scholarly journals Multi-Objective Optimization Models for Sustainable Perishable Intermodal Multi-Product Networks with Delivery Time Window

Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 379 ◽  
Author(s):  
Chia-Nan Wang ◽  
Nhat-Luong Nhieu ◽  
Yu-Chi Chung ◽  
Huynh-Tram Pham
Keyword(s):  
Supply Chain ◽  
Time Window ◽  
Mekong Delta ◽  
Network Design Problem ◽  
Future Research ◽  
Perishable Products ◽  
Delivery Time ◽  
Objective Functions ◽  
Multi Objective ◽  
Uncertainty Parameters

Supply chain network design problem is increasingly showing its importance, especially the perishable supply chain. This research develops a multi-objective mathematical model to design four-echelon intermodal multi-product perishable supply chain configuration in order to ensure a balance of the three pillars of sustainable development: economy, environment, and society. The optimization objective functions of the model are, respectively, minimizing costs, delivery time, emissions, and the supply-demand mismatch in time. The model addresses particular problems in the supply chain of fresh fruits, which is more challenging compared to other types of perishable products due to its seasonal characteristics. The study proposes a new approach that combines and standardizes the above objective functions into a single weighted objective function. The solution from the model supports the decision-making process at both strategic and tactical levels. Strategically, the model supports decisions about the location, size of facilities, product flows, and workforce level. Tactically, the decision variables provide information on harvest time, delivery time, the delivery route, and mode of transport. To demonstrate its practical applicability, the model is applied to Mekong Delta region, Vietnam, where a variety of fruit types, large yields, and high distribution demand in this region make designing a shared supply chain desirable for its overall economic, environmental, and social concerns. Moreover, sensitivity analysis regarding weights of different objectives is performed to assess possible changes in supply chain configurations. Application of this model to other perishable products, the addition of modes of transport, social policy, and uncertainty parameters may be suggested for future research.

scholarly journals Inventory Routing Problem in Supply Chain of Perishable Products under Cost Uncertainty

Mathematics ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 382 ◽  
Author(s):  
Muhammad Imran ◽  
Muhammad Salman Habib ◽  
Amjad Hussain ◽  
Naveed Ahmed ◽  
Abdulrahman M. Al-Ahmari
Keyword(s):  
Mathematical Model ◽  
Supply Chain ◽  
Surgical Instruments ◽  
Perishable Products ◽  
Inventory Routing ◽  
Routing Problem ◽  
Cost Uncertainty ◽  
Priority Index ◽  
Inventory Routing Problem ◽  
Multi Objective

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.

scholarly journals A robust multi-objective model for managing the distribution of perishable products within a green closed-loop supply chain

2021 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
Maedeh Agahgolnezhad Gerdrodbari ◽  
Fatemeh Harsej ◽  
Mahboubeh Sadeghpour ◽  
Mohammad Molani Aghdam
Keyword(s):  
Supply Chain ◽  
Closed Loop ◽  
Perishable Products ◽  
Closed Loop Supply Chain ◽  
Multi Objective ◽  
Objective Model

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

2021 ◽  
Author(s):  
Ali Mahmoodirad ◽  
Behzad Aghaei Fishani ◽  
Sadegh Niroomand ◽  
Mohammad Fallah
Keyword(s):  
Supply Chain ◽  
Social Impact ◽  
Heuristic Algorithms ◽  
Supply Chain Design ◽  
Grey Wolf Optimizer ◽  
Test Problems ◽  
Perishable Products ◽  
Heuristic Solution ◽  
Routing Problem ◽  
Multi Objective

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.

scholarly journals A Fuzzy Goal Programming Approach for Solving Multi-Objective Supply Chain Network Problems with Pareto-Distributed Random Variables

2019 ◽  
Vol 27 (04) ◽  
pp. 559-593 ◽  
Author(s):  
Vincent Charles ◽  
Srikant Gupta ◽  
Irfan Ali
Keyword(s):  
Supply Chain ◽  
Goal Programming ◽  
Fuzzy Goal Programming ◽  
Programming Approach ◽  
Supply Chain Network ◽  
Delivery Time ◽  
Demand And Supply ◽  
Multi Objective ◽  
Weighted Goal Programming ◽  
Fuzzy Goal

Uncertainty is unavoidable and addressing the same is inevitable. That everything is available at our doorstep is due to a well-managed modern global supply chain, which takes place despite its efficiency and effectiveness being threatened by various sources of uncertainty originating from the demand side, supply side, manufacturing process, and planning and control systems. This paper addresses the demand- and supply-rooted uncertainty. In order to cope with uncertainty within the constrained multi-objective supply chain network, this paper develops a fuzzy goal programming methodology, with solution procedures. The probabilistic fuzzy goal multi-objective supply chain network (PFG-MOSCN) problem is thus formulated and then solved by three different approaches, namely, simple additive goal programming approach, weighted goal programming approach, and pre-emptive goal programming approach, to obtain the optimal solution. The proposed work links fuzziness in transportation cost and delivery time with randomness in demand and supply parameters. The results may prove to be important for operational managers in manufacturing units, interested in optimizing transportation costs and delivery time, and implicitly, in optimizing profits. A numerical example is provided to illustrate the proposed model.

scholarly journals Metaheuristics in the Humanitarian Supply Chain

Algorithms ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 364
Author(s):  
Francisca Santana Robles ◽  
Eva Selene Hernández-Gress ◽  
Neil Hernández-Gress ◽  
Rafael Granillo Macias
Keyword(s):  
Supply Chain ◽  
Computational Time ◽  
Future Research ◽  
Objective Functions ◽  
Swarm Optimization ◽  
Solution Algorithms ◽  
Research Questions ◽  
Humanitarian Supply Chain ◽  
Mass Evacuation ◽  
Post Disaster

Everyday there are more disasters that require Humanitarian Supply Chain (HSC) attention; generally these problems are difficult to solve in reasonable computational time and metaheuristics (MHs) are the indicated solution algorithms. To our knowledge, there has not been a review article on MHs applied to HSC. In this work, 78 articles were extracted from 2016 publications using systematic literature review methodology and were analyzed to answer two research questions: (1) How are the HSC problems that have been solved from Metaheuristics classified? (2) What is the gap found to accomplish future research in Metaheuristics in HSC? After classifying them into deterministic (52.56%) and non-deterministic (47.44%) problems; post-disaster (51.28%), pre-disaster (14.10%) and integrated (34.62%); facility location (41.03%), distribution (71.79%), inventory (11.54%) and mass evacuation (10.26%); single (46.15%) and multiple objective functions (53.85%), single (76.92%) and multiple (23.07%) period; and the type of Metaheuristic: Metaphor (71.79%) with genetic algorithms and particle swarm optimization as the most used; and non-metaphor based (28.20%), in which search algorithms are mostly used; it is concluded that, to consider the uncertainty of the real context, future research should be done in non-deterministic and multi-period problems that integrate pre- and post-disaster stages, that increasingly include problems such as inventory and mass evacuation and in which new multi-objective MHs are tested.

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