Green road–rail intermodal routing problem with improved pickup and delivery services integrating truck departure time planning under uncertainty: an interactive fuzzy programming approach

AbstractThis paper addresses the multi-objective optimization for the road–rail intermodal routing problem that aims to minimize the total costs and carbon dioxide emissions of the routes. To achieve high timeliness of the entire transportation process, pickup and delivery services are simultaneously improved based on the employment of fuzzy soft time windows to measure their service levels. The modeling of road–rail intermodal routing considers fixed schedules of rail and time flexibility of road to match the real-world transportation scenario, in which travel times and carbon dioxide emission factors of road services are considered to be time-varying. To improve the feasibility of the routing, uncertainty of travel times and carbon dioxide emission factors of road services and capacities of rail services are incorporated into the problem. By applying trapezoidal fuzzy numbers to formulate the uncertainty, we propose a fuzzy multi-objective nonlinear optimization model for the routing problem that integrates the truck departure time planning for road services. After processing the model with fuzzy chance-constrained programming and linearization, we obtain an auxiliary equivalent crisp linear model and solve it by designing an interactive fuzzy programming approach with the Bounded Objective Function method. Based on an empirical case study, we demonstrate the validity of the proposed approach and discuss the effects of improving the confidence levels and service levels on the optimization results. The case analysis reveals several managerial insights that help to realize an efficient transportation organization by making effective trade-offs among lowering costs, reducing emissions, improving service levels, and enhancing feasibility.

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Finding optimal solutions for vehicle routing problem with pickup and delivery services with time windows: A dynamic programming approach based on state–space–time network representations

Transportation Research Part B Methodological ◽

10.1016/j.trb.2016.03.009 ◽

2016 ◽

Vol 89 ◽

pp. 19-42 ◽

Cited By ~ 98

Author(s):

Monirehalsadat Mahmoudi ◽

Xuesong Zhou

Keyword(s):

Dynamic Programming ◽

Vehicle Routing ◽

Vehicle Routing Problem ◽

Time Windows ◽

Programming Approach ◽

Pickup And Delivery ◽

Optimal Solutions ◽

Routing Problem ◽

Dynamic Programming Approach ◽

Delivery Services

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A Bi-Objective Fuzzy Credibilistic Chance-Constrained Programming Approach for the Hazardous Materials Road-Rail Multimodal Routing Problem under Uncertainty and Sustainability

Sustainability ◽

10.3390/su11092577 ◽

2019 ◽

Vol 11 (9) ◽

pp. 2577 ◽

Cited By ~ 16

Author(s):

Yan Sun ◽

Xinya Li ◽

Xia Liang ◽

Cevin Zhang

Keyword(s):

Carbon Dioxide ◽

Carbon Dioxide Emission ◽

Hazardous Materials ◽

Mixed Integer ◽

Programming Approach ◽

Population Exposure ◽

Chance Constrained Programming ◽

Routing Problem ◽

Multimodal Transportation ◽

Conflicting Objectives

Hazardous materials transportation involves extensive risk and cannot be avoided in practice. An advanced routing, however, can help to reduce the risk by planning the best transportation routes for hazardous materials that can make effective tradeoffs between the risk objective and the economic objective. In this study, we explore the hazardous materials routing problem in the road-rail multimodal transportation network with a hub-and-spoke structure, in which the risk is measured by the multiplication of population exposure and the associated volume of hazardous materials, and minimizing the total risk of all the transportation orders of hazardous materials is set as the risk objective. It is difficult to estimate the population exposure exactly during the routing decision-making process, which results in its uncertainty. In this study, we formulate the uncertain population exposure from a fuzzy programming perspective by using triangular fuzzy numbers. Moreover, the carbon dioxide emission constraint is formulated to realize the sustainable transportation of hazardous materials. To optimize the problem under the above framework, we first establish a bi-objective fuzzy mixed integer nonlinear programming model, and then develop a three-stage exact solution strategy that the combines fuzzy credibilistic chance constraint, linearization technique, and the normalized weighting method. Finally, a computational experiment is carried out to verify the feasibility of the proposed method in dealing with the problem. The experimental results indicate that tradeoffs between the two conflicting objectives can be effectively made by using the Pareto frontier to the hazardous materials routing problem. Furthermore, the credibility level and carbon dioxide emission cap significantly influence the hazardous materials routing optimization. Their effects on the optimization result are quantified by using sensitivity analysis, which can draw some useful insights to help decision makers to better organize the hazardous materials road-rail multimodal transportation under uncertainty and sustainability.

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A selective adaptive large neighborhood search heuristic for the profitable tour problem with simultaneous pickup and delivery services

RAIRO - Operations Research ◽

10.1051/ro/2018024 ◽

2018 ◽

Vol 52 (4-5) ◽

pp. 1295-1328 ◽

Cited By ~ 3

Author(s):

Hayet Chentli ◽

Rachid Ouafi ◽

Wahiba Ramdane Cherif-Khettaf

Keyword(s):

Vehicle Routing ◽

Vehicle Routing Problem ◽

Neighborhood Search ◽

Pickup And Delivery ◽

Large Neighborhood Search ◽

Adaptive Large Neighborhood Search ◽

Routing Problem ◽

Search Heuristic ◽

Delivery Services ◽

Simultaneous Pickup And Delivery

The Vehicle Routing Problem with Simultaneous Pickups and Deliveries (VRPSPD) is a variant of the Vehicle Routing Problem. In this variant, an unlimited fleet of capacitated vehicles is used to satisfy both pickup and delivery demands of each customer simultaneously. In many practical situations, such a fleet is costly. The present study extends the VRPSPD by assuming a fixed number of vehicles when the constraint of visiting all customers is relaxed. More specifically, profits are assigned to the customers with the goal of maximizing the difference between collected profits and routing costs. This variant is named Profitable Tour Problem with Simultaneous Pickup and Delivery services (PTPSPD). We present a mathematical model run with the CPLEX solver. We also present an extension of the Adaptive Large Neighborhood Search heuristic (ALNS) called selective ALNS (sALNS). sALNS uses a new operator selection that executes two phases alternately: the random and the score-dependent phases. An appropriate update of scores is employed. Furthermore, sALNS explores missed regions of the search space by evaluating solutions after the destruction step. Finally, we give tuned insertion and removal operators that handle the constraints of the PTPSPD, as well as a new update of temperature, that helps avoiding local optima, in the Simulated Annealing embedded in sALNS. sALNS is evaluated on 117 new instances with 50–199 customers. A comparison is made between the components of sALNS, the classical ALNS and a recent ALNS heuristic from the literature. sALNS is also evaluated on some VRPSPD instances from the literature. The computational results show that our heuristic provides good quality solutions in reasonable computing time.

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