The Importance of Data in Transportation and Retailing

Data in supply chains are known to be highly complex and voluminous. Such data, both structured and unstructured, are critical in many decisions which are being made on a regular basis. For example, in retailing, data related to demand, markets, customer engagements, prices, and many other relevant factors are constantly collected and leveraged in the decision-making process by demand and merchandise planners. In transportation, planners and dispatchers often rely on many sources of data involving real-time traffic, lead times, road conditions, costs, customer requirements and other sources of data in their planning and execution processes. Combining and extracting data from different sources and generating valuable insights from such data to support decision-making processes can be very challenging. In addition, the performance of data-driven decision-making methods depends heavily on the information and representations created from the original data. This research axis aims to tackle the data valuation aspect and its implications in decision-making, either in a fully automated manner or with human interventions.

GERAD researchers have carried out several studies which attempt to improve the quality and reliability of decisions through different quantitative approaches employed to enhance the value of data in multiple real-world supply chain and logistics applications. Several notable studies led by Carolina Osorio, Guy Desaulniers and Andrea Lodi have specifically addressed the prominent uncertainty issue in traffic and transportation management in the public domain. The research work carried out by Okan Arslan and Yichuan Daniel Ding demonstrate how data analytics can enhance planning and scheduling decisions in last-mile delivery and workforce scheduling. In a retail context, Andrea Lodi proposed an efficient decomposition method to learn latent customer preferences from retail data. Finally, the value of data availability and information sharing is also examined analytically in a retail context in the research papers of Georges Zaccour.

General note: in the text, only GERAD members involved in the mentioned research are indicated, but not those co-authors who are not members of GERAD. The information on co-authors, together with further information, can be found in the references.

References:

• Arslan, O., Abay, R., Data-driven vehicle routing in last mile delivery, Cirrelt-2021-30, 2021.

• Fields, E., Osorio, C., Zhou, T., A data-driven method for reconstructing a distribution from a truncated sample with an application to inferring car-sharing demand. Transportation Science, 55(3), 616-636, 2021.

• Jena, S. D., Lodi, A., Palmer, H.,Sole, C., A partially ranked choice model for large-scale data-driven assortment optimization. INFORMS Journal on Optimization, 2(4), 297-319, 2020.

• Lu, J., Osorio, C., A probabilistic traffic-theoretic network loading model suitable for large-scale network analysis. Transportation Science, 52(6), 1509-1530, 2018.

• Osorio, C., High-dimensional offline origin-destination (OD) demand calibration for stochastic traffic simulators of large-scale road networks. Transportation Research Part B: Methodological, 124, 18-43, 2019.

• Ricard, L., Desaulniers, G., Lodi, A., Rousseau, L.M., Predicting the probability distribution of bus travel time to move towards reliable planning of public transport services. arXiv:2102.02292, 2021.

• Yu, M., Ding, Y., Lindsey, R., Shi, C., A data-driven approach to manpower planning at US–Canada border crossings. Transportation Research Part A: Policy and Practice, 91, 34-47, 2016.

• Zhang, Q., Chen, J., Zaccour, G., Market targeting and information sharing with social influences in a luxury supply chain. Transportation Research Part E: Logistics and Transportation Review, 133, 101822, 2020.

Integrated Production and Transportation Planning

In a supply chain, different activities are done in sequence, starting from the initial suppliers to the final customers. Some of the most important activities include production, inventory management and transportation. In many cases, these different activities are managed in isolation. However, important gains can be achieved by explicitly considering the interaction between the various activities and hence optimizing them simultaneously.

In the context of a Vendor Managed Inventory (VMI) system, the supplier makes replenishment decisions for their customers. This leads to complex trade-offs. For example, when deciding when to produce and deliver two different customer orders, the supplier must consider several elements. If the two customers are located close to each other, some economies in transportation cost might be achieved by delivering the orders in the same route. However, this might also induce additional holding costs if these orders have different due dates. Furthermore, if the supplier is also producing the goods, then production decisions need to be incorporated: are there any possible economies of scale in production and is there enough capacity available?

Clearly, the consideration of multiple stages in the supply chain makes the planning process much more complex, and this therefore requires more sophisticated tools. Since the different activities impact each other, planning requires an integrated approach. Several members of GERAD have studied integrated supply chain planning problems, such as the Inventory Routing Problem and the Production Routing Problem.

