Designing a new mathematical model based on ABC analysis for inventory control problem: A real case study

In modern business today, organizations that hold large numbers of inventory items, do not find it economical to make policies for the management of individual inventory items. Managers, thus, need to classify these items according to their importance and fit each item to a certain asset class. The method of grouping and inventory control available in traditional ABC has several disadvantages. These shortcomings have led to the development of an optimization model in the present study to improve the grouping and inventory control decisions in ABC. Moreover, it simultaneously optimizes the existing business relationships among revenue, investment in inventory and customer satisfaction (through service levels) as well as a company's budget for inventory costs. In this paper, a mathematical model is presented to classify inventory items, taking into account significant profit and cost reduction indices. The model has an objective function to maximize the net profit of items in stock. Limitations such as budget even inventory shortages are taken into account too. The mathematical model is solved by the Benders decomposition and the Lagrange relaxation algorithms. Then, the results of the two solutions are compared. The TOPSIS technique and statistical tests are used to evaluate and compare the proposed solutions with one another and to choose the best one. Subsequently, several sensitivity analyses are performed on the model, which helps inventory control managers determine the effect of inventory management costs on optimal decision making and item grouping. Finally, according to the results of evaluating the efficiency of the proposed model and the solution method, a real-world case study is conducted on the ceramic tile industry. Based on the proposed approach, several managerial perspectives are gained on optimal inventory grouping and item control strategies

Lorlatinib with or without chemotherapy in ALK-driven refractory/relapsed neuroblastoma: phase 1 trial results.

Neuroblastomas harbor ALK aberrations clinically resistant to crizotinib yet sensitive pre-clinically to the third-generation ALK inhibitor lorlatinib. We conducted a first-in-child study evaluating lorlatinib with and without chemotherapy in children and adults with relapsed or refractory ALK-driven neuroblastoma. The trial is ongoing, and we report here on three cohorts that have met pre-specified primary endpoints: lorlatinib as a single agent in children (12 months to <18 years); lorlatinib as a single agent in adults (≥18 years); and lorlatinib in combination with topotecan/cyclophosphamide in children (<18 years). Primary endpoints were safety, pharmacokinetics and recommended phase 2 dose (RP2D). Secondary endpoints were response rate and 123I-metaiodobenzylguanidine (MIBG) response. Lorlatinib was evaluated at 45-115 mg/m2/dose in children and 100-150 mg in adults. Common adverse events (AEs) were hypertriglyceridemia (90%), hypercholesterolemia (79%) and weight gain (87%). Neurobehavioral AEs occurred mainly in adults and resolved with dose hold/reduction. The RP2D of lorlatinib with and without chemotherapy in children was 115 mg/m2. The single-agent adult RP2D was 150 mg. The single-agent response rate (complete/partial/minor) for <18 years was 30%; for ≥18 years, 67%; and for chemotherapy combination in <18 years, 63%; and 13 of 27 (48%) responders achieved MIBG complete responses, supporting lorlatinib's rapid translation into active phase 3 trials for patients with newly diagnosed high-risk, ALK-driven neuroblastoma. ClinicalTrials.gov registration: NCT03107988

Hybrid meta-heuristic algorithms for optimising a sustainable agricultural supply chain network considering CO2 emissions and water consumption

In this research, a new mixed-integer linear programming (MILP) formulation for the production-distribution-routing problem is developed in a sustainable agricultural product supply chain network (SAPSCN) considering CO2 emission. The objective functions of the SAPSCN model seek to minimise the economic effects containing total cost in SAPSCN and environmental impacts including production and operation emissions, water consumption in production, operational water consumption, and transportation emission, as well as to maximise social impacts including on the number of the created works. Due to the complexity and NP-hardness of the SAPSCN formulation, four multi-objective meta-heuristic algorithms were applied, and two new hybrid meta-heuristic algorithms were developed. To assess the efficiency of the suggested meta-heuristic algorithms, various test instances were used to solve the proposed model and comparisons and sensitivity analyses were carried out with various criteria. A real case study is provided to validate the mathematical model. Finally, the results of the hybrid simulated annealing and particle swarm optimisation algorithm emphasises that it is more robust than other proposed algorithms to solve the problem in a reasonable time

Hybrid meta-heuristic algorithms for a supply chain network considering different carbon emission regulations using big data characteristics

Big data (BD) approach has significantly impacted on the development and expansion of supply chain network management and design. The available problems in the supply chain network (SCN) include production, distribution, transportation, ordering, and inventory holding problems. These problems under the BD environment are challenging and considerably affect the efficiency of the SCN. The drastic environmental and regulatory changes around the world and the rising concerns about carbon emissions have increased the awareness of customers regarding the carbon footprint of the products they are consuming. This has enforced supply chain managers to change strategies to reframe carbon emissions. The decisions such as an optimization of the suitable network of the proper lot sizes can play a crucial role in minimizing the whole carbon emissions in the SCN. In this paper, a new integrated production–transportation–ordering–inventory holding problem for SCN is developed. In this regard, a mixed-integer nonlinear programming (MINLP) model in the multi-product, multi-level, and multi-period SCN is formulated based on the minimization of the total costs and the related cost of carbon emissions. The research also uses a chance-constrained programming approach. The proposed model needs a range of real-time parameters from capacities, carbon caps, and costs. These parameters along with the various sizes of BD, namely velocity, variety, and volume, have been illustrated. A lot-sizing policy along with carbon emissions is also provided in the proposed model. One of the important contributions of this paper is the three various carbon regulation policies that include carbon capacity-and-trade, the strict capacity on emission, and the carbon tax on emissions in order to assess the carbon emissions. As there is no benchmark available in the literature, this study contributes toward this aspect by proposing two hybrid novel meta-heuristics (H-1) and (H-2) to optimize the large-scale problems with the complex structure containing BD. Hence, a generated random dataset possessing the necessary parameters of BD, namely velocity, variety, and volume, is provided to validate and solve the suggested model. The parameters of the proposed algorithms are calibrated and controlled using the Taguchi approach. In order to evaluate hybrid algorithms and find optimal solutions, the study uses 15 randomly generated data examples having necessary features of BD and T test significance. Finally, the effectiveness and performance of the presented model are analyzed by a set of sensitivity analyses. The outcome of our study shows that H-2 is of higher efficiency