Journal of the Operational Research Society

Kelle, P; Schneider, H; Raschke, C; Shirazi, H

doi: 10.1057/jors.2012.55pmid: N/A

AbstractSince highway improvement project selection requires screening thousands of road segments with respect to crashes for further analysis and final project selection, we provide a two-step project selection methodology and describe an application case to demonstrate its advantages. In the first step of the proposed methodology, we will use odds against observing a given crash count, injury count, run-off road count and so on as measures of risk and a multi-criteria pre-selection technique with the objective to decrease the number of prospective improvement locations. In the second step, the final project selection is accomplished based on a composite efficiency measure of estimated cost, benefit and hazard assessment (odds) under budget constraints. To demonstrate the two-step methodology, we will analyze 4 years of accident data at 23 000 locations where the final projects are selected out of several hundred of potential locations.

Mostafaee, A

doi: 10.1057/jors.2012.56pmid: N/A

AbstractThis paper concerns the shared cost allocation problem by using Data Envelopment Analysis (DEA), which is observed in practical applications such as public services and production processes. In the management context, the cost allocation problem tries to balance the different desires of two management layers: central manager and each sector manager. The cost can be assigned in an equitable way to the various Decision Making Units (DMUs). To achieve this goal, we present a new DEA-based method for dividing a fixed cost among DMUs. In the proposed method, the fixed cost is assigned to DMUs such that the efficiency measures and the Returns to Scale classifications of all DMUs before and after assigning the fixed cost remain unchanged. Also, the gaps among the costs allocated to DMUs will be minimized. The proposed method has the flexibility to consider the management standpoints. Finally, numerical results of an elucidatory example are furnished to demonstrate the applicability and reliability of our scheme.

Swami, S; Shah, J

doi: 10.1057/jors.2012.44pmid: N/A

AbstractEnvironmental consciousness has become increasingly important in everyday life and business practice. The effort to reduce the impact of business activities on the environment has been labelled as green supply chain management. Any major greening project would require efforts on the part of the entire supply chain. However, very few studies have addressed the issue of coordinating the green supply chain. We consider the problem of coordination of a manufacturer and a retailer in a vertical supply chain, who put in efforts for ‘greening’ their operations. We address some pertinent questions in this regard such as extent of effort in greening of operations by manufacturer or retailer, level of cooperation between the two parties, and how to coordinate their operations in a supply chain. The greening efforts by the manufacturer and retailer result in demand expansion at the retail end. The decision variables of the manufacturer are wholesale price and greening effort, while those of the retailer are retail price and its greening effort. We find that the ratio of the optimal greening efforts put in by the manufacturer and retailer is equal to the ratio of their green sensitivity ratios and greening cost ratios. Further, profits and efforts are higher in the integrated channel as compared to the case of the decentralized channel. Finally, a two-part tariff contract is found to produce channel coordination in this problem. A numerical example illustrates the results.

Bernardi, S; Anjos, M F

doi: 10.1057/jors.2012.49pmid: N/A

AbstractWe present a two-stage method using mathematical-programming techniques for finding high-quality solutions to the multi-floor facility layout problem. The first stage is a mixed-integer linear program that assigns departments to floors such that the total of the vertical interaction costs between departments on different floors is globally minimized. The second stage finds a locally optimal layout for each floor. Two versions of the proposed approach are considered. The first solves the layout of each floor independently of the other floors, and is suitable for up to one elevator location. The second solves the layout of all floors simultaneously and can handle multiple elevator locations. Preliminary computational results show that both versions of the proposed method can efficiently provide a good variety of high-quality solutions in a short amount of time for medium and large-scale problem instances.

