Integrated airline crew scheduling: A bi-dynamic constraint aggregation method using neighborhoods

The integrated crew scheduling (ICS) problem consists of determining, for a set of available crew members, least-cost schedules that cover all flights and respect various safety and collective agreement rules. A schedule is a sequence of pairings interspersed by rest periods that may contain days off. A pairing is a sequence of flights, connections, and rests starting and ending at the same crew base. Given its high complexity, the ICS problem has been traditionally tackled using a sequential two-stage approach, where a crew pairing problem is solved in the first stage and a crew assignment problem in the second stage. Recently, Saddoune et al. (2010b) developed a model and a column generation/dynamic constraint aggregation method for solving the ICS problem in one stage. Their computational results showed that the integrated approach can yield significant savings in total cost and number of schedules, but requires much higher computational times than the sequential approach. In this paper, we enhance this method to obtain lower computational times. In fact, we develop a bi-dynamic constraint aggregation method that exploits a neighborhood structure when generating columns (schedules) in the column generation method. On a set of seven instances derived from real-world flight schedules, this method allows to reduce the computational times by an average factor of 2.3, while improving the quality of the computed solutions.     

Integrated capacity and inventory management with capacity acquisition lead times

We model a make-to-stock production system that utilizes permanent and contingent capacity to meet non-stationary stochastic demand, where a constant lead time is associated with the acquisition of contingent capacity. We determine the structure of the optimal solution concerning both the operational decisions of integrated inventory and flexible capacity management, and the tactical decision of determining the optimal permanent capacity level. Furthermore, we show that the inventory (either before or after production), the pipeline contingent capacity, the contingent capacity to be ordered, and the permanent capacity are economic substitutes. We also show that the stochastic demand variable and the optimal contingent capacity acquisition decisions are economic complements. Finally, we perform numerical experiments to evaluate the value of utilizing contingent capacity and to study the effects of capacity acquisition lead time, providing useful managerial insights.     

FISHERY SHARE SYSTEMS,ITQ MARKETS,AND THE DISTRIBUTION OF RENTS

Although, in most commercial fisheries, fishing crews are remunerated under a share system, the implications of share systems for individual transferable quotas markets have received relatively little attention. In this paper, we model the impact of extending crew shares of vessel operating costs to include payments for quota. Allocative efficiency is maintained as long as any share system is adopted consistently across the entire fleet. Making crews bear a share of quota costs, however, simply inflates the quota price: at market equilibrium the vessel owner's profit share is unaffected. Crews lose out if the vessel is leasing quota in, but gain if the vessel owner is a net seller of quota. We also consider the outcome if only net purchasers of quota involve crews in the cost of quota. Here, all vessel owners benefit, while all crews see a reduction in their earnings. These results are illustrated with a simple numerical example.     

Inventory control with an order-time constraint: optimality,uniqueness and significance

This paper analyzes a stochastic inventory problem with an order-time constraint that restricts the times at which a manufacturer places new orders to a supplier. This constraint stems from the limited upstream capacity in a supply chain, such as production capacity at a supplier or transportation capacity between a supplier and a manufacturer. Consideration of limited upstream capacity extends the classical inventory literature that unrealistically assumes infinite supplier/transporter capacity. But this consideration increases the complexity of the problem. We study the constraint under a Poisson demand process and allow for a fixed ordering cost. In presence of the constraint, we establish the optimality of an (s,S) policy under both the discounted and average cost objectives. Under the average cost objective, we show the uniqueness of the order-up-to level S. We numerically compare our model with the classical unconstrained model. We report significant savings in costs that can be achieved by using our model when the order time is constrained.     

Aircrew schedule generation using repeated matching

We consider the Aircrew Scheduling Problem of determining tours of duty (TODs) for aircrews, given a set of sectors (or flights) requiring regular crews. A regular crew consists of two crew members, but by including supplementary crew (a third pilot) on some sectors it is possible to extend duty periods to generate more cost efficient TODS. A related problem is thus to generate TODs for these third pilots, but the sectors requiring a third pilot are not known in advance. To solve these two related problems simultaneously, we apply a heuristic procedure that solves a sequence of matching problems, i.e. a repeated matching algorithm. Numerical results based on the solution of a real problem show that this approach is a valid and efficient method for solving the Aircrew Scheduling Problem, especially when there is the option of using supplementary crew to extend duty periods.     

