SCIENTIA IRANICA
Year:2024 | Volume:31 | Issue:Transactions on Industrial Engineering (E)11|Papers Count:5

In this paper, a mathematical negotiation mechanism is designed to minimize the negotiators’ costs in a distributed procurement problem at two echelons of an automotive supply chain. The buyer’s costs are procurement cost and shortage penalty in a one-period contract. On the other hand, the suppliers intend to solve a multi-period, multi-product production planning to minimize their costs. Such a mechanism provides an alignment among suppliers’ production planning and order allocation, also supports the partnership with the valued suppliers by taking suppliers’ capacities into account. Such a circumstance has been modeled via bi-level programming, in which the buyer acts as a leader, and the suppliers individually appear as followers in the lower level. To solve this nonlinear bi-level programming model, a hybrid algorithm by combining the particle swarm optimization (PSO) algorithm with a heuristic algorithm based on A* search is proposed. The heuristic A* algorithm is embedded to solve the mixed-integer nonlinear programming (MINLP) sub-problems for each supplier according to the received variable values determined by PSO system particles (buyer’s request for quotations (RFQs)). The computational analyses have shown that the proposed hybrid algorithm called PSO-A* outperforms PSO-SA and PSO-Greedy algorithms.

AbstractProviding appropriate home health care is one of the increasing concerns in the health care organizations. Home Health Care provides various services for disabled or elderly individuals at their homes. Also, deal with the current critical situation of the coronavirus disease (COVID-19) due to the limited capacity of hospitals and the feeling of insecurity in crowded places, home health care is more recommended. This paper addresses a Home Health Care Routing and Scheduling Problem (HHCRSP) with two modes of transportations including public and private modes. Also, multi-depot version of the problem is studied to enhance the service delivery in scattered points. In this study, a mathematical model is presented based on a Mixed Integer Linear P rogramming (MILP) whose objective function is minimization of the sum of the travel distance and overtime costs. Furthermore, three meta-heuristic algorithms including Invasive Weed Optimization (IWO), Grasshopper Optimization Algorithm (GOA) and Simulated Annealing (SA) are presented for solving the large-sized problems. Since the performance of meta-heuristic algorithms depends on setting the parameters, Taguchi method is used to statistically set parameters of the developed algorithms. The computational results have shown that the proposed IWO has worked better than the other two proposed algorithms statistically.

Drone delivery as a novel approach for parcel delivery has been under the focus of many scholars and practitioners. In this regard, this paper introduces a stochastic-fuzzy multi-objective optimization model for designing a last-mile delivery system with drones and ground vehicles. The first two objective functions aim to minimize the detrimental effects of the delivery system on the environment and the total costs. The last objective function maximized the system's reliability by considering the breakdown probability of both drones and ground vehicles. Then, AUGMECON2 is utilized as an exact method to solve the proposed model. besides determining the number of required drones and ground vehicles, the model indicates locations and capacities of facilities where vehicles start their one-to-one trips to meet the customer demands. The proposed model is then validated by applying it to a real case study of an e-commerce company in Karaj, Iran. The findings suggest that the system's total cost rises when the reliability increases and the environmental impacts decrease. Furthermore, when both drones and ground vehicles are considered for meeting the customer demands, the delivery system functions better in terms of costs, environmental impacts, and reliability than when only one mode of delivery is considered.

The success of any remanufacturing system depends on proper supply of used product. In the real-world, many remanufacturers are unable to collect it directly from customers. This article considers a closed-loop green supply chain consisting of a single remanufacturer, two competing retailers and collectors for dealing with used and remanufactured products. Considering the effect of acquisition price and greening level on collection quantity of used product, and the effect of selling price and greening level on the demand, closed-form solutions are derived for a centralized model and two decentralized models depending on collectors' cooperation and competition. Optimal results of the decentralized models are compared to find the best decentralized policy. Moreover, a novel multi-link two-part tariff contract among the channel members is proposed to resolve the channel coordination issue. The numerical analysis indicates that (i) cooperation between the collectors is only beneficial for themselves while competition between them is favorable from the viewpoint of the whole supply chain; (ii) utilization ratio has a noteworthy effect on the performance of the supply chain; (iii) the proposed contract assists the collectors with getting more than double transfer price and the remanufacturer to improve its profit over 21\% compared to the decentralized setting.

This paper proposes a new linearized mathematical model to solve integrated cell formation and job scheduling problem. The model aims to minimize the exceptional elements, voids and the make-span of the jobs. The results of test problems show that the proposed model is very effective to obtain best solutions for small sized problems in reasonable computation times. However, due to the NP-hard nature of the considered problem, the best solutions couldn’t be obtained in acceptable times for large sized test problems whereas the real-life applications of the problem addressed here are often much larger in size. To meet the requirement of solving larger sized problems, Genetic Algorithm, which is, today, considered as one of the artificial intelligence and machine learning technique and Marine Predators Algorithm as a new and a nature-inspired metaheuristic, are proposed. The success of the algorithms was investigated and compared. The test results reveal the fact that the Marine Predators algorithm with optimized parameters has a high potential to solve real life problems. At last, an attempt is made to re-design an existing real-life production system by the proposed algorithms. Eventually, a considerable improvement is obtained on performance compared to the current situation of the system.