ARMS: An automated resource management system for British Telecommunications plc

Accurate demand forecasting combined with resource planning is critical to a company’s performance and profitability. This paper describes ARMS (automated resource management system), an integrated system developed for the customer service operations of British Telecommunications plc to help with the operational/tactical planning and deployment of the company’s 20,000-strong field engineer workforce. ARMS integrates a forecasting tool with a resource planning tool and a resource balancing tool providing an end-to-end automated resource management solution for the organisation. OR techniques are used throughout the system, including ARIMA for forecasting, constraint satisfaction for problem modelling, heuristic search for problem solving thus demonstrating the value and relevance of OR in solving today’s business problems.     

Forest management optimisation models and habitat diversity: a goal programming approach

This paper proposes an analytical framework, based upon Goal Programming, to incorporate an operational measure of habitat diversity into a forest management optimisation model. First of all, the trade-off curve between the proposed measure of habitat diversity and financial returns is determined. Then, the measure of habitat diversity is integrated in conjunction with other relevant criteria into a compact forest management model. The way in which both models work is explained with the help of a simple but illustrative case study.     

An integrated staff-sizing approach considering feasibility of scheduling decision

This paper presents an integrated staff-sizing system for analyzing and determining workforce management policies with consideration of staff flexibility in service organizations, which addresses and captures the integrated requirements between long-term manpower planning and short-term staff scheduling in the service sector. Multiple Objective Linear Programming (MOLP) is applied to optimize several diversified goals. Solution methods to the MOLP models for the staff planning and staff scheduling are developed respectively, then a solution approach is proposed to iteratively revise the unacceptable staff-sizing plan or scheduling plan. Finally, an example of nurse sizing is analyzed and computational studies are carried out to investigate managerial insights.     

Using a multi-agent approach to optimise the train coupling and sharing system

This paper discusses a multi-agent scheduling system using the example of the railway transport system train coupling and sharing (TCS). The scheduling system includes the planning of travel units and the optimisation of solutions. The planning process is implemented as an incremental anytime algorithm that is capable of integrating new task specifications into the ongoing planning process. The initial solution is then optimised by a simulated trading approach. Furthermore, the multi-agent approach is tested on a practical example. The operational parameters and boundaries of the TCS-system are changed to get a comparable rail operation schedule.     

Integrated methodology to design and manage work-in-process buffers in repetitive building projects

An integrated methodology to design and manage buffers in repetitive building projects using work-in-process at conceptual level is proposed. The buffer design framework employs the Multiobjective Analytic Model and Simulation-Optimisation techniques, applied at strategic and tactical scheduling levels. The buffer management framework uses a statistical model to predict work progress, the Reliable Commitment Model, applied at operational scheduling level. The integrated methodology provides a new buffering approach for scheduling repetitive building projects, which considers: (1) a general production framework covering all the production levels from top to bottom; (2) a general modelling structure suitable to any type of repetitive building project; and (3) a sound theoretical and practical framework describing different production scenarios. The benefits of using this methodology are illustrated through a hypothetical project application.     

Equipment scheduling problem under disruptions in mail processing and distribution centres

This paper addresses the production and workforce scheduling problem under disruptions in United States Postal Service mail processing facilities. These facilities contain a large variety of equipment and employ a non-homogeneous workforce that work on shifts of various lengths and start times. Disruptions such as demand fluctuation and absenteeism happen and may significantly change demand and the size of workforce. How to adjust production plans and workforce schedules through the use of overtime and flexible employees in the face of these disruptions to meet the service commitment is a challenging problem yet to be solved. This problem is modelled as a large-scale integer program, which contains equipment scheduling, shift scheduling and overtime management, and break assignment modules. Problems of realistic size are solved efficiently through an LP-based decomposition algorithm. Comprehensive experiments have been designed to investigate the effects of the use of overtime, the control of absenteeism, and the importance of integrating equipment and workforce scheduling simultaneously. The model integrates seamlessly with other research studies and provides the necessary and critical tools to manage the resources in a facility on a routine basis.     

