Search

• Bike repositioning problem with service requirements for rental and return requests was studied. • A mathematical model with the goal of minimizing the travel and unsatisfied costs was developed. • An efficient two-phase heuristic approach based on linear programming was proposed. • The proposed approach performs better than CPLEX optimizer and a genetic algorithm-based method. • The effects of various system parameters on the system were also investigated. An approach to overcome the bike imbalance problem is to transfer excess bikes to branches with bike shortages. This study develops a constrained mathematical model to deal with a multi-vehicle bike-repositioning problem, and aims to minimize the sum of transportation and unmet demand costs over a planning horizon through bike-transfer strategies under a minimum service requirement. A two-phase heuristic based on linear programming was proposed to solve the problem and produce compromising solutions. In the first phase, the paper developed a linear programming model to quickly develop decisions related to bike inventory, unloading, and loading for all stations for each time slot. In the second phase, this paper proposed an iterative approach through two parameter sensitive mathematical models to sequentially reduce the problem scale to develop decisions related to bike transfers. Computational results show that the proposed approach is superior to a CPLEX optimizer and a hybrid heuristic based on a genetic algorithm. The proposed approach was used to analyze the bicycle system in Taiwan. The impacts of various system parameters on the system were also investigated. [ABSTRACT FROM AUTHOR]

In mixed-product assembly line sequencing, the production resources required for the assembly lines should be scheduled to minimize the overall cost and meet customer demand. In this paper, we study an assembly line sequencing problem for the door-lock industry in Taiwan and develop an integer programming formulation with realistic constraints. The complex solution space makes the resulting program difficult to solve using commercial optimization packages. Therefore, a heuristic based on the Lagrangian relaxation principle is developed to solve this problem efficiently. We evaluate the efficiency of the developed Lagrangian relaxation heuristic by comparing its solutions with those obtained using a commercial optimization package: the computational results show that the developed heuristic solves the real-world problem faster than the optimization package by almost 15 times in CPU time at a comparable solution quality. [ABSTRACT FROM AUTHOR]

Abstract: Harnessing the supply base is an important but complex task for enterprises. Supplier performance evaluation is increasingly seen as a strategic issue for companies to maintain and enhance the competitive edge. However, evaluating suppliers is complicated by the fact that various criteria must be considered in the decision-making process, and is inherently a multicriteria decision-making (MCDM) problem. It also concerns the evaluation by different experts of multiple attributes, both qualitative and quantitative. To perceive and to estimate effectively the capability of suppliers are real arduous tasks for executives. This paper takes advantage of the 2-tuple linguistic computing to coping with the heterogeneity and information loss problems while the evaluation processes of subjective integration. The proposed approach based on the group decision-making scenario assists executives in adroit manipulation of the heterogeneity during integration processes and averts the information loss effectively. Finally, we demonstrate the validity and feasibility by means of a high-technology company in Taiwan. [Copyright &y& Elsevier]

Abstract: This paper introduced a stochastic programming model to address the air freight hub location and flight routes planning under seasonal demand variations. Most existing approaches to airline network design problems are restricted to a deterministic environment. However, the demand in the air freight market usually varies seasonally. The model is separated into two decision stages. The first stage, which is the decision not affected by randomness, determines the number and the location of hubs. The second stage, which is the decision affected by randomness, determines the flight routes to transport flows from origins to destinations based upon the hub location and realized uncertain scenario. Finally, the real data based on the air freight market in Taiwan and China is used to test the proposed model. [Copyright &y& Elsevier]