Dynamic cost allocation for economic lot sizing games

We consider a cooperative game defined by an economic lot sizing problem with concave ordering costs over a finite time horizon, in which each player faces demand for a single product in each period and coalitions can pool orders. We show how to compute a dynamic cost allocation in the strong sequential core of this game, i.e. an allocation over time that exactly distributes costs and is stable against coalitional defections at every period of the time horizon.     

Optimal dynamic pricing of inventories with stochastic demand and discounted criterion

We consider a continuous time dynamic pricing problem for selling a given number of items over a finite or infinite time horizon. The demand is price sensitive and follows a non-homogeneous Poisson process. We formulate this problem as to maximize the expected discounted revenue and obtain the structural properties of the optimal revenue function and optimal price policy by the Hamilton-Jacobi-Bellman (HJB) equation. Moreover, we study the impact of the discount rate on the optimal revenue function and the optimal price. Further, we extend the problem to the case with discounting and time-varying demand, the infinite time horizon problem. Numerical examples are used to illustrate our analytical results.     

具有学习效应的三层供应链排序问题

研究了具有学习效应的三层供应链排序问题. 多个客户分布在不同位置,每个客户都有订 单需要制造商进行生产. 制造商需要针对每一个不同订单的客户从不同的地方进购对应的原材料进行生产,生产完工后需要利用有限的车辆将工件运输到相应客户处. 要求每辆运输车装载尽可 能多的货物才开始运输. 利用动态规划算法研究了最大流程时间、总流程时间以及最大延迟三个目标函数.     

Order-lot pegging for minimizing total tardiness in semiconductor wafer fabrication process

We consider a problem of order-lot pegging in semiconductor wafer fabrication process. In the problem, we determine an assignment of wafers in lots to orders and a plan for input release of wafers into a wafer fabrication facility with the objective of minimizing total tardiness of the orders over a finite time horizon. The problem is formulated as a mixed integer linear program and proved to be strongly NP-hard. We find properties for an optimal order-lot assignment of the problem and develop a pegging method based on the properties. Also, we prove that an optimal order-lot assignment can be obtained by finding an optimal order sequence of assigning wafers to orders when using the pegging method developed in this study. In addition, we suggest two search heuristic algorithms for finding the optimal order sequence of assigning wafers to orders. The test results on randomly generated problems show that the suggested algorithms work fairly well compared to the commercial optimization software package and solve industrial-sized problems in a reasonable amount of time.     

A tabu-based large neighbourhood search methodology for the capacitated examination timetabling problem

Neighbourhood search algorithms are often the most effective known approaches for solving partitioning problems. In this paper, we consider the capacitated examination timetabling problem as a partitioning problem and present an examination timetabling methodology that is based upon the large neighbourhood search algorithm that was originally developed by Ahuja and Orlin. It is based on searching a very large neighbourhood of solutions using graph theoretical algorithms implemented on a so-called improvement graph. In this paper, we present a tabu-based large neighbourhood search, in which the improvement moves are kept in a tabu list for a certain number of iterations. We have drawn upon Ahuja–Orlin's methodology incorporated with tabu lists and have developed an effective examination timetabling solution scheme which we evaluated on capacitated problem benchmark data sets from the literature. The capacitated problem includes the consideration of room capacities and, as such, represents an issue that is of particular importance in real-world situations. We compare our approach against other methodologies that have appeared in the literature over recent years. Our computational experiments indicate that the approach we describe produces the best known results on a number of these benchmark problems.     

Disjoint congruence classes and a timetabling application

We consider a combinatorial problem motivated by a special simplified timetabling problem for subway networks. Mathematically the problem is to find (pairwise) disjoint congruence classes modulo certain given integers; each such class corresponds to the arrival times of a subway line of a given frequency. For a large class of instances we characterize when such disjoint congruence classes exist and how they may be determined. We also study a generalization involving a minimum distance requirement between congruence classes, and a comparison of different frequency families in terms of their “efficiency”. Finally, a general method based on integer programming is also discussed.     

Optimal production plans and shipment schedules in a supply-chain system with multiple suppliers and multiple buyers

This research addresses an optimal policy for production and procurement in a supply-chain system with multiple non-competing suppliers, a manufacturer and multiple non-identical buyers. The manufacturer procures raw materials from suppliers, converts them to finished products and ships the products to each buyer at a fixed-interval of time over a finite planning horizon. The demand of finished product is given by buyers and the shipment size to each buyer is fixed. The problem is to determine the production start time, the initial and ending inventory, the cycle beginning and ending time, the number of orders of raw materials in each cycle, and the number of cycles for a finite planning horizon so as to minimize the system cost. A surrogate network representation of the problem developed to obtain an efficient, optimal solution to determine the production cycle and cycle costs with predetermined shipment schedules in the planning horizon. This research prescribes optimal policies for a multi-stage production and procurements for all shipments scheduled over the planning horizon. Numerical examples are also provided to illustrate the system.     

