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In the traditional agri-fresh food supply chain (AFSC), geographically dispersed small farmers transport their products individually to the market for sale. This leads to a higher transportation cost, which is the primary cause of farmers' low profitability. This paper formulates a traditional product movement problem in AFSC. First, the aggregate product movement model is combined with the vehicle routing model to redesign an existing AFSC (the ETKA Company; the most extensive domestic agri-fresh food supply chain in Iran) based on the available data. For the four-echelon, multi-period supply chain under investigation, a mixed integer linear programming (MILP) model is developed for the location-inventory-routing problem of perishable products via considering the clustering of farmers to minimize the total distribution cost. Considering the complexity of the problem, an efficient and effective \"matheuristic\" is introduced based on hybridizing the Lagrangian relaxation and genetic algorithm (GA). The solution obtained by the proposed \"matheuristic\" algorithm is robust and efficient in comparison with an exact solver, GA, and the Lagrangian relaxation approach individually. The comparison analysis reveals that the location-inventory-routing model is efficient, leading to a reduction in total distribution cost by 33% compared to the existing supply chain. Finally, the findings encourage further development and application of the proposed \"matheuristic\" to solve other complicated location-inventory-routing problems heuristically.

This paper introduces the Golf Sport Inspired Search (GSIS) algorithm as an evolutionary search method for numerical optimization. Each solution is generated with the aid of the step-length and search direction. The step-length is determined with the aid of the Tait’s model of the trajectory of the golf ball, which is a physical model. The search direction is from the current position in the search space toward the position of a different individual or its reflected position. Such a direction determines the movement direction in the optimization process. A crossover operator is introduced to increase exploration at the starting and exploitation at the ending stages of the search. Performance of the GSIS is compared with many algorithms on 23 + 14 unconstrained classic functions, 29 functions of CEC 2017 benchmark suite and six constrained engineering design problems. Experiments indicate that with the aid of its cleverly designed operators, GSIS is able to produce promising results. Besides a cooperative game theoretic approach is introduced, which is able to measure the effectiveness of different operators in reducing the search cost. Such an approach can be used to measure the effectiveness of different operators that an evolutionary or swarm-intelligence algorithm owns.

Coronavirus disease 2019 (COVID-19) has been reported to cause cardiovascular complications such as myocardial injury, thromboembolic events, arrhythmia, and heart failure. Multiple mechanisms—some overlapping, notably the role of inflammation and IL-6—potentially underlie these complications. The reported cardiac injury may be a result of direct viral invasion of cardiomyocytes with consequent unopposed effects of angiotensin II, increased metabolic demand, immune activation, or microvascular dysfunction. Thromboembolic events have been widely reported in both the venous and arterial systems that have attracted intense interest in the underlying mechanisms. These could potentially be due to endothelial dysfunction secondary to direct viral invasion or inflammation. Additionally, thromboembolic events may also be a consequence of an attempt by the immune system to contain the infection through immunothrombosis and neutrophil extracellular traps. Cardiac arrhythmias have also been reported with a wide range of implicated contributory factors, ranging from direct viral myocardial injury, as well as other factors, including at-risk individuals with underlying inherited arrhythmia syndromes. Heart failure may also occur as a progression from cardiac injury, precipitation secondary to the initiation or withdrawal of certain drugs, or the accumulation of des-Arg9-bradykinin (DABK) with excessive induction of pro-inflammatory G protein coupled receptor B1 (BK1). The presenting cardiovascular symptoms include chest pain, dyspnoea, and palpitations. There is currently intense interest in vaccine-induced thrombosis and in the treatment of Long COVID since many patients who have survived COVID-19 describe persisting health problems. This review will summarise the proposed physiological mechanisms of COVID-19-associated cardiovascular complications.

Policy-makers should focus on solar energy due to the increasing energy demand and adverse consequences such as global warming. Conflicting criteria influence choosing the most desirable place to construct a Solar Power Plant (SPP). Researchers have popularized multicriteria decision-making (MCDM) methods because of the potential. Although the simultaneous use of several methods increases the robustness and accuracy of the results, existing methods to integrate MCDM methods mainly consider the same weight for all methods and utilize the alternatives ranking for the final comparison. This paper presents a hybrid decision-making framework to determine the best location for SPPs in Iran using a set of criteria extracted from the literature and expert opinions. An initial list of decision-making alternatives is prepared and evaluated using GIS software in terms of criteria. Decision-makers prioritized the identified alternatives using the MCDM methods, including SWARA and different ranking methods (TOPSIS, TODIM, WASPAS, COPRAS, ARAS, and MULTIMOORA). Finally, the CCSD method aggregates the results and identifies the best location. Results highly correlate with the results of previous methods and demonstrate the robustness of the proposed approach and its capability to overcome the limitations of previous methods.

This paper introduces the three-dimensional bin packing problem with item fragmentation (3D-BPPIF) and explores its application in the storage location assignment problem (SLAP) to efficiently allocate warehouse spaces to product groups. Based on real-world constraints, the aim is to find an effective 3D-packing of the product groups into warehouse storage spaces to minimize the total distance. Given the internal limitations present in many warehouses, the storage spaces are not homogeneous, making the allocation to product groups a challenging task that can reduce space utilization efficiency. Accordingly, to effectively utilize warehouse storage spaces, we developed a MILP formulation incorporating the concepts of shape changeability and item fragmentation, significantly enhancing the flexibility of the arrangements. Due to the NP-hard nature of the problem, we proposed a simulated annealing-based meta-heuristic to solve large-scale real-world problems. Numerous computational experiments prove the validity of the proposed model and illustrate that the proposed algorithm can provide appropriate 3D assignments.

