Reactive Scheduling of a Distributed Network for the Supply of Perishable Products

This paper considers the problem of coordinating the production and distribution activities of a network of independent supply centers. In particular, we focus on the supply of rapidly perishable goods (ready-mixed concrete) that must be delivered to customers in strict time windows. The problem presents three main challenges. First, it includes several interrelated scheduling and routing problems, each affected by nearly prohibitive combinatorial complexity. Second, due to the perishable nature of the supplied products, effective solutions must not only optimize the objective function related to resource utilization and cost minimization, but also tolerate the small and frequent stochastic perturbations (transport delays) of the operating environment. Third, if major perturbations occur, the decision strategy must be able to respond in real time with effective rescheduling interventions restoring the indispensable synchronization of activities in progress, and avoiding extremely undesirable circumstances related to product decay. After providing a detailed mathematical model of the considered problem, this paper proposes a hybrid metaheuristic approach integrating a genetic algorithm with a number of problem-specific constructive heuristics. The effectiveness of the approach is evaluated against other scheduling heuristics on an industrial case study.

[1]  Hoong Chuin Lau,et al.  Vehicle routing problem with time windows and a limited number of vehicles , 2003, Eur. J. Oper. Res..

[2]  Hisao Ishibuchi,et al.  Balance between genetic search and local search in memetic algorithms for multiobjective permutation flowshop scheduling , 2003, IEEE Trans. Evol. Comput..

[3]  Roberto Musmanno,et al.  Real-time vehicle routing: Solution concepts, algorithms and parallel computing strategies , 2003, Eur. J. Oper. Res..

[4]  Y. Narahari,et al.  Design of six sigma supply chains , 2004, IEEE Transactions on Automation Science and Engineering.

[5]  Funda Sivrikaya-Serifoglu,et al.  Parallel machine scheduling with earliness and tardiness penalties , 1999, Comput. Oper. Res..

[6]  Wu Cheng,et al.  A genetic algorithm for minimizing the makespan in the case of scheduling identical parallel machines , 1999, Artif. Intell. Eng..

[7]  Nukala Viswanadham,et al.  Partner selection and synchronized planning in dynamic manufacturing networks , 2003, IEEE Trans. Robotics Autom..

[8]  Athanasios Migdalas,et al.  Annotated bibliography in vehicle routing , 2007, Oper. Res..

[9]  Eiichi Taniguchi,et al.  INTELLIGENT TRANSPORTATION SYSTEM BASED DYNAMIC VEHICLE ROUTING AND SCHEDULING WITH VARIABLE TRAVEL TIMES , 2004 .

[10]  Michael Pinedo,et al.  Scheduling: Theory, Algorithms, and Systems , 1994 .

[11]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[12]  Loo Hay Lee,et al.  Vehicle capacity planning system: a case study on vehicle routing problem with time windows , 2003, IEEE Trans. Syst. Man Cybern. Part A.

[13]  Mikkel T. Jensen,et al.  Generating robust and flexible job shop schedules using genetic algorithms , 2003, IEEE Trans. Evol. Comput..

[14]  Andreas C. Nearchou,et al.  The effect of various operators on the genetic search for large scheduling problems , 2004 .

[15]  Paolo Toth,et al.  Models, relaxations and exact approaches for the capacitated vehicle routing problem , 2002, Discret. Appl. Math..

[16]  Nukala Viswanadham,et al.  The past, present, and future of supply-chain automation , 2002, IEEE Robotics Autom. Mag..

[17]  Chung-Yee Lee,et al.  Machine scheduling with job delivery coordination , 2004, Eur. J. Oper. Res..

[18]  G. Villa,et al.  Coordinated scheduling of production and delivery from multiple plants and with time windows using genetic algorithms , 2002, Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02..

[19]  Peter B. Luh,et al.  Price-based approach for activity coordination in a supply network , 2003, IEEE Trans. Robotics Autom..

[20]  Iris D. Tommelein,et al.  Just-in-Time Concrete Delivery: Mapping Alternatives for Vertical Supply Chain Integration , 1999 .

[21]  Jae Young Choi,et al.  A genetic algorithm for job sequencing problems with distinct due dates and general early-tardy penalty weights , 1995, Comput. Oper. Res..

[22]  Chung-Wei Feng,et al.  Optimizing the schedule of dispatching RMC trucks through genetic algorithms , 2004 .

[23]  Slim Hammadi,et al.  A distributed scheduling for agro-food manufacturing problems , 2003, IEEE Trans. Syst. Man Cybern. Part C.

[24]  Uzay Kaymak,et al.  Genetic algorithms for supply-chain scheduling: A case study in the distribution of ready-mixed concrete , 2007, Eur. J. Oper. Res..

[25]  Gilbert Laporte,et al.  Classical and modern heuristics for the vehicle routing problem , 2000 .

[26]  George G. Lendaris,et al.  Intelligent supply chain management using adaptive critic learning , 2003, IEEE Trans. Syst. Man Cybern. Part A.

[27]  Michel Gendreau,et al.  A Tabu Search Heuristic for the Vehicle Routing Problem with Stochastic Demands and Customers , 1996, Oper. Res..

[28]  Kostas Zografos,et al.  A heuristic algorithm for solving hazardous materials distribution problems , 2004, Eur. J. Oper. Res..

[29]  Nikolaos F. Matsatsinis,et al.  Towards a decision support system for the ready concrete distribution system: A case of a Greek company , 2004, Eur. J. Oper. Res..

[30]  Marius M. Solomon,et al.  Algorithms for the Vehicle Routing and Scheduling Problems with Time Window Constraints , 1987, Oper. Res..

[31]  Loo Hay Lee,et al.  Heuristic methods for vehicle routing problem with time windows , 2001, Artif. Intell. Eng..