Object-oriented dynamic supply-chain modelling incorporated with production scheduling

In this paper we present a new modelling approach for realistic simulation of supply-chains. It is based on an object-oriented architecture, which enables flexible specification of the supply-chain configuration along with its operational decisions and policies.A model of a generic supply-chain node is developed to capture the features present in all supply-chain entities. The generic node models in detail activities such as inventory control, manufacturing processes and order handling. The supply-chain model is constructed by linking generic nodes and specifying the physical and business attributes of each supply-chain member. The generic-node model may also be linked to external software for greater accuracy, e.g., detailed production scheduling or optimisation.The model provides a fully dynamic simulation of the supply-chain and the effect of various uncertainties can be evaluated through Monte Carlo simulation and other, more efficient, sampling techniques (not described here).A case study is presented to illustrate the applicability of the model. The case study demonstrates how the effect of policy changes on the supply-chain performance under uncertainty can be evaluated before implementation.     

Optimizing the economic lot size of a three-stage supply chain with backordering derived without derivatives

Multi-stage supply chain management integration provides a key to successful international business operations. This is because the integrated approach improves the global system performance and cost efficiency. The integrated production inventory models using differential calculus to solve the multi-variable problems are prevalent in operational research. This paper extends the integrated vendor–buyer inventory problem by Yang and Wee [Yang, P.C., Wee, H.M., 2002. The economic lot size of the integrated vendor–buyer system derived without derivatives. Optimal Control Applications and Methods 23, 163–169] to solve the multi-variable problems in the supply chain, and simplifies the solution procedure using a simple algebraic method. As a result, the solution procedure may be easily understood and applied so as to derive the optimal solution.     

A three-phase matheuristic for the Packaging and Shipping Problem

E-commerce has been continuously growing in the last years to a primary retail market. Recently in France, the threshold of 1 billion of online transactions was overcome. Due to a high demand fluctuation of e-commerce, the workforce sizing for the logistic chain is a challenging problem. Companies have to develop good strategies to have a sustainable workforce size while guaranteeing a high-level service.In this paper, we consider the management of the workforce for a warehouse of an e-commerce company. Specifically, we address issues as i) How the workforce at the warehouse can be determined; ii) What is the daily operational production planning; iii) How the demand peaks can be smoothed, and the production maintained ideally constant over the time horizon.To provide answers to these issues, we introduce the Packaging and Shipping Problem (PSP). The PSP looks for a solution approach that jointly determines the workforce over a multi-period horizon and daily operational plans while minimizing the total logistics cost. We consider two strategies that aim to enhance the flexibility of the process and the efficiency of resources use: reassignment and postponement. To tackle the Packaging and Shipping Problem we propose a model, and a three-phase matheuristic. This heuristic is proved to be competitive with respect to the direct solution of the model with a commercial solver on real-life based instances.     

A decomposed branch-and-price procedure for integrating demand planning in personnel staffing problems

The personnel staffing problem calculates the required workforce size and is determined by constructing a baseline personnel roster that assigns personnel members to duties in order to cover certain staffing requirements. In this research, we incorporate the planning of the duty demand in the staff scheduling problem in order to lower the staffing costs. More specifically, the demand originates from a project scheduling problem with discrete time/resource trade-offs, which embodies additional flexibility as activities can be executed in different modes. In order to tackle this integrated problem, we propose a decomposed branch-and-price procedure. A tight lower and upper bound are calculated using a problem formulation that models the project scheduling constraints and the time-related resource scheduling constraints implicitly in the decision variables. Based upon these bounds, the strategic problem is decomposed into multiple tactical subproblems with a fixed workforce size and an optimal solution is searched for each subproblem via branch-and-price. Fixing the workforce size in a subproblem facilitates the definition of resource capacity cuts, which limit the set of eligible project schedules, decreasing the size of the branching tree. In addition, in order to find the optimal integer solution, we propose a specific search strategy based upon the lower bound and dedicated rules to branch upon the workload generated by a project schedule. The computational results show that applying the proposed search space decomposition and the inclusion of resource capacity cuts lead to a well-performing procedure outperforming different other heuristic and exact methodologies.     