Liu, C-L; Chen, S-L

doi: 10.1057/jors.2012.53pmid: N/A

AbstractThis study attempts to develop a supplier's risk sharing contract to gain an understanding of risk sharing for the automotive industry in Taiwan. The existing research has examined revenue-sharing contracts between retailers and manufacturers. However, the study of suppliers’ risk-sharing contracts between manufacturers and suppliers is neglected. This paper first employs a double moral hazard framework to obtain an optimal contract, and then uses the derived model to establish research hypotheses. The empirical analysis shows that manufacturers offer suppliers a type of supplier's risk sharing contract while maintaining long-term relationships with suppliers. The results also support the hypotheses that manufacturers absorb more risk when the suppliers are more uncertainty, more risk aversion and lower moral hazard, and suggest that manufacturers would be willing to absorb more risk as they deepen their involvement in the technological development of suppliers.

Mohiuddin, S G; Brailsford, S C; James, C J; Amor, J D; Blum, J M; Crowe, J A; Magill, E H; Prociow, P A

doi: 10.1057/jors.2012.57pmid: N/A

AbstractThis paper describes the role of mathematical modelling in the design and evaluation of an automated system of wearable and environmental sensors called PAM (Personalised Ambient Monitoring) to monitor the activity patterns of patients with bipolar disorder (BD). The modelling work was part of an EPSRC-funded project, also involving biomedical engineers and computer scientists, to develop a prototype PAM system. BD is a chronic, disabling mental illness associated with recurrent severe episodes of mania and depression, interspersed with periods of remission. Early detection of the onset of an acute episode is crucial for effective treatment and control. The aim of PAM is to enable patients with BD to self-manage their condition, by identifying the person's normal ‘activity signature’ and thus automatically detecting tiny changes in behaviour patterns which could herald the possible onset of an acute episode. PAM then alerts the patient to take appropriate action in time to prevent further deterioration and possible hospitalisation. A disease state transition model for BD was developed, using data from the clinical literature, and then used stochastically in a Monte Carlo simulation to test a wide range of monitoring scenarios. The minimum best set of sensors suitable to detect the onset of acute episodes (of both mania and depression) is identified, and the performance of the PAM system evaluated for a range of personalised choices of sensors.

Jozefowiez, N; Mancel, C; Mora-Camino, F

doi: 10.1057/jors.2012.20pmid: N/A

AbstractIn this paper, we present a heuristic method to solve an airline disruption management problem arising from the ROADEF 2009 challenge. Disruptions perturb an initial flight plan such that some passengers cannot start or conclude their planned trip. The developed algorithm considers passengers and aircraft with the same priority by reassigning passengers and by creating a limited number of flights. The aim is to minimize the cost induced for the airline by the recovery from the disruptions. The algorithm is tested on real-life-based data, as well as on large-scale instances and ranks among the best methods proposed to the challenge in terms of quality, while being efficient in terms of computation time.

Hadi-Vencheh, A; Esmaeilzadeh, A

doi: 10.1057/jors.2012.22pmid: N/A

AbstractThis paper proposes a new super-efficiency model when some factors (inputs/outputs) are negative. The proposed model employs the RDM model (Portela et al, 2004) for ranking decision-making units (DMUs). Our model ranks the DMUs and determines the type of efficiency for each DMU, simultaneously

Subramanian, A; Battarra, M

doi: 10.1057/jors.2012.24pmid: N/A

AbstractThe Travelling Salesman Problem with Pickups and Deliveries (TSPPD) consists in designing a minimum cost tour that starts at the depot, provides either a pickup or delivery service to each of the customers and returns to the depot, in such a way that the vehicle capacity is not exceeded in any part of the tour. In this paper, the TSPPD is solved by considering a metaheuris-tic algorithm based on Iterated Local Search with Variable Neighbourhood Descent and Random neighbourhood ordering. Our aim is to propose a fast, flexible and easy to code algorithm, also capable of producing high quality solutions. The results of our computational experience show that the algorithm finds or improves the best known results reported in the literature within reasonable computational time.

Gunawan, A; Lau, H C

doi: 10.1057/jors.2012.48pmid: N/A

AbstractWe 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.