Across the globe, the corollary of Green Cost Premium (GCP) obstructs the implementation of Sustainable Buildings (SB). Extensive studies into GCP proliferate, but the research norms rarely traversed theoretical contexts of GCP. The purpose of this paper was to explore the drivers of GCP from the contexts of the prevailing practice of SB using the theoretical lens of practice theory. Secondary literature comprising mainly peer-reviewed publications spanning 20years was critically reviewed. The results show some uncertainty regarding the effect of prevailing practice on the size of GCP due to the dearth of empirical studies. Secondary literature, however, showed that GCP is liable to variations in practice related to the level of knowledge and the implementation processes. The knowledge domain argued that the scope of GCP depends on regional issues including misperceptions, cost management deficiencies and sustainability accounting gaps. During implementation, GCP could also modify in response to changes in cost drivers, factors limiting innovative processes and challenges and barriers in the project environment. Engagements with practice have, however failed to embed this understanding into SB project implementation decisions and dynamics, as limited documented efforts aimed at mitigating the GCP exist. The paper offers a non-conventional perspective for assessing the dynamics of converging regional practices in SB that can contribute to GCP as well as lower the GCP when the practices are improved. GCP is susceptible to practice variations and answers to projects practices across regions. This portrays that the GCP can lessen through innovation of practice elements such as competencies and inputs (materials and procedures). The elements of scientific inquiry for GCP must be disconnected from currently established knowledge about SB systems to regional practices related to knowledge and procedures.

Education is the future. Education is the only way for a country to start developing and reducing poverty. In countries with medium incomes like Peru, the resources to spend on education is not unlimited. Therefore, it is necessary to have quality in investment. However, risks and uncertainty can make a project surpass its initial budget. Therefore, statistic based methods like Monte Carlo simulation is a powerful tool to forecast possible events that might endanger the profitability and sustainability of a project. Although there is not plenty of academic literature about Monte Carlo empirical usage, many projects employ this method to manage the possible risks the project could have. In consequence, the current research analyzed both risk and sensitivity of an elementary school building project. Both analyses showed that this project had huge probabilities to surpass the current profit and return estimations. However, the sensitivity analysis portrayed that the project could be endangered because of infrastructure overspending. Moreover, it indicated that students’ attendance is also a critical factor to ensure the sustainability of the project.

The integration between production and distribution to minimize total elapsed time is an important issue for industries that produce products with a short lifespan. However, the literature focus on production environments with a single stage. This paper enhances the complexity of the production system of an integrated production and distribution system by considering flowshop environment decisions integrated with a vehicle routing problem decision. In this case, each order is produced in a permutation flowshop subsystem and then shipped to its destination by a capacitated vehicle, and the objective is to sequence these orders to minimize the makespan of the schedule. This paper uses two approaches to address this integrated problem: a mixed-integer formulation and an Iterated Greedy algorithm. The experimentation shows that the Iterated Greedy algorithm yields results with a 0.02% deviation from the optimal for problems with five jobs, and is a viable option to be used in practical cases due to its short computational time.

We investigated a variant of the customer order scheduling problem taking into consideration due dates to minimize the total tardiness. Since the problem under study is NP-hard, we propose an efficient size reduction algorithm (SR). We perform an extensive computational experience and compare our proposition with JPO-20 matheuristic, the best existing algorithm for the problem under study. We use the Relative Deviation Index (RDI) and the Success Rate (SRa) as the statistical indicators for the performance measure. We must emphasize that SR presented the lowest average RDI (around 15.5 %), whereas the JPO-20 presented an average RDI approximately three times higher (around 52.5 %). Furthermore, the proposed SR presented a higher average SRa (around 66.9%), whereas the JPO-20 presented a lower average success (around 25.7%). Our proposal used a lower computational effort, resulting in a reduction for the computation times of approximately 22%. The obtained results point to the superiority of the proposed SR in comparison with the JPO-20.

The no-wait flow shop scheduling problem (NWFSP) plays a crucial role in the allocation of resources in multitudinous industries, including the steel, pharmaceutical, chemical, plastic, electronic, and food processing industries. The NWFSP consists of n jobs that must be processed in m machines in series, and no job is allowed to wait between consecutive operations. This project deals with NWFSP with sequence-dependent setup times for minimizing earliness and tardiness. From the literature review of the last five years in NWFSP, it is noticeable that only around 1.92% of the researchers have studied that multi-objective function, which could help to improve the productivity of industries where methods such as just in time are considered. Besides, there is no information about previous researchers that have solved this problem with sequence-dependent setup times. Firstly, a MILP model is proposed to solve small instances, and secondly, a genetic algorithm (GA) is developed as a solution method for medium and large instances. Compared with the mathematical model for small instances, the GA obtained the optimal solution in 100% of the cases. For medium and large instances, the GA improves in an average of 31.54%, 38.09%, 44.58%, 47.72%, and 37.33% the MDD, EDDP, ATC, SPT, and LPT dispatching rules, respectively.