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Supply chain (SC) research has boomed over the past two decades. Significant contributions have been made to the field from various analytical and decision-making perspectives. This paper, a comprehensive bibliometric study, aims to identify the key research contributors, institutions and themes.

A comprehensive knowledge domain visualization of over 1,000 articles, published between 2000 and 2022, is carried out to construct a bird’s eye view of the field in terms of research production, key authors, main publication outlets, geographic disparity of the contributions and emerging research trends. Additionally, collaboration patterns among researchers and institutions are mapped to highlight the communication networks underlying research initiatives.

Results show an explosive growth in the number of articles tackling supply chain optimization (SCO) issues with a significant concentration of the contributions in a relatively small cluster of authors, journals, institutions and countries. Among the many important findings, our analysis indicates that mixed-integer linear programming is the most commonly used model, while robust optimization is the method of choice for handling uncertainty. Furthermore, most SC models are developed at only one level of the organizational hierarchy and consider only one planning horizon. The importance of developing integrated SCO systems is key for future research.

The study fills the optimization techniques gap that exists in SC management bibliometric studies and presents a thematic map for the SCO research highlighting the various research foci.

The rise of remote work increasingly requires organizations to coordinate a single large, consolidated talent pool into ad-hoc, short-term project teams on demand. This problem involves many simultaneous considerations including project revenues and rejection costs, conflicting projects and roles, worker assignment costs, worker utilization preferences and limits, worker reassignment costs, and arbitrary role start and end times. Moreover, plans must be continuously updated in response to changing circumstances. This paper addresses the problem of dynamic virtual team planning and coordination.

We show this problem is NP-hard and provide a dynamic mixed integer linear programming (MILP) formulation for both optimal initial plan generation as well as continuous plan adjustment and re-optimization. We utilized a factorial experiment design to generate benchmark problems spanning a wide range of characteristics and conducted extensive computational experimentation using a common MILP solver.

Exactly optimal solutions to large, realistically sized problems were consistently obtained in short amounts of time. All observed solution times were sufficient to support the operational decision-making requirements of real-world virtual team coordination, demonstrating the viability of this approach.

The approach developed in this research can enable organizations to optimally coordinate virtual teams on a large scale and continually adjust plans in response to changing circumstances, all in an automated manner.

This paper addresses a new and complex problem of increasing importance to organizations due to the rise in remote work. We provide a problem formulation and exact approach for optimally solving both the planning and re-planning aspects of this problem.

The purpose of this paper is to propose a scenario-based grey methodology using clustering and optimizing with imprecise and uncertain body size data in an emergency assembly point area to assign the people on a campus to reach the emergency assembly points under uncertain disaster times.

Grey clustering and a new grey p-median linear programming model are developed to determine which units to assign to the pre-determined assembly points for a main campus in case of a disaster. The models have two scenarios: 70 and 100% occurrence capacities of administrative and academic personnel and students.

In this study, the academic and administrative units have been assigned to determine five different emergency assembly points on the main campus by using the numbers of the academic and administrative personnel and student and distances of the units to the assembly point areas of each other. The alternative solutions are obtained effectively by evaluating capacity utilization rates in the scenarios.

It is often unclear when disasters can occur and therefore, a preliminary preparation time must be required to minimize the risk. In the case of natural, man-made (unnatural) or technological disasters, the people are required to defend themselves and move away from the disaster area as soon as possible in a proper direction. The proposed assignment model yields a final solution that effectively eliminates uncertainty regarding the selection of emergency assembly points for administrative and academic staff as well as students, in the event of disasters.

Grey clustering suggests an assignment plan and concurrently, an investigation is underway utilizing the grey p-median linear programming model. This investigation aims to optimize various scenarios and body sizes concerning emergency assembly areas. All campus users who are present at the disaster in units of the campus are getting uncertainty about which emergency assembly point to use, and with this study, the vital risks aim to be ultimately reduced with reasonable plans.

This paper aims to develop a multi-objective problem for scheduling the operations of trucks entering and exiting cross-docks where the number of unloaded or loaded products by trucks is fuzzy logistic. The first objective function minimizes the maximum time to receive the products. The second objective function minimizes the emission cost of trucks. Finally, the third objective function minimizes the number of trucks assigned to the entrance and exit doors.

Two steps are implemented to validate and modify the proposed model. In the first step, two random numerical examples in small dimensions were solved by GAMS software with min-max objective function as well as genetic algorithms (GA) and particle swarm optimization. In the second step, due to the increasing dimensions of the problem and computational complexity, the problem in question is part of the NP-Hard problem, and therefore multi-objective meta-heuristic algorithms are used along with validation and parameter adjustment.

