Showing 3 results for Mousavi
Masoud Rabbani, Zahra Mousavi,
Volume 30, Issue 1 (IJIEPR 2019)
Abstract
In today's world, natural disasters such as earthquakes, floods, crises such as terrorist attacks and protests threaten the lives of many people. Hence, in this research we present a mathematical modeling that provide efficient and effective model to locate temporary depot, equitable distribution of resources and movement of injured people to health centers, with the aim of developing the multi-objective model and considering multiple central depot, multiple temporary depot and several type of relief items in the model . This paper is considered certainty state and uncertainty of influencing parameters of the models in robust optimization for three different levels uncertainty and in different size with consideration of traditional goals function and humanitarian purposes functions simultaneously. The model has been solved with multi-objective Particle Swarm optimization algorithm (MOPSO) and GAMS software to validate the model. Some numerical examples are presented. In Addition, we present sensitivity analyzes of model and study the relationship of the number of temporary depot location and the number of injured people to move to health centers and the number of uncovered damaged points.
Seyedhamed Mousavipour, Hiwa Farughi, Fardin Ahmadizar,
Volume 30, Issue 3 (IJIEPR 2019)
Abstract
Sequence dependent set-up times scheduling problems (SDSTs), availability constraint and transportation times are interesting and important issues in production management, which are often addressed separately. In this paper, the SDSTs job shop scheduling problem with position-based learning effects, job-dependent transportation times and multiple preventive maintenance activities is studied. Due to learning effects, jobs processing times are not fixed during plan horizon and each machine has predetermined number of preventive maintenance activities. A novel mixed integer linear programming model is proposed to formulate the problem for minimizing Make Span. Owing to the high complexity of the problem; we applied Grey Wolf Optimizer (GWO) and Invasive Weed Optimizer (IWO) to find nearly optimal solutions for medium and large instances. Finally, the computational Results are provided for evaluating the performance and effectiveness of the proposed solution approaches.
Mohammad Hasan Esmaili, Seyed Meysam Mousavi,
Volume 31, Issue 2 (IJIEPR 2020)
Abstract
To demonstrate the importance of customer satisfaction can mention numbers of the service providers that attempt to differentiate themselves by satisfied their customers, witnessed high growth. In this paper, some factors that increase retailers and customers’ satisfaction, such as driver consistent services and delivering fresh products, are considered in a perishable inventory routing problem (PIRP) under possibility and necessity class of fuzzy uncertainty measures. In a typical inventory routing problem (IRP), a distribution center delivers products to a set of customers through a limited time horizon, and simultaneously makes a decision about inventory and routing to minimize the total cost. The proposed model is formulated as mixed-integer programming. Two types of consistent driver services are regarded for different kinds of customers, including particular and typical customers. To investigate the validity of the model, the problem is solved for two values of possibility and necessity measures.
