Articles
Publication Date: 2025
International Journal of Management Science and Engineering Management (17509653)20(2)pp. 215-224
In this study, machine scheduling with variable capacity over time (SVCap) is investigated. The machine capacity is the maximum number of jobs that a machine can process at a time which can be either fixed or variable over time. In common machine scheduling problems, it is assumed that one machine can process one job at a time. However, in variable machine capacity, multiple jobs can be processed on a machine simultaneously. Unlike the current research, a mathematical formulation is not developed yet for solving this problem. In order to solve the problem, a novel mixed integer linear programming (MILP) is proposed. In addition, the SVCap is regarded as a special type of resource-constrained project scheduling problem (RCPSP). Thus, the discrete-time (DT) formulation is generalized to solve the SVCap. In these formulations, the total tardiness is minimized as the objective function. Proposed models are implemented on an irrigation scheduling problem in which water resources are allocated to each plot of farmland. The computational performances of proposed formulations are evaluated on problem instances with different sizes. Results show that the proposed formulations solved all problem instances. The results demonstrate that the proposed MILP formulation is more efficient than the generalized DT formulation in both solution quality and runtimes. © 2024 International Society of Management Science and Engineering Management.
Publication Date: 2024
Computers and Industrial Engineering (03608352)193
Designing an efficient production planning system will reduce production costs, increase customer satisfaction, and ensure sustainability in the global competitive market. This paper focuses on multi-objective optimization in production planning through the utilization of the Demand-Driven material requirement planning (DDMRP) approach. We employ a simulation–optimization method that uses a novel hybrid genetic algorithm and particle swarm optimization, to simultaneously optimize conflicting inventory cost and stockout objectives. To the best of our knowledge, there are rarely studies in the literature that optimized and reviewed the suitable value of lead time and variability factors in this approach. For the first time, this study considers the integration of the strategic level (strategic buffer positioning phase) and operational level (planning phase) of the DDMRP approach due to the interaction of these two issues. This study focuses on the subjective to prove the performance of the DDMRP approach by eliminating its effect. The results demonstrate that an integrated review of both the strategic and operational levels in determining the lead time and variability factors results in lower costs. The average inventory cost, stockout, and total cost are reduced by 21%, 68%, and 78% respectively, compared with the results of the state-of-the-art methods. © 2024
Publication Date: 2023
Journal of Modelling in Management (17465672)18(2)pp. 285-317
Purpose: This paper aims to minimize the total carbon emissions and costs and also maximize the total social benefits. Design/methodology/approach: The present study develops a mathematical model for a closed-loop supply chain network of perishable products so that considers the vital aspects of sustainability across the life cycle of the supply chain network. To evaluate carbon emissions, two different regulating policies are studied. Findings: According to the obtained results, increasing the lifetime of the perishable products improves the incorporated objective function (IOF) in both the carbon cap-and-trade model and the model with a strict cap on carbon emission while the solving time increases in both models. Moreover, the computational efficiency of the carbon cap-and-trade model is higher than that of the model with a strict cap, but its value of the IOF is worse. Results indicate that efficient policies for carbon management will support planners to achieve sustainability in a cost-effectively manner. Originality/value: This research proposes a mathematical model for the sustainable closed-loop supply chain of perishable products that applies the significant aspects of sustainability across the life cycle of the supply chain network. Regional economic value, regional development, unemployment rate and the number of job opportunities created in the regions are considered as the social dimension. © 2021, Emerald Publishing Limited.
Publication Date: 2023
International Journal on Interactive Design and Manufacturing (19552513)17(6)pp. 3305-3319
This research extends the constrained vehicle routing problem concept to solve flexible flow shop scheduling problems. Mixed-integer linear programming and constraint programming formulations are developed for a flow shop problem with no-wait, time lags and release time restrictions to minimize the makespan in both permutation and non-permutation schedules. The comparative analysis of various models reveals that constraint programming models have superior computational performance than mixed-integer linear programming models. However, the mixed-integer linear programming models are also timely-efficient. Moreover, the efficiency of developed models is also represented in comparison with several benchmark datasets. Based on the findings, while the objective function values of the mixed-integer linear programming and constraint programming models in non-permutation schedules exhibit lower values than their respective equivalents in permutation schedules, both models demonstrate longer runtime in non-permutation schedules. Results represent that the proposed constraint programming and mixed-integer linear programming models are among the top three models of the benchmark datasets in terms of the number of decision variables and computational performance. One of the limitations of the research is that there is no comprehensive dataset in the literature considering all the restrictions in permutation and non-permutation schedules. © 2023, The Author(s), under exclusive licence to Springer-Verlag France SAS, part of Springer Nature.