Continuous management of airlift and tanker resources: A constraint-based approach

Efficient allocation of aircraft and aircrews to transportation missions is an important priority at the USAF Air Mobility Command (AMC), where airlift demand must increasingly be met with less capacity and at lower cost. In addition to presenting a formidable optimization problem, the AMC resource management problem is complicated by the fact that it is situated in a continuously executing environment. Mission requests are received (and must be acted upon) incrementally, and, once allocation decisions have been communicated to the executing agents, subsequent opportunities for optimizing resource usage must be balanced against the cost of solution change. In this paper, we describe the technical approach taken to this problem in the AMC barrel allocator, a scheduling tool developed to address this problem and provide support for day-to-day allocation and management of AMC resources. The system utilizes incremental and configurable constraint-based search procedures to provide a range of automated and semi-automated scheduling capabilities. Most basically, the system provides an efficient solution to the fleet scheduling problem. More importantly to continuous operations, it also provides techniques for selectively reoptimizing to accommodate higher priority missions while minimizing disruption to most previously scheduled missions, and for selectively “merging” previously planned missions to minimize nonproductive flying time. In situations where all mission requirements cannot be met, the system can generate and compare alternative constraint relaxation options. The barrel allocator technology is currently transitioning into operational use within AMC's Tanker/Airlift Control Center (TACC). A version of the barrel allocator supporting airlift allocation was first incorporated as an experimental module of the AMC's Consolidated Air Mobility Planning System (CAMPS) in September 2000. In May 2003, a new tanker allocation module is scheduled for initial operational release to users as part of CAMPS Release 5.4.     

Scheduling electric power production at a wind farm

We present a model for scheduling power generation at a wind farm, and introduce a particle swarm optimization algorithm with a small world network structure to solve the model. The solution generated by the algorithm defines the operational status of wind turbines for a scheduling horizon selected by a decision maker. Different operational scenarios are constructed based on time series data of electricity price, grid demand, and wind speed. The computational results provide insights into management of a wind farm.     

A Comparative Study of Multiple-Objective Metaheuristics on the Bi-Objective Set Covering Problem and the Pareto Memetic Algorithm

Many organizations face employee scheduling problems under conditions of variable demand for service over the course of an operating day and across a planning horizon. These organizations are concerned with the tour scheduling problem that involves assigning shifts and break times to the work days of employees and allocating days off to individual work schedules. Nowadays, organizations try to adopt various scheduling flexibility alternatives to meet the fluctuating service demand. On the other hand, they have also realized that providing employee productivity and satisfaction is as much important as meeting the service demand. Up to date, tour scheduling solution approaches have neglected considering employee preferences and tried to develop work schedules for employees in a subsequent step. This paper presents a goal programming model that implicitly represents scheduling flexibility and also incorporates information about the preferred working patterns of employees. After solving the proposed model, a work schedule will be generated for each employee without requiring a further step for the assignment of shifts, break times, and work days to employees. The model is capable of handling multiple scheduling objectives, and it can produce optimal solutions in very short computing times.     

A decomposition approach for the integrated vehicle-crew-roster problem with days-off pattern

The integrated vehicle-crew-roster problem with days-off pattern aims to simultaneously determine minimum cost vehicle and daily crew schedules that cover all timetabled trips and a minimum cost roster covering all daily crew duties according to a pre-defined days-off pattern. This problem is formulated as a new integer linear programming model and is solved by a heuristic approach based on Benders decomposition that iterates between the solution of an integrated vehicle-crew scheduling problem and the solution of a rostering problem. Computational experience with data from two bus companies in Portugal and data from benchmark vehicle scheduling instances shows the ability of the approach for producing a variety of solutions within reasonable computing times as well as the advantages of integrating the three problems.     

Capacitated lot sizing and scheduling with parallel machines and shared buffers: A case study in a packaging company

The aim of this work is to propose a solution approach for a capacitated lot sizing and scheduling real problem with parallel machines and shared buffers, arising in a packaging company producing yoghurt. The problem has been formulated as a hybrid Continuous Set-up and Capacitated Lot Sizing Problem (CSLP–CLSP). A new effective two stage optimisation heuristic based on the decomposition of the problem into a lot sizing problem and a scheduling problem has been developed. An assignment of mixture to buffers is made in the first stage, and therefore the corresponding orders are scheduled on the production lines by performing a local search. Computational tests have been performed on the real data provided by the company. The heuristic exhibits near-optimal solutions, all obtained in a very short computational time.     

Planning and scheduling of selective harvest with management zones delineation

This paper considers an integrated approach to two common problems in a precision agriculture framework: management zone delineation and selective harvest scheduling. Our model minimizes the total costs of harvest operations, establishing planning and scheduling for selective harvest of each selected management zone. Therefore, this tool provides important information for decision making of farmers in the field. Our integrated model is contrasted with the hierarchical approach commonly used in the literature for these cases, where the result of zoning problem is an input to schedule the harvest problem. Both problems were solved through a complete enumeration of all the potential management zones and demonstrated the advantages of our proposed model over the hierarchical approach. Our model reached an average reduction of 10% in harvest operations costs for different instances in a case study.

