Yuvraj Gajpal

Hybridized ant colony algorithm for the Multi Compartment Vehicle Routing Problem

Hybridized ant colony algorithm has been proposed to solve the Multi Compartment Vehicle Routing Problem.Numerical experiments were performed to evaluate the performance of the algorithm.The numerical results showed that the average total length improvement of the proposed HAC over the existing ACS is 5.1%. In addition, the proposed HAC maintains its high performance in large problems on contrary of the existing ACS.The numerical result for the effect of hybridizing the ant colony algorithm with local search schemes has been presented.Illustration of the benefit of using two-compartment vehicles instead of single-compartment vehicles has been presented. Multi Compartment Vehicle Routing Problem is an extension of the classical Capacitated Vehicle Routing Problem where different products are transported together in one vehicle with multiple compartments. Products are stored in different compartments because they cannot be mixed together due to differences in their individual characteristics. The problem is encountered in many industries such as delivery of food and grocery, garbage collection, marine vessels, etc. We propose a hybridized algorithm which combines local search with an existent ant colony algorithm to solve the problem. Computational experiments are performed on new generated benchmark problem instances. An existing ant colony algorithm and the proposed hybridized ant colony algorithm are compared. It was found that the proposed ant colony algorithm gives better results as compared to the existing ant colony algorithm.

Multi-Zone Multi-Trip Vehicle Routing Problem with Time Windows

Abstract We introduce a new vehicle routing problem class in which customers are divided into a number of customer zones defined through geographical or timing characteristics. The customers of each of these zones must be serviced within time windows by dedicated routes originating at associated supply points characterized by hard time windows and very limited waiting facilities, if any. A key feature of the problem is that a vehicle can be used to cover routes in different zones at different times. The objective is to minimize the total transportation cost to ensure that the customers are serviced on time and that vehicles arrive at the next customer zone just in time for the next assignment. The problem is addressed by a decomposition-based heuristic. Lower-bound procedures and benchmark problem instances are introduced, highlighting the satisfactory performance of the heuristic. Finally, a wide range of sensitivity analyses on several key parameters reveal interesting facets of the behavior of this new problem class.

Enhanced heuristic for finance-based scheduling of construction projects

Typically, construction contractors operate under cash-constrained operating conditions. The lag between the time when contractors spend money to accomplish work on site and the time when payments are actually made by clients, which partially compensate contractors for the accomplished work, constantly creates a finance deficit. Contractors often supplement finance deficits using external funds procured through establishing credit-line bank accounts which typically allow contractors to withdraw cash up to specified credit limits. This makes the task of project scheduling considering the constraints of specified finance very important for financial and operational planning. This scheduling concept and technique are referred to as finance-based scheduling. An enhanced heuristic is proposed to devise finance-based schedules of multiple projects within contractors’ portfolios. The enhancement is achieved by replacing the exhaustive enumeration technique employed in the heuristic to specify activities’ start times with a polynomial shifting algorithm. This enhancement resulted in a substantial reduction in the number of solutions explored before a feasible solution is encountered. The enhanced heuristic was validated through comparison with the integer programming technique using 240 problems of randomly generated networks of sizes that range from 30 to 240 activities. Further, it was proved that the enhanced heuristic can be easily scaled up to handle portfolios of multiple large-size projects.

Two efficient heuristics to solve the integrated load distribution and production planning problem

This paper considers a multi-period production system where a set of machines are arranged in parallel. The machines are unreliable and the failure rate of machine depends on the load assigned to the machine. The expected production rate of the system is considered to be a non-monotonic function of its load. Because of the machine failure rate, the total production output depends on the combination of loads assigned to different machines. We consider the integration of load distribution decisions with production planning decision. The product demands are considered to be known in advance. The objective is to minimize the sum of holding costs, backorder costs, production costs, setup costs, capacity change costs and unused capacity costs while satisfying the demand over specified time horizon. The constraint is not to exceed available repair resources required to repair the machine breakdown. The paper develops two heuristics to solve the integrated load distribution and production planning problem. The first heuristic consists of a three-phase approach, while the second one is based on tabu search metaheuristic. The efficiency of the proposed heuristics is tested through the randomly generated problem instances.

