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"Scheduling (Management)"
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A worm optimization algorithm to minimize the makespan on unrelated parallel machines with sequence-dependent setup times
This paper addresses the unrelated parallel machine scheduling problem with setup times, with an objective of minimizing the makespan. The machines are unrelated in the sense that the processing speed depends on the job being executed and not the machine. Each job will have different processing times for each of the available machines, is available at the beginning of the scheduling horizon, and can be processed on any of the machines but needs to be processed by one machine only. Sequence-dependent and machine-dependent setup times are also considered. A Worm Optimization (WO) algorithm is introduced and is applied to this NP-hard problem. The novel WO is based on the behaviors of the worm, which is a nematode with only 302 neurons. Nevertheless, these neurons allow worms to achieve several intricate behaviors including finding food, interchanging between solitary and social foraging styles, alternating between dwelling and roaming, and entering a type of stasis/declining stage. WO’s performance is evaluated by comparing its solutions to solutions of six other known metaheuristics for the problem under study, and the extensive computational results indicated that the proposed WO performs best.
Journal Article
Project portfolio selection and scheduling optimization based on risk measure: a conditional value at risk approach
Project portfolios are considered “powerful strategic weapons” for implementing corporate strategy. Projects are exposed to different types of risks. Studies on project portfolio optimization have addressed risks either by maximizing the expected net present value or including constraints that place an upper bound on portfolio risk score. However, no study has attempted to minimize the risk of severe low returns by adopting a risk-averse measure. The present study contributes by addressing this research gap and utilizes a risk measure conditional value at risk (CVaR) for decision making. The present paper considers a case study of a dairy firm. It captures financial risk in the form of uncertain project cash inflows and evaluates strategic alignment scores and risk scores for technical, schedule, economic and political, organizational, and statutory clearance risks of projects using an analytical hierarchy process. Further, it formulates three project portfolio selection and scheduling models namely, risk-neutral (max_E), risk-averse (max_CVaR) and combined compromise (max_E_CVaR) models. A comparison of results shows that the max_CVaR model ensures that the lowest return in the worst scenario is maximized to the greatest extent possible, thereby yielding high returns even when the confidence levels are low. The model exploits the diversification approach for risk management and its portfolios contain at least one project from each project category (derivative, platform and breakthrough). The results obtained using max_E_CVaR model can be utilized by decision makers to select and schedule project portfolios according to their risk appetite and acceptable trade-off between risk-averse and risk-neutral objectives.
Journal Article
Value of information sharing in a multiple producers–distributor supply chain
This paper analyzes the coordination and the value of information sharing in a multiple producers–distributor supply chain, which can be viewed as a special multi-mode resource constrained project scheduling problem. In this problem, many independent manufacturers, which can produce different types of products, coordinate with a resource manager, who provides different types of resources (vehicles) to deliver products to the customers. The impact of sharing four vital pieces of information of this supply chain—(i) full information, (ii) production capacity, (iii) resource constraints, and (iv) basic information—is examined. Five solution approaches which share different levels of information are employed to solve this problem. Results and analysis based on extensive computational experiments with different solution approaches are presented. The results show that information-sharing significantly contributes to the strengthening of coordination in a decentralized supply chain. The basic information is found to have highest impact and the resource constraint information has a higher impact than the production capacity information.
Journal Article
A mixed integer linear programming modelling for the flexible cyclic jobshop problem
This paper addresses the Cyclic Jobshop Problem in a flexible context. The flexibility feature means that machines are able to perform several kinds of tasks. Hence, a solution of the scheduling problem does not only concern the starting times of the elementary tasks, but also the assignment of these tasks to a unique machine. The objective considered in this paper is the minimisation of the cycle time of a periodic schedule. We formulate the problem as a Mixed Integer Linear Problem and propose a Benders decomposition method along with a heuristic procedure to speed up the solving of large instances. It consists in reducing the number of machines available for each task. Results of numerical experiments on randomly generated instances show that the MILP modelling has trouble solving difficult instances, while our decomposition method is more efficient for solving such instances. Our heuristic procedure provides good estimates for difficult instances.
Journal Article
A multi-objective optimization approach to project scheduling with resiliency criteria under uncertain activity duration
Uncertainty is one of the main parts of the project management environment that can strongly affect the project objectives and cause unpredictable delays. This study presents a multi-objective optimization approach for constructing resilient project schedules under resource constraints to cope with uncertain activity durations. In this paper, the concept of resilient project scheduling is defined to measure the ability of schedules to deal with duration disruption. Since the direct evaluation of resiliency is computationally complicated and time-consuming, a new surrogate resilience measure is introduced. The proposed resiliency criteria measure the floating of activities and the risks associated with the completion of the project. Furthermore, a new model based on a combination of time buffer and float allocation approach is developed. To extend existing project scheduling models with uncertainty, general precedence relationships between activities have been considered. To validate the proposed approach, the construction project of a combined cycle power plant is used as a case study. Due to a large number of project activities in this case study, the non-dominated sorting genetic algorithm (NSGA II) has been used to solve the problem. The results of solving the mathematical model using the proposed method are assessed through extensive simulation experiments and compared with those of the baseline schedule. The results show that by taking the proposed resiliency measure and the optimal allocation of buffer time to activities, the project completed at the same duration with higher reliability.
Journal Article