Intelligent Vehicle Scheduling and Routing for a Chain of Retail Stores: A Case Study of Dhaka, Bangladesh

Abstract

Background: Retail chains aim to maintain a competitive advantage by ensuring product availability and fulfilling customer demand on-time. However, inefficient scheduling and vehicle routing from the distribution center may cause delivery delays and, thus, stock-outs on the store shelves. Therefore, optimization of vehicle routing can play a vital role in fulfilling customer demand. Methods: In this research, a case study is formulated for a chain of retail stores in Dhaka City, Bangladesh. Orders from various stores are combined, grouped, and scheduled for Region-1 and Region-2 of Dhaka City. The ‘vehicle routing add-on’ feature of Google Sheets is used for scheduling and navigation. An android application, Intelligent Route Optimizer, is developed using the shortest path first algorithm based on the Dijkstra algorithm. The vehicle navigation scheme is programmed to change the direction according to the shortest possible path in the google map generated by the intelligent routing optimizer. Results: With the application, the improvement of optimization results is evident from the reductions of traveled distance (8.1% and 12.2%) and time (20.2% and 15.0%) in Region-1 and Region-2, respectively. Conclusions: A smartphone-based application is developed to improve the distribution plan. It can be utilized for an intelligent vehicle routing system to respond to real-time traffic; hence, the overall replenishment process will be improved.

Genetic Crossover Operators for the Capacitated Vehicle Routing Problem

“…Fig. 2a displays an example chromosome (1,6,9,8,5,2,10,4,3,7) and its corresponding tour {1→6→9→ 8→5→2→ 10→4→3→7 →1} with 9 customers and 2 vehicles, where the integers 1 and 10 are the depots, the others are customers. Fig.…”

“…Tab. 3 shows the generation of the chromosome (1,6,9,8,5,3,10,2,4,7). Similarly, a population of chromosomes is generated.…”

“…Similarly, the 2 nd * in that offspring would be 5 which is from 2 nd parent, however, it is available in the offspring, so we add 4 using the map 5↔4. So, the 1 st offspring leads to O 1 : (1,6,9,8,5,3,7,4,2). Likewise, the 2 nd offspring is created as: O 2 : (1, 8, 6, 9, 4, 3, 2, 5, 7).…”

“…The CVRP is a mixture of the standard travelling salesman problem (TSP) and the bin packing problem (BPP). The CVRP has applications in many real-life problems, such as renting-sharing problems for urban bicycles [3], scheduling and routing of retail stores [4], dispensing medical supplies [5], etc. The classical CVRP is NP-hard [6] and is a very complicated problem.…”

A Novel Algorithm for Capacitated Vehicle Routing Problem for Smart Cities

“…As depicted in Figure 1, the CVRP problem is concerned with discovering the optimal paths for a given fleet of motor vehicles to fulfill the demands of physically distributed customers [3]. The CVRP and its variants are widely used in many real-life applications, such as smart logistics [1,2], critical data collection in IoT platforms [4], renting-sharing problems for urban bicycles [5], the routing and scheduling of chains of retail stores [6], distributing medical supplies for emergencies [7], crop harvesting and transportation [8], and the dynamic vehicle routing problem with traffic congestion to name a few. The CVRP is a widely-discussed NP-hard [3] problem, and therefore, optimization methods including exact methods (dynamic programming, branch-and-bound); heuristics (the Fisher-Jaikumar algorithm and the Clarke-Wright saving algorithm); swarm and evolutionary algorithms (GA, ACO, and firefly algorithms); local search operators (swap, inversion, scramble) and hybrid approaches have widely been employed to crack the CVRP problem [3,[8][9][10][11][12][13][14].…”