Combining integer programming and the randomization method to schedule employees

Ingolfsson, Armann; Campello, Fernanda; Wu, Xudong; Cabral, Edgar Alberto

doi:10.1016/j.ejor.2009.04.026

Cited by 82 publications

(64 citation statements)

References 24 publications

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“…In a similar spirit, noting traditional methods assume that service level goals are "hard constraints" that must be met during each interval, Koole and van der Sluis (2003) instead develop a scheduling methodology that seeks to meet only an overall service level objective over the course of an entire scheduling period (typically a day or a week). Ingolfsson, Cabral, and Wu (2003) note that the traditional staffing methods use steady-state staffing models for individual intervals and seek to eliminate errors induced by this approximation by using transient results on a period-by-period basis, which they refer to as the "randomization method," along with integer programming to create agent schedules. Motivated by the potential impact of understaffing on call abandonment, Saltzman (2005) and Saltzman and Mehrotra (2007) develop and test a scheduling methodology that combines linear programming, tabu search, and simulation while including costs to staff, waiting times, and abandoned calls in the objective function.…”

Section: Personnel Planning: Resource Acquisitionmentioning

confidence: 99%

The Modern Call Center: A Multi‐Disciplinary Perspective on Operations Management Research

Akşin

Armony

Mehrotra

2007

Production and Operations Management

616

515

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C all centers are an increasingly important part of today's business world, employing millions of agents across the globe and serving as a primary customer-facing channel for firms in many different industries. Call centers have been a fertile area for operations management researchers in several domains, including forecasting, capacity planning, queueing, and personnel scheduling. In addition, as telecommunications and information technology have advanced over the past several years, the operational challenges faced by call center managers have become more complicated. Issues associated with human resources management, sales, and marketing have also become increasingly relevant to call center operations and associated academic research.In this paper, we provide a survey of the recent literature on call center operations management. Along with traditional research areas, we pay special attention to new management challenges that have been caused by emerging technologies, to behavioral issues associated with both call center agents and customers, and to the interface between call center operations and sales and marketing. We identify a handful of broad themes for future investigation while also pointing out several very specific research opportunities.

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Section: Personnel Planning: Resource Acquisitionmentioning

confidence: 99%

The Modern Call Center: A Multi‐Disciplinary Perspective on Operations Management Research

Akşin

Armony

Mehrotra

2007

Production and Operations Management

616

515

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“…The goal is to choose a minimum cost set of employees whose schedule meets the demand at all the timeslots. The problem framework is quite general and captures many other situations arising in sensor networks, cloud computing, energy management and distributed computing (see [12,7,4]). …”

Section: Introductionmentioning

confidence: 99%

Resource Allocation for Covering Time Varying Demands

Chakaravarthy

Kumar

Roy

et al. 2011

Algorithms – ESA 2011

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Abstract. We consider the problem of allocating resources to satisfy demand requirements varying over time. The input specifies a demand for each timeslot. Each resource is specified by a start-time, end-time, an associated cost and a capacity. A feasible solution is a multiset of resources such that at any point of time, the sum of the capacities offered by the resources is at least the demand requirement at that point of time. The goal is to minimize the total cost of the resources included in the solution. This problem arises naturally in many scenarios such as workforce management, sensor networks, cloud computing, energy management and distributed computing. We study this problem under the partial cover setting and the zero-one setting. In the former scenario, the input also includes a number k and the goal is to choose a minimum cost solution that satisfies the demand requirements of at least k timeslots. For this problem, we present a 16-approximation algorithm; we show that there exist "well-structured" near-optimal solutions and that such a solution can be found in polynomial time via dynamic programming. In the zero-one setting, a feasible solution is allowed to pick at most one copy of any resource. For this case, we present a 4-approximation algorithm; our algorithm uses a novel LP relaxation involving flow-cover inequalities.

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“…The objective of the model is to maximize profit, where profit has three components: productivity, quality costs and training costs. Ingolfsson et al [12] combined integer programming and the randomization method to schedule employees by using an integer programming heuristic to generate schedules; they used the randomization method to compute service levels. They described a method to find low cost shift schedules with a time-varying service level that is always above a specified minimum.…”

Section: Literature Reviewmentioning

confidence: 99%

Knowledge‐Based Assignment Model for Allocation of Employees in Engineering‐to‐Order Production

Roblek¹,

Zajec²,

Urh³

2017

Knowledge Management Strategies and Applications

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In today's rapidly changing business environment, it is necessary to react promptly in response to the product changes that happen constantly in an Engineering-to-Order production environment. Very often, there is not sufficient time to educate employees regarding new and necessary knowledge. If we insist on the standardization of a process execution, the process always requires appropriate knowledge from among available employees. In this chapter, an option for adjusting processes to available knowledge is studied. Following calculations, it was concluded that a partial corruption of a perfect process leads to a better knowledge alignment of employees. At first, with the corruption of a perfect process, its efficiency is decreased, but with better knowledge alignment, process efficiency is consequently increased to a level better than the original one. The optimization model presented in this chapter is based on a modified classic assignment problem and it includes a numerical example based on the data of ETO company. We proved our findings from the aspects of balance, employee capacity load and process efficiency.

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Combining integer programming and the randomization method to schedule employees

Cited by 82 publications

References 24 publications

The Modern Call Center: A Multi‐Disciplinary Perspective on Operations Management Research

The Modern Call Center: A Multi‐Disciplinary Perspective on Operations Management Research

Resource Allocation for Covering Time Varying Demands

Knowledge‐Based Assignment Model for Allocation of Employees in Engineering‐to‐Order Production

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