SUPPLY CHAIN NETWORK DESIGN: RISK-AVERSE VS. RISK-NEUTRAL DECISION MAKING

Recent events, such as the Heparin tragedy, highlight the necessity for designers and planners of supply chain networks to consider the risk of disruptions in spite of their low probability of occurrence. One effective way to hedge against supply chain network disruptions is to have a robustly designed supply chain network. This involves strategic decisions, such as choosing which markets to serve, which suppliers to source from, the location of plants, the types of facilities to use, and tactical decisions, such as production and capacity allocation. In this dissertation, we focus on models for designing supply chain networks that are resilient to disruptions. We consider two types of decision making policies. A risk-neutral decision making policy is based on the cost minimization approach, and the decision-maker defines the set of decisions that minimize expected cost. We also consider a risk-averse policy wherein rather than selecting facilities that minimize expected cost, the decision-maker uses a Conditional Value-at-Risk approach to measure and quantify risk. However, such network design problems belong to class of NP hard problems. Accordingly, we develop efficient heuristic algorithms and metaheuristic approaches to obtain acceptable solutions to these types of problems in reasonable runtimes so that the decision making process is facilitated with at most a moderate reduction in solution quality. Finally, we perform statistical analyses (e.g., logistic regression) to assess the likelihood of selection for each facility. These models allow us to identify the factors that impact facility selection in both the risk-neutral and risk-averse policies

Supply chain management: An opportunity for metaheuristics

In today’s highly competitive and global marketplace the pressure on organizations to find new ways to create and deliver value to customers grows ever stronger. In the last two decades, logistics and supply chain has moved to the center stage. There has been a growing recognition that it is through an effective management of the logistics function and the supply chain that the goal of cost reduction and service enhancement can be achieved. The key to success in Supply Chain Management (SCM) require heavy emphasis on integration of activities, cooperation, coordination and information sharing throughout the entire supply chain, from suppliers to customers. To be able to respond to the challenge of integration there is the need of sophisticated decision support systems based on powerful mathematical models and solution techniques, together with the advances in information and communication technologies. The industry and the academia have become increasingly interested in SCM to be able to respond to the problems and issues posed by the changes in the logistics and supply chain. We present a brief discussion on the important issues in SCM. We then argue that metaheuristics can play an important role in solving complex supply chain related problems derived by the importance of designing and managing the entire supply chain as a single entity. We will focus specially on the Iterated Local Search, Tabu Search and Scatter Search as the ones, but not limited to, with great potential to be used on solving the SCM related problems. We will present briefly some successful applications.Supply chain management, metaheuristics, iterated local search, tabu search and scatter search

A Problem-Specific and Effective Metaheuristic for Flexibility Design

Matching uncertain demand with capacities is notoriously hard. Operations managers can use mix-flexible resources to shift excess demands to unused capacities. To find the optimal configuration of a mix-flexible production network, a flexibility design problem (FDP) is solved. Existing literature on FDPs provides qualitative structural insights, but work on solution methods is rare. We contribute the first metaheuristic which integrates these structural insights and is specifically tailored to solve FDPs. Our genetic algorithm is compared to commercial solvers on instances of up to 15 demand types, resources, and 500 demand scenarios. Experimental evidence shows that in the realistic case of flexible optimal configurations, it dominates the comparison methods regarding runtime and solution quality.Flexibility, Metaheuristic, Network Design

On the use of biased-randomized algorithms for solving non-smooth optimization problems

Soft constraints are quite common in real-life applications. For example, in freight transportation, the fleet size can be enlarged by outsourcing part of the distribution service and some deliveries to customers can be postponed as well; in inventory management, it is possible to consider stock-outs generated by unexpected demands; and in manufacturing processes and project management, it is frequent that some deadlines cannot be met due to delays in critical steps of the supply chain. However, capacity-, size-, and time-related limitations are included in many optimization problems as hard constraints, while it would be usually more realistic to consider them as soft ones, i.e., they can be violated to some extent by incurring a penalty cost. Most of the times, this penalty cost will be nonlinear and even noncontinuous, which might transform the objective function into a non-smooth one. Despite its many practical applications, non-smooth optimization problems are quite challenging, especially when the underlying optimization problem is NP-hard in nature. In this paper, we propose the use of biased-randomized algorithms as an effective methodology to cope with NP-hard and non-smooth optimization problems in many practical applications. Biased-randomized algorithms extend constructive heuristics by introducing a nonuniform randomization pattern into them. Hence, they can be used to explore promising areas of the solution space without the limitations of gradient-based approaches, which assume the existence of smooth objective functions. Moreover, biased-randomized algorithms can be easily parallelized, thus employing short computing times while exploring a large number of promising regions. This paper discusses these concepts in detail, reviews existing work in different application areas, and highlights current trends and open research lines