Scheduling programs with repetitive projects: A comparison of a simulated annealing,a genetic and a pair-wise swap algorithm

We address the problem of scheduling in programs involving the production of multiple units of the same product. Our study was motivated by a construction program for fast naval patrol boats. Other applications of this problem include procurement of multiple copies of aircraft, spacecraft, and weapon systems. In this problem we must decide how many units of the product to assign to each of a number of available crews (individuals, teams, subcontractors, etc.). These types of problems are characterized by two potentially conflicting considerations: 1) the need to complete each unit by its contractual due date, and 2) learning effects. Because of the first consideration, there is a tendency to use multiple crews for simultaneous production, so that meeting due dates is assured. However, the second consideration encourages assigning many units to a single crew so that learning effects are maximized. We study this scheduling problem with two different penalty cost structures and develop models for both versions. The models trade-off the penalty associated with late deliveries and the savings due to learning (and possibly incentive payments for early completion). We discuss different heuristic algorithms — simulated annealing, a genetic algorithm, and a pair-wise swap heuristic — as well as an exhaustive search to determine a baseline for comparisons. Our computational results show that the pair-wise swap algorithm is the most efficient solution procedure for these models.     

Simultaneous optimization of capacity and planned lead time in a two-stage production system with different customer due dates

We consider a two-stage make-to-order manufacturing system with random demands, processing times, and distributed customer due dates. The work to each stage is released based on a planned lead time. A general approach to minimize total inventory holding and customer order tardiness cost is presented to find the optimal manufacturing capacities and planned lead times for each manufacturing stage. Expressions are derived for work-in process inventories, finished-goods-inventory and expected backorders under the assumption of a series of M/M/1 queuing systems and exponentially distributed customer required lead times. We prove that the distribution of customer required lead time has no influence on the optimal planned lead times whenever capacity is predefined but it influences the optimal capacity to invest into. For the simultaneous optimization of capacity and planned lead times we present a numerical study that shows that only marginal cost decreases can be gained by setting a planned lead time for the upstream stage and that a considerable cost penalty is incurred if capacity and planned lead time optimization are performed sequentially.     

航空公司机组排班计划研究

以中国国际航空公司北京-成都航班为例,提出一种航空公司制定机组排班计划的新方法。首先以机组异地停留时间最短为目标,应用匈牙利算法生成“机组航班串”;然后,应用人员排班方法求得保证机组每周连休两日的条件下完成“机组航班串”飞行任务的最少机组数;最后,对这些机组制定具体的排班计划。应用该方法制定的机组排班计划使得航空公司在保证机组每周连休两日的条件下能够以最少的机组完成航班飞行任务,且机组在异地的停留时间最短。     

Production capacity planning and control in multi-stage manufacturing

This paper develops a mathematical model for determining the optimum lot-sizes for a set of products and the capacity required to produce them in a multi-stage production system. The purpose of the modelling is to support capacity planning at the production function level and the basic criterion considered for the optimisation is the minimisation of the total system cost (TSC) per unit time. The TSC consists of (i) set-up cost, (ii) cost due to the quenching of batches, and (iii) hiring cost of the machines. An example is presented to explain the model.     

The crew timetabling problem: An extension of the crew scheduling problem

In some urban transportation companies driving periods are short when compared with the total duty time, leading to long non-driving periods that can be used as cover time. This paper presents the Crew Timetabling Problem, an extension of the Crew Scheduling Problem in which crew timetables are obtained by levelling the cover crew resources. An objective function for this problem is proposed in order to balance the number of driving and cover crews. A Lisbon Underground case study is used to illustrate the Crew Timetabling Problem. The problem is represented in a multigraph and solved by a tabu search-based heuristic.     