Stock repurchase with an adaptive reservation price: A study of the greedy policy

We consider the problem of stock repurchase over a finite time horizon. We assume that a firm has a reservation price for the stock, which is the highest price that the firm is willing to pay to repurchase its own stock. We characterize the optimal policy for the trader to maximize the total number of shares that they can buy over a fixed time horizon. In particular, we study a greedy policy, which involves in each period buying a quantity that drives stock price to the reservation price.     

Risk-sensitive stopping problems for continuous-time Markov chains

In this paper we consider stopping problems for continuous-time Markov chains under a general risk-sensitive optimization criterion for problems with finite and infinite time horizon. More precisely our aim is to maximize the certainty equivalent of the stopping reward minus cost over the time horizon. We derive optimality equations for the value functions and prove the existence of optimal stopping times. The exponential utility is treated as a special case. In contrast to risk-neutral stopping problems it may be optimal to stop between jumps of the Markov chain. We briefly discuss the influence of the risk sensitivity on the optimal stopping time and consider a special house selling problem as an example.     

A decomposed metaheuristic approach for a real-world university timetabling problem

In this paper we present a decomposed metaheuristic approach to solve a real-world university course timetabling problem. Essential in this problem are the overlapping time slots and the irregular weekly timetables. A first stage in the approach reduces the number of subjects through the introduction of new structures that we call ‘pillars’. The next stages involve a metaheuristic search that attempts to solve the constraints one by one, instead of trying to find a solution for all the constraints at once. Test results for a real-world instance are presented.     

工作有到达时间且拒绝工件总个数受限的单机平行分批排序问题的近似算法

考虑了工件有到达时间且拒绝工件总个数不超过某个给定值的单机平行分批排序问题.在该问题中,给定一个工件集和一台可以进行批处理加工的机器.每个工件有它的到达时间和加工时间;对于每个工件来说要么被拒绝要么被接受安排在机器的某一个批次里进行加工;一个工件如果被拒绝,则需支付该工件对应的拒绝费用.为了保证一定的服务水平,要求拒绝工件的总个数不超过给定值.目标是如何安排被接受工件的加工批次和加工次序使得其最大完工时间与被拒绝工件的总拒绝费用之和最小.该问题是NP-难的,对此给出了伪多项式时间动态规划精确算法,2-近似算法和完全多项式时间近似方案.     

Planning and scheduling of multistage multiproduct batch plants operating under production campaigns

When plants are operated under stable conditions during reasonable time periods, operation with campaigns is particularly appropriate. The regular operation of the facilities simplifies the production control, the inventory management, the plant operability, etc. A?campaign includes several batches of different products that are going to be manufactured and the same one is cyclically repeated over the time horizon. In this work, a mixed integer linear programming formulation is proposed for the planning and scheduling of given multiproduct batch plants operating with campaigns. The number and size of batches for each product, the campaign composition, the assignment of batches to units and their sequencing, and the number of times that the campaign is repeated over the time horizon must be determined. Taking into account this scenario, an appropriate performance measure is the minimization of the cycle time. An asynchronous slot-based continuous-time representation for modeling the assignment of batches to units and their sequencing is employed, and a novel rule for determining the maximum number of slots postulated for each unit is proposed.     

A multi-period order selection problem in flexible manufacturing systems

We consider a multi-period order selection problem in flexible manufacturing systems, which is the problem of selecting orders to be produced in each period during the upcoming planning horizon with the objective of minimising earliness and tardiness costs and subcontracting costs. The earliness and tardiness costs are incurred if an order is not finished on time, while subcontracting cost is incurred if an order is not selected within the planning horizon (and must be subcontracted) due to processing time capacity or tool magazine capacity. This problem is formulated as a 0–1 integer program which can be transformed into a generalised assignment problem. To solve the problem, a heuristic algorithm is developed using a Lagrangian relaxation technique. Effectiveness of the algorithm is tested on randomly generated problems and results are reported.     

