Constraint programming approach for multi-resource-constrained unrelated parallel machine scheduling problem with sequence-dependent setup times

This paper studies the multi-resource-constrained unrelated parallel machine scheduling problem under various operational constraints with the objective of minimising maximum completion time among the scheduled jobs. Sequence-dependent setup times, precedence relations, machine eligibility restrictions and release dates are incorporated into the problem as operational constraints to reflect real-world manufacturing environments. The considered problem is in NP-hard class of problems, which cannot be solved in deterministic polynomial time. Our aim in this study is to develop an exact solution approach based on constraint programming (CP), which shows good performance in solving scheduling problems. In this regard, we propose a CP model and enrich this model by adding lower bound restrictions and redundant constraints. Moreover, to achieve a reduction in computation time, we propose two branching strategies for the proposed CP model. The performance of the CP model is tested using randomly generated and benchmark instances from the literature. The computational results indicate that the proposed CP model outperforms the best solutions with an average gap of 15.52%

A machine learning-based two-stage approach for the location of undesirable facilities in the biomass-to-bioenergy supply chain

Biomass-to-bioenergy supply chain management is an integral part of the sustainable industrialization of energy conversion through biomass to bioenergy by managing economic, environmental, and social challenges encountered in each supply chain stage. Motivated by a real-world biomass-to-bioenergy supply chain network design (BSCND) problem, this study addresses the location of undesirable facilities for the first time in the BSCND literature. The motivation of this study is to develop a machine learning-based two-stage approach for solving the BSCND problem with undesirable facilities that have a negative impact on surrounding communities. The first stage employs the k-means clustering algorithm to alleviate the complexity of the problem, and the second stage utilizes a novel pre-emptive goal programming (PGP) approach to optimize two distinct objectives hierarchically. The first objective maximizes the sum of the distances between all clients and the open facilities, which is the well-known objective of the obnoxious p-median (OpM) problem. The second objective maximizes the total profit of the entire supply chain. The applicability of the proposed solution approach is shown through the case problem, and performance of the two-stage approach is validated using randomly generated test problems. The computational results indicate the effectiveness of the clustering methodology in reducing the complexity of the problem while the PGP achieves the optimal configuration of the biomass-to-bioenergy supply chain handling the hierarchical objectives. The optimal solution of the case problem was achieved within 25,239.36&nbsp;s execution time, and the total profit of the supply chain is $6,776,870.22 with 735&nbsp;km total distance to clients. The average optimality gap for the first phase of the PGP is 4.97%, and the average optimality gap for the second phase of the PGP is 0.01% for the generated test problems.</p

Daily Production Planning Problem of an International Energy Management Company

This study is about real-life production and capacity planning problem in an international company which operates in energy management sector in Manisa, Turkey. The company produces different types of circuit breakers and delivers its products to different countries and distribution center, located in France. Within the scope of this problem, the production plan is done for nine products that are manufactured on six production lines. Each product has a unique production line, but some of the products are processed on common production lines. In this study, the production lot amount is determined each day by considering the due date and quantity of the customer orders without exceeding the capacity of the production lines. In the existing system, there are many tardy and early customer orders and the production plan is done manually which causes time loss for the company. A preemptive goal programming model is proposed for solving this problem where the main goal is to minimize total lateness in customer orders and minimizing the number of customer orders that have been split is considered as the secondary objective. The proposed mathematical model is solved optimally for real life instances in IBM ILOG CPLEX Optimization Studio 12.6.3. In addition, a heuristic method is presented in order to decrease the daily production planning duration and the fulfill the company’s needs. Moreover, a user-friendly decision support system is developed where both solution techniques are embedded.</p