Solving large scale crew scheduling problems

The crew pairing problem is posed as a set partitioning zero-one integer program. Variables are generated as legal pairings meeting all work rules. Dual values obtained from solving successive large linear program relaxations are used to prune the search tree. In this paper we present a graph based branching heuristic applied to a restricted set partitioning problem representing a collection of ‘best’ pairings. The algorithm exploits the natural integer properties of the crew pairing problem. Computational results are presented to show realized crew cost savings.     

Optimization of operating strategies in a community solar heating system

Minimization of auxiliary energy costs is discussed for heating in a district solar heating system with effective heat storage. The minimization problem is approached by dynamic programming which gives an optimal operating strategy for the auxiliary energy system. The effects of different pricing schemes of auxiliary energy (electricity) have been studied. The results show that with an adequate heat storage capacity, the optimization of the auxiliary energy use in a community solar heating system may lead to considerable cost savings.     

Airline Crew Scheduling: State-of-the-Art

The airline industry is faced with some of the largest scheduling problems of any industry. The crew scheduling problem involves the optimal allocation of crews to flights. Over the last two decades the magnitude and complexity of crew scheduling problems have grown enormously and airlines are relying more on automated mathematical procedures as a practical necessity. In this paper we survey different approaches studied and discuss the state-of-the-art in solution methodology for the airline crew scheduling problem. We conclude with a discussion about promising areas for further work to make it possible to get very good solutions for the crew scheduling problem.     

A tree search algorithm for the crew scheduling problem

In this paper we consider the crew scheduling program, that is the problem of assigning K crews to N tasks with fixed start and finish times such that each crew does not exceed a limit on the total time it can spend working.A zero-one integer linear programming formulation of the problem is given, which is then relaxed in a lagrangean way to provide a lower bound which is improved by subgradient optimisation. Finally a tree search procedure incorporating this lower bound is presented to solve the problem to optimality.Computational results are given for a number of randomly generated test problems involving between 50 and 500 tasks.     

Reducing lost-sales rate in (T, R, L) inventory model with controllable lead time

In order to establish a good image and to enhance customer’s loyalty, many efforts such as upgrading the servicing facilities, maintaining a high quality of products and increasing expenditure on advertisement could be made by a selling shop. Naturally, an extra-added cost must be spent for these efforts and it is expected to have a result to reduce the shortage cost of lost-sales and the total expected annual cost. This paper explores a probabilistic inventory model with optimal lost-sales caused by investment due to two different types of cost functions. We consider that the lead time can be shortened at an extra crashing cost, which depends on the length of the lead time. Moreover, we assume that the lost-sales rate can also be reduced by capital investment. The purpose of this paper is to establish a (T, R, L) inventory model with controllable lead time and to analyze the effects of increasing two different types of investments to reduce the lost-sales rate, in which the review period, lead time and lost-sales rate are treated as decision variables. We first formulate the basic periodic review model mathematically with the capital investment to reduce lost-sales rate. Then two models are discussed, one with normally distributed protection interval demand and another with distribution-free case. For each model, two investment cost functional forms, logarithmic and power, are employed for lost-sales rate reduction. Two computational algorithms with the help of the software Matlab are furnished to determine the optimal solution. In addition, six numerical examples and sensitivity analysis are presented to illustrate the theoretical results and obtain some managerial insights. Finally, the effect of lost-sales rate reduction is investigated. By framing this new model, we observe that a significant amount of savings can be easily achieved to increase the competitive edge in business. The results in the numerical examples indicate that the savings of expected annual total cost are realized through lost-sales reduction.     

Balancing capacity and lot sizes

In many companies there is an on-going discussion about capacity, capacity utilization and capital tied up in inventories. However, traditional models such as the EOQ model only include capacity considerations in the set-up cost, or in the cost of a replenishment order. This implies e.g. that they do not consider the set-up time as a capacity constraint. Furthermore, in these models the set-up cost is usually treated as a constant, even though the opportunity cost for capacity in general is dependent upon the capacity utilization.The purpose of this paper is to derive an analytical model for the balancing of capacity and lot sizes. The model includes costs for capacity, work-in-process (queueing, set-up, and processing time), and finished goods inventory. The total costs are minimized with respect to capacity. Then, the corresponding, recommended lot sizes are determined. The model was tested with data from a Swedish manufacturing company. The results turned out to coincide with experiences of the company in many important respects. The model offers production management an opportunity to discuss the relationship between capacity, work-in-process, and lot sizes.     