Master physician scheduling problem

We study a real-world problem arising from the operations of a hospital service provider, which we term the master physician scheduling problem. It is a planning problem of assigning physicians’ full range of day-to-day duties (including surgery, clinics, scopes, calls, administration) to the defined time slots/shifts over a time horizon, incorporating a large number of constraints and complex physician preferences. The goals are to satisfy as many physicians’ preferences and duty requirements as possible while ensuring optimum usage of available resources. We propose mathematical programming models that represent different variants of this problem. The models were tested on a real case from the Surgery Department of a local government hospital, as well as on randomly generated problem instances. The computational results are reported together with analysis on the optimal solutions obtained. For large-scale instances that could not be solved by the exact method, we propose a heuristic algorithm to generate good solutions.     

The periodic capacitated arc routing problem with irregular services

The aim of this paper is to introduce the periodic capacitated arc routing problem with irregular services. Some applications can be found in road maintenance operations and road network surveillance. The problem consists of determining a set of routes to cover a given network over a time horizon. The roads must be serviced a number of times in sub-periods over the time horizon, according to a hierarchy of arc classes. We present a mathematical model and a heuristic solution approach.     

Mixed-integer linear programming for resource leveling problems

We consider project scheduling problems subject to general temporal constraints, where the utilization of a set of renewable resources has to be smoothed over a prescribed planning horizon. In particular, we consider the classical resource leveling problem, where the variation in resource utilization during project execution is to be minimized, and the so-called “overload problem”, where costs are incurred if a given resource-utilization threshold is exceeded. For both problems, we present new mixed-integer linear model formulations and domain-reducing preprocessing techniques. In order to strengthen the models, lower and upper bounds for resource requirements at particular points in time, as well as effective cutting planes, are outlined. We use CPLEX 12.1 to solve medium-scale instances, as well as instances of the well-known test set devised by Kolisch et al. (1999). Instances with up to 50 activities and tight project deadlines are solved to optimality for the first time.     

Customer selection problem with profit from a sideline

This paper deals with the problem of selecting profitable orders to accept out of customers sequentially arriving at companies operating in service industries which provide specialized services designed to meet the various needs of their customers. When all the orders accepted up to a point of time are completed and delivered, the companies provide subsidiary services as a sideline in order to prevent their system from being idle, and to yield extra income, referred to as the profit from a sideline. Further, a cost is paid to search for customers, called the search cost. We discuss the admission control problem and pricing control problem in an identical framework. Properties of the optimal decision rule maximizing the total expected present discounted net profit gained over an infinite planning horizon are examined and clarified. It is shown that when the profit from the sideline is large, the optimal policies may not be monotone in the number of orders in the system.     

Scheduling with batch setup times and earliness-tardiness penalties

We consider a scheduling model in which several batches of jobs need to be processed by a single machine. During processing, a setup time is incurred whenever there is a switch from processing a job in one batch to a job in another batch. All the jobs in the same batch have a common due date that is either externally given as an input data or internally determined as a decision variable. Two problems are investigated. One problem is to minimize the total earliness and tardiness penalties provided that each due date is externally given. We show that this problem is NP-hard even when there are only two batches of jobs and the two due dates are unrestrictively large. The other problem is to minimize the total earliness and tardiness penalties plus the total due date penalty provided that each due date is a decision variable. We give some optimality properties for this problem with the general case and propose a polynomial dynamic programming algorithm for solving this problem with two batches of jobs. We also consider a special case for both of the problems when the common due dates for different batches are all equal. Under this special case, we give a dynamic programming algorithm for solving the first problem with an unrestrictively large due date and for solving the second problem. This algorithm has a running time polynomial in the number of jobs but exponential in the number of batches.     

Joint replacement in an operational planning phase

We consider the problem of combining replacements of multiple components in an operational planning phase. Within an infinite or finite time horizon, decisions concerning replacement of components are made at discrete time epochs. The optimal solution of this problem is limited to only a small number of components. We present a heuristic rolling horizon approach that decomposes the problem; at each decision epoch an initial plan is made that addresses components separately, and subsequently a deviation from this plan is allowed to enable joint replacement. This approach provides insight into why certain actions are taken. The time needed to determine an action at a certain epoch is only quadratic in the number of components. After dealing with harmonisation and horizon effects, our approach yields average costs less than 1% above the minimum value.     

An optimal sequential procedure for a multiple selling problem with independent observations

We consider a sequential problem of selling K identical assets over the finite time horizon with a fixed number of offers per time period and no recall of past offers. The objective is to find an optimal sequential procedure which maximizes the total expected revenue. In this paper, we derive an effective number of stoppings for an optimal sequential procedure for the selling problem with independent observations.