There are many real-life applications in which these problems appear. Guy Desaulniers studied an application for a catering service company delivering meals to various clients. In this case, an employee shift schedule needed to be established in addition to the production and distribution plan. Jean-François Cordeau and Raf Jans studied another real-life application of integrated production and routing planning for a meat producer who needs to deliver a whole range of products to close to 200 retailers having different time windows. In both cases, the problem is made more difficult because of the short life span of the products. Jean-François Cordeau and Raf Jans considered another application of the Production Routing Problem in the furniture industry. Guy Desaulniers, Jacques Desrosiers considered the Inventory Routing Problem in a maritime setting for liquefied natural gas. Leandro Coelho and Gilbert Laporte solved variants of the Inventory Routing Problem in various real-life applications: one for a bottled water manufacturer and another one for the replenishment of automated teller machines.

Together with the aforementioned researchers, other GERAD members such as Yossiri Adulyasak have also focused on developing efficient optimization algorithms for various integrated production, inventory and transportation planning problems, including efficient algorithms for the Inventory-Routing problem, the Production-Routing problem and the integrated Vessel Service Planning problem.

Other GERAD members have also considered different types of integrated logistics planning problems, such as the integration of vehicle routing and load planning by Marilène Cherkesly.

References:

• Adulyasak, Y., Cordeau, J.F., Jans, R., Optimization-based adaptive large neighborhood search for the production routing problem. Transportation Science, 48(1), 20-45, 2014.

• Adulyasak, Y., Cordeau, J.F., Jans, R., Formulations and branch-and-cut algorithms for multivehicle production and inventory routing problems. INFORMS Journal on Computing, 26(1), 103-120, 2014.

• Bertazzi, L., Coelho, L.C., De Maio, A., Laganà, D., A matheuristic algorithm for the multi-depot inventory routing problem. Transportation Research Part E: Logistics and Transportation Review, 122, 524-544, 2019.

• Cherkesly, M., Desaulniers, G., Laporte, G., Branch-price-and-cut algorithms for the pickup and delivery problem with time windows and last-in-first-out loading. Transportation Science, 49(4), 752-766, 2015.

• Chitsaz, M., Cordeau, J.F., Jans, R., A unified decomposition matheuristic for assembly, production, and inventory routing. INFORMS Journal on Computing, 31(1), 134-152, 2019.

• Dayarian, I., Desaulniers, G., A branch-price-and-cut algorithm for a production-routing problem with short-life-span products. Transportation Science, 53(3), 829-849, 2019.

• Desaulniers, G., Rakke, J.G., Coelho, L.C., A branch-price-and-cut algorithm for the inventory-routing problem. Transportation Science, 50(3), 1060-1076, 2016.

• Grønhaug, R., Christiansen, M., Desaulniers, G., Desrosiers, J., A branch-and-price method for a liquefied natural gas inventory routing problem. Transportation Science, 44(3), 400-415, 2010.

• Guimarães, T. A., Coelho, L.C., Schenekemberg, C.M., Scarpin, C.T., The two-echelon multi-depot inventory-routing problem. Computers & Operations Research, 101, 220-233, 2019.

• Li, Y., Chu, F., Côté, J.F., Coelho, L.C., Chu, C., The multi-plant perishable food production routing with packaging consideration. International Journal of Production Economics, 221, 107472, 2020.

• Lmariouh, J., Coelho, L.C., Elhachemi, N., Laporte, G., Jamali, A., Bouami, D., Solving a vendor-managed inventory routing problem arising in the distribution of bottled water in Morocco. European Journal of Industrial Engineering, 11(2), 168-184, 2017.

• Neves-Moreira, F., Almada-Lobo, B., Cordeau, J.F., Guimarães, L., Jans, R., Solving a large multi-product production-routing problem with delivery time windows. Omega, 86, 154-172, 2019.

• Van Anholt, R.G., Coelho, L.C., Laporte, G., Vis, I.F., An inventory-routing problem with pickups and deliveries arising in the replenishment of automated teller machines. Transportation Science, 50(3), 1077-1091, 2016.

• Wu, L., Adulyasak, Y., Cordeau, J.-F., Wang, S., Vessel Service Planning in Seaports. Operations Research (Forthcoming), 2021.

Demand Uncertainty in Inventory, Routing, and Location Decisions

One of the main complexities in supply chain planning comes from the unavoidable uncertainties which must be dealt with. GERAD members have developed methods based on robust and stochastic optimization to make better decisions in the face of uncertainty in supply chain applications. These problems range from operational and tactical problems such as routing or production and inventory planning, to strategic problems such as facility location.

