While there are many general methods for case retrieval, case adaptation usually requires problem-specific knowledge and it is still an open problem. In this paper we propose a general method for solving case adaptation problems for the large class of problems which can be formulated as Constraint Satisfaction Problems. This method is based on the concept of interchangeability between values in problem solutions. The method is able to determine how change propagates in a solution set and generate a minimal set of choices which need to be changed to adapt an existing solution to a new problem. The paper presents the proposed method, algorithms and test results for a resource allocation domain.
Substitutability and interchangeability in constraint satisfaction problems (CSPs) have been used as a basis for search heuristics, solution adaptation and abstraction techniques. In this paper, we consider how the same concepts can be extended to soft constraint satisfaction problems (SCSPs). We introduce two notions: threshold α and degradation δ for substitutability and interchangeability, ( α substitutability/interchangeability and δ substitutability/interchangeability respectively). We show that they satisfy analogous theorems to the ones already known for hard constraints. In α interchangeability, values are interchangeable in any solution that is better than a threshold α, thus allowing to disregard differences among solutions that are not sufficiently good anyway. In δ interchangeability, values are interchangeable if their exchange could not degrade the solution by more than a factor of δ. We give efficient algorithms to compute ( δ /α)interchangeable sets of value for a large class of SCSPs.
Abstract:Purpose: Network design of the supply chain is an important and strategic aspect of logistics management. In this paper, we address the network design problem specific to packaged gases (cylinder) supply chain. We propose an integrated framework that allows for the determination of the optimal facility locations, the filling plant production capacities, the inventory at plants and hubs, and the number of packages to be routed in primary and secondary transportation.Design/methodology/approach: We formulate the problem as a mixed integer program and then develop a decomposition approach to solve it. We illustrate the proposed framework with numerical examples from real-life packaged gases supply chain. The results show that the decomposition approach is effective in solving a broad range of problem sizes. Findings:The main finding of this paper is that decomposing the network design problem into two sub-problems is very effective to tackle the real-life large scale network design problems occurring in cylinder gas distribution by optimizing strategic and tactical decisions and approximating the operational decisions. We also benchmark the results from the decomposition approach by solving the complete packaged gases network design model for smaller test cases. Originality/value:The main contribution of our work is that it integrates supply chain network design decisions without fixing the fillings plant locations with inventory and resource allocation decisions required at the plants. We also consider the transportation costs for the -85-Journal of Industrial Engineering and Management -http://dx.doi.org/10.3926/jiem.1140 entire supply chain including the transhipment costs among different facilities by deciding the replenishment frequency.
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