Important Logistics Problem Research Articles

A multi-objective berth allocation problem in fuzzy environment

The Berth Allocation Problem (BAP) is an important seaside problem in port logistics, which involves the determination of berthing times and positions of arriving vessels in a wharf. Most studies have considered that vessels arrival and handling times are known fixed quantities available to wharves operators beforehand. However, due to many uncontrollable factors that can affect vessels arrival and handling times, those quantities are highly uncertain and may have a significant impact in the wharf operation. This paper takes fuzzy uncertainty into account and presents a fully fuzzy BAP with two objective functions. Specifically, it is considered the minimisation of the total waiting time of vessels and the makespan of the wharf operation. The problem is solved by using two lexicographic methods for fully fuzzy multi-objective linear programming (FFMOLP). Multiple Pareto optimal fuzzy solutions are obtained using a fuzzy epsilon-constraint method. Results demonstrate the effectiveness of the proposed approaches in handling fuzziness and conflicting objectives in a BAP. Moreover, from a methodological point of view, results show that it is best to use lexicographic ranking criteria instead of linear ranking functions for solving the associated FFMOLP problem.

A heuristic approach applied to the fleet sizing problem for military ground vehicles

Purpose This paper aims to propose two solution approaches to determine the number of ground transport vehicles that are required to ensure the on-time delivery of military equipment between origin and destination node pairs in some geographic region, which is an important logistics problem at the US Transportation Command. Design/methodology/approach The author uses a mathematical program and a traditional heuristic to provide optimal and near-optimal solutions, respectively. The author also compares the approaches for random, small-scale problems to assess the quality and computational efficiency of the heuristic solution, and also uses the heuristic to solve a notional, large-scale problem typical of real problems. Findings This work helps analysts identify how many ground transport vehicles are needed to meet cargo delivery requirements in any military theater of operation. Research limitations/implications This research assumes all problem data is deterministic, so it does not capture variations in requirements or transit times between nodes. Practical implications This work provides prescriptive details to military analysts and decision-makers in a timely manner. Prior to this work, insights for this type of problem were generated using time-consuming simulation taking about a week and often involving trial-and-error. Originality/value This research provides new methods to solve an important logistics problem. The heuristic presented in this paper was recently used to provide operational insights about ground vehicle requirements to support a geographic combatant command and to inform decisions for railcar recapitalization within the US Army.

Single-machine scheduling with release times, deadlines, setup times, and rejection

Single-machine scheduling where jobs have a penalty for being late or for being rejected altogether is an important (sub)problem in manufacturing, logistics, and satellite scheduling. It is known to be NP-hard in the strong sense, and there is no polynomial-time algorithm that can guarantee a constant-factor approximation (unless P=NP). We provide an exact algorithm that is fixed-parameter tractable in the slack and the maximum number of time windows overlapping at any point in time, i.e., the width. This algorithm has a runtime exponential in these parameters, but quadratic in the number of jobs, even when modeling sequence-dependent setup times. We further provide a fixed-parameter fully-polynomial time approximation scheme (FPTAS) with only this width as a parameter, having a runtime bound that is cubic. Finally, we propose a neighbourhood heuristic similar to the Balas-Simonetti neighbourhood. All algorithms use an efficient representation of the state space inspired by decision diagrams, where partial solutions that are provably dominated are excluded from further consideration. Experimental evidence shows that the exact method significantly outperforms the state-of-the-art on instances where the width is smaller than one third of the number of jobs and finds optimal solutions to previously unsolved instances. The FPTAS is competitive to state-of-the-art heuristics only when the width is significantly smaller, but the neighbourhood heuristic outperforms most other heuristics in runtime or quality.