Shrimp closed-loop supply chain network design

none3Recent developments in food industries have attracted both academic and industrial practitioners. Shrimp as a well-known, rich, and sought-after seafood, is generally obtained from either marine environments or aquaculture. Central prominence of Shrimp Supply Chain (SSC) is brought about by numerous factors such as high demand, market price, and diverse fisheries or aquaculture locations. In this respect, this paper considers SSC as a set of distribution centers, wholesalers, shrimp processing factories, markets, shrimp waste powder factory, and shrimp waste powder market. Subsequently, a mathematical model is proposed for the SSC, whose aim is to minimize the total cost through the supply chain. The SSC model is NP-hard and is not able to solve large-size problems. Therefore, three well-known metaheuristics accompanied by two hybrid ones are exerted. Moreover, a real-world application with 15 test problems are established to validate the model. Finally, the results confirm that the SSC model and the solution methods are effective and useful to achieve cost savings.openMosallanezhad B.; Hajiaghaei-Keshteli M.; Triki C.Mosallanezhad, B.; Hajiaghaei-Keshteli, M.; Triki, C

Simheuristics to support efficient and sustainable freight transportation in smart city logistics

La logística urbana intel·ligent constitueix un factor crucial en la creació de sistemes de transport urbà eficients i sostenibles. Entre altres factors, aquests sistemes es centren en la incorporació de dades en temps real i en la creació de models de negoci col·laboratius en el transport urbà de mercaderies, considerant l’augment dels habitants en les ciutats, la creixent complexitat de les demandes dels clients i els mercats altament competitius. Això permet als que planifiquen el transport minimitzar els costos monetaris i ambientals del transport de mercaderies a les àrees metropolitanes. Molts problemes de presa de decisions en aquest context es poden formular com a problemes d’optimació combinatòria. Tot i que hi ha diferents enfocaments de resolució exacta per a trobar solucions òptimes a aquests problemes, la seva complexitat i grandària, a més de la necessitat de prendre decisions instantànies pel que fa a l’encaminament de vehicles, la programació o la situació d’instal·lacions, fa que aquestes metodologies no s’apliquin a la pràctica. A causa de la seva capacitat per a trobar solucions pseudoòptimes en gairebé temps real, els algorismes metaheurístics reben una atenció creixent dels investigadors i professionals com a alternatives eficients i fiables per a resoldre nombrosos problemes d’optimació en la creació de la logística de les ciutats intel·ligents. Malgrat el seu èxit, les tècniques metaheurístiques tradicionals no representen plenament la complexitat dels sistemes més realistes. En assumir entrades (inputs) i restriccions de problemes deterministes, la incertesa i el dinamisme experimentats en els escenaris de transport urbà queden sense explicar. Els algorismes simheurístics persegueixen superar aquests inconvenients mitjançant la integració de qualsevol tipus de simulació en processos metaheurístics per a explicar la incertesa inherent a la majoria de les aplicacions de la vida real. Aquesta tesi defineix i investiga l’ús d’algorismes simheurístics com el mètode més adequat per a resoldre problemes d’optimació derivats de la logística de les ciutats. Alguns algorismes simheurístics s’apliquen a una sèrie de problemes complexos, com la recollida de residus urbans, els problemes de disseny de la cadena de subministrament integrada i els models de transport innovadors relacionats amb la col·laboració horitzontal entre els socis de la cadena de subministrament. A més de les discussions metodològiques i la comparació d’algorismes desenvolupats amb els referents de la bibliografia acadèmica, es mostra l’aplicabilitat i l’eficiència dels algorismes simheurístics en diferents casos de gran escala.Las actividades de logística en ciudades inteligentes constituyen un factor crucial en la creación de sistemas de transporte urbano eficientes y sostenibles. Entre otros factores, estos sistemas se centran en la incorporación de datos en tiempo real y la creación de modelos empresariales colaborativos en el transporte urbano de mercancías, al tiempo que consideran el aumento del número de habitantes en las ciudades, la creciente complejidad de las demandas de los clientes y los mercados altamente competitivos. Esto permite minimizar los costes monetarios y ambientales del transporte de mercancías en las áreas metropolitanas. Muchos de los problemas de toma de decisiones en este contexto se pueden formular como problemas de optimización combinatoria. Si bien existen diferentes enfoques de resolución exacta para encontrar soluciones óptimas a tales problemas, su complejidad y tamaño, además de la necesidad de tomar decisiones instantáneas con respecto al enrutamiento, la programación o la ubicación de las instalaciones, hacen que dichas metodologías sean inaplicables en la práctica. Debido a su capacidad para encontrar soluciones pseudoóptimas casi en tiempo real, los algoritmos metaheurísticos reciben cada vez más atención por parte de investigadores y profesionales como alternativas eficientes y fiables para resolver numerosos problemas de optimización en la creación de la logística de ciudades inteligentes. A pesar de su éxito, las técnicas metaheurísticas tradicionales no representan completamente la complejidad de los sistemas más realistas. Al asumir insumos y restricciones de problemas deterministas, se ignora la incertidumbre y el dinamismo experimentados en los escenarios de transporte urbano. Los algoritmos simheurísticos persiguen superar estos inconvenientes integrando cualquier tipo de simulación en procesos metaheurísticos con el fin de considerar la incertidumbre inherente en la mayoría de las aplicaciones de la vida real. Esta tesis define e investiga el uso de algoritmos simheurísticos como método adecuado para resolver problemas de optimización que surgen en la logística de ciudades inteligentes. Se aplican algoritmos simheurísticos a una variedad de problemas complejos, incluyendo la recolección de residuos urbanos, problemas de diseño de la cadena de suministro integrada y modelos de transporte innovadores relacionados con la colaboración horizontal entre los socios de la cadena de suministro. Además de las discusiones metodológicas y la comparación de los algoritmos desarrollados con los de referencia de la bibliografía académica, se muestra la aplicabilidad y la eficiencia de los algoritmos simheurísticos en diferentes estudios de casos a gran escala.Smart city logistics are a crucial factor in the creation of efficient and sustainable urban transportation systems. Among other factors, they focus on incorporating real-time data and creating collaborative business models in urban freight transportation concepts, whilst also considering rising urban population numbers, increasingly complex customer demands, and highly competitive markets. This allows transportation planners to minimize the monetary and environmental costs of freight transportation in metropolitan areas. Many decision-making problems faced in this context can be formulated as combinatorial optimization problems. While different exact solving approaches exist to find optimal solutions to such problems, their complexity and size, in addition to the need for instantaneous decision-making regarding vehicle routing, scheduling, or facility location, make such methodologies inapplicable in practice. Due to their ability to find pseudo-optimal solutions in almost real time, metaheuristic algorithms have received increasing attention from researchers and practitioners as efficient and reliable alternatives in solving numerous optimization problems in the creation of smart city logistics. Despite their success, traditional metaheuristic techniques fail to fully represent the complexity of most realistic systems. By assuming deterministic problem inputs and constraints, the uncertainty and dynamism experienced in urban transportation scenarios are left unaccounted for. Simheuristic frameworks try to overcome these drawbacks by integrating any type of simulation into metaheuristic-driven processes to account for the inherent uncertainty in most real-life applications. This thesis defines and investigates the use of simheuristics as a method of first resort for solving optimization problems arising in smart city logistics concepts. Simheuristic algorithms are applied to a range of complex problem settings including urban waste collection, integrated supply chain design, and innovative transportation models related to horizontal collaboration among supply chain partners. In addition to methodological discussions and the comparison of developed algorithms to state-of-the-art benchmarks found in the academic literature, the applicability and efficiency of simheuristic frameworks in different large-scaled case studies are shown

