Service Level Requirements Research Articles

Evaluation of the impact of strategic staff planning in a university using a MILP model

A mathematical model for optimising the strategic staff planning in universities is used to analyse the impact of different personnel and academic policies on the strategic staff plan, considering a preferable staff composition. The personnel policies are evaluated allowing or not the dismissals of permanent workers; the ratio of internal promotion for workers and the personnel budget. The academic policies are tested through the impact of different demand trends. Addressing the specificities of the university, the optimisation model considers not only economic criteria, i.e., personnel costs, but also other factors related to the fulfilment of the required service level and the achievement of a preferable workforce composition. Several computational scenarios are used, based on real data from the Universitat Politecnica de Catalunya (Barcelona, Spain). The results show the adjustment to the preferable workforce composition through the available mechanisms (dismissals, hiring and internal promotions). [Received 12 June 2015; Revised 24 November 2015; Revised 24 July 2016; Accepted 7 December 2016]

Evaluating the impact of strategic personnel policies using a MILP model: The public university case

Purpose: The main purpose of the paper is to evaluate the impact of diverse personnel policies around personnel promotion in the design of the strategic staff plan for a public university. The strategic staff planning consists in the determination of the size and composition of the workforce for an organization.Design/methodology/approach: The staff planning is solved using a Mixed Integer Linear Programming (MILP) model. The MILP model represents the organizational structure of the university, the personnel categories and capacity decisions, the demand requirements, the required service level and budget restrictions. All these aspects are translated into a set of data, as well as the parameters and constraints building up the mathematical model for optimization. The required data for the model is adopted from a Spanish public university.Findings: The development of appropriate policies for personnel promotion can effectively reduce the number of dismissals while proposing a transition towards different preferable workforce structures in the university.Research limitations/implications: The long term staff plan for the university is solved by the MILP model considering a time horizon of 8 years. For this time horizon, the required input data is derived from current data of the university. Different scenarios are proposed considering different temporal trends for input data, such as in demand and admissible promotional ratios for workers.Originality/value: The literature review reports a lack of formalized procedures for staff planning in universities taking into account, at the same time, the regulations on hiring, dismissals, promotions and the workforce heterogeneity, all considered to optimize workforce size and composition addressing not only an economic criteria, but also the required workforce expertise and the quality in the service offered. This paper adopts a formalized procedure developed by the authors in previous works, and exploits it to assess the impact of various personnel policies in the staff planning for a particular university case, and this is the principal contribution of the paper.

Order variability in perishable product supply chains

Empirical research has shown that the degree of order variability in supply chains is significantly influenced by product- and industry-specific factors. This paper analyzes the impact of perishability on order variability and the bullwhip effect in supply chains. We decompose the ordering process of a retailer into a sales and an outdating process and quantify their short- and long-term variability and correlation. We find differences to non-perishable product supply chains driven by the impact of the inventory depletion policy, stock-out management, and retailers service level requirement. These three factors significantly affect the retailer’s order variability and thus the decision making process and the profitability of the upstream supply stage. For the majority of instances, the perishable nature of a product results in the ordering process having a lower variability than the demand process. Only when inventory depletion is dominated by last-in-first-out in high service level environments, variability amplification can be observed. We propose a dynamic ordering policy for the upstream supply stage, taking into account negative correlation of retailer orders between periods. This dynamic policy may lead to substantial performance improvements. In a sensitivity analysis, we investigate the impact of shelf life, lead time and demand correlation.

