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The adverse interactions between disruptions can increase the supply chain's vulnerability. Accordingly, establishing supply chain resilience to deal with disruptions and employing business continuity planning to preserve risk management achievements is of considerable importance. The aforementioned idea is discussed in this study.

This study proposes a multi-objective optimization model for employing business continuity management and organizational resilience in a supply chain for responding to multiple interrelated disruptions. The improved augmented e-constraint and the scenario-based robust optimization methods are adopted for multi-objective programming and dealing with uncertainty, respectively. A case study of the automotive battery manufacturing industry is also considered to ensure real-world conformity of the model.

The results indicate that interactions between disruptions remarkably increase the supply chain's vulnerability. Choosing a higher fortification level for the supply chain and foreign suppliers reduces disruption impacts on resources and improves the supply chain's resilience and business continuity. Facilities dispersion, fortification of facilities, lateral transshipment, order deferral policy, dynamic capacity planning and direct transportation of products to markets are the most efficient resilience strategies in the under-study industry.

Applying resource allocation planning and portfolio selection to adopt preventive and reactive resilience strategies simultaneously to manage multiple interrelated disruptions in a real-world automotive battery manufacturing industry, maintaining the long-term achievements of supply chain resilience using business continuity management and dynamic capacity planning are the main contributions of the presented paper.

There have been numerous extensions of the maximum covering location problem that has been developed in the last decade to deal with facility location. Most of the research, however, addresses a single attribute or objective. In the case when a single criterion such as minimizing average response time to access a service facility is insufficient to address the interests of the decision maker, multiple objectives must be employed. Qualitative factors like customer service and market demand as well as quantitative factors like distribution and operating costs need to be appropriately weighted and used in a mathematical programming model. We develop a multi‐objective model for a service facility location problem that simultaneously sites facilities and allocates demand for products from different customer zones. We apply this model to “real‐world” data and show the practical advantages of using this model to solve capacitated service logistics problems.

Recently, there has been increasing focus on the development of multi-skilled workforce in project management. The purpose of this paper is to investigate a multi-skill project scheduling problem (MSPSP), which combines project scheduling and multi-skill personnel assignment. The distinct features of skill evolution and cooperation effectiveness are considered in the problem to maximize the total project effectiveness and skill development simultaneously.

The Bi-objective non-linear integer programming (LIP) models are formulated for the problem using three types of skill development objective function: number of experts, total skill increment and “bottleneck” skill increment. Non-linear models are then linearized through several linearization techniques, and the ε-constraint method is used to convert the bi-objective models into single-objective models.

A construction project case is used to validate the proposed models. In comparison with models that do not consider skill evolution and cooperation effectiveness, the models proposed in this paper offer more realistic solutions and show better performance with regard to both project effectiveness and skill development.

This research extends the current MSPSP by considering skill evolution based on the “learning effect” as well as the influence of cooperation in an activity-based team, which are common phenomena in practice but seldom studied. LIP models formulated in this paper can be solved by any off-the-shelf optimization solver, such as CPLEX. Besides, the proposed LIP models can offer better project scheduling and personnel assignment plan, which would be of immense practical value in project management applications.

Achieving project objectives in constructionprojects such as time, cost and quality is a challenging task. Minimizing project cost often results in additional project duration and might jeopardize quality, and minimizing project duration often results in additional cost and might jeopardize quality. Also, increasing construction quality often results in additional cost and time. The purpose of this paper is to identify and analyze trade-offs among the project objectives of time, cost and quality.

The optimization model adopted a quantitative research method and is developed in two main steps formulation step that focuses on identifying model decision variables and formulating objective functions, and implementation step that executes the model computations using multi-objective optimization of Non-Dominated Sorting Genetic Algorithms to identify the aforementioned trade-offs, and codes the model using python. The model performance is verified and tested using a case study of construction project consisting of 20 activities.

The model was able to show practical and needed value for construction managers by identifying various trade-off solutions between the project objectives of time, cost and quality. For example, the model was able to identify the shortest project duration at 84 days while keeping cost under $440,000 and quality higher than 85 percent. However, with an additional budget of $20,000 (4.5 percent increase), the quality can be increased to 0.935 (8.5 percent improvement).

The present research work is limited to project objectives of time, cost and quality. Future expansion of the model will focus on additional project objectives such as safety and sustainability. Furthermore, new optimization models can be developed for construction projects with repetitive nature such as roads, tunnels and high rise buildings.

