Modified savings heuristics and genetic algorithm for bi-objective vehicle routing problem with forced backhauls

► Vehicle routing problem (VRP) incorporating forced backhauls, routing cost minimisation and minimisation of span of travel tour is considered. ► Extended variant termed as bi-objective vehicle routing problem with forced backhauls (BVFB). ► Three heuristics for BVFB are: modified savings heuristic with arc removal procedure, with node swap procedure and adapted genetic algorithms. ► Randomly generated data-sets of BVFB and nine real-life cases of BVFB are considered. ► It is evident from the results that GA promises to be a useful tool for solving BVFB. The cost of distribution and logistics accounts for a sizable part of the total operating cost of a company. However, the cost associated with operating vehicles and crews for delivery purposes form an important component of total distribution costs. Small percentage saving in these expenses could result in a large amount of savings over a number of years. Increase in the number of automated teller machines (ATMs) in the bank industry enforced the researchers to concentrate much on the optimization of distribution logistics problem. The process of replenishing money in the ATMs is considered as a scope with bi-objectives such as minimizing total routing cost and minimizing the span of travel tour. Some of the pick-up routes of the problem are forced and it is termed as forced backhauls. This problem is termed as bi-objective vehicle routing problems with forced backhauls (BVFB). We developed three heuristics to solve BVFB. Two heuristics are modified savings heuristics and the third heuristic is based on adapted genetic algorithm (GA). Standard data sets of VRPB of real life cases for BVFB and randomly generated datasets for BVFB are solved using all the three heuristics. The results are compared and found that all the three heuristics are competitive in solving BVFB. GA yields better solution compared to the other two heuristics.