Joint optimization of fleet size and maintenance capacity in a fork-join cyclical transportation system

This article presents an asset management-oriented multi-criteria methodology for the joint estimation of a mobile equipment fleet size, and the maintenance capacity to be allocated in a productive system. Using a business-centred life-cycle perspective, we propose an integrated analytical model and evaluate it using global cost rate, availability and throughput as performance indicators. The global cost components include: (i) opportunity costs associated with lost production, (ii) vehicle idle time costs, and (iii) maintenance resources idle time costs. This multi-criteria approach allows a balanced scorecard to be built that identifies the main trade-offs in the system. The methodology uses an improved closed network queueing model approach to describe the production and maintenance areas. We test the proposed methodology using an underground mining operation case study. The decision variables are the size of a load-haul-dump fleet and specialized maintenance crew levels. Our model achieves savings of 20.6% in global cost terms with respect to a benchmark case. We also optimize the system to achieve desired targets of vehicle availability and system throughput (based on system utilization). The results show increments of 7.1% in vehicle availability and 13.5% in system throughput with respect to baseline case. For the case studied, these criteria also have a maximum, which allows for further improvement if desired. The results also show the importance of using balanced performance measures in the decision process. A multi-criteria optimization was also performed, showing the Pareto front of considered indicators. We discuss the trade-offs among different criteria, and the implications in finding balanced solutions. The proposed analytical approach is easy to implement and requires low computational effort. It also allows for an easy re-evaluation of resources when the business cycle changes and relevant exogenous factors vary.     

Algorithms for railway crew management

Crew management is concerned with building the work schedules of crews needed to cover a planned timetable. This is a well-known problem in Operations Research and has been historically associated with airlines and mass-transit companies. More recently, railway applications have also come on the scene, especially in Europe. In practice, the overall crew management problem is decomposed into two subproblems, called crew scheduling and crew rostering. In this paper, we give an outline of different ways of modeling the two subproblems and possible solution methods. Two main solution approaches are illustrated for real-world applications. In particular we discuss in some detail the solution techniques currently adopted at the Italian railway company, Ferrovie dello Stato SpA, for solving crew scheduling and rostering problems.     

A model and computational tool for crew scheduling in train transportation of mine materials by using a local search strategy

This work introduces a model of the crew scheduling problem for the operation of trains in the mining industry in the North of Chile. The model possesses particular features due to specific regulations with which train operators in mine material transportation are required to comply: every week, a fixed set of trips must be made according to current demand for the transportation of mine products and supplies. In order to balance the loads of the crews in the long term, the proposed model generates an infinite horizon schedule by means of a rotative scheme in which each crew takes the place of the previous one at the beginning of the next week. This gives rise to a medium/large size 0–1 linear optimization problem, whose solution represents the optimal assignment of drivers to trips with the number of working hours per week distributed equally among crews. The model and algorithm have been implemented with a user interface suitable for the remote execution of real instances on a High Performance Computing platform. The transportation company regularly uses this computerized tool for planning crew schedules and generating efficient assignments for emerging and changing operational conditions.     

An inventory model for slow moving items subject to obsolescence

In this paper, we consider a continuous review inventory system of a slow moving item for which the demand rate drops to a lower level at a known future time instance. The inventory system is controlled according to a one-for-one replenishment policy with a fixed lead time. Adapting to lower demand is achieved by changing the control policy in advance and letting the demand take away the excess stocks. We show that the timing of the control policy change primarily determines the tradeoff between backordering penalties and obsolescence costs. We propose an approximate solution for the optimal time to shift to the new control policy minimizing the expected total cost during the transient period. We find that the advance policy change results in significant cost savings and the approximation yields near optimal expected total costs.