The main uncertainty which complicates supply chain planning at all levels is the uncertainty in demand. When making production and inventory decisions, safety stock is used to protect against this demand uncertainty. The planner must balance the cost of not having enough inventory versus the cost of having too much inventory. Determining the right amount of safety stock is hence difficult and requires sophisticated optimization approaches. Yossiri Adulyasak, Jean-François Cordeau, Erick Delage, Raf Jans and Gilbert Laporte have leveraged stochastic programming and robust approaches to solve such production and inventory problems. Other uncertainties come from the production process itself, e.g., stochastic setup time, and must be considered in order to make efficient plans.

GERAD has a long and rich tradition of research on vehicle routing problems. Many GERAD members, including Yossiri Adulyasak, Leandro Coelho, Jean-François Cordeau, Guy Desaulniers, Fausto Errico, Raf Jans, Gilbert Laporte, and Andrea Lodi have considered the incorporation of stochastic elements into various vehicle routing problems. This includes not only the consideration of stochastic demand, but also stochastic travel times or service times. Furthermore, integrated planning problems considering production, inventory and routing decisions have also been studied in a stochastic environment.

Uncertainty also needs to be considered for more tactical and strategic decisions. An extremely important supply chain decision relates to the location of new facilities. These are long-term investment decisions and hence they must be taken when there is a lot of uncertainty related to future demand. Since it is difficult to have good information about such long-term demand, robust approaches have been used by Erick Delage and Yossiri Adulyasak. Also, other types of uncertainty, such as the possibility of disruptions at a facility, have been incorporated in such models. At a tactical level, Errico Fausto considered the demand uncertainty in a two-tier city logistics planning problem.

References:
Stochastic VRP

• Adulyasak, Y., Jaillet, P., Models and algorithms for stochastic and robust vehicle routing with deadlines. Transportation Science, 50(2), 608-626, 2016.

• Errico, F., Desaulniers, G., Gendreau, M., Rei, W., Rousseau, L.M., The vehicle routing problem with hard time windows and stochastic service times. EURO Journal on Transportation and Logistics, 7(3), 223-251, 2018.

• Errico, F., Desaulniers, G., Gendreau, M., Rei, W., Rousseau, L.M., A priori optimization with recourse for the vehicle routing problem with hard time windows and stochastic service times. European Journal of Operational Research, 249(1), 55-66, 2016.

• Gauvin, C., Desaulniers, G., Gendreau, M., A branch-cut-and-price algorithm for the vehicle routing problem with stochastic demands. Computers & Operations Research, 50, 141-153, 2014.

• Hoogeboom, M., Adulyasak, Y., Dullaert, W., Jaillet, P., The robust vehicle routing problem with time window assignments. Transportation Science, 55(2). 275-552, 2021.

• Markov, I., Bierlaire, M., Cordeau, J. F., Maknoon, Y., Varone, S., A unified framework for rich routing problems with stochastic demands. Transportation Research Part B: Methodological, 114, 213-240, 2018.

• Rostami, B., Desaulniers, G., Errico, F., Lodi, A., Branch-price-and-cut algorithms for the vehicle routing problem with stochastic and correlated travel times. Operations Research, 69(2), 436-455, 2021.

Stochastic integrated vehicle routing and inventory problems

• Adulyasak, Y., Cordeau, J.F., Jans, R., Benders decomposition for production routing under demand uncertainty. Operations Research, 63(4), 851-867, 2015.

• Alvarez, A., Cordeau, J.F., Jans, R., Munari, P., Morabito, R., Inventory routing under stochastic supply and demand. Omega, 102, 102304, 2021.

• Coelho, L.C., Cordeau, J.F., Laporte, G., Heuristics for dynamic and stochastic inventory-routing. Computers & Operations Research, 52, 55-67, 2014.

• Markov, I., Bierlaire, M., Cordeau, J.F., Maknoon, Y., Varone, S., Waste collection inventory routing with non-stationary stochastic demands. Computers & Operations Research, 113, 104798, 2020.

• Roldán, R.F., Basagoiti, R., Coelho, L.C., Robustness of inventory replenishment and customer selection policies for the dynamic and stochastic inventory-routing problem. Computers & Operations Research, 74, 14-20, 2016.

• Solyalı, O., Cordeau, J.F., Laporte, G., Robust inventory routing under demand uncertainty. Transportation Science, 46(3), 327-340, 2012.