     

Workforce planning at USPS mail processing and distribution centers using stochastic optimization

Service organizations that operate outside the normal 8-hour day and face wide fluctuations in demand constantly struggle to optimize the size and composition of their workforce. Recent research has shown that improved personnel scheduling methods that take demand uncertainty into account can lead to significant reductions in labor costs. This paper addresses a staff planning and scheduling problem that arises at United States Postal Service (USPS) mail processing & distribution centers (P&DCs) and develops a two-stage stochastic integer program with recourse for the analysis. In the first stage, before the demand is known, the number of full-time and part-time employees is determined for the permanent workforce. In the second stage, the demand is revealed and workers are assigned to specific shifts during the week. When necessary, overtime and casual labor are used to satisfy demand. This paper consists of two parts: (1) the analysis of the demand distribution in light of historical data, and (2) the development and analysis of the stochastic integer programming model. Using weekly demand for a three-year period, we first investigate the possibility that there exists an end-of-month effect, i.e., the week at the end of month has larger volume than the other weeks. We show that the data fail to indicate that this is the case. In the computational phase of the work, three scenarios are considered: high, medium, and low demand. The stochastic optimization problem that results is a large-scale integer program that embodies the full set of contractual agreements and labor rules governing the design of the workforce at a P&DC. The usefulness of the model is evaluated by solving a series of instances constructed from data provided by the Dallas facility. The results indicate that significant savings are likely when the recourse problem is used to help structure the workforce. This work was supported in part by the National Science Foundation under grants DMI-0218701 and DMI-0217927.     

Non-block scheduling with priority for radiotherapy treatments

In this paper, a quite challenging operational problem within health care delivery has been considered: the optimal management of patients waiting for radiotherapy treatments. Long waiting times for radiotherapy treatments of several cancers are largely documented all over the world. This problem is mainly due to an imbalance between supply and demand of radiotherapy services, which negatively affects the effectiveness and the efficiency of the health care delivered. Within this context, the paper presents an innovative solution approach for effectively scheduling a set of patients waiting to start the radiotherapy plan. The proposed approach is based on a well tailored integer linear optimization program, modelling a non-block scheduling strategy, with the aim to minimize the mean waiting time or maximize the number of new scheduled patients. The model has been tested and evaluated by carrying out some numerical experiments on suitable use-case scenarios, and the obtained results demonstrate the effectiveness and reliability of the proposed approach.     

Optimized pump scheduling in water distribution systems

A method based on nonlinear programming for determining the optimal operation of general water distribution systems containing multiple sources and reservoirs is presented. The problem is formulated and solved so that, given the forecasted demands for the coming 24 hours, the initial and final conditions in the reservoirs, the unit and maximum demand electricity charge, and the constraints in the hydraulic properties of all system components, an optimized pumping schedule is found. An optimization algorithm which employs the generalized reduced gradient method and the nonlinear sensitivity analysis has been developed for a basic scheduling problem in which only unit charges are considered. The maximum demand charge, which is weighted by varying degrees from day to day, is incorporated into the scheduling problem. The algorithm uses a feasible initial solution as the starting solution and iterates so that all the interim solutions are feasible.The work described here was undertaken at Brunel University and the University of Durham, UK. The authors are grateful to SERC for dunding the project and also thank Professor Uri Shamir, Department of Civil Engineering, Israel Institute of Technology for his review and many useful comments.     

Scheduling of road vehicles in sugarcane transport: A case study at an Australian sugar mill

Pressure to remain internationally competitive has forced Australian sugar mills to reduce capital and operational costs. Improved scheduling of road transport vehicles provides one such opportunity, as it would reduce vehicle queue and mill idle times and hence the number of vehicles needed. It is difficult for mill traffic officers to produce good transport schedules manually due to the need to service a large number of harvesters in different locations. To address this issue, research was undertaken participatively with a sugar milling company in Australia to produce and implement a mixed integer programming model that represents the road transport operations. Two meta-heuristics were applied to find a solution to the model, leading to potential cost savings of AU$240,000 per year versus schedules produced manually by the mill traffic officer. The model was also applied to explore regional planning options for a more integrated harvesting and transport system.