Stochastic multi-period multi-product multi-objective Aggregate Production Planning model in multi-echelon supply chain

In this paper a multi-period multi-product multi-objective aggregate production planning (APP) model is proposed for an uncertain multi-echelon supply chain considering financial risk, customer satisfaction, and human resource training. Three conflictive objective functions and several sets of real constraints are considered concurrently in the proposed APP model. Some parameters of the proposed model are assumed to be uncertain and handled through a two-stage stochastic programming (TSSP) approach. The proposed TSSP is solved using three multi-objective solution procedures, i.e., the goal attainment technique, the modified ε-constraint method, and STEM method. The whole procedure is applied in an automotive resin and oil supply chain as a real case study wherein the efficacy and applicability of the proposed approaches are illustrated in comparison with existing experimental production planning method

Hybrid Meta-Heuristics for Robust Scheduling

The production and delivery of rapidly perishable goods in distributed supply networks involves a number of tightly coupled decision and optimization problems regarding the just-in-time production scheduling and the routing of the delivery vehicles in order to satisfy strict customer specified time-windows. Besides dealing with the typical combinatorial complexity related to activity assignment and synchronization, effective methods must also provide robust schedules, coping with the stochastic perturbations (typically transportation delays) affecting the distribution process. In this paper, we propose a novel metaheuristic approach for robust scheduling. Our approach integrates mathematical programming, multi-objective evolutionary computation, and problem-specific constructive heuristics. The optimization algorithm returns a set of solutions with different cost and risk tradeoffs, allowing the analyst to adapt the planning depending on the attitude to risk. The effectiveness of the approach is demonstrated by a real-world case concerning the production and distribution of ready-mixed concrete.Meta-Heuristics;Multi-Objective Genetic Optimization;Robust Scheduling;Supply Networks

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

A case analysis of a sustainable food supply chain distribution system—A multi-objective approach

Sustainable supply chain management is a topical area which is continuing to grow and evolve. Within supply chains, downstream distribution from producers to customers plays a significant role in the environmental performance of production supply chains. With consumer consciousness growing in the area of sustainable food supply, food distribution needs to embrace and adapt to improve its environmental performance, while still remaining economically competitive. With a particular focus on the dairy industry, a robust solution approach is presented for the design of a capacitated distribution network for a two-layer supply chain involved in the distribution of milk in Ireland. In particular the green multiobjective optimisation model minimises CO2 emissions from transportation and total costs in the distribution chain. These distribution channels are analysed to ensure the non-dominated solutions are distributed along the Pareto fronts. A multi-attribute decision-making approach, TOPSIS, has been used to rank the realistic feasible transportation routes resulting from the trade-offs between total costs and CO2 emissions. The refined realistic solution space allows the decision-makers to geographically locate the sustainable transportation routes. In addition to geographical mapping the decision maker is also presented with a number of alternative analysed scenarios which forcibly open closed distribution routes to build resiliency into the solution approach. In terms of model performance, three separate GA based optimisers have been evaluated and reported upon. In the case presented NSGA-II was found to outperform its counterparts of MOGA-II and HYBRID

The relevance of outsourcing and leagile strategies in performance optimization of an integrated process planning and scheduling

Over the past few years growing global competition has forced the manufacturing industries to upgrade their old production strategies with the modern day approaches. As a result, recent interest has been developed towards finding an appropriate policy that could enable them to compete with others, and facilitate them to emerge as a market winner. Keeping in mind the abovementioned facts, in this paper the authors have proposed an integrated process planning and scheduling model inheriting the salient features of outsourcing, and leagile principles to compete in the existing market scenario. The paper also proposes a model based on leagile principles, where the integrated planning management has been practiced. In the present work a scheduling problem has been considered and overall minimization of makespan has been aimed. The paper shows the relevance of both the strategies in performance enhancement of the industries, in terms of their reduced makespan. The authors have also proposed a new hybrid Enhanced Swift Converging Simulated Annealing (ESCSA) algorithm, to solve the complex real-time scheduling problems. The proposed algorithm inherits the prominent features of the Genetic Algorithm (GA), Simulated Annealing (SA), and the Fuzzy Logic Controller (FLC). The ESCSA algorithm reduces the makespan significantly in less computational time and number of iterations. The efficacy of the proposed algorithm has been shown by comparing the results with GA, SA, Tabu, and hybrid Tabu-SA optimization methods

Genetic Algorithms in Supply Chain Scheduling of Ready-Mixed Concrete

The coordination of just-in-time production and transportation in a network of partially independent facilities to guarantee timely delivery to distributed customers is one of the most challenging aspects of supply chain management. From the theoretical perspective, the timely production/distribution can be viewed as a hybrid combination of planning, scheduling and routing problem, each notoriously affected by nearly prohibitive combinatorial complexity. From a practical viewpoint, the problem calls for a trade-off between risks and profits. This paper focuses on the ready-made concrete delivery: in addition to the mentioned complexity, strict time-constraints forbid both earliness and lateness of the supply. After developing a detailed model of the considered problem, we propose a novel meta-heuristic approach based on a hybrid genetic algorithm combined with constructive heuristics. A detailed case study derived from industrial data is used to illustrate the potential of the proposed approach

Hybrid Meta-Heuristics for Robust Scheduling

The production and delivery of rapidly perishable goods in distributed supply networks involves a number of tightly coupled decision and optimization problems regarding the just-in-time production scheduling and the routing of the delivery vehicles in order to satisfy strict customer specified time-windows. Besides dealing with the typical combinatorial complexity related to activity assignment and synchronization, effective methods must also provide robust schedules, coping with the stochastic perturbations (typically transportation delays) affecting the distribution process. In this paper, we propose a novel metaheuristic approach for robust scheduling. Our approach integrates mathematical programming, multi-objective evolutionary computation, and problem-specific constructive heuristics. The optimization algorithm returns a set of solutions with different cost and risk tradeoffs, allowing the analyst to adapt the planning depending on the attitude to risk. The effectiveness of the approach is demonstrated by a real-world case concerning the production and distribution of ready-mixed concrete