Optimizing garbage collection vehicle routing problem with alternative fuel-powered vehicles

Abstract This paper considers the garbage collection problem in which vehicles with multiple compartments are used to collect the garbage. The vehicles are considered to be Alternative Fuel-powered Vehicles (AFVs). Compared with the traditional fossil fuel powered vehicles, the AFVs have limited fuel tank capacity. In addition, AFVs are allowed to refuel only at the depot. We provide a mathematical formulation and develop two solution approaches to solve the problem. The first approach is based on the saving algorithm, while the second is based on the ant colony system (ACS) metaheuristic. New problem instances have been generated to evaluate the performance of the proposed algorithms.

Two-agent-based single-machine scheduling with switchover time to minimize total weighted completion time and makespan objectives

We consider a single-machine scheduling problem with two-agents, each with a set of non-pre-emptive jobs, where two agents compete for the use of a single processing resource. A switchover time arises whenever a job of one agent is processed after a job of another agent. Each agent wants to minimize a certain objective function, which depends upon the completion time and switchover time of their own jobs only. This paper considers the minimization of total weighted completion time of the first agent subject to an upper bound on the makespan of the second agent. We introduce some properties to the problem. The properties describe the structure of an optimal solution which is being used for developing an optimal algorithm. We propose an optimal algorithm, a simple heuristic algorithm, and a particle-swarm-based meta heuristic algorithm to solve the problem. The heuristic algorithm is based on the weighted shortest process time-first rule. The performances of the heuristic and particle swarm algorithms are evaluated on randomly generated problem instances. We perform the numerical analysis to reveal the properties of the proposed problem.

A meta-heuristic for capacitated green vehicle routing problem

The capacitated green vehicle routing problem is considered in this paper as a new variant of the vehicle routing problem. In this problem, alternative fuel-powered vehicles (AFVs) are used for distributing products. AFVs are assumed to have low fuel tank capacity. Therefore, during their distribution process, AFVs are required to visit alternative fuel stations (AFSs) for refueling. The design of the vehicle routes for AFVs becomes difficult due to the limited loading capacity, the low fuel tank capacity and the scarce availability of AFSs. Two solution methods, the two-phase heuristic algorithm and the meta-heuristic based on ant colony system, are proposed to solve the problem. The numerical experiment is performed on the randomly generated problem instances to evaluate the performance of the proposed algorithms.

Ant Colony Algorithm for Routing Alternate Fuel Vehicles in Multi-depot Vehicle Routing Problem

A Multi-depot Green Vehicle Routing Problem (MDGVRP) is considered in this paper. An Ant Colony System-based metaheuristic is proposed to find the solution to this problem. The solution for MDGVRP is useful for companies, who employ the Alternative Fuel-Powered Vehicles (AFVs) to deal with the obstacles brought by the limited number of the Alternative Fuel Stations. This paper adds an important constraint, vehicle capacity to the model, to make it more meaningful and closer to real-world case. The numerical experiment is performed on randomly generated problem instances to understand the property of MDGVRP and to bring the managerial insights of the problem.

Single machine scheduling with two-agent for total weighted completion time objectives

Abstract This paper considers two-agent scheduling problem with a single machine which is responsible for processing jobs from two agents. The objective is to minimize the objective function of one agent, subject to an upper bound on the objective function of the other agent. The objectives considered in this paper are, (1) the minimization of total completion time and (2) the minimization of total weighted completion time. To solve these problems, one heuristic and an Ant Colony Optimization algorithm are proposed. The heuristic suggested in the paper are motivated by the Weighted Shortest Processing Time first (WSPT) rule. A numerical experiment is performed on randomly generated problem instances. The performance of the algorithm is evaluated by comparing it with the lower bound value of all three problems considered in the present paper.