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Integrating project scheduling and material ordering problems is vital in realistically estimating project cost and duration. Also, the quality level of materials is important as one of the key project success factors.

In this paper, a three-objective mathematical model is presented for green project scheduling with materials ordering problems considering rental resources. The first objective is to minimize the total cost of the project site and logistics. The second objective is to minimize the environmental impacts of producing materials and the third objective is to maximize the total quality of materials. Since costs trigger several challenges in projects, cost constraints are considered in this model for the first time and also the cost of delay in supplying of materials by the suppliers has been deducted from the project costs. Subsequently, the model was implemented in a real case and solved by the Lagrangian Relaxation algorithm as an exact method on GAMS software for model validation.

Based on sensitivity analysis of some parameters, the findings indicate that the cost constraint and lead time have considerable effects on the project duration. Also, integrating project scheduling and material ordering improves the robustness of the project schedule.

The primary contributions of the present research can be stated as follows: considering the cost constraints in the project scheduling with material ordering problem, incorporating the rental resources and taking the quality levels of materials as well as the environmental impacts into account.

The purpose of this study is to develop an integrated model for the stochastic multiproject scheduling and material ordering problems, where some of the prominent features of offshore projects and their environmental-degrading effects have been embraced as well. The durations of activities are uncertain in this model. The developed formulation is tri-objective that seeks to minimize the expected time, total cost and CO₂ emission of all projects.

A new version of the multiobjective multiagent optimization (MOMAO) algorithm has been proposed to solve the amalgamated model. To empower the MOMAO, various procedures of this algorithm have been modified based on the multiattribute utility theory (MAUT) technique. Along with the MOMAO, this study has employed four other meta-heuristic methodologies to solve the model as well.

The outputs of the MOMAO have been put to test against four other optimizers in terms of convergence, diversity, uniformity and computation times. The results of the Mean Ideal Distance (MID) metric have revealed that the MOMAO has strongly prevailed its rival optimizers. In terms of diversity of the acquired solutions, the MOMAO has ranked the first among all employed optimizers since this algorithm has offered the best solutions in 56.66 and 63.33% of the test problems regarding the diversification metric and hyper-volume metrics. Regarding the uniformity of results, which is measured through the spacing metric (SP), the MOMAO has presented the best SP values in more than 96% of the test problems. The MOMAO has needed more computation times in comparison to its rivals.

A real case study comprising two concurrent offshore projects has been offered. The proposed formulation and the MOMAO have been implemented for this case study, and their effectiveness has been appraised.

Very few studies have focused on presenting an integrated formulation for the stochastic multiproject scheduling and material ordering problems. The model embraces some of the characteristics of the offshore projects which have not been adequately studied in the literature. Limited capacities of the offshore platforms and cargo vessels have been embedded in the proposed model. The offshore platforms have spatial limitations in storing the required materials. The vessels are also capacitated and they also have limited shipment capacities. Some of the required materials need to be transported from the base to the offshore platform via a fleet of cargo vessels. The workforces and equipment can become idle on the offshore platform due to material shortage. Various offshore-related costs have been integrated as a minimization objective function in the model. The cargo vessels release CO₂ detrimental emissions to the environment which are sought to be minimized in the developed formulation. To the best of the authors' knowledge, the MOMAO has not been sufficiently employed as a solution methodology for the stochastic multiproject scheduling and material ordering problems.

Construction material management plays a significant role in achieving successful project delivery of a construction project. However, ineffective material management is a critical issue in the construction industry, especially in developing economies, of which Sri Lanka is not an exception. Therefore, this study aims to focus on exploring the causes of ineffective material management practices in civil engineering construction projects in Sri Lanka and their impact on successful project delivery.

Furthermore, the literature findings were validated through the preliminary survey. Subsequently, a quantitative research approach was adopted to pursue the research aim. Questionnaire responses were obtained from 215 construction professionals in civil engineering projects who were selected using the judgemental and snowball sampling techniques. Collected data were analysed through Statistical Package for the Social Sciences (SPSS) V26 and Microsoft Excel 2016.

Moreover, the study revealed that material price fluctuation, shortage of material in the market, delay in material procurement, inadequate planning and delays in material delivery are the most frequent causes of ineffective material management in civil engineering projects. In addition, it was evidenced that most ineffective material management practices cause both time and cost overruns in civil engineering construction projects. Most respondents emphasized inadequate planning, inadequate qualified and experienced staff, lack of supervision and lack of leadership as the causes for both time and cost overruns.