Integrated strategic and tactical optimization of animal-waste sourced biopower supply chains

International audience—Many models have been recently developed for the optimization of biomass related supply chains. However, models for biopower supply chains powered by animal waste have not received much attention yet. In this paper, we propose a mixed integer linear programming model for supplier selection and procurement planning for a biopower plant. The model integrates time window constraints for the collection of animal waste as well as inventory constraints. We show that the model is intractable with a state-of-the art commercial solver and propose a heuristic approach based on the Adaptive Large Neighbourhood Search (ALNS) framework. We show the efficiency of this approach on a case study in central France

Integrated Models and Algorithms for Automotive Supply Chain Optimization

The automotive industry is one of the most important economic sectors, and the efficiency of its supply chain is crucial for ensuring its profitability. Developing and applying techniques to optimize automotive supply chains can lead to favorable economic outcomes and customer satisfaction. In this dissertation, we develop integrated models and algorithms for automotive supply chain optimization. Our objective is to explore methods that can increase the competitiveness of the automotive supply chain via maximizing efficiency and service levels. Based on interactions with an automotive industry supplier, we define an automotive supply chain planning problem at a detailed operational level while taking into account realistic assumptions such as sequence-dependent setups on parallel machines, auxiliary resource assignments, and multiple types of costs. We model the research problem of interest using mixed-integer linear programming. Given the problem’s NP-hard complexity, we develop a hybrid metaheuristic approach, including a constructive heuristic and an effective encoding-decoding strategy, to minimize the total integrated cost of production setups, inventory holding, transportation, and production outsourcing. Furthermore, since there are often conflicting objectives of interest in automotive supply chains, we investigate simultaneously optimizing total cost and customer service level via a multiobjective optimization methodology. Finally, we analyze the impact of adding an additional transportation mode, which offers a cost vs. delivery time option to the manufacturer, on total integrated cost. Our results demonstrate the promising performance of the proposed solution approaches to analyze the integrated cost minimization problem to near optimality in a timely manner, lowering the cost of the automotive supply chain. The proposed bicriteria, hybrid metaheuristic offers decision makers several options to trade-off cost with service level via identified Pareto-optimal solutions. The effect of the available additional transportation mode’s lead time is found to be bigger than its cost on the total integrated cost measure under study