A modified priority-based encoding for design of a closed-loop supply chain network using a discrete league championship algorithm

In a closed-loop supply chain network, the aim is to ensure a smooth fow of materials and attaining the maximum value from returning and end-of-life goods. Tis paper presents a single-objective deterministic mixed integer linear programming (MILP) model for the closed-loop supply chain (CLSC) network design problem consisting of plants, collection centers, disposal centers, and customer zones. Our model minimizes the total costs comprising fxed opening cost of plants, collection, disposal centers, and transportation costs of products among the nodes. As supply chain network design problems belong to the class of NP-hard problems, a novel league championship algorithm (LCA) with a modifed priority-based encoding is applied to fnd a near-optimal solution. New operators are defned for the LCA to search the discrete space. Numerical comparison of our proposed encoding with the existing approaches in the literature is indicative of the high quality performance of the proposed encoding

A robust fuzzy mathematical programming model for the closed-loop supply chain network design and a whale optimization solution algorithm

The closed-loop supply chain (CLSC) management as one of the most significant management issues has been increasingly spotlighted by the government, companies and customers, over the past years. The primary reasons for this growing attention mainly down to the governments-driven and environmental-related regulations which has caused the overall supply cost to reduce while enhancing the customer satisfaction. Thereby, in the present study, efforts have been made to propose a facility location/allocation model for a multi-echelon multi-product multi-period CLSC network under shortage, uncertainty, and discount on the purchase of raw materials. To design the network, a mixed-integer nonlinear programming (MINLP) model capable of reducing total costs of network is proposed. Moreover, the model is developed using a robust fuzzy programming (RFP) to investigate the effects of uncertainty parameters including customer demand, fraction of returned products, transportation costs, the price of raw materials, and shortage costs. As the developed model was NP-hard, a novel whale optimization algorithm (WOA) aimed at minimizing the network total costs with application of a modified priority-based encoding procedure is proposed. To validate the model and effectiveness of the proposed algorithm, some quantitative experiments were designed and solved by an optimization solver package and the proposed algorithm. Comparison of the outcomes provided by the proposed algorithm and exact solution is indicative of high quality performance of the applied algorithm to find a near-optimal solution within the reasonable computational time

A food bank network design examining food nutritional value and freshness: a multi objective robust fuzzy model

One main reason for food scarcity is its improper and uneven distribution amongst those who require aid. To overcome this issue, charities and food banks serve as the connection between beneficiaries and donors. They are mostly nonprofit organizations but they incur operational costs for storage and delivery of the donated food items. The donated food items are either canned and cold, or hot meals from over production of businesses; and therefore their freshness, inventory and shelf-life bring about additional operational challenges in distribution and logistics. The uncertainty of demand and supply is another challenge to overcome, which necessitates a robust plan. This article proposes a multi-objective mathematical programming model for a food bank network design to optimize the cost, food freshness and its nutritional value. A robust fuzzy counterpart of the model is developed together with three solution methods including -constraint, MOGWO and NSGA II. The MOGWO algorithm shows a better performance in our numerical experiment with large instances. Its application on a case study resulted in a supply network with lower cost, smaller fleet size and higher food quality, although less fresh distributed foods compared to the benchmark network. The trade-off between the cost and freshness of food is depicted here by examining shelf-life of products and vehicle capacity. The long lasting products incur less transportation cost due to compactness of packaging, and similarly, higher capacity vehicles lead to more cost efficient dispatch with longer routes which decrease the freshness of food. According to our numerical results, higher uncertainty rate in the network increases total cost, but also overall nutritional value of the distributed food over the network due to greater supply of food

A multi-objective robust fuzzy model for food bank network design examining food nutritional value and freshness

To overcome food poverty, charities, and food banks serve as the connection between beneficiaries and donors. They are mostly non-profit organizations and they incur operational costs for storage and delivery of the donated food items. The uncertainty of demand and supply is a challenge to overcome, which necessitates a robust plan. The donated food items are either cold canned food, or hot meals from overproduction of businesses. Thus, their freshness, inventory, and shelf-life cause additional operational challenges in distribution and logistics. Our study proposes a multi-objective mathematical programming model for a food bank network design to optimize the cost, food freshness, and nutritional value. A robust fuzzy counterpart of the model is developed together with three solution methods including epsilon-constraint, MOGWO, and NSGA II. According to our numerical study, the MOGWO algorithm shows a better performance in our large instances. Its application in a case study resulted in a supply network with lower cost, smaller fleet size, and higher food quality, although less freshly distributed foods compared to the benchmark network. The trade-off between the cost and freshness of food is depicted here by examining the shelf-life of products and vehicle capacity. The long-lasting products incur less transportation cost due to the compactness of packaging, and similarly, higher capacity vehicles lead to more cost efficient dispatch with longer routes which decreases the freshness of food

A bi-objective blood supply chain model under uncertain donation, demand, capacity and cost: a robust possibilistic-necessity approach

This paper addresses a multi-objective blood supply chain network design, considering economic and environmental aspects. The objective of this model is to simultaneously minimize a blood supply chain operational cost and its logistical carbon footprint. In order to embed the uncertainty of transportation costs, blood demand, capacity of facilities and carbon emission, a novel robust possibilistic-necessity optimization used regarding a hybrid optimistic-pessimistic form. For solving our bi-objective model, three multi-objective decision making approaches including LP-metric, Goal-Programming and Torabi- Hassini methods are examined. These approaches are assessed and ranked with respect to several attributes using a statistical test and TOPSIS method. Our proposed model can accommodate a wide range of decision-makers’ viewpoints with the normalized objective weights, both at the operational or strategic level. The trade-offs between the cost and carbon emission for each method has been depicted in our analyses and a Pareto frontier is determined, using a real case study data of 21 cities in the North-West of Iran considering a 12-month implementation time window