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© 2017 Elsevier Ltd In this paper, we consider Resource Constrained Project Scheduling Problems (RCPSPs) with known deterministic renewable resource requirements but uncertain activity durations. In this case, the activity durations are represented by random variables with different probability distribution functions. To deal with this problem, we propose an approach based on the robust optimization concept, which produces reasonably good solutions under any likely input data scenario. Depending on different uncertainty characteristics, we have developed six different heuristics to incorporate the uncertain duration as a deterministic constraint in a robust optimization model. The resulting optimization model is then solved by using a Coin-Branch & Cut (CBC) solver. To judge the performance of the algorithm, we solved 30, 60, 90 and 120-activity benchmark problems from the project scheduling problem library (PSPLIB). Our proposed approach guarantees the feasibility of solutions and produces high-quality solutions, particularly for larger activity instances, compared to other existing approaches.

A real-world make-to-stock production system has been studied.We modified the existing model for this problem.A new genetic algorithm based Real-Time strategy has been proposed to solve the problem.Performance of the proposed approach has been tested against the traditional approaches.Numerical results illustrate that the proposed approach helps to reduce the production costs. The permutation flow shop scheduling is a well-known combinatorial optimization problem that arises in many manufacturing systems. Over the last few decades, permutation flow shop problems have widely been studied and solved as a static problem. However, in many practical systems, permutation flow shop problems are not really static, but rather dynamic, where the challenge is to schedule n different products that must be produced on a permutation shop floor in a cyclical pattern. In this paper, we have considered a make-to-stock production system, where three related issues must be considered: the length of a production cycle, the batch size of each product, and the order of the products in each cycle. To deal with these tasks, we have proposed a genetic algorithm based lot scheduling approach with an objective of minimizing the sum of the setup and holding costs. The proposed algorithm has been tested using scenarios from a real-world sanitaryware production system, and the experimental results illustrates that the proposed algorithm can obtain better results in comparison to traditional reactive approaches.

The Multi-Mode Resource Constrained Project Scheduling Problem (MMRCPSP) is a challenging NP-hard optimization problem, that schedules activities under a set of resource constraints. Although, over the last few decades, different solution approaches have been proposed, no single algorithm has consistently been the best for a wide range of MMRCPSPs. In this paper, we have proposed an effective hybrid algorithm, in which two multi-operator evolutionary algorithms perform sequentially under two sub-populations, with their sizes dynamically adapted based on their performance during the evolutionary process. In addition, two heuristics are proposed, the first one is based on a linear programming approach with an aim to obtain feasible modes, while the second one is based on a modified forward and backward justification approach with an aim of obtaining feasible schedules. Also, a classification technique is used to determine the complexity of a given problem, based on its resource’s availability. The proposed approach is tested by solving a wide-range of multi-mode resource-constrained project scheduling problems, including available larger test problems, with the results revealing that the proposed method outperforms well-known algorithms.

Considered an aggregate production planning problem with imprecise parameters.Developed a mathematical model for representing the problem.A variant of PSO algorithm is proposed to solve the model.A case study is conducted to demonstrate the usefulness of the proposed approach.Our approach provides better solutions than the known approaches compared. Development of an Aggregate Production Plan (APP), the top most level in a hierarchical production planning system, is a difficult task, especially when input and other production planning parameters are uncertain because of their inherent impreciseness. This therefore makes generation of a master production schedule highly complex. Regarding this point, in this paper, we present a scheme of a multi-period and multi-product APP which is formulated as an integer linear programming model. The proposed approach uses a triangular possibility distribution for handling all the imprecise operating costs, demands, and also for the capacity data. The proposed approach uses the strategy of simultaneously minimizing the most possible value of the imprecise total costs, maximizing the possibility of obtaining lower total costs and minimizing the risk of obtaining higher total costs. A modified variant of a possibilistic environment based particle swarm optimization (PE-PSO) approach is used to solve the APP model. A numerical model for demonstrating the feasibility of the proposed model is also carried out. In the computational study, the considered case study data were experimented with and analyzed to evaluate the performance of the PE-PSO over both a standard genetic algorithm (GA) and a fuzzy based genetic algorithm (FBGA). The experimental results demonstrate that the PE-PSO variant provides better qualities in the aspects of its accuracy when compared to the other two algorithms.

A new real-time disruption recovery plan is developed.An imperfect production-inventory system is considered.A new mathematical and dynamic solution approach is developed for managing disruptions.We solved the problem for the both single and multiple disruptions on a real-time basis.We compared the results for a good number of randomly generated disruption test problems. In this paper, a disruption recovery model is developed for an imperfect single-stage production-inventory system. For it, the system may unexpectedly face either a single disruption or a mix of multiple dependent and/or independent disruptions. The system is usually run according to a user defined production-inventory policy. We have formulated a mathematical model for rescheduling the production plan, after the occurrence of a single disruption, which maximizes the total profit during the recovery time window. The model thereby generates a revised plan after the occurrence of the disruption. The mathematical model, developed for a single disruption, is solved by using both a pattern search and a genetic algorithm, and the results are compared using a good number of randomly generated disruption test problems. We also consider multiple disruptions, that occur one after another as a series, for which a new occurrence may or may not affect the revised plan of earlier occurrences. We have developed a new dynamic solution approach that is capable of dealing with multiple disruptions on a real-time basis. Some numerical examples and a set of sensitivity analysis are presented to explain the usefulness and benefits of the developed model. The proposed quantitative approach helps decision makers to make prompt and accurate decisions for managing disruption.

Supply chains are becoming increasingly competitive and complex in order to effectively meet customer demands. These characteristics make supply chains vulnerable to various risks, including disruptions. In this study, a recovery model is explored for a two-stage production and inventory system with the possibility of transportation disruption. This model is capable of determining the optimal ordering and production quantities during the recovery window, and ensuring that the total relevant costs are minimized, while seeking to recover the original schedule. An efficient heuristic was developed to solve the model. The results showed that the optimal recovery schedule is highly dependent on the relationship between the backorder cost and the lost sales cost parameters. In addition, the heuristic was able to give quality solutions for the model, with very small deviations of the heuristic solutions from the optimal value. Such tools are useful in assisting managers towards effective decision making, particularly in determining the optimal recovery strategy for the longevity and sustainability of their firms undergoing disruptions.