The study was concluded by proposing strategies for effective material management. Education/training/enlightenment of staff in charge of materials management, use of software like Microsoft Project, Primavera and similar software to eliminate manual errors in material management, and providing clear specifications to suppliers were the most agreed strategies for effective material management in civil engineering construction projects.

This paper aims to investigate the challenges faced by the transport infrastructure sector in its civil construction material supply processes, following the 2016 Kaikoura earthquake in New Zealand.

A case study approach was adopted, which included on-site observations, semi-structured interviews and literature-based desktop reviews.

The research findings show that there were supply problems for aggregates, concrete, stormwater pipes and some specialised products for the repair and rebuild of transport facilities. Those supply problems were largely caused by (1) difficulty in predicting material requirements, (2) constrained supply capability, (3) inadequate local freight capacity, (4) legal, cultural and/or environmental considerations on resource exploitation and utilisation, (5) impacts of COVID-19 and (6) ineffective communication and coordination.

For future research, it is recommended that a comparative analysis of multiple disaster cases be undertaken to further explore the generalisability of the research findings.

The research findings will inform the development of post-earthquake recovery policies and material supply chain operation strategies, in order to expedite the recovery of transport networks if a future earthquake strikes.

The purpose of this study is to investigate a joint economic lot-size model with the possibility of cofinancing between members of a three-echelon supply chain (SC) including one supplier, one manufacture and one retailer. Given the differences in credit as well as differences in access to capital markets, SC members will be able to create a financial alliance to maximize the profits of each member. This study proposed a model to maximize the annuity stream of the SC by considering the financial interaction between SC members.

This joint economic lot-sizing problem was described and modeled mathematically. To evaluate the mathematical model, different scenarios were considered with (and without) the possibility of financial interaction.

It is suggested that, in addition to the goods and information flow among SC members, proper financial flow can also have an impact on the improvement of SC performance.

While previous studies consider cofinancing between members of a two-echelon SC, this paper considers a three-echelon SC including one supplier, one manufacturer and one retailer where financial cooperation between different levels of the SC in both upstream and reverse directions will be possible.

The unstable labor productivity and periodic planning method cause barriers to improving construction logistics management. This paper aims to explore a demand-driven mechanism for efficient construction logistics planning to record the material consumption, report the real-time demand and trigger material replenishment from off-site to on-site, which is aided by Building Information Modeling (BIM) and the Kanban technique.

This paper follows the design science research (DSR) principles to propose a system of designing and applying Kanban batch with 4D BIM for construction logistics planning and monitoring. Prototype development with comparative simulation experiments of a river remediation project is conducted to analyze the conventional and Kanban-triggered supply. Two-staged industrial interviews are conducted to guide and evaluate the system design.

The proposed BIM-enabled Kanban system enables construction managers and suppliers to better set integrated on- and off-site targets, report real-time demands and conduct collaborative planning and monitoring. The simulation results present significant site storage and schedule savings applying the BIM-enabled Kanban system. Feedback and constructive suggestions from practitioners are collected via interviews and analyzed for further development.

This paper brings to the limelight the benefits of implementing BIM-enabled demand-driven replenishment to remove waste from the material flow. This paper combines lean production theory with advanced information technology to solve construction logistics management problems.

This study aims to propose a general methodology to handle multimaterial filtering for density-based topology optimization containing periodic or antiperiodic boundary conditions, which are expected to reduce the simulation time of electrical machines. The optimization of the material distribution in a permanent magnet synchronous machine rotor illustrates the relevance of this approach.

The optimization algorithm relies on an augmented Lagrangian with a projected gradient descent. The 2D finite element method computes the physical and adjoint states to evaluate the objective function and its sensitivities. Concerning regularization, a mathematical development leads to a multimaterial convolution filtering methodology that is consistent with the boundary conditions and helps eliminate artifacts.

The method behaves as expected and shows the superiority of multimaterial topology optimization over bimaterial topology optimization for the chosen test case. Unlike the standard approach that uses a cropped convolution kernel, the proposed methodology does not artificially reflect the limits of the simulation domain in the optimized material distribution.