Therefore, non-dominated sorting genetic algorithm (NSGA-II) and non-dominated ranking genetic algorithm (NRGA) are used to solve 30 random problems in high dimensions. Then, the algorithms were ranked using the TOPSIS method for each problem according to the results obtained from the evaluation criteria. The analysis of the results confirms the applicability of the proposed model and solution methods.

This paper proposes mathematical model of truck scheduling for a real problem, including cross-docks that play an essential role in supply chains, as they could reduce order delivery time, inventory holding costs and shipping costs. To solve the proposed multi-objective mathematical model, as the problem is NP-hard, multi-objective meta-heuristic algorithms are used along with validation and parameter adjustment. Therefore, NSGA-II and NRGA are used to solve 30 random problems in high dimensions.

This work conducts a comprehensive analysis of how to incorporate resilience and sustainability into capacity expansion strategies for business-to-business (B2B) chemical supply chains. This study aims to guide both researchers and managers on ensuring profitability in B2B chemical supply chains while minimizing environmental impacts, complying with regulations and mitigating disruptions and risks.

A systematic literature review is conducted to analyze the interplay between sustainability and resilience in chemical B2B supply chains, specify the quantitative and qualitative methods used to tackle this challenge and identify the drivers and barriers concerning capacity expansion. In addition, a comprehensive conceptual framework is suggested to outline a compelling research agenda.

The findings emphasize the increasing importance of modeling and resolving decision-making challenges related to sustainable and resilient supply chains, particularly in capital-intensive chemical industries. Yet, there is no standardized strategy for addressing these challenges. The predominant solution methods are heuristic and metaheuristic, and the selection of performance metrics tends to be empirical and tailored to specific cases. The main barriers to achieving sustainability and resilience arise from resource limitations within the supply chain. Conversely, the key drivers of performance focus on enhancing efficiency, competitiveness, cost effectiveness and risk management.

This work offers practitioners a conceptual framework that synthesizes the knowledge and tackles the challenges of designing sustainable and resilient supply chains as well as managing their operations in the context of B2B chemical supply chains. Results provide a practical guide for navigating the complex interplay of sustainability, resilience and chemical supply chain expansion.

The key concepts and dimensions associated with capacity expansion planning for a resilient and sustainable chemical supply chain are identified through structured and comprehensive analyses of existing literature. A conceptual framework is proposed for delineating the intersections among sustainability, resilience and chemical supply chain expansions. This mapping endeavor aims to facilitate a future characterized by the deployment of a nexus of resilience and sustainability in chemical supply chains. To this end, a promising future research agenda is accordingly outlined.

Considering sustainability and resilience together is crucial in food supply chain (FSC) management, as it ensures a balanced approach that meets environmental, economic and social needs while maintaining the system's capacity to withstand disruptions. Towards this, a multi-objective optimisation model is proposed in this study to create an integrated sustainable and resilient FSC. The proposed model employs four objective functions – each representing a dimension of sustainability and one for resilience and utilises an augmented ϵ-constraint method for solving. The findings highlight the interplay between sustainability aspects and resilience, illustrating that overemphasis on any single dimension can adversely affect others. Further, the proposed model is applied to the case of Indian mango pulp supply chain and several inferences are derived. The proposed model would assist decision-makers in making a well-balanced choice based on sustainability and resilience considerations.

From a supply chain perspective, logistics firms collaborate with other supply chain members to extend their business scope. Investment in circular economy projects in the supply chain can not only broaden the scope of business but also increase the value of the entire supply chain. Third-party logistics companies are gradually participating in the construction and operation of many circular economy projects. How to coordinate multiple circular economy supply chain projects is at the core of its operation.

This paper first analyzes some typical supply chain projects in China and summarizes the main features of these projects. Secondly, considering the benefits of the project and the stakes of each project, a multi-stage stochastic programming model is established. Finally, Cplex, nested decomposition, LocalSolver and other methods are adopted to simulate and analyze the model.

The final experimental results find that the importance of coordinating multiple circular economy supply chain projects to increase the value of the entire supply chain. The multi-stage stochastic programming model presented in this research can provide a useful tool for logistics enterprises and third-party logistics companies to optimize their investment decisions and maximize their profits in the context of a circular economy.

There are still some limitations to this study; for example, it is limited to the analysis of circular economy supply chain projects in China. The study focused on third-party logistics companies, and other enterprises in the circular economy supply chain were not considered. The research also assumed that the benefits of each circular economy project and the stakes of each project were known, which may not always be the case in real-world scenarios.

This manuscript found that investing in other circular economy projects in the supply chain can broaden the scope of business and increase the value of the entire supply chain. Third-party logistics companies are gradually participating in the construction and operation of many circular economy projects, such as recycling and repurposing initiatives. It highlights the importance of coordinating multiple circular economy supply chain projects to increase the value of the entire supply chain. The multi-stage stochastic programming model presented in this research can provide a useful tool for logistics enterprises and third-party logistics companies to optimize their investment decisions and maximize their profits in the context of a circular economy.