When bid price is not enough: Taking better allotment decisions for Camping Revenue Management

In the hospitality industry, an allotment is a block of pre-negociated “rooms” which have been bought by a tour operator. In the context of campsites, allotments represent a significant share of the mobile homes sales. For the campsite owner, dealing with tour operator allotment requests is a poisoned chalice. On one hand these pre-booked sales are more or less a guarantee of selling a good share of its inventory. On the other hand, the discount level is so high that selling the whole inventory through allotments could potentially ruin the business. Hence, a balance must be found between allotment contracts and estimated direct sales to final customers (at full price, or lightly discounted price). The purpose of the present paper is to show that the stochastic optimisation problem at stake is highly combinatorial and that algorithmic approaches relying on continuous relaxations of the demand (“bid price”) behave poorly. For multi-site allotment optimisation with service level requirements from tour operators, we developed a Lagrange decomposition technique based on local Markov Decision Process solvers that outperforms classic "fluid displacement" approaches. We provide experimental results on instances with 200 campsites 20,000 mobile homes and 15 tour operators inspired from a leading European actor of the campsite industry.

Modeling Deployment Decisions for Elastic Services with ABS

The use of cloud technology can offer significant savings for the deployment of services, provided that the service is able to make efficient use of the available virtual resources to meet service-level requirements. To avoid software designs that scale poorly, it is important to make deployment decisions for the service at design time, early in the development of the service itself. ABS offers a formal, model-based approach which integrates the design of services with the modeling of deployment decisions. In this paper, we illustrate the main concepts of this approach by modeling a scalable pool of workers with an auto-scaling strategy and by using the model to compare deployment decisions with respect to client traffic with peak loads.

Integrated location-inventory modelling under forward and reverse product flows in the used merchandise retail sector: A multi-echelon formulation

This study presents a joint three-echelon location inventory model for a donation-demand driven industry in which the main warehouse (MW), distribution centers (DC), retail stores (RS) and donation-only centers (ADCs) exist. This unique inventory-location problem involves demand and supply uncertainties, coverage radius limitations, service level requirements, and multiple products consideration. Each retailer has two classes of products flowing from the assigned DC due to demands minus donations occurring in that retailer. The proposed model simultaneously determines the number of DCs to open, DC locations, and assignments of retailers to the open DCs for particular product types. The objective is to minimize the total annual cost including: facility location costs, transportation costs, inventory costs, and the lost sale costs. Due to the complexity of the problem, the proposed model structure allows for relaxing complicating constraints through recourse to Lagrangian relaxation. The use of robust branch-cut and price heuristics solves the mixed integer nonlinear problem to obtain a lower bound and a distance-based heuristic to get an upper bound. We formulate essential features of this novel problem, solve several numerical example problems and evaluate solution performance. We believe this is a novel problem environment, and that this initial study extends integrated location-inventory modeling to a new context.

Designing an efficient humanitarian supply network

Increasingly, humanitarian organizations have opened regional warehouses and pre-positioned resources locally. Choosing appropriate locations is not easy and frequently based on opportunities rather than rational decisions. Dedicated decision-support systems could help humanitarian practitioners design their supply networks. Academic literature suggests the use of commercial sector models but rarely considers the constraints and specific context of humanitarian operations, such as obtaining accurate data, high uncertainties, limited budgets and increasing pressure on cost efficiency. We propose a tooled methodology to properly support humanitarian decision makers in the design of their supply chains. Our contribution is based on the definition of aggregate scenarios to reliably forecast demand using past disaster data and future trends. Demand for relief items based on these scenarios is then fed to a mixed-integer linear programming model in order to improve current supply networks. The specifications of this model have been defined in close collaboration with humanitarian workers. The model allows analysis of the impact of alternative sourcing strategies and service level requirements on operational efficiency. It provides clear and actionable recommendations for a given context, bridging the gap between academics and humanitarian logisticians. The methodology was developed to be useful to a broad range of humanitarian organizations, and a specific application to the supply chain design of the International Federation of Red Cross and Red Crescent Societies is discussed in detail.

