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In response to the station design and flexible resources allocation of the aircraft moving assembly line, a new problem named flexible resource investment problem based on project splitting (FRIP_PS), which minimizes total cost of resources with a given deadline are proposed in this paper.

First, a corresponding mathematical model considering project splitting is constructed, which needs to be simultaneously determined together with job scheduling to acquire the optimized project scheduling scheme and resource configurations. Then, an integrated nested optimization algorithm including project splitting policy and job scheduling policy is designed in this paper. In the first stage of the algorithm, a heuristic algorithm designed to get the project splitting scheme and then in the second stage a genetic algorithm with local prospective scheduling strategy is adopted to solve the flexible resource investment problem.

The heuristic algorithm of project splitting gets better project splitting results through the job shift selection strategy and meanwhile guides the algorithm of the second stage. Furthermore, the genetic algorithm solves resources allocation and job schedule through evaluation rules which can effectively solve the delayed execution of jobs because of improper allocation of flexible resources.

This paper represents a new extension of the resource investment problem based on aircraft moving assembly line. An effective integrated nested optimization algorithm is proposed to specify station splitting scheme, job scheduling scheme and resources allocation in the assembly lines, which is significant for practical engineering applications.

This study was steered to establish how buyer–supplier collaboration's commitment attributes serve as an antecedent for procurement performance in large manufacturing entities in Tanzania.

A parallel, concurrent, mixed method was used in the study. Quantitatively, 52 firms were surveyed from Temeke Municipality, Tanzania, using questionnaire that specified 1 procurement manager and 1 store manager from those firms, totaling a sample size of 104 respondents. Qualitatively, expressive opinions to supplement the numeric data were gathered from supply chain managers using the saturation principle. Explanatory design analyzed the existing cause–effect relationship, and the null hypotheses were tested using binary logistic regression at p values < 0.05 and ExpB > 1.

Fidelity and enthusiasm to suggest improvements to suppliers and the duration of the collaboration antecede the procurement performance of the manufacturing firms in Tanzania, while devotion to invest resources and initiatives on joint problem solving have no significant impact.

The causality between buyer–supplier collaboration and procurement performance has been revealed. Since there might be third party logistics in collaborations, future research should center on their moderating effect.

A framework has been developed for liberating procurement performance in the context of large manufacturing firms in Tanzania.

Based on Transaction Cost Economics and Resource Dependency Theories, the study revealed the root cause of procurement performance in the context of Tanzanian manufacturing firms, while also considering commitment to buyer–supplier collaboration as a prerequisit for the commendable target.

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.

Building information modeling (BIM) is a popular technology in modern construction, but its implementation faces numerous difficulties, which are known as problematic issues of BIM implementation (PIBIs). This study aims to identify and analyze PIBIs based on the perceptions of practitioners in Vietnamese BIM-implemented construction contractors and similar BIM adoption-level countries.

The research is conducted in three stages. After a comprehensive literature review, PIBIs are identified and ranked based on the Delphi method and questionnaire survey. Next, interpretive structural modeling (ISM) is used to provide a hierarchy of system PIBIs. Finally, the driving and dependence power of PIBIs are determined using Fuzzy-Matrice d'Impacts Croisés-Multiplication Appliquée à un Classement (MICMAC) analysis.

This study evaluates 32 PIBIs for small and medium contractors (SMCs) and 28 PIBIs for large contractors (LCs) in Vietnam. This study also develops a systematic framework for contextual relationships and analyzes the relationships among PIBIs using ISM-Fuzzy MICMAC analysis. The results show that different standards or techniques, modeling software limitations and information management significantly impacted LCs, while a lack of senior leadership support and incompatible software affected SMCs, causing other PIBIs.

This study is among the first attempts to investigate the problematic issues emerging during the BIM implementation process at the organizational level in Vietnam as well as other developing countries. The study results could be considered valuable references for BIM practitioners. Furthermore, this knowledge can enable contractors to avoid problems while adopting BIM, thereby increasing their ability to implement BIM successfully and using critical resources more efficiently.

The purpose of this paper is to propose a new mixed-integer formulation for the time-dependent multi-skilled resource-constrained project scheduling problem (MSRCPSP/t) considering learning effect. The proposed model extends the basic form of the MSRCPSP by three concepts: workforces have different efficiencies, it is possible for workforces to improve their efficiencies by learning from more efficient workers and the availability of workforces and resource requests of activities are time-dependent. To spread dexterity from more efficient workforces to others, this study has integrated the concept of diffusion maximization in social networks into the proposed model. In this respect, the diffusion of dexterity is formulated based on the linear threshold model for a network of workforces who share common skills. The proposed model is bi-objective, aiming to minimize make-span and total costs of project, simultaneously.

The MSRCPSP is an non-deterministic polynomial-time hard (NP-hard) problem in the strong sense. Therefore, an improved version of the non-dominated sorting genetic algorithm II (IM-NSGA-II) is developed to optimize the make-span and total costs of project, concurrently. For the proposed algorithm, this paper has designed new genetic operators that help to spread dexterity among workforces. To validate the solutions obtained by the IM-NSGA-II, four other evolutionary algorithms – the classical NSGA-II, non-dominated ranked genetic algorithm, Pareto envelope-based selection algorithm II and strength Pareto evolutionary algorithm II – are used. All algorithms are calibrated via the Taguchi method.

Comprehensive numerical tests are conducted to evaluate the performance of the IM-NSGA-II in comparison with the other four methods in terms of convergence, diversity and computational time. The computational results reveal that the IM-NSGA-II outperforms the other methods in terms of most of the metrics. Besides, a sensitivity analysis is implemented to investigate the impact of learning on objective function values. The outputs show the significant impact of learning on objective function values.

