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

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

Integrated capture process for purification of plasmid DNA based on aqueous two phase separation

Facility systems may be vulnerable to a disaster, whether caused by intention, an accident, or by an act of nature. When disrupting events do occur, services may be degraded or even destroyed. This chapter addresses problems of disruption associated with facility based service systems. Three main questions often arise when dealing with a possible disaster: 1) how bad can it get? 2) is there a way in which we can protect our system from such an outcome? and 3) is there a way in which we can incorporate such issues in our future designs and plans? This chapter addresses each of these main questions with respect to several classic location problems. Specifically, it discusses recent location models under disaster events along three main streams of research: facility interdiction, facility protection, and resilient design