Search

Due to increasing awareness on the treatment of end-of-use/life products, disassembly has been a fast-growing research area of interest for many researchers over recent decades. This paper introduces a novel lot-sizing problem that has not been studied in the literature, which is the service-parts lot-sizing with disassembly option. The disassembly option implies that the demands of service parts can be fulfilled by newly manufactured parts, but also by disassembled parts. The disassembled parts are the ones recovered after the disassembly of end-of-use/life products. The objective of the considered problem is to maximize the total profit, i.e., the revenue of selling the service parts minus the total cost of the fixed setup, production, disassembly, inventory holding, and disposal over a planning horizon. This paper proves that the single-period version of the considered problem is NP-hard and suggests a heuristic by combining a simulated annealing algorithm and a linear-programming relaxation. Computational experiment results show that the heuristic generates near-optimal solutions within reasonable computation time, which implies that the heuristic is a viable optimization tool for the service parts inventory management. In addition, sensitivity analyses indicate that deciding an appropriate price of disassembled parts and an appropriate collection amount of EOLs are very important for sustainable service parts systems.

The air transportation industry is one big network that is expected to grow continuously, and Asia-Pacific is the fastest growing region as of 2018 data. We apply weighted network analysis to explore the key network characteristics of the Asian international passenger aviation market in 2014 and 2018. For the analysis, networks are constructed based on international passengers' origin-destination pair data from 28 Asian airlines in 2014 and 2018. Previously published social network analysis (SNA) on the air transportation network largely used unweighted centrality metrics, whereas the actual situation is a weighted network. We implement Mean Associations (MA), Triangle Betweenness Centrality (TBC), and Weighted PageRank (WPR), as new weighted centrality metrics, to effectively reflect actual passenger traffic among airports beyond simple connections. Our weighted network analysis indicates that SIN, ICN, HKG, NRT, MNL, BKK, TPE, HND, PEK, and KUL are the top-ranked hub airports, followed by SGN, PVG, CAN, DEL, SYD, KIX and CGK, and that the low-cost carriers have almost fully developed a ‘hub-and-spoke’ structure over the 4-year study period and are increasing their impact on the centrality of airports in the region.

In South Korea, Jeju Island has a role as a test bed for electric vehicles (EVs). All conventional cars on the island are supposed to be replaced with EVs by 2030. Accordingly, how to effectively set up EV charging stations (EVCSs) that can charge EVs is an urgent research issue. In this paper, we present a case study on planning the locations of EVCS for Jeju Island, South Korea. The objective is to determine where EVCSs to be installed so as to balance the load of EVCSs while satisfying demands. For a public service with EVCSs by some government or non-profit organization, load balancing between EVCS locations may be one of major measures to evaluate or publicize the associated service network. Nevertheless, this measure has not been receiving much attention in the related literature. Thus, we consider the measure as a constraint and an objective in a mixed integer programming model. The model also considers the maximum allowed distance that drivers would detour to recharge their EV instead of using the shortest path to their destination. To solve the problem effectively, we develop a heuristic algorithm. With the proposed heuristic algorithm, a variety of numerical analysis is conducted to identify effects of the maximum allowed detour distance and the tightness of budget for installing EVCSs. From the analysis, we discuss the effects and draw practical implications.

This paper aims to provide a practical method for forecasting potential container cargo volume that can be induced by a port development project on a container transport network by combining port choice and an autoregressive integrated moving average (ARIMA) model. The entrance channel improvement plan for Incheon New Port in South Korea is used as a case study. Based on the stated preference data collected from domestic shippers, a discrete choice analysis is performed to estimate the future market share of three major ports in South Korea: Busan, Gwangyang, and Incheon. The estimated market share of Incheon New Port is used to forecast its future container volume derived by the ARIMA model and the potential port development scenarios.

This study addresses the problem of determining the ship speed and bunkering ports in a ship route. All of the previous research has investigated the ship speed optimization issues by assuming that the ship navigates at constant speed or by ignoring the bunkering port decision. In this study, the problem of determining the variable speed and bunkering port is formulated mathematically with a nonlinear program in order to minimize the bunker fuel, ship time costs, and carbon tax imposed on greenhouse gas emissions. This study then provides a Lagrangian heuristic by deriving a property for a relaxed problem. The performance of the heuristic is evaluated and analysed using the data obtained from the literature, real practice and random generation.

This paper presents a case study on the problems which result when loading air containers onto DHL and Air Hong Kong air express carriers. Problems often arise when trying to determine which containers should be loaded and where the containers should be located in an aircraft in order for it to maintain stability. The objective of this activity is to maximize the revenue obtained from delivering containers. We will present an integer programming model which will attempt to optimally solve the problem. Computational experiments which have been done on a number of randomly generated test instances show that the integer programming model can be a viable tool for generating loading plans within the companies since optimal or near-optimal solutions were obtained during the test instances within a reasonable amount of computation time.

This paper considers a multimodal transportation problem, which is the problem of determining the transportation flow, i.e. volume of container cargoes, and the transportation mode in each trade route, for the objective of minimizing the sum of shipping and inland transportation costs. The problem takes account of two restrictions: maximum cargo volumes capacitated at each seaport and maximum number of vehicles available at each transportation mode. To solve optimally the problem, this paper employs a mixed integer programming, which is an operations research technique. A case study is performed on the container cargo data in Korea and we draw several implications to improve efficiency in the transportation of international trade cargoes in Korea.

Disassembly scheduling, one of the important operational problems in disassembly systems, can be generally defined as the problem of determining the quantity and timing of the end-of-use/life products while satisfying the demand of their parts over a planning horizon. This paper presents a literature review on this planning problem in disassembly systems. First, the basic form of the problem is defined with a mathematical formulation. To characterize the differences between assembly and disassembly processes, the effects of the divergence property are also explained with respect to the zero inventory property, indispensable surplus inventory, and mathematical representation. Then, we review the existing research articles on the basic problem and its generalizations. A systematic scheme for classifying problems is also suggested. Finally, we suggest several future research directions.

This paper considers the problem of determining the disassembly schedule (quantity and timing) of products in order to satisfy the demand of their parts or components over a finite planning horizon. The objective is to minimize the sum of set-up, disassembly operation, and inventory holding costs. As an extension of the uncapacitated versions of the problem, we consider the resource capacity restrictions over the planning horizon. An integer program is suggested to describe the problem mathematically, and to solve the problem, a heuristic is developed using a Lagrangean relaxation technique together with a method to find a good feasible solution while considering the trade-offs among different costs. The effectiveness of the algorithm is tested on a number of randomly generated problems and the test results show that the heuristic suggested in this paper can give near optimal solutions within a short amount of computation time.

This paper addresses the problem of determining the ship speed, fleet size, and chartered ship number subject to environmental regulations consisting of a carbon tax and an emission trading scheme. The objective of the problem is to minimise bunker consumption cost, ship operating cost, ship charterage and either the carbon tax or the revenue/cost of selling/purchasing CO2 emissions permits. We formulate the problems as non-linear programmes by considering the two regulations independently. This study also suggests Lagrangian heuristics to solve the problems. A case study is performed to evaluate the applicability of the heuristics.