A Decomposition-Based Local Search for Large-Scale Many-Objective Vehicle Routing Problems With Simultaneous Delivery and Pickup and Time Windows

Vehicle routing problem with simultaneous delivery and pickup and time windows (VRPSDPTW) is an important logistics problem in supply chain network optimization. However, at present, existing approaches are only applied to solve small-scale many-objective VRPSDPTW. For the large-scale problem, to obtain high-quality solutions (effectively) and solve the problem in a short running time (efficiently), we focus on two issues: 1) how to handle many-objective property and 2) how to tackle large-scale property of the problem. For the first issue, to enhance the convergence property, a novel local search algorithm utilizing seven neighborhood operators is proposed. Meanwhile, to maintain the diversity property, the concept of decomposition is adopted. The problem is first decomposed into multiple single-objective problems, then local search is utilized to optimize these subproblems. For the second issue, since optimizing a subproblem by local search is time consuming for large-scale problems, the key point is how to allocate the computational resources and balance the convergence and diversity. To handle this problem, a weight space partition strategy is introduced. Finally, a decomposition-based local search algorithm is proposed for many-objective VRPSDPTW. Experimental results on the large-scale instances provided by Chinese logistic company show the effectiveness of the proposed algorithm.

Offline–Online Approximate Dynamic Programming for Dynamic Vehicle Routing with Stochastic Requests

Although increasing amounts of transaction data make it possible to characterize uncertainties surrounding customer service requests, few methods integrate predictive tools with prescriptive optimization procedures to meet growing demand for small-volume urban transport services. We incorporate temporal and spatial anticipation of service requests into approximate dynamic programming (ADP) procedures to yield dynamic routing policies for the single-vehicle routing problem with stochastic service requests, an important problem in city-based logistics. We contribute to the routing literature as well as to the field of ADP. We combine offline value function approximation (VFA) with online rollout algorithms resulting in a high-quality, computationally tractable policy. Our offline–online policy enhances the anticipation of the VFA policy, yielding spatial and temporal anticipation of requests and routing developments. Our combination of VFA with rollout algorithms demonstrates the potential benefit of using offline and online methods in tandem as a hybrid ADP procedure, making possible higher-quality policies with reduced computational requirements for real-time decision making. Finally, we identify a policy improvement guarantee applicable to VFA-based rollout algorithms, showing that base policies composed of deterministic decision rules lead to rollout policies with performance at least as strong as that of their base policy.

A Local Search-Based Multiobjective Optimization Algorithm for Multiobjective Vehicle Routing Problem With Time Windows

Vehicle routing problem with time windows (VRPTW) is an important logistics problem, which appears to be multiobjective in real world. Recently, a general multiobjective VRPTW (MOVRPTW) with five objectives has been defined, and a set of MOVRPTW problem instances based on data from real world have been proposed. These instances indicate more truly multiobjective nature and represent more realistic and challenging MOVRPTW cases. In this paper, a local search-based multiobjective optimization algorithm is proposed for the real-world MOVRPTW instances. Considering the problem structure of MOVRPTW, we design different local search methods for different objectives. These simple but effective local search methods cooperate to optimize different objectives simultaneously. More problem-specific knowledge can be extracted by using objectivewise local search components, and thus, high-quality solutions are expected to be generated. The proposed algorithm is tested on 45 realistic and challenging MOVRPTW benchmark instances from real world. Experimental results show that the proposed algorithm can obtain better solutions than the previous evolutionary algorithm-based multiobjective algorithm on new MOVRPTW cases. Additional results on 56 Solomon instances show the stability of the proposed algorithm across data sets.

Multiobjective Vehicle Routing Problems With Simultaneous Delivery and Pickup and Time Windows: Formulation, Instances, and Algorithms

This paper investigates a practical variant of the vehicle routing problem (VRP), called VRP with simultaneous delivery and pickup and time windows (VRPSDPTW), in the logistics industry. VRPSDPTW is an important logistics problem in closed-loop supply chain network optimization. VRPSDPTW exhibits multiobjective properties in real-world applications. In this paper, a general multiobjective VRPSDPTW (MO-VRPSDPTW) with five objectives is first defined, and then a set of MO-VRPSDPTW instances based on data from the real-world are introduced. These instances represent more realistic multiobjective nature and more challenging MO-VRPSDPTW cases. Finally, two algorithms, multiobjective local search (MOLS) and multiobjective memetic algorithm (MOMA), are designed, implemented and compared for solving MO-VRPSDPTW. The simulation results on the proposed real-world instances and traditional instances show that MOLS outperforms MOMA in most of instances. However, the superiority of MOLS over MOMA in real-world instances is not so obvious as in traditional instances.

