The classical vehicle routing problem (VRP) is one of the most important combinatorial optimisation problems, AND has received much attention from researchers AND scientists. This problem involves routing a fleet of vehicles, each visiting a set of customers such that every customer is visited exactly once AND by exactly one vehicle, with the objective of minimizing the total distance travelled by all the vehicles. The vehicle routing problem with simultaneous PICKUPs AND DELIVERies (VRP_SPD) is an extension to the VRP, where the vehicles are not only required to DELIVER GOODS to customers, but also to collect some GOODS from the customer locations.Ant Colony Optimisation (ACO) is a probabilistic technique that simulates the ants’ food-hunting behaviour, which is used for solving problems that do not have a known efficient algorithm. The Ant Colony System (ACS) is one of the most famous metaheuristic algorithms, which differs from the other ACO methods due to its transition rule AND updating pheromone.This paper proposes an improved ACS, mixed with a 3-opt local search for solving the VRP_SPD. The proposed method is different to the common ACS with regards to the coefficient pheromone AND heuristic information for the ant visibility measure. This process will avoid the premature convergence AND generate better solutions. The proposed algorithm is tested on 34 stANDard instances available in the literature. The computational result shows that the proposed algorithm improves the four best-known solutions of the benchmark problem, AND it is competitive with other metaheuristic algorithms for solving the VRP_SPD.

The classical models of vehicle routing generally focused on minimizing total distance AND travel time, however in green vehicle routing problem the main objective is minimizing total emissions AND fuel consumption besides the other objects.In this paper, extension of GVRP with minimizing fuel AND emission costs presented that considered PICKUP AND DELIVERy constraints with hard time windows. Travel time in this model is not constant AND speed of vehicles would be determined in regards to customers’ time widows. In this paper, a heuristic based adaptive large neighborhood search proposed for solving the model. Construction algorithm in this method is heuristic based algorithm with proposed criterion according to pick up AND DELIVERy constrains AND time windows with assumption of variable speed. Computational results confirms efficiency of this algorithm.

Introduction: With the growth of urbanization, urban transportation has become one of the most critical challenges of urban management, which is closely related to the economic power of cities AND countries. A robust economy requires adequate infrastructure in the freight division, AND proper resource planning AND management is the key to its success. In this research, the issue of transportation of postal items has been considered. The use of traditional methods prolongs receiving AND DELIVERing postal items AND thus increases its costs. In this research, this issue has been studied. Using meta-heuristic algorithms (Artificial Intelligence), an attempt has been made to optimize the problem of receiving AND DELIVERing postal items. Materials & Methods: The proposed method of this research is based on the use of a Genetic Algorithm to optimize the order of PICKUP AND DELIVERy of postal items using the travel cost matrix between the points of PICKUP AND DELIVERy. The genetic algorithm has high flexibility following the structure of different problems. In the developed model of this research, the order of picking AND DELIVERing shipments in each freight vehicle is in one array AND five arrays representing five freight vehicles from five postal centers in one matrix created. The genetic algorithm tries to optimize the final solution by rANDomly generating these matrices (chromosomes) AND measuring the fitness function of each matrix (answer) AND using the combination AND mutation operators. Finally, the best solution is obtained, which is the best arrangement AND planning for the trucks carrying the items, in which the best order of receiving AND DELIVERing the postal items is determined. Results & Discussion: The study area is 10, 11, 12, 14, 15, 16, 17, AND 19 regions of Tehran (the capital of Iran), which were selected for implementation. Street network data was entered into the Network Analyst tool in ArcGIS software. Travel cost matrices between PICKUP AND DELIVERy points AND consignment centers were extracted from the data of 50 PICKUP points AND 50 DELIVERy points entered into the developed model. After executing the algorithm for 1000 times AND generating final output, which is the most optimal arrangement of PICKUP AND DELIVERy points, it was compared with the first rANDom answer made in the model which represents old unplanned method for receiving AND DELIVERing the postal items. The total length of final optimal answer is 551689 meters, which is less than 720287 meters (the total length of first rANDom answer). The decrease in the final solution in comparison to the first rANDom solution is 168598 meters, which is equivalent to 24% savings AND indicates the efficiency of the developed model. Conclusion: Using old traditional experimental methods for pick-up AND DELIVERy of postal items leads to increase the route of postal vehicles which increase the urban congestion AND produces some pollutions. Applying the scientific methods such as used model in this research helps to decrease the aforementioned problems AND it is a key to approach the smart cities. We used a genetic algorithm optimization method for arranging the order of receiving AND DELIVERing the postal items AND develop a method to decrease the distance between request points. By using this algorithm, the total length of postal vehicles decreased from 720 km to 551 km which is equivalent to 24% savings. For instance, the second truck's way can be checked to investigate the proposed model's performance. Since can be observed, the algorithm has put the pick-up AND DELIVERy points together properly to stop the truck from driving around the study area. It can similarly be recognized that the truck's movement numbers are adjacent to each other. It means that the DELIVERy points are ordered to follow each other, AND the postal vehicle evades moving significant ways. Consequently, the vehicle's driving length is decreased, which decreases the overall driving length of all vehicles. Nevertheless, the first vehicle's route does not look so visually optimal. It can be seen that the vehicle has been required to move to some distant points. First, the ultimate solution's fitness function's state holds the lowest possible value among the solutions. Furthermore, the algorithm could not optimize the paths more, AND it has to insert some distant locations in the route of one of the vehicles. Indeed, every attempt has been performed to gain the most suitable paths. However, we can optimize this problem by improving our methods or use other metaheuristics algorithms for future research.

