The fleet size and mix location-routing problem with time windows: Formulations and a heuristic algorithm

Abstract

This paper introduces the fleet size and mix location-routing problem with time windows (FSML-RPTW) which extends the location-routing problem by considering a heterogeneous fleet and time windows. The main objective is to minimize the sum of vehicle fixed cost, depot cost and routing cost. We present mixed integer programming formulations, a family of valid inequalities and we develop a powerful hybrid evolutionary search algorithm (HESA) to solve the problem. The HESA successfully combines several metaheuristics and offers a number of new advanced efficient procedures tailored to handle heterogeneous fleet dimensioning and location decisions. We evaluate the strengths of the proposed formulations with respect to their ability to find optimal solutions. We also investigate the performance of the HESA. Extensive computational experiments on new benchmark instances have shown that the HESA is highly effective on the FSMLRPTW.

The impact of depot location, fleet composition and routing on emissions in city logistics

“…This algorithm is partly based on the ALNS framework of Demir et al (2012) which was initially put forward by Ropke and Pisinger (2006a,b) to solve several variants of the VRP (see Laporte et al, 2014). This metaheuristic has since provided very good results on several complicated variants of the VRP (see Pisinger and Ropke, 2007;Koç et al, 2015a), of the LRP (see Koç et al, 2016a), and of the PRP (see Demir et al, 2012Demir et al, , 2014aKoç et al, 2014).…”

A multiperiod location-routing problem arising in the collection of Olive Oil Mill Wastewater

“…This metaheuristic and its variations have since provided very good results on complicated variants of the VRP (see Ropke and Pisinger, 2006a,b;Koç et al, 2015) and of the LRP (see Koç et al, 2016). Given its evidenced success, our methodology will also be based on this search paradigm.…”

“…For the first day of the week, an initial solution ω 0 for a given day is generated by using the procedure described in Koç et al (2016). This feasible solution is then iteratively copied to remaining six days (line 1).…”

Modelling and analysis of a strategic fleet sizing problem for a furniture distributor

“…Koç et al (2015) study the heterogeneous fleet vehicle routing problem with time windows and propose a hybrid evolutionary algorithm. Koç et al (2016a) consider the fleet size and mix location-routing problem with time-windows and heterogeneous fleet assumptions. They propose a MILP, valid inequalities and a heuristic.…”