Inventory rebalancing and vehicle routing in bike sharing systems

Bike sharing systems have been installed in many cities around the world and are increasing in popularity. A major operational cost driver in these systems is rebalancing the bikes over time such that the appropriate number of bikes and open docks are available to users. We combine two aspects that have previously been handled separately in the literature: determining service level requirements at each bike sharing station, and designing (near-)optimal vehicle routes to rebalance the inventory. Since finding provably optimal solutions is practically intractable, we propose a new cluster-first route-second heuristic, in which a polynomial-size Clustering Problem simultaneously considers the service level feasibility and approximate routing costs. Extensive computational results on real-world data from Hubway (Boston, MA) and Capital Bikeshare (Washington, DC) are provided, which show that our heuristic outperforms a pure mixed-integer programming formulation and a constraint programming approach.

Impacts of decision sequences on a random yield supply chain with a service level requirement

The last decade has witnessed a rising trend of social network structures where decision making may be influenced by members’ channel power. This paper considers a decentralized supply chain that comprises a random yield supplier and a manufacturer with a service level requirement. The supplier determines the production quantity while the manufacturer decides on the replenishment quantity. We investigate several game-theoretic models with respect to decision sequences of the supplier and manufacturer with a service level requirement. We compare firms’ equilibrium production/order quantity and profits under these models. Both firms achieve a higher payoff in the manufacturer-leader game than in the supplier-leader and simultaneous move games, which indicates that the manufacturer-leader game is Pareto efficient from the channel structure perspective. A critical value is observed at the service level, beyond which the supplier’s profit decreases, while the manufacturer’s profit increases in the service level requirement. Interestingly, the critical value is independent of the models and it just depends on the wholesale price and production cost. We also examine the decentralized random yield supply chain under the vendor-managed inventory arrangement. Finally, numerical computations are conducted to evaluate supply chain performance with respect to various parameters.

Service facility relocation decision: a case study

Purpose The purpose of this paper is to present issues and challenges faced during a firm’s facility relocation decision aimed at improving both cost and service performance in an innovative service context. Design/methodology/approach The reader is given background of the decision-making process behind single service facility relocation decision using a detailed case study. Key financial, operational and business data of the firm are collected, compiled and analysed. The solution methodology uses a combination of qualitative and quantitative analyses to choose the best among the three possible discrete location choices. For propriety reasons, some information has been disguised, and some data have been sanitized. Findings The factors that significantly influence relocation decision are proximity to high transaction customers, infrastructure and other input costs, customer service level requirements and extant regulations. Transportation has a direct impact on cost as well as service level. Most of the findings are in line with literature, but some of them differ too. Research limitations/implications The approach is focused on a single case study of a pooling container firm in the Indian context and thereby limits the ability to generalize the findings. Nevertheless, this study may serve as a significant starting point for future research. Practical implications Firms can create a rational, efficient and even-handed approach for relocation of facilities applying a mix of qualitative and quantitative models judiciously. It provides managers better understanding and insights and actions needed for single service facility relocation. Originality/value This work is perhaps the first on facility relocation in emerging economies covering actual interventions and experiences. It gives new insights to a limited literature of relocating single service facility reflecting both theoretical imperatives and practitioner requirements.

An integrated approach for airline scheduling, aircraft fleeting and routing with cruise speed control

To place an emphasis on profound relations among airline schedule planning problems and to mitigate the effect of unexpected delays, we integrate schedule design, fleet assignment and aircraft routing problems within a daily planning horizon while passengers’ connection service levels are ensured via chance constraints. We propose a nonlinear mixed integer programming model due to the nonlinear fuel consumption and CO2 emission cost terms in the objective function, which is handled by second order conic reformulation. The key contribution of this study is to take into account the cruise time control for the first time in an integrated model of these three stages of airline operations. Changing cruise times of flights in an integrated model enables to construct a schedule to increase utilization of fuel efficient aircraft and even to decrease total number of aircraft needed while satisfying the same service level and maintenance requirements for aircraft fleeting and routing. There is a critical tradeoff between the number of aircraft needed to fulfill the required flights and overall operational expenses. We also propose two heuristic methods to solve larger size problems. Finally, computational results using real data obtained from a major U.S. carrier are presented to demonstrate potential profitability in applying the proposed solution methods.