In online direct selling, three effective elements, namely price, refund and quality, affect the increment (or decrement) of demand and product return. This paper considers forward and backward (i.e., return) pricing decisions under uncertainty and develops a fuzzy mathematical model based on the Stackelberg game approach utilizing the proper action and reaction between a manufacturer and a retailer. Moreover, media advertising and manufacturer’s desire for accepting massive payments made us take into account the advertising as another factor influencing the demand. By an agreement between the manufacturer and the retailer, the costs of advertising and raising the level of the product quality are shared by two agreed rates. Two numerical examples are considered and the associated results are analyzed under fuzzy and crisp conditions when customers are sensitive or insensitive to the quality of the product. It is found that incorporation of the quality factor under a fuzzy environment has a better performance compared with the case of ignoring the quality and uncertainty in the parameters.

This research develops a sustainable supply chain model for time-varying deteriorating items with customers’ credit period, customers’ credit amount, promotional efforts, and selling price-dependent demand. The model incorporates joint policies of promotional cost-sharing, three-level trade credit financing, and carbon tax. Under three-level trade credit financing, the supplier and the wholesaler offer some credit periods to the wholesaler and the retailer, respectively. As a result of this opportunity, the retailer allows customers to delay the payment of some portion of the total purchased amount. Here, shortages are assumed to occur in the form of partial backorder. The main objective of this investigation is to minimize the carbon emissions and maximize the joint profit of the retailer and the wholesaler simultaneously. To achieve this, the model is formulated as a Signomial Geometric Programming problem and solved efficiently using a global optimization method. The performance of the developed model and solution method is evaluated through several numerical examples and sensitivity analysis, providing valuable managerial insights. The computed results reveal that the optimal selling price varies depending on the nature of deterioration rates. Constant functions result in higher prices compared to linear and three-parameter Weibull functions. Coordination strategy and promotional cost-sharing policy among supply chain partners are shown to impact profits positively. Additionally, the wholesaler's credit period is a crucial factor influencing pricing decisions, logistics operations, and carbon emissions. The findings further demonstrate that extending the wholesaler's credit period under a carbon tax policy leads to a 26% increase in total joint profit, a 10% decrease in the wholesaler's carbon emissions, and a 21% decrease in the retailer's carbon emissions.

Differential evolution ( DE ) is one of the most powerful stochastic search methods which was introduced originally for continuous optimization. In this sense, it is of low efficiency in dealing with discrete problems. In this paper we try to cover this deficiency through introducing a new version of DE algorithm, particularly designed for binary optimization. It is well-known that in its original form, DE maintains a differential mutation, a crossover and a selection operator for optimizing non-linear continuous functions. Therefore, developing the new binary version of DE algorithm, calls for introducing operators having the major characteristics of the original ones and being respondent to the structure of binary optimization problems. Using a measure of dissimilarity between binary vectors, we propose a differential mutation operator that works in continuous space while its consequence is used in the construction of the complete solution in binary space. This approach essentially enables us to utilize the structural knowledge of the problem through heuristic procedures, during the construction of the new solution. To verify effectiveness of our approach, we choose the uncapacitated facility location problem ( UFLP )—one of the most frequently encountered binary optimization problems—and solve benchmark suites collected from OR-Library. Extensive computational experiments are carried out to find out the behavior of our algorithm under various setting of the control parameters and also to measure how well it competes with other state of the art binary optimization algorithms. Beside UFLP , we also investigate the suitably of our approach for optimizing numerical functions. We select a number of well-known functions on which we compare the performance of our approach with different binary optimization algorithms. Results testify that our approach is very efficient and can be regarded as a promising method for solving wide class of binary optimization problems.

Order picking, which is collecting a set of products from different locations in a warehouse, has repeatedly been described as one of the most laborious and time-consuming internal logistic processes. Each order is issued to pick some products located at given locations in the warehouse. In this paper, we consider an order picking problem, in which a number of orders with different delivery due dates are going to be retrieved by a limited number of order pickers in multiperiods such that the total tardiness is minimized. The aim is to determine a retrieval plan in terms of order batching and order picker multitrip routing as decision variables. Besides, products are arrived and replenished at the predetermined locations at different periods. Therefore, products sitting in those locations should be delivered soon to provide empty rooms for replenishment. A mixed integer linear programming formulation is proposed for this new problem. The model is optimally solved for small-size problems. For larger instances, grouping metaheuristic algorithms are proposed based on particle swarm optimization and the league championship algorithm that use group-based operators to generate reasonable batches of orders. Improvement heuristics are designed as well. The performance of the MILP formulation and metaheuristic algorithms is analyzed for different problem instances whose designs are based on real data gathered from an auto parts warehouse. Results indicate that our algorithms can stably solve large instances of the problem in a reasonable time.

This article considers a biobjective location-routing problem to deliver relief resources to the victims affected by a disaster under uncertainty in demand, transportation infrastructure, and travel time. Since transportation networks are exposed to a considerable level of uncertainty, choosing the reliable path for relief goods to be transmitted to the affected areas ensures the arrival of these supplies. For the first time, route reliability is calculated based on the multistate theory, and the universal generating function technique is used for network reliability assessment. The problem is formulated as a multiperiod robust biobjective mixed-integer programming model. Two objective functions are considered: (a) decreasing the sum of arrival times of relief vehicles at the demand nodes for delivering aids to the affected areas, and (b) increasing the minimum route reliability for all the serving vehicles. A novel multiobjective grouping algorithm is proposed to obtain the Pareto-optimal solutions of the problem. Then, its performance is compared with two other multiobjective grouping algorithms. To evaluate the solution method, the algorithms are implemented on various test problems and compared statistically. A case study is presented to illustrate the potential applicability of our model. Additionally, to determine the effect of the changes in the main parameters of the problem on the value of objective functions, the sensitivity analyses are performed and the managerial insights are given.