scholarly journals Multi-Objective Location-Allocation-Routing Problem of Perishable Multi-Product Supply Chain With Direct Shipment and Open Routing Possibilities Under Sustainability

Author(s):  
Ali Mahmoodirad ◽  
Behzad Aghaei Fishani ◽  
Sadegh Niroomand ◽  
Mohammad Fallah

Abstract In this study a multi-objective formulation is proposed for designing a supply chain of perishable products including suppliers, plants, distributors, and customers under sustainable development. In addition to the studies of the literature, direct shipment between producers and customers and also alternative products possibility are allowed. In this problem the objectives like facilities establishment costs, transportation costs, negative environmental impacts, and social impact (fixed and variable employment rates) are optimized simultaneously. As in real situations, most of the transportation activities of such supply chain are performed by hiring transportation devices, the open routing logic is applied to form the travelling path of each hired transportation device. Furthermore, the possibility of direct shipment from the plants to the customers is considered in order to increase profitability of the plants. Because of the NP-hard nature of the supply chain design problems, some meta-heuristic solution approaches of the literature are modified to multi-objective form and applied to solve the problem. Several test problems from small to large sizes are generated randomly to evaluate the meta-heuristic algorithms. As a result, among the proposed algorithms, the multi-objective grey wolf optimizer (MGWO) perform better than others by considering four well-known evaluation metrics. At the end, a case study from perishable products supply chain of Iran is solved and analyzed to show the applicability of the proposed problem.

Mathematics ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 382 ◽  
Author(s):  
Muhammad Imran ◽  
Muhammad Salman Habib ◽  
Amjad Hussain ◽  
Naveed Ahmed ◽  
Abdulrahman M. Al-Ahmari

This paper presents a multi-objective, multi-period inventory routing problem in the supply chain of perishable products under uncertain costs. In addition to traditional objectives of cost and greenhouse gas (GHG) emission minimization, a novel objective of priority index maximization has been introduced in the model. The priority index quantifies the qualitative social aspects, such as coordination, trust, behavior, and long-term relationships among the stakeholders. In a multi-echelon supply chain, the performance of distributor/retailer is affected by the performance of supplier/distributor. The priority index measures the relative performance index of each player within the supply chain. The maximization of priority index ensures the achievement of social sustainability in the supply chain. Moreover, to model cost uncertainty, a time series integrated regression fuzzy method is developed. This research comprises of three phases. In the first phase, a mixed-integer multi-objective mathematical model while considering the cost uncertainty has been formulated. In order to determine the parameters for priority index objective function, a two-phase fuzzy inference process is used and the rest of the objectives (cost and GHG) have been modeled mathematically. The second phase involves the development of solution methodology. In this phase, to solve the mathematical model, a modified interactive multi-objective fuzzy programming has been employed that incorporates experts’ preferences for objective satisfaction based on their experiences. Finally, in the third phase, a case study of the supply chain of surgical instruments is presented as an example. The results of the case provide optimal flow of products from suppliers to hospitals and the optimal sequence of the visits of different vehicle types that minimize total cost, GHG emissions, and maximizes the priority index.


Author(s):  
Javad Ansarifar ◽  
Reza Tavakkoli-Moghaddam ◽  
Faezeh Akhavizadegan ◽  
Saman Hassanzadeh Amin

This article formulates the operating rooms considering several constraints of the real world, such as decision-making styles, multiple stages for surgeries, time windows for resources, and specialty and complexity of surgery. Based on planning, surgeries are assigned to the working days. Then, the scheduling part determines the sequence of surgeries per day. Moreover, an integrated fuzzy possibilistic–stochastic mathematical programming approach is applied to consider some sources of uncertainty, simultaneously. Net revenues of operating rooms are maximized through the first objective function. Minimizing a decision-making style inconsistency among human resources and maximizing utilization of operating rooms are considered as the second and third objectives, respectively. Two popular multi-objective meta-heuristic algorithms including Non-dominated Sorting Genetic Algorithm and Multi-Objective Particle Swarm Optimization are utilized for solving the developed model. Moreover, different comparison metrics are applied to compare the two proposed meta-heuristics. Several test problems based on the data obtained from a public hospital located in Iran are used to display the performance of the model. According to the results, Non-dominated Sorting Genetic Algorithm-II outperforms the Multi-Objective Particle Swarm Optimization algorithm in most of the utilized metrics. Moreover, the results indicate that our proposed model is more effective and efficient to schedule and plan surgeries and assign resources than manual scheduling.


2021 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
Maedeh Agahgolnezhad Gerdrodbari ◽  
Fatemeh Harsej ◽  
Mahboubeh Sadeghpour ◽  
Mohammad Molani Aghdam

Sign in / Sign up

Export Citation Format

Share Document