The present model advances existing research in planning construction projects efficiently and achieving important project objectives. On the practical side, the optimization model will support the construction industry by allowing construction managers to identify the highest quality to deliver a construction project within specified budget and duration, lowest cost for specified duration and quality or shortest duration for specified budget and quality.

The present model introduces new and innovative method of increasing working hours per day and number of working days per shift while analyzing labor working efficiency and overtime rate to identify optimal trade-offs among important project objectives of time, cost and quality.

The purpose of this paper is to provide feasible and fast solutions for the multi-objective airport gate assignment problem (AGAP) considering both passenger-oriented and airline-oriented objectives, which is the total walking distance from gate to baggage carousels (TWD) and the total aircraft fuel consumption during taxi operations (TFC). In addition, obtaining feasible and near-optimal solutions in a short time reduces the gate planning time to be spent by air traffic controllers.

The mixed integer linear programming (MILP) approach is implemented to solve the multi-objective AGAP. The weighted sum approach technique was applied in the model to obtain non-dominated solutions. Because of the complexity of the problem, the simulated annealing (SA) algorithm was used for the proposed model. The results were compared with baseline results, which were obtained from the algorithm using the fastest gate assignment and baggage carousel combinations without any conflict taking place at the gate assignments.

The proposed model noticeably decreased both the TWD and TFC. The improvement of the TWD and TFC changed from 22.8% to 46.9% and from 4.7% to 7.1%, respectively, according to the priorities of the objectives. Additionally, the average number of non-dominated solutions was calculated as 6.94, which presents many feasible solutions for air traffic controllers to manage ground traffic while taking the airline and passenger objectives into consideration.

The proposed MILP model includes the objectives of different stakeholders: air traffic controllers, passengers and airlines. In addition, the proposed model can provide feasible gate and baggage carousel assignments together in a short time. Therefore, the model creates a flexibility for air traffic controllers to re-arrange assignments if any unexpected situations take place.

The proposed MILP model combines the TWD and TFC together for the AGAP problem using the SA. Moreover, the proposed model integrates passenger-oriented and airline-oriented objectives together and reveals the relationships between the objectives in only a short time.

This paper aims to propose an innovative building information modelling (BIM)-based framework for multi-objective and dynamic temporary construction site layout design (SLD), which uses a hybrid approach of systematic layout planning (SLP) and mathematical modelling.

The hybrid approach, which follows a step-by-step process for site layout planning, is designed to facilitate both qualitative and quantitative data collection and processing. BIM platform is usedto facilitate the determination of the required quantitative data, while the qualitative data are generated through knowledge-based rules.

The multi-objective layout model represents two important aspects: layout cost and adjacency score. The result shows that the model meets construction managers’ requirements in not only saving cost but also assuring the preferences of temporary facility relationships. This implies that the integration of SLP and mathematical layout modelling is an appropriate approach to deliver practical multi-objective SLD solutions.

The proposed framework is expected to serve as a solution, for practical application, which takes the advantage of technologies in data collection and processing. Besides, this paper demonstrates, by using numerical experimentation and applying Microsoft Excel Solver for site layout optimisation, how to reduce the complexity in mathematical programming for construction managers.

The original contribution of this paper is the attempt of developing a framework in which all data used for the site layout modelling are collected and processed using a systematic approach, instead of being predetermined, as in many previous studies.

Appropriate logistic planning for the pharmaceutical supply chain can significantly improve many financial and performance aspects. To this aim, a multi-objective optimization model is proposed in this paper that considers different types of pharmaceuticals, different vehicles with determining capacities and multi-period logistic planning. This model can be updated based on new information about resources and newly identified requests.

The main objective function of the proposed model in this paper is minimizing the unsatisfied prioritized requests for pharmaceuticals in the network. Besides, the total transportation activities of different types of vehicles and related costs are considered as other objectives. Therefore, these objectives are optimized hierarchically in the proposed model using the Lexicographic method. This method finds the best value for the first objective function. Then, it tries to optimize the second objective function while maintaining the optimality of the first objective function. The third objective function is optimized based on the optimality of other objective functions, as well. A non-dominated sorting genetic algorithm II-multi-objective particle swarm optimization heuristic method is designed for this aim.

The performances of the proposed model were analyzed in different cases and its results for different problems were shown within the framework of a case study. Besides, the sensitivity analysis of results shows the logical behavior of the proposed model against various factors.

The proposed methodology can be applied to find the best logistic plan in real situations.

In this paper, the authors have tried to use a multi-objective optimization model to guide and correct the pharmaceutical supply chain to deal with the related requests. This is important because it can help managers to improve their plans.