Although filtering is a standard tool in topology optimization, no attention has previously been paid to the influence of periodic or antiperiodic boundary conditions when dealing with different natures of materials. The comparison between the bimaterial and multimaterial topology optimization of a permanent magnet machine rotor without symmetry constraints constitutes another originality of this work.

This exploratory study aims to explore the operational and financial constraints faced by small and medium enterprises (SMEs) in India during the COVID-19 pandemic. The paper highlights the role of supply chain finance (SCF) in the uncertain business environment caused by the pandemic.

The study adopts an inductive approach and conducts convergent interviews with 32 SME owners and bank officials who are associated with SME-related financial transactions. The analysis of the interview data has been done through a grounded theory approach.

The findings portray four key themes representing the operational and financial constraints faced by SMEs during the pandemic. Further, the study identifies four drivers of SCF adoption among SMEs, including capital constraints, high inventory turnover cycle time, high order fulfilment cycle time and long debtors’ collection period.

The study provides various insights to the managers and owners of SMEs to deal with the economic crisis and eliminate the financial pressure created by the pandemic. The study enlightens the policymakers about the struggles of the SMEs during the economic turmoil created by the pandemic and guides them to introduce the relevant policies to resolve their problems.

To the best of the authors’ knowledge, this is the first study to identify the factors driving the SMEs to adopt SCF due to the economic chaos created by the pandemic. Also, the study theoretically contributes to the literature by developing a theoretical framework for SCF adoption based on grounded theory.

After completion of the case study, students will be able to understand general and specific challenges associated with carrying on a family business that faces market challenges including stiff competition from existing and newer players, understand the plywood manufacturing process and its supply chain management, understand the businesses operating in an organized versus the unorganized market, comprehend the marketing strategies adopted and identify a reasonable solution to address the challenges associated with the operations of a business.

This case study focuses on Gattani Industries (a plywood manufacturing company) located in the northeastern region of the Indian state of Assam. Headquartered at Cinnamara industrial zone of Jorhat district, Assam, the company began its operation in 1992 under the leadership of Makhan Gattani (Director). Gattani Industries catered to both residential and commercial demand. Its clients included the departments of central and state governments in India, public sector undertakings and civil contractors. The company had a wider distribution network across the country and adopted the one- and two-level marketing channels to reach consumers. It aimed to sell its products through dealers across the cities in India. However, in December 2019, Gattani faced the challenge of developing a growth strategy to overcome competition and use the upcoming market opportunities for business growth in the diverse and complex environment that existed in the country.

This case study is designed for use in graduate or undergraduate programs. This case study can be used in strategy, supply chain and marketing courses at Bachelor of Business Administration and Master of Business Administration levels.

Teaching notes are available for educators only.

CSS11: Strategy.

Construction organisations cannot underestimate the improvement in public–private partnership (PPP) projects’ implementation. At the same time, construction organisations cannot overlook the risk arising from engaging in PPP construction projects. Hence, this study aims to establish the influence of risk resource management (RRM) in managing PPP risk in the construction industry in Ghana.

The researchers adopted qualitative and quantitative research methods to achieve the aim of the study, in which Delphi questions and a close-ended questionnaire were developed. A total of 650 construction specialists, including procurement officers, consultants, project managers, quantity surveyors, site engineers and planning officers were chosen using random and purposive sampling techniques. Recovered data were analysed using descriptive statistics and confirmatory factor analysis (CFA). The CFA maximum likelihood estimation extractor compresses 19 variables into 3 pattern matrices.

The results of the study revealed three factors that measure RRM in Ghana’s PPP construction industry, including financial resource management which was influenced by communicating the budget to project team members and project partners understanding the budget, and material resource management which was influenced by the provision of materials transportation and provision of delivery programs and labour resource management which was impacted by a commitment to pay social security and taxes and provision of good salaries, to address RRM in PPP construction organisations.

To incessantly improve the PPP risk management (RM) in construction through RRM, there should be a strong liaison between the universities, government agencies and the construction industry, and such collaboration will assist the industry to obtain first-hand information regarding the study findings and how they can be implemented to help the development of RM in the construction industry. This study is limited to Ghana and CFA and further study should explore structural equation model to determine the structure and measurement model of the risk resource variables.

The study may be valuable to industry stakeholders looking for new approaches to improve RM in their construction activities, particularly in PPP projects. Also, to assist reduce PPP risk, construction companies should use RRM in their organisations.