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Sustainability is one of the most significant problems in today's world and the concept of sustainable development is being an important objective in construction projects. Since the large projects have enormous and different activities that should be scheduled by considering resource constraint and precedence relationship between them, the resource-constrained project scheduling problem (RCPSP) is a NP-hard problem. In this study, fuzzy project scheduling model is presented to solve RCPSP under uncertainty in availability of the resources and activities duration simultaneously. Subcontractors have incentive to share their resources in the form of coalition in order to reduce activities time and cost. In this research, cooperative game methods are introduced for fair allocating utility of the project, as well as, present supper-additivity, stability and satisfaction level of each coalition. Finally, sustainability concept is analyzed in RCPSP and cooperation of subcontractors in a coalition form, whereas in the previous researches these topics addressed separately or generally in project scheduling to minimize the makespan and maximize the profit of project. The results of the proposed model indicate that the subcontractors can obtain more profit and the balance between sustainability indicators in project management arises by collaboration. [ABSTRACT FROM AUTHOR]

Production scheduling has a considerable impact on productivity and resource assignment. In many situations, every job has an owner, which is called an agent. Since the agents are independent and selfish, it is possible that they have not any incentive to cooperate. Scheduling games help us to understand interactions between the agents. In this study, we consider a real firm with a flow shop manufacturing system that receives various orders from different agents so that each order belongs to a unique agent and includes some jobs. We propose a Nash bargaining model to find a compromise solution among agents. We suppose the utilities of the agents in disagreement point are non-deterministic. Therefore, to overcome this problem, we used linear programming with interval coefficients in order to find the best and the worst Nash bargaining solution. To find a compromised solution, we propose an improved genetic algorithm and compare it with other meta-heuristic algorithms. The comparisons indicate that the proposed algorithm has a good potential to evaluation of Nash bargaining problem in hybrid flow shop environment. Based on the results to reach an agreement between agents, it is required to create a trade-off between usage rates of fastest machines at each stage especially in bottleneck stages and total processing time of orders. The results indicate that the Nash bargaining solution is suitable to solve real-life agent-based production scheduling with the consideration of interactions among the agents when disagreement points are under uncertainty. [ABSTRACT FROM AUTHOR]

Metaheuristics have been extensively used for solving difficult optimization problems in recent years. However, according to the famous theory (No Free Lunch), it is infeasible for a metaheuristic to optimally solve all problems. As a result, novel metaheuristics have been incessantly introduced. In this paper, we provide a bibliography on recent development in metaheuristics. This work lists and categorizes 112 metaheuristic articles published from 2009 to 2015. The key aim of this work is to gather a group of metaheuristics that plays a fundamental role in development of metaheuristics in the coming years. This paper reveals that the growth of new metaheuristics continues as well as has not seen any interruption. Although this study cannot assert to be comprehensive, it involves a main part of novel publications and therefore, is a helpful guide for new metaheuristic researches. [ABSTRACT FROM AUTHOR]

• Proposing a competitive approach in hybrid flow shop scheduling. • Using multiple leaders and multiple followers game for hybrid flow shop scheduling. • Using a competitive genetic algorithm for evaluating Nash equilibrium. It is logical that organizations pay more attention to their major customers which have more impacts on their abilities or benefits. In many real cases, decision makers in scheduling system must prioritize the sequence of processing orders according to the agent of orders while the classic production scheduling do not have the ability to consider the relationships between agents. These problems can be modeled by the Stackelberg problems in the field of game theory. In this study, an agent-based hybrid flow shop problem is formulated by using the multi-leader multi-follower mathematical model. Each level is evaluated by Nash equilibrium and Nash bargaining with the new co-evolutionary and genetic algorithm. The proposed model is validated in a real case problem in the tire manufacturing industry. The results show that Nash equilibrium is superior to Nash bargaining for all agents. [ABSTRACT FROM AUTHOR]

• Proposing a game-based simulation–optimization approach in HFS scheduling. • Using bargaining approach to determine the optimal priority processing rule in HFS. • Using scheduling game in an HFS exposed to the stochastic failure of machinery. • The efficiency of the approach due to the significant reduction of makespan. Dynamic scheduling using real-time data in manufacturing systems enables quick response to unforeseen system events to reduce costs and makespan while increasing customer satisfaction. Since many production systems are multi-product, each product's customers aim to receive the product in the shortest possible time, thus competing with each other. Extant research neglects to consider not only competition between customers, but also bargaining strategies. In this paper, a hybrid flow shop system with multi-product is regarded. The production system studied is the Alborz Tire Company (Iran), which uses multi-type machines subject to stochastic failure. The objective is to determine the product processing prioritization in workstations, based on the Nash bargaining model, to minimize makespan. To this end, a simulation–optimization approach based on discrete-event simulation and Simulated annealing is employed. The results of the case study show that makespan is reduced significantly for all players. [ABSTRACT FROM AUTHOR]