Given the growing interest in modern construction techniques and the emergence of innovative technologies, construction site layout planning research has progressively been investigating approaches to adopt innovative concepts and incorporate renewed approaches to improve widespread efficiency. This research develops a decision-making tool that optimizes construction site layout plans. The developed model targets two main objectives: minimizing material transportation costs and maximizing safety by optimally placing facilities on construction sites.

A novel approach is devised based on the integration of Building Information Modeling and Generative Design (BIM-GD). This engine is used to optimize the multi-objective site layout problems to identify layout alternatives in the early project stages. Parametric modeling uses Dynamo to construct the model and explore constraints initially. Finally, the GD environment is utilized to create different design alternatives, and then the decision-making procedure selects the most appropriate design alternative. Additionally, a case study is applied to validate the effectiveness of the developed model.

The results indicate the effectiveness of the proposed GD tool and its potential for more complex applications. The GD engine examined optimal layout plans, balancing different objectives and adhering to appointed geometric constraints. A case study was conducted to assess the model's effectiveness and showcase its suitability. Construction Site Layout Planning (CSLP) is an essential step in design that can influence considerable aspects, such as material transportation expenses and different safety standards on the site. Employing visual programming for parametric modeling within Dynamo-Revit creates an expedient and user-friendly platform for planning engineers who may require more programming expertise to create and program algorithmic models visually. Utilizing GD in CSLP has proven to be a powerful tool with consequential prospects for improving applications and executing more models.

The findings from this framework are intended to help construction practitioners select the most appropriate site layout during early project stages while incorporating different safety criteria inside construction sites to alleviate actual safety risks.

A new approach is proposed that utilizes an integrated BIM-GD engine to optimize multi-objective site layout problems. This approach targets two main objectives: minimizing material transportation costs and maximizing safety by optimally placing facilities in construction sites.

Grey relational analysis (GRA) has already proved itself as an efficient tool for multi-objective optimization of many of the machining processes. In GRA, the distinguishing coefficient (ξ) plays an important role in identifying the optimal parametric combinations of the machining processes and almost all the past researchers have considered its value as 0.5. In this paper, based on past experimental data, the application of GRA is extended to dynamic GRA (DGRA) to optimize two electrochemical machining (ECM) processes.

Instead of a static distinguishing coefficient, this paper considers dynamic distinguishing coefficient for each of the responses for both the ECM processes under consideration. Based on these coefficients, the application of DGRA leads to determination of the dynamic grey relational grade (DGRG) and grey relational standard deviation (GRSD), helping in initial ranking of the alternative experimental trials. Considering the ranks obtained by DGRG and GRSD, a composite rank in terms of rank product score is obtained, aiding in final rankings of the experimental trials for both the ECM processes.

In the first example, the maximum material removal rate (MRR) would be obtained at an optimal combination of ECM parameters as electrolyte concentration = 2 mol/l, voltage = 16V and current = 4A, while another parametric intermix as electrolyte concentration = 2 mol/l, voltage = 14V and current = 2A would result in minimum radial overcut and delamination. For the second example, an optimal combination of ECM parameters as electrode temperature = 30°C, voltage = 12V, duty cycle = 90% and electrolyte concentration = 15 g/l would simultaneously maximize MRR and minimize surface roughness and conicity.

In this paper, two ECM operations are optimized using a newly developed but yet to be popular multi-objective optimization tool in the form of the DGRA technique. For both the examples, the derived rankings of the ECM experiments exactly match with those obtained by the past researchers. Thus, DGRA can be effectively adopted to solve parametric optimization problems in any of the machining processes.

The purpose of this study is developing a new buy-back coordination contract in the symbiotic supply chain. In this new contract, the goal of the supply chain members (profit maximization) is realized.

This paper encourages the manufacturer to order products optimally by presenting a new buy-back coordination contract, and in return, the supplier undertakes to buy the unsold products from the manufacturer at the buy-back price. By using data-driven decision-making and multiobjective decision-making and considering the existing conditions in the symbiosis industry, a contract has been presented that guarantees the profits of supply chain members.

In this paper, it was found out how the authors can determine the order quantity, buy-back price and wholesale price in a symbiotic supply chain in such a way that it makes a profit for both the supplier and the manufacturer. In other words, how to determine these variables to encourage the manufacturer to order more quantity to the supplier so that both will benefit.

To the best of the authors’ knowledge, this is the first paper that defines a new buy-back coordination contract in the symbiotic supply chain by considering uncertain demand and a multiobjective model. Due to the importance of environmental issues, the sharing of resources by companies and organizations with each other, and the necessity of their cooperation, industries are moving toward a symbiosis industry.