Hossein Karimi

Bio: Hossein Karimi is an academic researcher from University of Bojnord. The author has contributed to research in topics: Tabu search & Metaheuristic. The author has an hindex of 12, co-authored 35 publications receiving 405 citations. Previous affiliations of Hossein Karimi include K.N.Toosi University of Technology & Shahed University.

Time-dependent vehicle routing problem with path flexibility

Abstract: Conventionally, vehicle routing problems are defined on a network in which the customer locations and arcs are given. Typically, these arcs somehow represent the distances or expected travel time derived from the underlying road network. When executed, the quality of the solutions obtained from the vehicle routing problem depends largely on the quality of the road network representation. This paper explicitly considers path selection in the road network as an integrated decision in the time-dependent vehicle routing problem, denoted as path flexibility (PF). This means that any arc between two customer nodes has multiple corresponding paths in the road network (geographical graph). Hence, the decisions to make are involving not only the routing decision but also the path selection decision depending upon the departure time at the customers and the congestion levels in the relevant road network. The corresponding routing problem is a time-dependent vehicle routing problem with path flexibility (TDVRP–PF). We formulate the TDVRP–PF models under deterministic and stochastic traffic conditions. We derive important insights, relationships, and solution structures. Based on a representative testbed of instances (inspired on the road network of Beijing), significant savings are obtained in terms of cost and fuel consumption, by explicitly considering path flexibility. Having both path flexibility and time-dependent travel time seems to be a good representation of a wide range of stochasticity and dynamics in the travel time, and path flexibility serves as a natural recourse under stochastic conditions. Exploiting this observation, we employ a Route-Path approximation method generating near-optimal solutions for the TDVRP–PF under stochastic traffic conditions.

Group multi-criteria supplier selection using combined grey systems theory and uncertainty theory

Abstract: Developing framework for reducing purchasing risks associated with suppliers.Combination of grey system theory and uncertainty theory is used.It neither requires any probability distribution nor fuzzy membership function.It selects the most appropriate suppliers and allocates optimal purchase quantity. Supplier selection in supply chain is critical strategic decision for organization's success and has attracted much attention of both academic researchers and practitioners. Supplier selection problem consists of stochastic and recognitive uncertainties. However, the requirement of large sample size and strong subject knowledge to build suitable fuzzy membership function restrict the applicability of probability and fuzzy theories in supplier selection problem. In response, this study proposed a new tool for supplier selection. In this paper, we applied the combination of grey system theory and uncertainty theory which neither requires any probability distribution nor fuzzy membership function. The objective of this paper is to develop framework for reducing the purchasing risks associated with suppliers. The proposed supplier selection method not only selects the most appropriate supplier(s) but also allocate optimal purchase quantity under stochastic and recognitive uncertainties. An example is shown to highlight the procedure of the proposed model at the end of this paper.