Single liner shipping service design

The design of container shipping networks is an important logistics problem, involving assets and operational costs measured in billions of dollars. To guide the optimal deployment of the ships, a single vessel round trip is considered by minimizing operational costs and flowing the best paying demand under commercially driven constraints. This paper introduces the Single Liner Shipping Service Design Problem. Arc-flow and path-flow models are presented using state-of-the-art elements from the wide literature on pickup and delivery problems. A Branch-and-Cut-and-Price algorithm is proposed, and implementation details are discussed. The algorithm can solve instances with up to 25 ports to optimality, a very promising result as real-world vessel roundtrips seldom involve more than 20 ports.

Vehicle routing problem with time windows considering overtime and outsourcing vehicles

The vehicle routing problem with time windows (VRPTW) is an important problem in third-party logistics and supply chain management. We extend the VRPTW to the VRPTW with overtime and outsourcing vehicles (VRPTWOV), which allows overtime for drivers and the possibility of using outsourced vehicles. This problem can be applied to third-party logistics companies for managing central distributor-local distributors, local distributor-retailers (or customers), and manufacturers. We developed a mixed integer programming model, a genetic algorithm (GA), and a hybrid algorithm based on simulated annealing. The computational results demonstrate the efficiency of the developed algorithms. We also develop a decision support system for the VRPTWOV that is equipped with a vehicle route rescheduling function for realistic situations based on the GA.

Constructing initial solutions for the multiple vehicle pickup and delivery problem with time windows

The Multiple Vehicle Pickup and Delivery Problem with Time Windows (MV-PDPTWs) is an important problem in logistics and transportation. However, this problem is characterized by having a large number of constraints that are difficult to deal with in a solution algorithm. Indeed, merely constructing a feasible solution to this hard problem is a challenge in itself. In this research, we compare several construction algorithms that generate initial feasible solutions to the problem. The suggested algorithms all utilize a simple routing heuristic to create individual vehicle routes. The algorithms differ, though, in whether routes are generated sequentially or in parallel. They also have different criteria for selecting requests and the routes in which they will be inserted. Inserting a request in a route is either based on a first acceptance criterion, in which a request is inserted in the first route where a feasible insertion is found, or a best acceptance criterion, in which a request is inserted in the estimated best route for insertion. Experimental results on several benchmark problem instances indicate that the sequential construction heuristic may be the most suitable construction algorithm for this problem, in terms of simplicity of coding, solution quality as well as processing speed.1This paper is part of the PhD thesis of the first author (Hosny, 2010), and it is an expanded version of the MIC2009 conference paper (Hosny and Mumford, 2009).1

A Two-Stage Heuristic with Ejection Pools and Generalized Ejection Chains for the Vehicle Routing Problem with Time Windows

The vehicle routing problem with time windows (VRPTW) is an important problem in logistics. The problem is to serve a number of customers at minimum cost without violating the customers’ time-window constraints or the vehicle-capacity constraint. In this paper, we propose a two-stage algorithm for the VRPTW. The algorithm first minimizes the number of vehicles with an ejection pool to hold temporarily unserved customers, which enables the algorithm to go through the infeasible solution space. Then it minimizes the total travel distance using a multi-start iterated hill-climbing algorithm with classical and new operators including generalized ejection chains, which enable the algorithm to search a larger neighborhood. We applied the algorithm to Solomon’s 56 VRPTW instances and Gehring and Homberger’s 300 extended instances. The experimental results showed that the algorithm is effective and efficient in reducing the number of vehicles and is also very competitive in terms of distance minimization. The m-VRPTW is a variant of the VRPTW in which a limited number of vehicles is available. A feasible solution to m-VRPTW may contain some unserved customers due to the insufficiency of vehicles. The primary objective of m-VRPTW is to maximize the number of customers served. We extended our VRPTW algorithm to solve m-VRPTW and the experimental results showed consistently good performance of the algorithm when compared with other methods.