A way for reducing drug supply chain cost for a hospital district: A case study

Purpose: This work aims at providing insights to optimise healthcare logistic of the drug management, in order to deal with the healthcare expenditure cut. In this paper the effects of different drug supply chain configurations, on the resulting average stock, service level and Bullwhip effect, of the studied supply chain, is quantitatively assessed. Design/methodology/approach: A case study of an Italian district has been studied, taking into account three echelons: suppliers, central stock, and hospitals. A model of the various supply chain configurations has been created with the use of the simulation. Specifically, 24 supply chain configurations have been examined, stemming from the combination of several supply chain design parameters, namely: transshipment policies (Emergency Lateral Transshipment or Total Inventory Equalization); re-order and inventory management policies (Economic Order Quantity or Economic Order Interval); required service levels (90% or 95%); the number of available vans (one or two). For each configuration, hospital average stock, service level and a “Bullwhip effect” analysis are computed. To know which input variables are statistically significant, a DoE (Design of Experiments) analysis has been executed. Findings: The output of this paper provides useful insights and suggestions to optimize the healthcare logistic and drug supply chain. According to the developed DoE analysis, it can be stated that the introduction of transshipment policies provides important improvement in terms of service and stock levels. To reduce the Bullwhip effect, which results in a service level decreasing, and in a managing stock costs increasing, it is worth to adopt an EOQ re-order policy. Practical implications: This research gives practical recommendations to the studied system, in order to reduce costs and maintain a very satisfactory service level. Originality/value: This paper fulfils an identified need to study which combination of transshipment policies, re-order/inventory management policies and required service levels, can be the best one to reduce costs and maintain a very satisfactory service level, in the specific logistic system.

FPA method for optimal production planning under availability/degradation machine and subcontracting constraint

This work treats an industrial problematic of a manufacturing system M1 subject to random failure and that satisfies a random demand under required service level. To ensure the satisfaction of the demand, it calls up on a second manufacturing system M2 that is a subcontractor and has a stochastic service level. This paper aims to determine an optimal production plan taking into account the degradation of the manufacturing system following its production rate and its availability. The unavailability of the machine M1 is due to the non-negligible preventive and corrective maintenance actions duration. To consider a realistic case of the subcontractor, we consider that is not able to respond to the variation of the demand, and then it assumed that the manufacturer can order a minimum fixed quantity. We then derive an optimal production plan taking into account the degradation of the machine and simultaneously minimising: inventory, degradation and production cost. An algorithm based on finite perturbation analysis is proposed to determine the optimal production plan and the sensitivity study of the availability is presented.

Optimal ordering policy for newsvendor models with bidirectional changes in demand using expert judgment

Demand forecast is a critical determinant of order quantity under newsvendor problem (NVP) framework and warrants major revision in the event of changing circumstances or happening of some unforeseen events having potential to alter the demand. Retailers of single period products such as fashion apparels are required to pass their orders far ahead of selling seasons and apply preseason two-stage ordering procedure, where an initial order (first stage) is followed by a final confirmed order (second stage). The enterprise forecasting experts may get additional information related to the occurrence of some unforeseen events that may significantly impact the initial demand estimation. In this paper, the potential impact of such events is combined using a weight factor to obtain revised demand forecasts. In this context, this paper develops inventory models under NVP framework to determine the optimal order quantity and weight factor on the basis of revised forecasts. Considering the bidirectional changes in demand, we formulate a unique objective function that operates as a profit maximization function for the positive demand adjustment and turns into a cost minimization function for the negative demand adjustment. Models developed without constraints at first instance are extended subsequently by incorporating constraints of budget limits, storage space capacity and required service level. Near closed form expressions of decision variables for four demand distributions with multiplicative demand forms are presented. The results demonstrate economic benefits of using revised demand through models developed, negative impact of constraints, and role of demand distribution entropy in determining the order size and expected profit.