Stochastic production and inventory planning

• Ardestani-Jaafari, A., Delage, E., Robust optimization of sums of piecewise linear functions with application to inventory problems. Operations research, 64(2), 474-494, 2016.

• Rodrigues, F., Agra, A., Requejo, C., Delage, E., Lagrangian duality for robust problems with decomposable functions: the case of a robust inventory problem. INFORMS Journal on Computing, 33(2), 685-705, 2021.

• Sereshti, N., Adulyasak, Y., Jans, R., The value of aggregate service levels in stochastic lot sizing problems. Omega, 102, 10233, 2021.

• Solyalı, O., Cordeau, J.F., Laporte, G., The impact of modeling on robust inventory management under demand uncertainty. Management Science, 62(4), 1188-1201, 2016.

• Taş, D., Gendreau, M., Jabali, O., Jans, R., A capacitated lot sizing problem with stochastic setup times and overtime. European Journal of Operational Research, 273(1), 146-159, 2019.

• Thevenin, S., Adulyasak, Y., Cordeau, J.F., Material requirements planning under demand uncertainty using stochastic optimization. Production and Operations Management, 30(2), 475-493, 2021.

Tactical and strategic stochastic problems

• Ardestani-Jaafari, A., Delage, E., The value of flexibility in robust location–transportation problems. Transportation Science, 52(1), 189-209, 2018.

• Cheng, C., Adulyasak, Y., Rousseau, L.M., Robust facility location under disruptions. INFORMS Journal on Optimization, 2021.

• Cheng, C., Adulyasak, Y., Rousseau, L.M., Robust facility location under demand uncertainty and facility disruptions. Omega, 103, 102429, 2021.

• Crainic, T.G., Errico, F., Rei, W., Ricciardi, N., Modeling demand uncertainty in two-tier city logistics tactical planning. Transportation Science, 50(2), 559-578, 2016.

• Saif, A., Delage, E., Data-driven distributionally robust capacitated facility location problem. European Journal of Operational Research, 291(3), 995-1007, 2021.

Rescheduling Decisions in Logistics Applications

In many logistics applications, it is of the utmost importance to be able to take into account the latest data and make decisions to readjust existing plans on a real-time basis.

In retail operations, it is not only important to make the right inventory decisions, but personnel planning is also an essential task which contributes to customer satisfaction. This is a complex task because a personnel schedule must take into account various constraints such as union rules and legislative requirements, as well as employee preferences and cost considerations. However, the planned schedule cannot always be executed due to sudden changes in demand or workforce availability. In such a case, it is very important to be able to generate a new, feasible schedule taking the new situation into account. Ideally, this new schedule does not lead to too many modifications or a big cost increase. Since such a new schedule must be generated in real time, Guy Desaulniers and Issmail El Hallaoui developed effective and quick heuristics that can solve large rescheduling problems in a matter of seconds.

Transportation provides another application area for real-time decision-making. Antoine Legrain considers the case of ride-sharing, where requests from different customers must very frequently be updated and reoptimized. To make efficient and cost-effective real-time decisions in the context of sequential decision-making under uncertainty, Yossiri Adulyasak has proposed decomposition frameworks for Markov decision processes (MDPs) under uncertainty and demonstrated the efficiency of the resulting policies in inventory management and robot planning. This axis is also perfectly aligned with the research mission of the Canada Excellence Research Chair in Data Science for Real-Time Decision-Making led by Andrea Lodi.

References:

• Adulyasak, Y., Varakantham, P., Jaillet, P., Solving uncertain MDPs with objectives that are separable over instantiations of model uncertainty. 29th Conference on Artificial Intelligence (AAAI), 29(1), 3454-3460, 2015.

• Ahmed, A., Varakantham, P., Adulyasak, Y., Jaillet, P., Regret based robust solutions for uncertain Markov decision processes. Advances in Neural Information Processing Systems (NeurIPS) 26, 2013.

• Hassani, R., Desaulniers, G., El Hallaoui, I., Real-time personnel re-scheduling after a minor disruption in the retail industry. Computers & Operations Research, 120, 104952, 2020.

• Hassani, R., Desaulniers, G., El Hallaoui, I., Real-time bi-objective personnel re-scheduling in the retail industry. European Journal of Operational Research, 293(1), 93-108, 2021.

• Riley, C., Legrain, A., Van Hentenryck, P., Column generation for real-time ride-sharing operations. In International Conference on Integration of Constraint Programming, Artificial Intelligence, and Operations Research. Springer, Cham, 472-487, 2019.