up to 38% and premature convergence rate up to 60% compared to other algorithms.

Emissions produced by oceangoing vessels not only negatively affect the environment but also may deteriorate health of living organisms. Several regulations were released by the International Maritime Organization (IMO) to alleviate negative externalities from maritime transportation. Certain polluted areas were designated as “Emission Control Areas” (ECAs). However, IMO did not enforce any restrictions on the actual quantity of emissions that could be produced within ECAs. This paper aims to perform a comprehensive assessment of advantages and disadvantages from introducing restrictions on the emissions produced within ECAs. Two mixed-integer non-linear mathematical programs are presented to model the existing IMO regulations and an alternative policy, which along with the established IMO requirements also enforces restrictions on the quantity of emissions produced within ECAs. A set of linearization techniques are applied to linearize both models, which are further solved using the dynamic secant approximation procedure. Numerical experiments demonstrate that introduction of emission restrictions within ECAs can significantly reduce pollution levels but may incur increasing route service cost for the liner shipping company.

Two mixed-integer non-linear mathematical programs are presented to model the existing IMO regulations and an alternative policy, which along with the established IMO requirements also enforces restrictions on the quantity of emissions produced within ECAs. A set of linearization techniques are applied to linearize both models, which are further solved using the dynamic secant approximation procedure.

Numerical experiments were conducted for the French Asia Line 3 route, served by CMA CGM liner shipping company and passing through ECAs with sulfur oxide control. It was found that introduction of emission restrictions reduced the quantity of sulfur dioxide emissions produced by 40.4 per cent. In the meantime, emission restrictions required the liner shipping company to decrease the vessel sailing speed not only at voyage legs within ECAs but also at the adjacent voyage legs, which increased the total vessel turnaround time and in turn increased the total route service cost by 7.8 per cent.

This study does not capture uncertainty in liner shipping operations.

The developed mathematical model can serve as an efficient practical tool for liner shipping companies in developing green vessel schedules, enhancing energy efficiency and improving environmental sustainability.

Researchers and practitioners seek for new mathematical models and environmental policies that may alleviate pollution from oceangoing vessels and improve energy efficiency. This study proposes two novel mathematical models for the green vessel scheduling problem in a liner shipping route with ECAs. The first model is based on the existing IMO regulations, whereas the second one along with the established IMO requirements enforces emission restrictions within ECAs. Extensive numerical experiments are performed to assess advantages and disadvantages from introducing emission restrictions within ECAs.

Driven by sustainable production, mobile robots are introduced as a new clean-energy material handling tool for mixed-model assembly lines (MMALs), which reduces energy consumption and lineside inventory of workstations (LSI). Nevertheless, the previous part feeding scheduling method was designed for conventional material handling tools without considering the flexible spatial layout of the robotic mobile fulfillment system (RMFS). To fill this gap, this paper focuses on a greening mobile robot part feeding scheduling problem with Just-In-Time (JIT) considerations, where the layout and number of pods can be adjusted.

A novel hybrid-load pod (HL-pod) and mobile robot are proposed to carry out part feeding tasks between material supermarkets and assembly lines. A bi-objective mixed-integer programming model is formulated to minimize both total energy consumption and LSI, aligning with environmental and sustainable JIT goals. Due to the NP-hard nature of the proposed problem, a chaotic differential evolution algorithm for multi-objective optimization based on iterated local search (CDEMIL) algorithm is presented. The effectiveness of the proposed algorithm is verified by dealing with the HL-pod-based greening part feeding scheduling problem in different problem scales and compared to two benchmark algorithms. Managerial insights analyses are conducted to implement the HL-pod strategy.

The CDEMIL algorithm's ability to produce Pareto fronts for different problem scales confirms its effectiveness and feasibility. Computational results show that the proposed algorithm outperforms the other two compared algorithms regarding solution quality and convergence speed. Additionally, the results indicate that the HL-pod performs better than adopting a single type of pod.

This study proposes an innovative solution to the scheduling problem for efficient JIT part feeding using RMFS and HL-pods in automobile MMALs. It considers both the layout and number of pods, ensuring a sustainable and environmental-friendly approach to production.

Facility design standardization strategy has considerable advantages, highlighted by its widespread and consistent use in the shipbuilding and manufacturing industries. However, capital projects have failed to realize these benefits. The primary rationale behind this problem is the lack of proper understanding of design standardization, more specifically the benefits and equally importantly, the trade-offs of design standardization in capital projects. Therefore, this study highlights 13 benefits and six trade-offs of standardization in connection to design standardization, along with specific examples.

To achieve the study objectives, the researchers identified the most impactful benefits and trade-offs in terms of economic impact by surveying prominent players in the industry. Furthermore, the researchers examined 43 actual case projects (a case study) executed with the standardization strategy to evaluate the industry's status in terms of the levels of advantage achievement and disadvantage incurrence.

The results of this survey show that design once, reuse multiple times and design and procurement in advance are the most impactful benefits. Similarly, susceptible to changes in the market conditions is one of the top trade-offs that can be incurred in capital projects when implementing standardization. The results also highlight that design once, reuse multiple times is one of the most achieved benefits in standardized capital projects today, while cost of establishing the design standard is the most incurred trade-off.

This study provides important insight into how standardization strategy can be advantageous while also enriching the literature about pitfalls expected from standardization. Moreover, this study's results will help the industrial sector achieve higher levels of design standardization by providing a better understanding of the benefits and trade-offs of design standardization.

Facing the challenge of increasing energy cost and requirement of reducing the emissions, identifying the potential factors of them in the manufacturing factories is an important prerequisite to control energy consumption. This paper aims to present a bi-objective green in-house transportation scheduling and fleet size determination problem (BOGIHTS&FSDP) in automobile assembly line to schedule the material delivery tasks, which jointly take the energy consumption into consideration as well.

This research proposes an optimal method for material handling in automobile assembly line. To solve the problem, several properties and definitions are proposed to solve the model more efficiently. Because of the non-deterministic polynomial-time-hard nature of the proposed problem, a Multi-objective Discrete Differential Evolution Algorithm with Variable Neighborhood Search (VNS-MDDE) is developed to solve the multi-objective problem.

The performances of VNS-MDDE are evaluated in simulation and the results indicate that the proposed algorithm is effective and efficient in solving BOGIHTS&FSDP problem.

This study is the first to take advantage of the robot's interactive functions for part supply in automobile assembly lines, which is both the challenge and trend of future intelligent logistics under the pressure of energy and resource. To solve the problem, a VNS-MDDE is developed to solve the multi-objective problem.

This study is carried out in one public and one private health-care centers based on different probabilities of patient’s no-show rate. The present study aims to determine the optimal queuing system capacity so that the expected total cost is minimized.

In this study an M/M/1/K queuing model is used for analytical properties of optimal queuing system capacity and appointment window so that total costs of these cases could be minimized. MATLAB software version R2014a is used to code the model.

In this paper, the optimal queuing system capacity is determined based on the changes in effective parameters, followed by a sensitivity analysis. Total cost in public center includes the costs of patient waiting time and rejection. However, the total cost in private center includes costs of physician idle time plus costs of public center. At the end, the results for public and private centers are compared to reach a final assessment.

Today, determining the optimal queuing system capacity is one of the most central concerns of outpatient clinics. The large capacity of the queuing system leads to an increase in the patient’s waiting-time cost, and on the other hand, a small queuing system will increase the cost of patient’s rejection. The approach suggested in this paper attempts to deal with this mentioned concern.