جستجوی مقالات مرتبط با کلیدواژه "mixed integer programming" در نشریات گروه "مواد و متالورژی"

The resilient supply chain considers many capabilities for companies to overcome financial crises and to supply and distribute products. In this study, we address the allocation of inventory distribution for a distribution network, including a factory, a number of potential locations for distribution centers and a number of retailers. Customers demand is assumed to be certain and deterministic for all periods but time varying in the limited planning horizon. The proposed model in this research is a linear complex integer programming model with two-objective functions. The first objective function minimizes the total costs of the entire distribution system in the planning horizon, and the second objective function seeks to minimize the difference between the maximum and minimum distances traveled by vehicles over the planning horizon. Therefore, the model tries to satisfy the demand and at the same time reduce costs using the best route transportation option configuration and transportation option. The routing problem is developed, and as the problem is a NP-hard problem, a meta-heuristic method is used to solve it. In this model, the demand volume for each customer in a period of the network, vehicle capacity, factory capacity, constant transportation cost, variable transportation cost, etc., are considered as factors affecting the model. The results show that the model proposed in the network can be used as a lever to improve the performance of the financial economic supply network through saving in routes.

The ever-increasing demands for surgeries and the limited resources force hospitals to have efficient management of resources, especially the expensive ones like operating rooms (ORs). Scheduling surgeries including sequencing them, assigning resources to them and determining their start times is a complicated task for hospital managers. Surgery referrals usually include elective surgeries that are admitted before the planning horizon of the schedule and emergency surgeries that arrive during this horizon and require fast services. In this paper, we presented a mathematical model for scheduling electives and emergencies. In our model, we considered surgeries as projects with multi-activities. We implemented the Break-in-Moments (BIMs) technique in this structure, which to our best knowledge has not been implemented in the literature before. We examined this method with real data from a medium-sized Norwegian hospital and observed that this method reduces the waiting time of emergencies to be inserted into the schedule without dedicating any OR merely to emergencies. In such a way, this method counterbalances between efficient OR usage and responsiveness for emergency surgeries.

In this paper, a green transportation location problem is considered with uncertain demand parameter. Increasing robustness influences the number of trucks for sending goods and products, caused consequently, increase the air pollution. In this paper, two green approaches are introduced which demand is the main uncertain parameter in both. These approaches are addressed to provide a trade-off between using available trucks and buying new hybrid trucks for evaluating total costs beside air pollution. Due to growing complexity, a Lagrangian decomposition algorithm is applied to find a tight lower bound for each approach. In this propounded algorithm, the main model is decomposed into master and subproblems to speed up convergence with a tight gap. Finally, the suggested algorithm is compared with commercial solver regarding total cost and computational time. Due to computational results for the proposed approach, the Lagrangian decomposition algorithm is provided a close lower bound in less time against commercial solver.

One of the most important goals of disaster management teams is to protect the assets and infrastructures of the community in the event of accidents such as wildfires and floods. This issue requires appropriate operations of all disaster management teams and analysis of available information for suitable decision making and consequently timely response. A mixed integer mathematical model is presented and solved for allocating resources to different districts to protect more assets in an available time. The proposed model tries to protect more valuable assets in pre-determined districts with optimized team allocation strategy. Finally, for validating the model, a numerical example is solved with an exact method and the results of various sensitivity analyses have been reported. The computational results indicate the efficiency and applicability of the proposed model in real conditions comparing to existing classic models.

This paper addresses an unrelated multi-machine scheduling problem with sequence-dependent setup time, release date and processing set restriction to minimize the sum of weighted earliness/tardiness penalties and the sum of completion times, which is known to be NP-hard. A Mixed Integer Programming (MIP) model is proposed to formulate the considered multi-criteria problem. Also, to solve the model for real-sized applications, a Pareto-based algorithm, namely controlled elitism non-dominated sorting genetic algorithm (CENSGA), is proposed. To validate its performance, the algorithm is examined under six performance metric measures, and compared with a Pareto-based algorithm, namely NSGA-II. The results are statistically evaluated by the Mann–Whitney test and t-test methods. From the obtained results based on the t-test, the proposed CENSGA significantly outperforms the NSGA-II in four out of six terms. Additionally, the statistical results from Mann–Whitney test show that the performance of the proposed CENSGA is better than the NSGA- II in two out of six terms. Finally, the experimental results indicate the effectiveness of the proposed algorithm for different problems.

In this study, a two echelons supply chain system in which a supplier is producing perishable product and distribute it to multiple customers is considered. By allowing lateral transshipment mechanism, it is also possible to deliver products to some customers in some periods in bulk, then customers using their own vehicle to transship goods between each other seeking further reduction in the overall cost. The aim here is minimizing the production, inventory carrying cost, and distribution as the first objective, and transshipment cost as the second objective, which is contrary objectives, without facing any shortage anywhere in the chain during the planning horizon. This problem is formulated as a bi-objectives mixed integer programming (BOMIP), and then a proper Pareto front as a set of multiple decision alternatives is provided using NSGAII and NRGA approach. Novelty of this research is providing a bi-objectives mathematical modeling of perishable product inventory routing with production and transshipment (BO-P-PIRPT) that help the decision maker to choose the best mixture of routing and transshipment.

Switchgrass is known as one of the best second generation lignocellulosic feedstock for bioethanol production. Designing an efficient switchgrass-based bioethanol supply chain (SBSC) is an essential requirement for commercialization of bioethanol production. This paper presents a mixed integer linear programming (MILP) model to design SBSC in which the bioethanol demand is under ARMA time series models. It is studied how ARMA time series structure of bioethanol demand affect the supply chain design. A case study based on North Dakota state in the United States demonstrates application of the proposed model to design the most optimal SBSC.in addition, to provide insights for efficiently designing the SBSC, the ARMA models of bioethanol demand is used to forecast SBSC design for the period 2013 to 2020.

In this paper, we study the location routing problem with intermediate replenishment facilities (LRPIRF), an extension of the location routing problem (LRP), where the vehicles can replenish at some intermediate facilities. Vehicles leave the depot with load on-board, serve customers until out of load, may return to an intermediate facility to replenish, and finally return to the depot, completing their route. In this paper, we initiate a mathematical mixed integer programming model with new kind of subtour elimination constraints for this problem. Moreover, the facility location phase is consideredbesides vehicle routing phase in our problem. The objective of the problem is to find routes for vehicles to serve all customers at a minimal cost in terms of total travel cost and total facility location cost, without violating the capacity constraint of the vehicles. The solution to the LRPIRF is obtained through CPLEX solver in commercial software GAMS 23.5.1, Genetic Algorithm and Tabu Search algorithm. Computational results are obtained on a set of randomly generated instances and indicate the effectiveness of the proposed algorithms in terms of solution time and quality.