A tabu search heuristic for the heterogeneous vehicle routing problem on a multigraph

We study a time-constrained heterogeneous vehicle routing problem on a multigraph where parallel arcs between pairs of vertices represent different travel options based on criteria such as time, cost, and distance. We formulate the problem as a mixed-integer linear programming model and develop a tabu search heuristic that efficiently addresses computational challenges due to parallel arcs. Numerical experiments show that the heuristic is highly effective and that freight operators can achieve advantages in cost and customer service by considering alternative paths, especially when route duration limits are restrictive and/or when vehicles of smaller capacity are dispatched to serve remote customers.     

A tabu search heuristic for the local container drayage problem under a new operation mode

This paper examines the Local Container Drayage Problem (LCDP) under an operation mode in which a tractor can be detached from its companion trailer and assigned to a new task. We have incorporated a set of temporal constraints into the classical VRP to realize this operation by utilizing the idle time available to tractors and coordinating the empty containers moving between customers. A tabu search algorithm is proposed. Some numerical experiments are conducted to assess the performance of the proposed algorithm, quantify the benefit of the new operation mode, and identify the conditions under which the mode is effective.     

A reactive tabu search algorithm for the multi-depot container truck transportation problem

A container truck transportation problem that involves multiple depots with time windows at both origins and destinations, including the reposition of empty containers, is formulated as a multi-traveling salesman problem with time windows (m-TSPTW) with multiple depots. Since the problem is NP-hard, a cluster method and a reactive tabu search (RTS) algorithm are developed to solve the problem. The two methods are compared with the mixed integer program which can be used to find optimum solutions for small size problems. The computational results show that the developed methods, particularly the RTS algorithm, can be efficiently used to solve the problem.     

Container fleet sizing and empty repositioning in liner shipping systems

This paper considers the joint container fleet sizing and empty container repositioning problem in multi-vessel, multi-port and multi-voyage shipping systems with dynamic, uncertain and imbalanced customer demands. The objective is to minimize the expected total costs including inventory-holding costs, lifting-on/lifting-off costs, transportation costs, repositioning costs, and lost-sale penalty costs. A simulation-based optimization tool is developed to optimize the container fleet size and the parameterized empty repositioning policy simultaneously. The optimization procedure is based on Genetic Algorithms and Evolutionary Strategy combined with an adjustment mechanism. Case studies are given to demonstrate the results.     

The airfare pricing and seat allocation problem in full-service carriers and subsidiary low-cost carriers

This study investigates the competitive market situation in the air transport industry considering full-service carriers (FSC), subsidiary low-cost carriers (LCC) and rival LCCs on the flight-leg level while subsidiary LCCs are established by FSCs against rival LCCs to keep the market share and to make more profit. It is assumed that the demand of economy class for each airline follows a known distribution, and the mean value of that distribution is a function of its airfare and the airfare differences with other airlines. In addition, no-shows and cancellations are introduced to reflect a real situation. Based on this situation, a mathematical model is developed to derive efficient airfare pricing and seat allocation for each airline for maximizing the profit sum of both FSCs and subsidiary LCCs using a repeated game. A repeated game model integrated with a Tabu search algorithm and an EMSR based heuristic is suggested to deal with the proposed repeated game. A numerical example is provided to validate the model and solution procedure with hypothetical system parameter values under two kinds of market situations that show before and after the emergence of subsidiary LCCs.     

An adaptive large-neighborhood search heuristic for a multi-period vehicle routing problem

This problem involves optimizing product collection and redistribution from production locations to a set of processing plants over a planning horizon. This horizon consists of several days, and the collection-redistribution is performed on a repeating daily basis. A single routing plan must be prepared for the whole horizon, taking into account the seasonal variations in the supply. We model the problem using a sequence of periods, each corresponding to a season. We propose an adaptive large-neighborhood search with several specifically designed operators and features. The results show the excellent performance of the algorithm in terms of solution quality and computational efficiency.     

Lack of progress in tourist information search research: a critique of citation behaviour and knowledge development

ABSTRACT

Knowledge construction is a crucial factor in the development of any field of study. While empirical papers sustain knowledge development in a field, systematic analysis of the literature and critiques of influential papers are core components of high-level knowledge construction. The aim of this paper is to critically evaluate Fodness and Murray’s ([1999]. A model of tourist information search behavior. Journal of Travel Research, 37(3), 220–230.) seminal paper on information search and to assess how the model has been developed by the subsequent literature. A critique of the model highlighted that the original work had a number of shortcomings, particularly given recent advances in information technologies. The method consisted of a meta-analysis of 575 studies that cited Fodness and Murray’s work. The meta-analysis demonstrated that none of the subsequent studies attempted to further develop this well-cited model. The analysis also highlighted that many of the citations to the original study were superficial, and in some cases, incorrect, raising serious questions about citation practices in the tourism field more broadly. The main implication is that, in order to advance our understanding of tourism, we need robust studies that confirm and build on past research efforts to move the field forward.     

A large neighborhood search heuristic to establish an optimal ad-hoc hubbing strategy in the wake of a large-scale airport outage

Abrupt airport outages can cause diversions and fuel-critical situations for flights, leading to costly passenger misconnections. We develop a large neighborhood search heuristic to optimize the rerouting of flights bound for a disrupted airport to a hub airport that is not disrupted, with the goal of accommodating passengers on existing flights departing the non-disrupted hub. The objective of the heuristic is to identify and reroute flights to the ad-hoc hub(s) – non-disrupted hub airport(s) – that minimize the sum of passenger travel time and wait time. We minimize the passenger cost as the sum of passenger travel time to the diversion airport and wait time for a connecting flight at the ad-hoc hub airport, subject to on-board fuel and diversion airport capacity constraints. We use the heuristic to determine how a coordinated traffic management strategy could have diverted flights immediately following a real-world airport outage.     

Variable neighborhood search heuristic for storage location assignment and storage/retrieval scheduling under shared storage in multi-shuttle automated storage/retrieval systems

This paper examines the joint optimization of storage location assignment and storage/retrieval scheduling in multi-shuttle automated storage/retrieval systems (AS/RSs) under shared storage, in which the reuse of empty location yielded by retrieval operation is allowed. From the view of analytical model, the advantage of operational mode under shared storage is verified. A variable neighborhood search (VNS) algorithm is developed to solve the large-sized problems. Various numerical experiments are conducted to evaluate the performance of the proposed algorithm and investigate the impact of different parameters on computational efficiency.