A decision method for supplier selection in multi-service outsourcing

Although supplier selection in multi-service outsourcing is a very important decision problem, research concerning this issue is still relatively scarce. This paper proposes a decision method for selecting a pool of suppliers for the provision of different service process/product elements. It pioneers the use of collaborative utility between partner firms for supplier selection. A multi-objective model is built to select desired suppliers. This model is proved to be NP-hard, so we develop a multi-objective algorithm based on Tabu search for solving it. We then use an example to show the applicability of the proposed model and algorithm. Extensive computational experiments are also conducted to further test the performance of the proposed algorithm.     

Non-permutation flowshop scheduling in a supply chain with sequence-dependent setup times

In this paper, we consider a flowshop scheduling problem with sequence-dependent setup times and a bicriteria objective to minimize the work-in-process inventory for the producer and to maximize the customers' service level. The use of a bicriteria objective is motivated by the fact that successful companies in today's environment not only try to minimize their own cost but also try to fulfill their customers' need. Two main approaches, permutation and non-permutation schedules, are considered in finding the optimal schedule for a flowshop. In permutation schedules the sequence of jobs remains the same on all machines whereas in non-permutation schedule, jobs can have different sequence on different machines. A linear mathematical model for solving the non-permutation flowshop is developed to comply with all of the operational constraints commonly encountered in the industry, including dynamic machine availabilities, dynamic job releases, and the possibility of jobs skipping one or more machines, should their operational requirements deem that it was necessary. As the model is shown to be NP-hard, a metasearch heuristic, employing a newly developed concept known as the Tabu search with embedded progressive perturbation (TSEPP) is developed to solve, in particular, industry-size problems efficiently. The effectiveness and efficiency of the search algorithm are assessed by comparing the search algorithmic solutions with that of the optimal solutions obtained from CPLEX in solvable small problem instances.     

Optimization of postal express line network under mixed driving pattern of trucks

Details about the movement of trucks on postal express lines are investigated to improve the performances of mail distribution. A mixed driving pattern of trucks is introduced to minimize the transportation cost of a postal express line network with a service level requirement. We formulate this problem as a mixed p meeting depots location with shipment scheduling problem and build a MINLP model. A two-level tabu search procedure based on shipment grouping method is developed. Through a series of computational experiments and sensitivity analysis on different instances, some managerial insights of the network under mixed driving pattern are revealed.     

A tabu search heuristic for redesigning a multi-echelon supply chain network over a planning horizon

This paper addresses the problem of redesigning a supply chain network with multiple echelons and commodities. The problem belongs to a comprehensive class of network redesign problems previously introduced in the literature. Redesign decisions comprise the relocation of existing facilities to new sites under an available budget over a finite time horizon, the supply of commodities by upstream facilities, the inventory levels at storage facilities, and the flow of commodities through the network. The problem is modeled as a large-scale mixed-integer linear program. Feasible solutions are obtained by using a tabu search procedure that explores the space of the facility location variables. The latter prescribe the time periods in which changes in the network configuration occur. They are triggered by the setup of new facilities, which operate with capacity transferred from the existing facilities, and by closing the latter upon their entire relocation. As the problem is highly constrained, infeasible solutions with excess budget are allowed during the course of the search process. However, such solutions are penalized for their infeasibility. Computational experiments on realistically sized randomly generated instances indicate that this strategic oscillation scheme used in conjunction with tabu search performs very well.     

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.     

Optimising flight connection times in airline bank structure through Simulated Annealing and Tabu Search algorithms

In hub and spoke airline networks, flight arrivals and departures generally have a bank structure to increase connections among spoke cities through a hub airport in order to provide cheaper service for higher volumes of air traffic. In this study, we introduce the airline bank optimisation problem with a novel mathematical model for improving flight connection times. The mathematical model aims to minimise the total connection times for transfer passengers and generates flight schedules regarding slot capacities in the hub airports. Since the problem is a combinatorial optimisation problem NP-hard and computational complexity increases rapidly for real-world problems, we employ the simulated annealing and the tabu search algorithms to achieve better solutions in a reasonable time. We generate sub-problems using real-world data and investigate the effectiveness of the algorithms. Finally, we present the results of a real case study of a Turkish airline company which has a hub airport connecting the flights between Middle Eastern and European cities.     

Solving a static repositioning problem in bike-sharing systems using iterated tabu search

In this paper, we study the static bike repositioning problem where the problem consists of selecting a subset of stations to visit, sequencing them, and determining the pick-up/drop-off quantities (associated with each of the visited stations) under the various operational constraints. The objective is to minimize the total penalties incurred at all the stations. We present an iterated tabu search heuristic to solve the described problem. Experimental results show that this simple heuristic can generate high quality solutions using small computing times.     

单车场大规模车辆路径优化问题研究

针对传统优化技术在解决大规模车辆路径问题中存在的缺陷,提出了一种解决单车场大规模车辆路径优化问题的综合启发式算法。首先,采用Sweep技术将区域分解成几个子区。其次,设计了分区的禁忌搜索算法,并采用相邻区域综合优化技术,提高了算法的全局搜索能力。仿真试验表明,该算法能够有效解决大规模车辆路径优化问题。     

禁忌搜索算法在编组站调机运用计划中的应用

在分析论述调机运用计划编制方法的基础上,提出应用禁忌搜索算法进行编组站调机运用计划的编制。分别以最小化延迟解体列车和编组列车加权数量为目标建立数学模型,以解编顺序作为优化对象,设计禁忌搜索算法对其进行求解,并以解体顺序为例,采用两两交换(2-opt)方式构建邻域,以该操作前后列车解体顺序的变化作为禁忌对象构建禁忌表,利用软件编程实现模型计算,并通过算例验证该算法的可行性和有效性。     

Stochastic Optimization: a Review

We review three leading stochastic optimization methods—simulated annealing, genetic algorithms, and tabu search. In each case we analyze the method, give the exact algorithm, detail advantages and disadvantages, and summarize the literature on optimal values of the inputs. As a motivating example we describe the solution—using Bayesian decision theory, via maximization of expected utility—of a variable selection problem in generalized linear models, which arises in the cost-effective construction of a patient sickness-at-admission scale as part of an effort to measure quality of hospital care.