This paper aims to develop a grey decentralized bi-level multi-objective programming (MOP) model. A solution approach is also proposed for the given model. A production and transportation plan for a closed loop supply chain network under an uncertain environment and different scenarios is also developed.

In this paper, we combined grey linear programming (GLP) and fuzzy set theory to present a solution approach for the problem. The proposed model first solves the given problem using GLP. Membership functions for the decision variables under the control of the leader and for the goals are created. These membership functions are then used to generate the final solutions.

This paper provides insight for fomenting the decision-making process while providing a more flexible approach in uncertain logistics problems. The deviations of the final solution from the individual best solutions of the two levels are very little. These deviations can further be reduced by adjusting the tolerances associated with the decision variables under the control of the leader.

The proposed approach uses the concept of membership functions of linear form, and thus, requires less computational efforts while providing effective results. Most of the organizations exhibit decentralized decision-making under the presence of uncertainties. Therefore, the present study is helpful in dealing with such scenarios.

This is the first time, formulation of a decentralized bi-level multi-objective model under a grey environment is carried out as per the best knowledge of the authors. A solution approach is developed for bi-level MOP under grey uncertainty.

The purpose of this paper is to explore why objective setting is often found difficult and consider what to do about it. The paper critically assesses the two main managerial perspectives on objective setting before summarising evidence-based research on what works. Based on this literature review, the paper develops a contingency model of objective setting. It then describes how to use this model in practice.

The paper uses a review of the managerial and evidence-based literature on objective setting to develop a contingency model of objective setting. It describes how this model is operationalised by developing a scale to measure the differences between jobs and the situations they operate in. The model is represented diagramatically. Guidance is given on how to use the model in practice.

Result-centred and process-centred approaches to objective setting are described and critically assessed. Evidence-based research describing the relationship between objective setting and performance is also presented. In general, clear and specific goals that are challenging but realistic have a moderate effect on performance. However, this only holds for straightforward and predictable tasks. When prior knowledge is needed to perform a task or when the task is complex, a general goal, behavioural goal or learning goal is more effective. Parallels between the managerial perspectives and the contrasting situations form the basis of a contingency model of objective setting.

The relevant theory is described and critically examined. This provides useful descriptions of two different ways to go about setting objectives. The conclusions of recent studies and reviews using evidence-based research are described. They establish both what works and when it works. Taken together, these insights provide a foundation on which to develop a contingency model of objective setting.

There is no one right way to set objectives. Different situations require different approaches. It is possible to assess situations and establish the appropriate combination of perspectives. It is then possible to develop an appropriate set of objectives for the situation. Guidance is given on how to use this approach in practice. The overall approach is rooted in theory and evidence-based research.

The application of this model in the workplace can help individuals to perform more effectively. It can also help line managers, learning and development specialists, and human resource professionals to help individuals to perform more effectively. In so doing, the model helps organisations to function more effectively. This has wider implications for the economy and society.

The paper is original in that it brings together both management theory and evidence-based research to develop a contingency model of objective setting. This model as a whole and the method of assessing job characteristics are original.

This study aims to address the inherent uncertainties within closed-loop supply chain (CLSC) networks through the application of a multi-objective approach, specifically focusing on the optimization of integrated production and transportation processes. The primary purpose is to enhance decision-making in supply chain management by formulating a robust multi-objective model.

In dealing with uncertainty, this study uses Pythagorean fuzzy numbers (PFNs) to effectively represent and quantify uncertainties associated with various parameters within the CLSC network. The proposed model is solved using Pythagorean hesitant fuzzy programming, presenting a comprehensive and innovative methodology designed explicitly for handling uncertainties inherent in CLSC contexts.

The research findings highlight the effectiveness and reliability of the proposed framework for addressing uncertainties within CLSC networks. Through a comparative analysis with other established approaches, the model demonstrates its robustness, showcasing its potential to make informed and resilient decisions in supply chain management.

This study successfully addressed uncertainty in CLSC networks, providing logistics managers with a robust decision-making framework. Emphasizing the importance of PFNs and Pythagorean hesitant fuzzy programming, the research offered practical insights for optimizing transportation routes and resource allocation. Future research could explore dynamic factors in CLSCs, integrate real-time data and leverage emerging technologies for more agile and sustainable supply chain management.

This research contributes significantly to the field by introducing a novel and comprehensive methodology for managing uncertainty in CLSC networks. The adoption of PFNs and Pythagorean hesitant fuzzy programming offers an original and valuable approach to addressing uncertainties, providing practitioners and decision-makers with insights to make informed and resilient decisions in supply chain management.