A hybrid algorithm for stochastic single-source capacitated facility location problem with service level requirements

A hybrid algorithm for stochastic single-source capacitated facility location problem with service level requirements

Oversubscription Dimensioning of Next-Generation PONs with Different Service Levels

This letter provides a methodology for planning passive optical networks (PONs) using the oversubscription concept and shows its applicability in the dimensioning of 1 Gb/s access to both the basic (i.e., residential) and premium (i.e., business) users with different service level requirements and activity patterns. We show that only next-generation PON networks can reach a large number of users with acceptable service levels.

Layout of Urban Distribution Center Using Possibilistic Programming

AbstractDistribution centers, link supply chain to the customers. Layout of the distribution centers is very important in terms of product conservation at the required service level and delivering of products with minimum cost to the customer from distribution centers. Therefore, the layout design of distribution centers should be handled with a smart decision system. In this study, the layout design of a fruits and vegetable hall, which is a kind of urban distribution center, is investigated for two different scenarios: (1) assignments of the brokers to the stores and (2) assignments of the products to the stores. The study suggests the most suitable allocation models for the inner layout of distribution center. In the first phase, the requirements of the mathematical model to develop the decision making system for the layouts of distribution center that will be determined. In the second phase, allocation models will be developed and solved for fruits and vegetables halls in Istanbul.

Production and Maintenance Optimization for Multi-Machines under Degradation Constraint

Abstract: In this paper, we have developed an integrated maintenance policy for a manufacturing system, composed by numerous parallel machines, which has to satisfy a random demand during a finite horizon given a required service level. The aim of this study is to determine the production plan of each machine in the manufacturing system minimizing the total production cost and the optimal maintenance strategy for different machines taking into account the degradation of each one according to its production rate and minimizing the total preventive and corrective maintenance cost

A Study on Competitive Advantage of Retail Stores Adopting Supply Chain Strategies and Responsiveness in Trichy

Supply chain management is a set of approaches utilized to efficiently integrate suppliers, manufacturers, warehouses, and stores, so that merchandise is produced and distributed at the right quantities, to the right locations, and at the right time, in order to minimize system wide costs while satisfying service level requirements. In order to do a business efficiently, effective supply chain is must in today's fierce competition. A study has been conducted among 200 respondents to pick selected retail stores to study the supply chain strategies and responsiveness and its impact on competitive advantage in Trichy district. The main objective of the study is to examine the supply chain strategies adopted by the retail stores and to study the impact of strategies on responsiveness and the combined impact of supply chain strategies and responsiveness on competitive advantage.

Food Rescue and Delivery

This paper addresses a special case of periodic vehicle routing problem in which each node has a nonnegative supply or demand of a single product that is unpaired. The product collected from a pickup node can be delivered to any one node or multiple delivery nodes, and the demand of a delivery node can be met by the product collected from any one node or multiple pickup nodes. This periodic unpaired pickup and delivery vehicle-routing problem is a novel variant of the periodic vehicle routing problem. The objective of the problem was to design the pickup and delivery vehicle routes to meet required service levels of delivery nodes, minimizing the total transportation cost while satisfying certain operational constraints. This problem was driven by food relief operations in Sydney, Australia. The logistics aspect of the approach was to design and execute a vehicle routing problem for a food rescue and delivery network. The specific goals were to develop an integrated linear programming model for this new variant of the periodic vehicle routing problem and to propose an integer programming–based heuristic solution approach to solve the problem introduced in the paper. The heuristic algorithm was tested with small instances created from Cordeau's benchmark instances, and the solution approach was validated against optimal solutions obtained through the exact method before implementation on a food rescue and delivery network. The heuristic approach was found to be comparable with the optimal solution and can solve the real-world scenarios with significantly fewer resources than are used in practice.