The proposed model and algorithm can be used for scheduling activities of small- and large-size real-world projects.

Based on the previous studies reviewed in this paper, one of the research gaps is the MSRCPSP with time-dependent resource capacities and requests. Therefore, this paper proposes a multi-objective model for the MSRCPSP with time-dependent resource profiles. Besides, the evaluation of learning effect on efficiency of workforces has not been studied sufficiently in the literature. In this study, the effect of learning on efficiency of workforces has been considered. In the scarce number of proposed models with learning effect, the researchers have assumed that the efficiency of workforces increases as they spend more time on performing a skill. To the best of the authors’ knowledge, the effect of learning from more efficient co-workers has not been studied in the literature of the RCPSP. Therefore, in this research, the effect of learning from more efficient co-workers has been investigated. In addition, a modified version of the NSGA-II algorithm is developed to solve the model.

To meet the requirement of establishing an effective schedule for the assembly process with overall detection and rework, this paper aims to address a new problem named resource-constrained multi-project scheduling problem based on detection and rework (RCMPSP-DR).

First, to satisfy both online and offline scheduling, a mixed integer programming model is established with a weighted bi-objective minimizing the expected makespan and the solution robustness. Second, an algorithm that combines a tabu search framework with a critical chain-based baseline generation scheme is designed. The tabu search framework focuses on searching for a reasonable resource flow representing the execution sequence of activities, while the critical chain-based baseline generation scheme establishes a buffered baseline schedule by estimating the tradeoff between two aspects of bi-objective.

The proposed algorithm can get solutions with gaps from −4.45% to 2.33% when compared with those obtained by the commercial MIP solver CPLEX. Moreover, the algorithm outperforms four other algorithms in terms of both objective performance and stability over instances with different weighting parameters, which reveals its effectiveness.

The represented RCMPSP-DR considering the overall detection and rework is an extension of the scheduling problem for large-scale equipment. An effective algorithm is proposed to establish the baseline schedule and determine the execution sequence of activities for the assembly process, which is significant for practical engineering applications.

Developing an optimized project schedule that considers all decision criteria represents a challenge for project managers. The purpose of this paper is to provide a multi-objectives overall optimization model for project scheduling considering time, cost, resources, and cash flow. This development aims to overcome the limitations of optimizing each objective at once resulting of non-overall optimized schedule.

In this paper, a multi-objectives overall optimization model for project scheduling is developed using particle swarm optimization with a new evolutionary strategy based on the compromise solution of the Pareto-front. This model optimizes the most important decisions that affect a given project including: time, cost, resources, and cash flow. The study assumes each activity has different execution methods accompanied by different time, cost, cost distribution pattern, and multiple resource utilization schemes.

Applying the developed model to schedule a real-life case study project proves that the proposed model is valid in modeling real-life construction projects and gives important results for schedulers and project managers. The proposed model is expected to help construction managers and decision makers in successfully completing the project on time and reduced budget by utilizing the available information and resources.

The paper presented a novel model that has four main characteristics: it produces an optimized schedule considering time, cost, resources, and cash flow simultaneously; it incorporates a powerful particle swarm optimization technique to search for the optimum schedule; it applies multi-objectives optimization rather than single-objective and it uses a unique Pareto-compromise solution to drive the fitness calculations of the evolutionary process.

This purpose of this study is to develop a simple framework for designing a warehouse incorporating lean principles. Multiple objectives like resource planning, material handling, storage, inventory management, including internal and external logistics, are considered.

A design procedure to incorporate lean principles for designing a warehouse for a complex multi-model production line has been proposed. The preferred standards and factors affecting warehouse design, the inputs and outputs of process flow characteristics, are incorporated into the design. Current and future state value stream mappings are drawn to bring out the challenges in the value flow.

The framework for designing a lean warehouse have been implemented and validated in a heavy machinery manufacturer. This framework will ease the work of the future lean-based warehouse designers to apply simple step-by-step processes to achieve the goal with the nearest accuracy. The steps followed can be summarized as defining the lean processes, making the lean process as the design base, collecting inputs like stock-keeping unit master, inventory and space details, and building the lean warehouse design with the step-by-step processes.

Practical tips on warehouse design have been explained focusing on the part volume, quantity handled, inventory and throughput. This will assist the practitioners in designing a lean warehouse and leading to an improved operational performance.

A simplified design procedure for designing a lean warehouse, along with a real-time case study has been enumerated in detail. Effective use of space and resources with lean tools and techniques lead to better storage and picking efficiency resulting in an overall reduction in cost.

Colfax Corporation was a young, privately held collection of pump-manufacturing companies from the United States and Europe. Intending to go public, it was eager to find a story for investors of how it could grow at rates faster than its subsidiaries had historically grown in their home regions and core-customer industrial markets. This case describes a singular new-growth opportunity: selling Colfax solutions into state-owned petroleum enterprises in the Middle East at a time when these producers were straining to add capacity. Designing the optimal marketing system required Colfax to weigh a complex of issues, including global resource allocation and deployment, a process for customer-relationship building, and estimates for revenue streams versus investment outlays. The design process was, in short, far more than “sticking sales rep pins in the map.” Case readers are asked to think along with the Colfax global management team in deciding, “How much can we afford to risk our current income model in order to build new capacity in a new region in a new way?”

Understanding issues related to global B2B marketing channel strategy development, as well as complexities of entering unfamiliar new international markets such as Middle East oil and gas.