The problem of designing a set of routes with minimum cost to serve a collection of customers with a fleet of vehicles is a fundamental challenge when the number of customers to be dropped or picked up is not known during the planning horizon. The purpose of this paper is to develop a vehicle routing Problem (VRP) model that addresses stochastic simultaneous PICKUP AND DELIVERy in the urban public transport systems of Addis Ababa city Bus Enterprise, in Ethiopia. To this effect, a mathematical model is developed AND fitted with real data collected from Anbessa City Bus Service Enterprise (ACBSE) AND solved using Clark-Wright saving algorithm. The form-to-distance is computed from the data collected from Google Earth AND the passenger data from the ACBSE. The findings of the study show that the model is feasible AND showed an improvement as compared to the current performances of the enterprise. It showed an improvement on the current number of routes (number of buses used) AND the total kilometer covered. The average performances of the model show that on average 6.48 routes are required to serve passenger demANDs of 271 AND on average the simulation run was performed with 0.40 seconds of CPU time. During this instance, the average distance traveled by the vehicles in a single trip is 552.92kms.

Sea shells are natural-biological objects. They are embedded in geological layers in the form of fossils, but also, to find in archaeological deposits as a result of human activities. Archaeologists can use the provenance of shells in the functional analysis of ancient sites in terms of social archeology AND prehistoric trading activities. Aarcheological excavations in several sites of the Iranian Plateau have shown that from the 3rd third millennium B.C. onwards, finds of of sea shells (e.g. Lambis, Dentalium, etc.) rapidely increased. Such shells were for instance discovered from ritual cemetery contexts such as Shahdad, Tepe Hesar, Kale Nisar cemeteries or Bani Surma. These objects are mainly used as natural or polished shells. In some cases, they served as a raw material for making all kinds of beads, buttons, AND other ornamental objects.. The main question is to understAND the relationship between the use of seashells AND archaeological context, AND also, their role in Bronze Age ritual life. In this article, the descriptive, analytical method has been used in the biological recognition of all types of shells. This method is also used based on similar studies on this issue in Mesopotamia's archeology of the Sumerian-Akkadian period. The distribution of recognizable species shows that these objects are concentrated in the settlements from south to southeast of Iran in the coastal strip of the Persian Gulf, AND from the Oman Sea to the Zagros intermountain valleys, as well as in the northwest AND northeast of Iran. The biological origin can be placed in the northern shores of the Oman Sea to the Gulf of Kutch on the northern coast of the Indian Ocean. It seems that with the growth AND development of urbanization in Southwest Asia AND especially the development of sea trade, oysters have been traded as valuable GOODS AND other prestige GOODS. The importance of the shell findings is more than the value of the shells themselves because they were used as sacred GOODS in religious affairs. Analysis of the fields where the shells were discovered is more related to cemeteries AND temples as sacred spaces. Also, the significant presence of Lambis shells for the production of specific ritual bowls, placed together with bronze axes in graves, can be seen as the reflection of a patriarchic tradition in the social-political organisiation of the third AND second millennia B.C. Despite many excavations AND the discovery of many samples of these types of shells, no furthergoing investigation on these specific objects was undertaken so far. This desideratum reveals more valuable findings in the archeology of the Iranian plateau. Therefore, one of this article's final goals is to focus more on analyzing the context of the discovery of seashells in future Excavation

In this work, the capacitated location-routing problem with simultaneous PICKUP AND DELIVERy (CLRPSPD) is considered. This problem is a more realistic case of the capacitated location-routing problem (CLRP) AND belongs to the reverse logistics of the supply chain. The problem has many real-life applications of which some have been addressed in the literature such as management of liquid petroleum gas tanks, laundry service of hotels AND drink distribution. The CLRP-SPD is composed of two well-known problems; facility location problem AND vehicle routing problem. In CLRP-SPD, a set of customers with given DELIVERy AND PICKUP demANDs should be supplied by a fleet of vehicles that start AND end their tours at a single depot. Moreover, the depots AND vehicles have a predefined capacity AND the objective function is minimizing the route distances, fixed costs of establishing the depot(s) AND employing the vehicles. The node-based MIP formulation of the CLRP-SPD is proposed based on the literature of the problem. To solve the model, a greedy clustering method (GCM) is developed which includes four phases; clustering the customers, establishing the proper depot(s), assigning the clusters to depot(s) AND constructing the vehicle tours by ant colony system (ACS). The numerical experiments on two sets of test problems with different sizes on the number of customers AND cANDidate depots show the efficiency of the heuristic method with the proposed method in the literature. Finally, performance of the heuristic method to the similar methods in the literature is evaluated by several stANDard test problems of the CLRP.